Prepared by

the Australian Centre for Diabetes Strategies

Prince of Wales Hospital, Sydney

for the Diabetes Australia Guideline Development Consortium

APPROVED BY THE NHMRC 14 DECEMBER 2001

2.0 Guideline for Primary Prevention of Type 2 Diabetes ......................................... 4

2.1 Introduction ............................................................................................................... 4

2.2 Issues for the Primary Prevention of Type 2 Diabetes............................................... 5

2.3 Summary of Recommendations ................................................................................ 6

2.4 Recommendations ...................................................................................................... 8

Section 1: Obesity in childhood and adult life ...................................................... 8

Section 2: Fat distribution, total adiposity, and ethnicity..................................... 17

Section 3: Simple measures for body fat distribution ......................................... 24

Section 4: Physical activity and sedentary lifestyle ............................................. 29

Section 5: Simple measures of physical activity .................................................. 37

Section 6: High-fat diets, GDM, and fat type....................................................... 44

Section 7: GDM and subsequent risk of Type 2 diabetes ................................... 51

Section 8: Birth weight as a later risk of Type 2 diabetes .................................... 56

Section 9: Low glycaemic index diets.................................................................. 60

Section 10: Psychosocial stress ............................................................................. 64

Section 11: Diet and physical activity to prevent excessive weight gain............... 69

References ................................................................................................................82

Evidence References ...................................................................................... 82

General References......................................................................................... 90

Other References Identified............................................................................ 95

2.5 Prevention Guideline Search Strategy and Yield Table......................................... 100

3

Primary Prevention - Part 2 December 2001

Centre for Population Health and Nutrition

Institute of Public Health and Health Services Research

Monash University, MELBOURNE VIC

Centre for Population Health and Nutrition

Institute of Public Health and Health Services Research

Monash University, MELBOURNE VIC

Professor David Simmons

Department of Rural Health

University of Melbourne, MELBOURNE VIC

Dr Garry Egger

Gutbusters, SYDNEY NSW

Professor Jim Mann

Department of Human Nutrition

University of Otago, AUCKLAND NZ

Southern Public Health Unit

ALBANY WA

Department of Biomedical Science

University of Wollongong, WOLLONGONG NSW

Centre for Health Program Evaluation, MELBOURNE VIC

32 Badminton Street, MOUNT GRAVATT QLD

Australian Centre for Diabetes Strategies

Prince of Wales Hospital, SYDNEY NSW

Ms Melanie Colagiuri

Human Nutrition Unit

University of Sydney, SYDNEY NSW

4

Primary Prevention - Part 2 December 2001

This guideline covers issues related to the primary prevention of Type 2 diabetes. Its aim is

to inform and guide health promotion and Preventive activities for Type 2 diabetes with

evidence based information on what non-pharmacological interventions work and what does

not. While this guideline contains information that would be relevent for a range of audiences,

the focus of the systematic review is primarily on the efficacy and effectiveness of one to one

interventions. The guideline targets clinicians, health promotion practitioners, planners and

policy makers.

Since certain risk factors are common to both the Prevention Guideline and the Case

Detection and Diagnosis Guideline, where possible overlap has been minimized. For

example, family history as a risk factor for the development of Type 2 diabetes is covered in

the Case Detection and Diagnosis Guideline but was omitted from the Prevention Guideline

as it is a non-modifiable risk and therefore is not subject to Preventive interventions.

In addition to the methods used to identify and critically appraise the evidence to formulate

the guideline recommendations which are described in detail in Part 1 of the document, the

Project Management Team reviewed and checked each step of the methods process and:

• repeated a selection of the searches

• double culled the yield from a selection of the database searches

• double reviewed the majority of the articles used as evidence references

• checked all recommendations, evidence statements, evidence tables and search strategy

and yield tables

Issues identified by the EWG and from the literature as critical to the primary prevention of

Type 2 diabetes are shown in point 2.2 (next page).

Each of these issues is addressed in a separate section in a format presenting:

• Recommendation(s)

• Evidence Statements - supporting the recommendations

• Background - to issues for the guideline

• Evidence - detailing and interpreting the key findings

• Summary - of major evidence found

• Evidence tables - summarising the evidence ratings for the articles reviewed

For all issues combined, supporting material appears at the end of the guideline topic and

includes:

• Evidence references

• General references

• Other references identified

5

Primary Prevention - Part 2 December 2001

􀂄 Does obesity in adult life and/or in childhood increase the risk of Type 2 diabetes?

􀂄 Is the pattern of fat distribution an additional contributor to total adiposity in determining

the risk of Type 2 diabetes and how does this vary with ethnicity?

􀂄 What simple measures can be used for body fat distribution?

􀂄 What is the evidence that physical activity can decrease, and sedentary behaviour

increase, the risk of Type 2 diabetes?

􀂄 What simple measures can be used to determine physical activity and inactivity?

􀂄 Do high-fat diets increase the risk of Type 2 diabetes and how is this affected by fat type?

􀂄 Does GDM increase the subsequent risk of Type 2 diabetes in mothers and/or infants?

􀂄 How does weight at birth affect later risk of Type 2 diabetes?

􀂄 Do low glycaemic index (GI) diets decrease risk of Type 2 diabetes?

􀂄 Does psychosocial stress increase the risk of Type 2 diabetes?

􀂄 What forms of diet and physical activity are recommended to prevent excessive weight

gain?

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Primary Prevention - Part 2 December 2001

• Regular physical activity is recommended to reduce the risk of Type 2 diabetes

• Since obesity is associated with an increased risk of Type 2 diabetes, interventions to

reduce obesity may reduce the risk of Type 2 diabetes

• Abdominal obesity is an important indicator of increased risk of Type 2 diabetes in all

ethnic groups and should be a particular focus of weight loss programs

• Individuals at risk of developing Type 2 diabetes should have dietary intake assessed and

should receive individualised dietary advice and continued dietetic support

• Individuals at risk should consume a diet with <30% energy as fat and <10% energy as

saturated fat

• Diets of low energy density and containing a wide range of carbohydrate foods rich in

dietary fibre and of low glycaemic index (cereals, vegetables, legumes and fruits) are

recommended to reduce the risk of Type 2 diabetes

7

Primary Prevention - Part 2 December 2001

• Waist circumference (> 100 cm for European men and > 90 cm for European women)

should be used in addition to body weight and body mass index (BMI) to identify

individuals who should seek and be offered weight management programs

• Physical activity should be measured in free-living subjects by:

· movement recorders, particularly the pedometer

· questionnaires focussing on leisure-time activities

· heart rate monitoring

• Functional aerobic capacity should be measured from predictive equations based on

gender, age, self-reported physical activity and body composition or BMI

Identification of women with GDM would allow:

• Postnatal clinical interventions in those with diabetes persisting after delivery

• The option to use preventive methods to reduce the risk of Type 2 diabetes

• In view of the association between low birth weight and the later development of

diabetes, studies are required to evaluate interventions aimed at reducing low birth

weight and the impact this has on the development of Type 2 diabetes

• Programs of diet and exercise education in children should include parental involvement

and use behavioural techniques to reinforce lifestyle change

• Adults who have achieved weight loss require long-term programs involving diet,

exercise and social support to prevent weight regain, however the effectiveness of

specific long term programs requires further evaluation

• More research is required to assess the impact of psychosocial stress or a major

depressive episode in affecting the risk of Type 2 diabetes

8

Primary Prevention - Part 2 December 2001

Does obesity in adult life and/or childhood increase the risk of Type 2 diabetes?

Since obesity is associated with an increased risk of Type 2 diabetes, interventions to reduce

obesity may reduce the risk of Type 2 diabetes

• Underweight in childhood or adolescence may increase the risk of central adiposity,

insulin resistance and diabetes in adult life but more research will be required to

substantiate this

• Rapid weight gain in childhood may increase insulin resistance

• Intervention programs to increase exercise levels in addition to dietary change can reduce

obesity in children

• Programs in children aimed at reducing sedentary behaviour produce more body fat loss

after a year than programs aimed at increasing exercise activity or programs that combine

both approaches

9

Primary Prevention - Part 2 December 2001

• Obesity in adult life increases the risk of diabetes in both men and women;

the greater the magnitude of the obesity, the greater the risk

• Weight gain in adult life increases the risk of diabetes

• Risk of diabetes increases with the duration of the obese state

• In people with impaired glucose tolerance, obesity increases the risk of progression to

Type 2 diabetes

• Weight loss is associated with reduced progression of IGT to Type 2 diabetes

• Targeted dietary and exercise programs based on an empowerment model comprising

diabetes awareness sessions, exercise groups and cooking demonstrations have prevented

weight gain in a high-risk population

• Although body weight can be readily lost during active supervised treatment, it is often

gradually regained during the subsequent unsupervised period

• In men taught dietary and exercise strategies, waist circumference may decline during

active supervised treatment and remain reduced for up to 2 years

• Men and women who have successfully maintained long-term weight loss report the

continued consumption of a low energy, low fat diet

• In women, an ad libitum low fat diet is superior to a low energy diet in maintaining weight

after a major weight loss

• A program of combined diet and exercise is more effective in maintaining weight loss

than either diet alone or exercise alone

• Overweight men with impaired glucose tolerance who followed a long-term program of

diet and exercise exhibited reduced all-cause mortality after a period of 12 years

10

Primary Prevention - Part 2 December 2001

Obesity is a serious public health problem in Australia. Moreover, its prevalence is rising in

both men and women in all five-year age groups from 25 to 64 years (Risk Factor Prevalence

Study Management Committee, 1984). Overall, 75% of men and 65% of women aged 45 - 64

are overweight or obese (McLennan and Podger, 1997). Obesity is also a growing problem in

children in Australia. NHMRC data from 1985 indicated that 4% of boys and 6% of girls

between the ages of 9 and 15 were overweight (ie they had body mass index (BMI) values

development of Type 2 diabetes (see below), its increasing prevalence in Australia has

important implications for diabetes incidence.

Both within and between populations there is a strong association between body weight and

diabetes prevalence. A strong association (r =0.89) has been reported between the prevalence

of diabetes and population mean body weight based on data from 11 countries of widely

ranging socioeconomic development (West and Kalbfleisch, 1971). Himsworth in 1949

observed that diabetes-related mortality fell markedly in the UK with food rationing during

the first and second World War, a probable consequence of reduced population body weight

in older people (Himsworth, 1949). A much more frequent occurrence worldwide, is an

increase in diabetes prevalence with increased prevalence of overweight and obesity, as has

happened in Australia (Welborn et al, 1968; Glatthar et al, 1985; Risk Factor Prevalence

Study Management Committee, 1990). The relationship between increased obesity and

development of diabetes is particularly evident in populations that have rapidly made a

transition from a traditional to an urbanised way of life. Examples are the Pima Indians

(Knowler et al, 1990), Nauruans (Zimmet et al, 1990) and in Australian Aboriginal people

(Rowley et al, 1997).

In children between the ages of 2 to 18 years the presence of obesity should be determined by

measurement of height and weight and reference to standard gender-specific growth charts.

A simple measure to determine obesity in adults is the body mass index (BMI). This is

(National Institutes of Health Expert Committee, 1998). The health risks associated with

various BMI categories are shown in Table 1.

<18.5 Underweight Long term hazard to health

18.5-24.9 Normal Healthy range

25.0-29.9 Overweight Some risk to health

30.0-34.9 Obesity class I High risk to health

35-39.9 Obesity class II Severe risk to health

>40 Extreme obesity

Obesity class III

Very severe risk to health

It must be noted however, that although BMI is simple to determine, it is an imperfect

measure of total adiposity. When BMI is compared with the more accurate standard of

densitometry, correlation coefficients are found to lie between 0.6 and 0.8 (Spiegelman et al,

11

Primary Prevention - Part 2 December 2001

1992). The relationship between BMI and total adiposity moreover may differ between

Europeans and other ethnic groups such as Asian Indians and Australian Aborigines. For

example, for any given BMI, Aboriginal people have more body fat than Europeans

(Rutishauser and McKay, 1986).

In childhood the presence of Type 2 diabetes is uncommon although it may be found in some

sedentary, severely obese adolescents and its prevalence is increasing (Glaser, 1997; ADA,

2000). Nevertheless, body weight in childhood may influence the risk of diabetes in later

adult life. A study of 2,000 Americans aged 50 years or more, has indicated that self-reported

underweight as a child or as a teenager was associated with a slightly increased risk of Type 2

diabetes in adult life [Relative Risk (RR) 1.3 and 1.4, respectively] compared to those who

had maintained an appropriate childhood or adolescent weight (Holbrook et al, 1989).

Moreover, the relative risk was further increased in those who reported underweight in

childhood and overweight (BMI>26) in middle-age (RR 1.7, p< 0.01). Prospective studies,

based on measured rather than self-reported body weight are necessary to fully delineate the

effects of childhood and adolescent underweight on diabetes risk. Evidence relating to the

question of whether the low weight in childhood and adolescence leading to increased risk of

diabetes in adult life reflects low birth weight or intrauterine growth retardation will be

discussed separately in Prevention Issue 7.

As yet there is little direct evidence linking obesity in childhood to increased risk of diabetes

in adult life and more studies are needed in this area. Nevertheless, overweight or obesity in

childhood appears strongly related to the presence of obesity in adulthood. For example, in

the Minneapolis Childrens’ Blood Pressure Study, weight and BMI at age 7 years were

closely related to young adult weight and BMI (Sinaiko et al, 1999). In addition, the

Bogalusa Heart Study, indicated that in a biracial cohort of 783 children, followed from age

persisted into young adulthood (Srinivasan et al, 1996). Once overweight children have

become overweight adults, they are then at an increased risk of developing diabetes (evidence

outlined above).

A prospective study followed 137 Afro-American children from the Philadelphia Perinatal

Collaborative Project for 35 years (Hulman et al, 1998). In this study, BMI at age seven years

correlated with adult weight, and body weight at age 14 and was negatively correlated with

measures of insulin-stimulated glucose utilisation indicating that obese adolescents are at

greater risk for developing insulin resistance (Hulman et al, 1998). Similarly, a study in 1,258

Pima Indian children aged 5-19 years found that increased weight in childhood and

adolescence was predictive for later development of diabetes (McCance et al, 1994). The rate

of weight gain in childhood may also be of importance. In a longitudinal study in 152 black

South African children examined until age 7 years, rapid weight gain during childhood was

weakly related to insulin resistance as measured by homeostasis model assessment (HOMA)

(r= 0.18, p = 0.04) (Crowther et al, 1998). While people who become obese in adult life are

12

Primary Prevention - Part 2 December 2001

at increased risk of developing diabetes, the risk is not greater if they were obese from

childhood. In the study by Holbrook and coworkers (Holbrook et al, 1989), overweight adults

who reported that they were overweight as children or teenagers were at a similar risk of

diabetes to overweight adults who did not have a history of obesity in childhood or

adolescence.

The effect of BMI at age 21 on later risk of diabetes was examined in 27,983 American men

from the Men’s Health Professionals Study. After controlling for age, family history, smoking

same age (Chan et al, 1994). In a study of 895 Japanese men, it was also found that weight

gain in early adult life (ie between the ages of 21 and 25 years) was significantly related to

risk of Type 2 diabetes. If 10 kg or more was gained in this period, the Odds Ratio (OR) for

risk of Type 2 diabetes was 3.9 with a confidence interval (CI) 1.5-10.0 (Sakurai et al, 1997).

Many studies have demonstrated the importance of obesity in adult life as a major

independent risk factor for the development of Type 2 diabetes. Of 27,983 American men

aged 40-75 years, selected from the Men’s Health Professionals Study and followed over a

period of five years, 272 developed diabetes (Chan et al, 1994). The risk of Type 2 diabetes

was strongly associated with the presence of obesity as measured by the BMI (weight in kg

divided by height in metres, squared). Moreover, after adjustment for age, family history of

diabetes and smoking, the relative risk of diabetes was found to increase continuously with

men.

Clear positive associations between obesity, as measured by BMI, and increased risk of

diabetes have also been demonstrated in American men enrolled in the Usual Care Group of

the Multiple Risk Factor Intervention Trial (MRFIT) (Shaten et al, 1993). Associations were

also evident in British men participating in the British Regional Heart Study (Perry et al,

1995; Shaper et al, 1997) or in the 20 year follow-up to the Wickham Study (Vanderpump et

al, 1996); as well as in Norwegian men participating in the Finmark Study (Njolstad et al,

1998); in men from the Kinman Islands (Chou et al, 1998); in African American and

European men participating in the Atherosclerosis Risk in Communities Trial (Liese et al,

1997); in Japanese American men (Kahn et al, 1996), and in Pima Indian men (Hanson et al,

1995).

There is also evidence that obesity increases the risk of diabetes in women. In a cohort of

113,861 women aged 30-55 years and initially free of known diabetes, BMI was the most

based on a sub-cohort of 43,581 of these same American women derived after application of

tighter exclusion criteria. In this study, the relative risk of Type 2 diabetes was 11.2 (CI 7.9-

13

Primary Prevention - Part 2 December 2001

Norwegian study, the association between BMI and diabetes seen in women was attenuated

after multiple adjustment for other risk factors whereas in men the association remained

marked (Njolstad et al, 1998).

Absolute weight gain throughout adulthood is also a significant independent risk factor for

diabetes. For example, in men from the Mens’ Health Professionals Study, it was found that

men who had gained 5 kg since age 21 had no increased risk of diabetes in middle age [RR

0.9 (CI 0.5-1.8)]. In contrast, in men who had gained 15 kg the relative risk increased to 8.9

(CI 5.5-14.7) (Chan et al, 1994). Similarly, a study of 2,272 men from the Kuopio Ischemic

Heart Disease Risk Factor Study in Finland indicated that men who had gained more than

30% in body weight since the age of 20 were 10.6 times as likely to develop insulin resistance

than men who had remained within 10% of their earlier body weight (Everson et al, 1998). A

study of 2,000 American men and women found that a weight gain of more than 4.5 kg

between the ages of 40 and 60 years was associated with an increased risk of diabetes (RR

1.4, P < 0.05) (Holbrook et al, 1989). A weight fluctuation of 4.5 kg between the ages of 40

and 60 years also increased risk (RR 1.7, p< 0.01) (Holbrook et al, 1989).

Risk of diabetes also increases with the duration of obesity. A study of 1,598 Japanese men

employed by a railway company in Yokohama has shown that men who had a BMI greater

compared with non-obese men. If obesity continued for up to 20 years the relative risk of

diabetes climbed to 58.6 (CI 10.8-317.3) (Sakurai et al, 1999). In American Pima Indians, a

population group at very high risk of diabetes, a highly significant association (p< 0.001) was

evident between the duration of obesity and the incidence of obesity after adjustment for age,

sex and current BMI. The adjusted incidence of diabetes was 24.8 cases per 1,000 person

years in people with less than five years of obesity and 59.8 cases per 1,000 person years in

people with at least ten years of obesity (Everhart et al, 1992). The relative risk however, in

this population has to be seen within the background of known high incidence. There is also a

need for prospective studies on this question in other populations including those of European

origin.

The effect of obesity on the risk of progression from impaired glucose tolerance (IGT) to

Type 2 diabetes has been examined in a study on 183 middle-aged Finnish men and women.

These subjects were classified as having IGT by WHO criteria, then retested after 2 years.

Type 2 diabetes than those who were not obese [OR 4.4 (CI 1.3-13.5), p=0.04] (Qiao et al,

1996). In South African Indians with IGT, who were followed for four years, the absence of

obesity was also shown to be a significant predictive factor for reversion to normal glucose

tolerance [OR 4.2 (CI 1.4-13.1), p=0.013] (Motala et al, 1997).

The recently published Diabetes Prevention Study (Tuomilehto et al, 2001) conducted in

Finland with a 3.2 year follow-up showed that individual diet and exercise intervention could

14

Primary Prevention - Part 2 December 2001

reduce the risk of diabetes by 58% (p<0.001) in a high risk population. This was achieved

with 7 sessions in the first year and regular 3 monthly sessions after that during which goals

were set on how to lose weight (>5%), decrease total fat intake to <30% of energy, decrease

saturated fat intake to <10% of energy, increase fibre intake to >15 g/1000 kcal consumed and

to exercise for at least 30 min per day. The groups were individually given guidance to

increase their exercise levels (30 min per day) with a combination of endurance exercise and

supervised resistance training. The control group were set goals and given general written and

oral information about diet and exercise initially and then annually but no specific

individualized programs were offered. The incidence of diabetes in the intervention group was

significantly lower than the control group (11% vs 23%). The intervention group lost 4.7% of

their body weightsignificantly more than the 0.9% lost by the control group. Diabetes did not

develop in any of the groups who achieved 4 or 5 goals, however significantly more people

were able to achieve these goals in the intervention group (49 in the intervention group vs 15

in the control group, p< 0.001 for each of the goals). In those who did not achieve any of the

goals the incidence of diabetes was 38% vs 31% in the intervention and control groups

respectively (Tuomilehto et al, 2001).

The Diabetes Prevention Program was a major clinical trial conducted in the United States

over 3 years in a group of people with IGT and therefore at high risk of developing Type 2

diabetes. The intensive lifestyle intervention included instructed on a low fat diet, exercising

for 150 minutes per week and behaviour modification skills. The results show on average that

this group had a 7% weight loss in the first year and sustained a 5% loss for the study’s

duration and maintained 30 minutes of exercise per day. A 58% reduction in the risk of

developing Type 2 diabetes was found compared to the control group who received only basic

diet and exercise advice. This study also included another group which was treated with

Metformin which was effective in reducing the risk of developing diabetes by approximately

30% (National Institutes of Diabetes and Digestive and Kidney Diseases, 2001).

Type 2 diabetes. However, the risk of type 2 diabetes and associated insulin resistance can be

greatly ameliorated by substantive weight loss. This was clearly demonstrated in a study

where a group of 109 patients from a total of 136 individuals with severe obesity received

bariatric surgery to induce marked weight loss. In the control group who did not receive

surgery and who were followed on average for 4.8 years, the conversion to diabetes (by WHO

criteria) was 4.72 cases per 100 person years. In contrast, among those who received surgical

treatment, and who were followed on average for 6.2 years, only one individual developed

diabetes (this person overcame the effect of the surgery and regained the initial weight lost), a

conversion rate of only 0.15 cases per 100 person years (p < 0.0001). Weight loss thus

prevented progression to diabetes by more than 30-fold in this patient group (Long et al,

1994). In a second study, gastric bypass surgery was used to induce marked weight loss in

severely obese individuals, and it was found that 121 of 146 patients (82.9%) with Type 2

diabetes reverted to normal glucose tolerance which was maintained for up to 14 years of

follow up. Furthermore, 150 of 152 patients with glucose intolerance at baseline maintained

normal levels of plasma glucose, glycosylated haemoglobin and insulin up to 14 years later

(Pories et al, 1995).

15

Primary Prevention - Part 2 December 2001

• There is moderately strong evidence that underweight in childhood and adolescence

followed by over-weight in middle-age will increase the risk of diabetes in adult life.

Further studies are required on this question however and links need to be made to both

birth weight and the pattern of growth in utero (Prevention Section 7)

• As yet there is little evidence directly linking obesity in childhood to increased risk of

diabetes in adult life, although it has been shown that black South African children who

gain weight rapidly have increased insulin resistance. Nevertheless, there is good evidence

that overweight children are at high risk of becoming overweight adults. Overweight

adults are then at increased risk of developing diabetes

• There is strong evidence, from well designed longitudinal studies highly applicable to

Australia, indicating that obesity in adult life increases the risk of Type 2 diabetes

particularly in men

• There is strong to moderate evidence that weight gain in adult life increases diabetes risk

• There is moderately strong evidence in at least one ethnic population that the duration of

adult obesity is related to diabetes risk, but further research is needed in other populations

including those of European origin

• There is moderate evidence that obesity increases the risk that people with IGT will

progress to Type 2 diabetes

• In severely obese patients surgically - induced weight loss not only markedly decreases

the risk of progression from impaired glucose tolerance to diabetes, but also reverses

established Type 2 diabetes

• Research will be required in the future to:

- clarify the role of early life influences (fetal, neonatal, childhood) on the development

of obesity and the risk of Type 2 diabetes in later life

- undertake large cohort studies in women where body weight and waist circumference

are measured rather than self-reported

16

Primary Prevention - Part 2 December 2001

Rating

Rating

(Adult women – US) III-2 Cohort Medium High+ High

(Adult men - US) III-2 Cohort Medium High+ High

(Adult men – Kinmen, Tiawan) III-2 Cohort High High+ Medium

(Adult women - US) III-2 Cohort Medium High+ High

(Children - Sth Africa: Black Sth

African) III-2 Cohort Medium High+ Low

(Adults – US: Pima Indian) III-2 Cohort High High+ Low

(Adult men – Finland) III-2 Cohort Medium High+ High

(Adults – US: Pima Indian) III-2 Cohort Medium High+ Low

(Adults - US) III-2 Cohort Medium High+ High

(Children – US: African

American) III-2 Cohort Medium Low Low

(Adult men – US: Japanese) III-2 Cohort Medium High+ Low

(Adults – US) III-2 Cohort Medium High+ High

(Adults - US) III-2 Cohort Medium High+ High

(Adults & Children – US: Pima

Indian)

III-2 Cohort Medium High+ Low

(Adults – South Africa: Indian) III-2 Cohort Medium High+ Medium

(Adults – US) II RCT High High + High

(Adults – Norway) III-2 Cohort Medium High+ High

(Adult men - UK) III-2 Cohort Medium High+ High

(Adults & adolescents – US) III-2 Cohort Medium High+ High

(Adults - Finland) III-2 Cohort Medium High+ High

(Adult men – Japan) III-2 Cohort High High+ Low

(Adult men – Japan) III-2 Cohort High High+ Low

(Adult men – UK) III-2 Cohort Medium High+ High

(Adult men – US) III-2 Cohort High High+ High

(Children – US) III-2 Cohort Medium High+ High

(Adolescents – US) III-2 Cohort High High+ High

(Adults – Finland) II RCT High High + High

(Adults – UK) III-2 Cohort Medium High+ High

+ Indicates that obesity is associated with Type 2 diabetes: Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and

‘-‘ for a negative effect. Low = no statistically sigificant effect

17

Primary Prevention - Part 2 December 2001

Is the pattern of fat distribution an additional contributor to total adiposity in determining the

risk of Type 2 diabetes and how does this vary with ethnicity?

Abdominal obesity is an important indicator of increased risk of Type 2 diabetes in all ethnic

groups and should be a particular focus of weight loss programs.

• In men, waist circumference is a better predictor of Type 2 diabetes than waist-hip ratio

particularly when used with body mass index

• In women, android obesity (as indicated by the waist:hip ratio or waist circumference)

increases the risk of Type 2 diabetes and there is less agreement in women than in men

whether BMI contributes more to risk than WHR or waist circumference

• Ethnic groups differ in their patterns of body fat distribution

• In some ethnic groups total adiposity (as indicated by BMI) is more strongly related to

risk of Type 2 diabetes, while in other ethnic groups android obesity is more strongly

related to risk

18

Primary Prevention - Part 2 December 2001

Obesity in adult life is an important independent risk factor for the development of Type 2

diabetes (Ferrannini and Camastra, 1998). Anthropometry is the term for techniques used to

the measure the size, weight and proportions of the human body. Generalised obesity can be

estimated anthropometrically using the Body Mass Index (BMI) (weight in kg divided by

height in metres squared). A high BMI has been shown in many studies to be a strong

determinant for the development of Type 2 diabetes both in men (Feskens and Kromhout,

1989; McPhillips et al, 1990; Haffner et al, 1990; Cassano et al, 1992; Shaten et al, 1993;

Chan et al, 1994; Perry et al, 1995), and in women (Lundgren et al, 1989; McPhillips et al,

1990; Haffner et al, 1990; Kaye et al, 1991; Carey et al, 1997). However, in addition to total

adiposity, the pattern of body fat distribution also appears to play an important part in

determining diabetes risk.

In premenopausal women, excess body fat is deposited on the hips and thighs in what is

known as a gynoid pattern of fat distribution (Kissebah and Krakower, 1994). In relation to

health risks, this pattern of fat distribution appears relatively benign (Terry et al, 1991;

Williams et al, 1997). It is of interest therefore, that women who develop Type 2 diabetes

retain significantly less fat on the hips and thighs than women of the same age and total

adiposity who have not developed diabetes (Stoney, 1998). Seidell and co-workers (1997),

similarly found in a cross-sectional study that Type 2 diabetes was most prevalent in men and

women with larger waist and smaller hip circumferences.

In men and in many women after menopause, excess body fat is deposited not on the hips and

thighs, but in the abdominal region. This is known as an android or centralised pattern of fat

distribution (Kissebah and Krakower, 1994; Kissebah, 1996). Women who develop Type 2

diabetes are characterised by their accumulation of abdominal fat (Hartz et al, 1983). As

detailed below, an android pattern of body fat distribution has been implicated with increased

risk of Type 2 diabetes both in men and in women.

Abdominal fat can be deposited both subcutaneously as abdominal subcutaneous fat and

internally around the body organs as visceral fat. The amount of abdominal subcutaneous fat

and the amount of visceral fat can now be measured by imaging techniques such as

Computerised Tomography (CT) and Magnetic Resonance Imaging (MRI) (van der Kooy and

Seidell, 1993). Visceral fat comprises about 10% of total fat mass whereas abdominal

subcutaneous fat comprises about 20% of total fat mass (Abate et al, 1995). Interest has

focussed on the important role that visceral fat plays in the metabolic consequences of obesity

because of its venous drainage into the portal system and directly into the liver where excess

non-esterified fatty acids and glycerol may adversely affect liver metabolism (Bouchard et al,

1993; Kissebah and Krakower, 1994). Loss of visceral fat leads to reductions in the risk of

cardiovascular disease and also improves metabolic control in Type 2 diabetes (Kissebah and

Krakower, 1994). Abdominal subcutaneous fat is less lipolytically active than visceral fat

(Rebuffé-Scrive, 1989), but evidence is accumulating that subcutaneous abdominal fat may be

an important contributor to insulin resistance (Abate et al, 1995; Abate et al, 1996;

Goodpaster et al, 1997).

The waist: hip ratio (WHR) is the most commonly used simple anthropometric measure to

indicate android obesity (van der Kooy and Seidell, 1993). A high WHR (WHR > 1.0 in men

and > 0.85 in women) is now accepted as a method of identifying people with android obesity

19

Primary Prevention - Part 2 December 2001

(WHO, 1998). Android adiposity may also be indicated by waist circumference (National

Institutes of Health Expert Committee, 1998), which may be an even better predictor of

diabetes risk in men and women than the WHR. Lean and co-workers (1995) have

recommended that at a waist circumference of ≥ 94 cm in men and ≥ 80 cm in women,

subjects should gain no further weight; at a waist circumference of ≥ 102 cm in men and ≥88

cm in women, subjects should reduce their weight. In Australia, 67% of men aged 45-64 have

a waist circumference >90 cm and 46% of women aged 45-64 have a waist circumference

greater than 80 cm (Risk Factor Prevalence Study Management Committee, 1990). More

recent Australian data from the National Nutrition Survey will soon be available.

In European men BMI appears to be a stronger indicator for risk of Type 2 diabetes than

WHR. This is evidenced in a study based on self-reported measurements made by 51,529

American men who were followed for five years (Chan et al, 1994). After adjustment for age,

smoking status, family history and quintiles of WHR, men in the highest BMI quintile (BMI

However, after adjustment for age, family history, smoking status and BMI, the risk of

diabetes in men from the highest quintile of WHR (above 0.99) versus men in the lowest

quintile of WHR (< 0.90) was only 1.7 (CI 1.1-2.7). An earlier, much smaller study on a

cohort of 1,972 American men, came to the opposite conclusion that android adiposity might

have a higher effect on diabetes risk than BMI (Cassano et al, 1992).

There is also evidence to suggest that WHR may not indicate diabetes risk as clearly as waist

circumference. After adjustment for age, family history of diabetes, smoking and BMI, the

relative risk of Type 2 diabetes for the highest quintile of waist circumference (102-175 cm)

versus the lowest quintile (75-86 cm) was 3.5 (CI 1.7-7.0). After similar adjustment, the

relative risk of Type 2 diabetes for the highest quintile of WHR (WHR > 0.99) versus the

lowest quintile (WHR <0.90 cm) was only 1.7 (CI 1.1-2.7) (Chan et al, 1994).

In women, as in men, android obesity appears to be a strong marker for insulin resistance. In a

study of 22 Australian women, the level of total and central obesity was measured both by

anthropometry and by Dual Energy X-ray Absorptiometry (DXA) while insulin sensitivity

was determined by euglycemic-hyperinsulinemic clamp (Carey et al, 1996). Insulin sensitivity

was found to be significantly and inversely related to BMI (r = 0.48) but a similar level of

relationship was also seen with waist circumference and the WHR (r= -0.43 and r= -0.46,

respectively). Rissanen and coworkers (1997) also found in Finnish women that both the

WHR and the waist circumference had a similar relationship to the metabolic risk profile.

Android obesity in women has also been shown to be associated with increased risk of Type 2

diabetes. This was indicated by anthropometry in the Nurses’ Health Study where 43,582 US

women who self-reported weight, waist and hip measurements, were followed from 1986 to

1994 (Carey et al, 1997). Controlling for BMI and other cofounders, the relative risk of Type

20

Primary Prevention - Part 2 December 2001

5.1 (CI 2.9-8.9). The waist circumference in these American women was therefore a stronger

indicator of risk than the WHR.

In the Nurses Health Study, it was also found that BMI was a stronger predictor of diabetes

1997). This finding contrasts with that of an earlier study on women from Gothenberg,

Sweden (Lundgren et al, 1989). Here the incidence of diabetes was found to increase 8 times

between the highest and lowest quintile for BMI, while the incidence of diabetes increased

13.6 times between the highest and lowest quintile of WHR. Another study in Mexican

Americans enrolled in the San Antonio Heart Study also found a slightly greater effect of

android adiposity (measured as the centrality index or the ratio of subscapular to triceps

skinfold) than total adiposity (measured by BMI) on diabetes risk in women (Haffner et al,

was 2.25 (CI 0.62-8.11), while the OR for the centrality index was 3.28 (CI 0.41-26.). In a

later study, where the WHR was used to indicate android obesity, it was again confirmed that

android obesity had a greater effect on diabetes risk in Mexican American women than in

Mexican American men (Haffner et al, 1992).

Android fat distribution has been implicated as a risk factor for the development of Type 2

diabetes in a number of different ethnic groups. These include Europeans (Ohlson et al,

1988); Mexican Americans (Haffner et al, 1990; Haffner et al, 1991; Wei et al, 1997); Asian

Indians, Creoles and Chinese from Mauritius (Dowse et al, 1991) and Chinese living in the

UK (Unwin et al, 1997).

Some ethnic groups appear to be more prone to deposit body fat in an android pattern than

Europeans. For example, Indians from Mauritius have a greater amount of abdominal fat (as

indicated by the WHR) for any given level of BMI than the Creole or Chinese Mauritians

(Hodge et al, 1996). Another study comparing European and Pakistani men living in the UK,

distinctly different patterns of body fat deposition (Bose, 1996). By anthropometry, the

European men had more fat accumulated over both the upper and lower body, whereas in the

Pakistani men, fat deposition was more concentrated on the abdomen. Asian Indian men from

the United States have similarly been reported to have relatively high levels of abdominal fat

(Banerji et al, 1999). Anthropometric measures moreover, may not always reveal differences

in visceral fat between ethnic groups. Body fat distribution was compared by CT in 23 Afro-

American and 15 European women who had similar mean BMI and WHR (Lovejoy et al,

1996). Despite the close similarity by anthropometry, African-American women by CT were

found to have a smaller area of visceral fat.

It is evident that ethnicity can influence relationships between total adiposity, body fat

distribution and diabetes risk. In Japanese men the WHR may have a greater influence on risk

than the BMI. A cohort study of 2,228 men from the Japanese military indicated that after

adjusting for WHR, the OR for Type 2 diabetes in the highest versus the lowest quintile of

BMI was 1.2 (CI 0.6-2.5). In contrast, after adjustment for WHR, the OR for the highest

versus the lowest quintile of WHR was 3.5 (CI 1.5-2.9) (Sakurai et al, 1995). In this study, the

mean BMI of men studied was within the normal range (mean ± SD, 23.8 ± 2.6) while the

mean WHR was relatively high (0.902 ± 0.049).

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Primary Prevention - Part 2 December 2001

In 721 Mexican Americans followed for 7.2 years, a comparison was made between BMI,

waist circumference and the WHR as predictors of Type 2 diabetes (Wei et al, 1997). In this

population, waist circumference was clearly found to be the best predictor of diabetes risk, the

risk of diabetes being 11 times greater in those in the highest quartile for waist circumference

than those in the lowest quartile (CI 4.2-28.8). In addition, by multivariate analysis, the waist

circumference was the only significant predictor of Type 2 diabetes in this population, in

models where WHR and BMI were also entered either separately or simultaneously. A crosssectional

study has reported that an increased WHR was associated with higher risk in non-

Hispanic Whites than in Hispanics (Marshall et al, 1993). Another study compared the effect

of WHR on risk of Type 2 diabetes in Asian Indians from Madras, and Mexican Americans

and non-Hispanic whites recruited into the San Antonio Heart Study (Ramachandran et al,

1997). Here it was found that although the non-Hispanic whites had a WHR significantly

above the other groups (p<0.05), WHR was only predictive of Type 2 diabetes in the Asian

Indians and the Mexican Americans.

A cross-sectional comparison has also been made of the effects of both WHR and waist

circumference on risk of Type 2 diabetes in African-Americans and Jamaicans (Okosun et al,

1998). In Jamaican men (n= 510), a waist circumference equal or greater than 94 cm was

associated with an age-adjusted OR of 2.09 (CI 1.38-3.16), whereas in African-American men

(n=844) this waist circumference was associated with an age-adjusted OR of 3.90 (CI 2.67-

5.72). In Jamaican women (n=776), a waist circumference equal or greater than 80 cm was

associated with an age-adjusted OR of 3.38 (CI 1.84-6.21), whereas in African-American

women (n=983) this waist circumference was associated with an age-adjusted OR of 1.63 (CI

0.94-2.81).

The evidence indicates that different ethnic groups differ in the level of risk associated with

waist circumference, WHR or BMI. At present, these measures provide a practical method of

identifying Europeans with android obesity but grading systems developed for the European

population must be applied with caution to other ethnic groups.

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Primary Prevention - Part 2 December 2001

• Waist circumference in European men is a better predictor of Type 2 diabetes than waisthip

ratio particularly when used with Body Mass Index

• While there is strong evidence in European women that android obesity increases the risk

of Type 2 diabetes, there is less agreement on whether the risk associated with android

obesity in women is greater than that associated with total adiposity

• There is strong evidence that different ethnic groups differ in whether total adiposity (as

indicated by BMI) or android adiposity (as indicated by WHR or waist circumference) is

the stronger indicator of diabetes risk. There is a need for further research to establish cutoff

values for waist circumference or WHR relative to diabetes risk in different ethnic

groups

23

Primary Prevention - Part 2 December 2001

(Adult men – US: Asian

Indian)

III-2 Cross-sectional Medium HighA+ Medium

(Adult men – UK: Caucasian

& Pakistani)

III-2 Case-control Medium HighA+; E+ Medium

(Adult women – Australia) III-2 Cross-sectional Medium HighA+ High

(Adult women – US) III-2 Cohort Low HighA+ High

(Adult men – US) III-2 Cohort Medium HighA+ High

(Adult men – US) III-2 Cohort Medium HighA+ High

(Adults – Mauritius) III-2 Cross-sectional Medium High A+; E+ Low

(Adults – US: Mexican

American)

III-2 Cohort High High A+ Low

(Adults – US: Mexican

American)

III-2 Cohort High High A+ Low

(Adults – US: Mexican

American)

III-2 Cross-sectional High High A+ Low

(Adults – Mauritius) III-2 Cohort High Medium A+; E+ Low

(Adults – Scotland, UK) III-2 Cross-sectional Medium High A+ High

(Adult women – US) III-2 Case-control Medium High A+; E+ High

(Adult women – Sweden) III-2 Cohort Medium High A+ High

(Adults – US) III-2 Case-control Medium High A+; E+ High

(Adult men - Sweden) III-2 Cohort Medium High A+ High

(Adults – Jamaica; US;

Nigeria)

III-2 Cross-sectional High High A+; E+ Low

(Adults – US; India) III-2 Cross-sectional Medium High A+; E+ Medium

(Adult women – Finland) III-2 Cross-sectional High High A+ High

(Adult men – Japan) III-2 Cross-sectional High High A+ Low

(Adults – Korea) III-2 Cohort with nested

Case-control High Low Low

(Adults – UK: Chinese,

Europid)

III-2 Cross-sectional High High A+; E+ Medium

(Adults – US: Mexican

American)

III-2 Cohort High High A+ Low

+ Indicates that ethnicity or adiposity is associated with increased risk of Type 2 diabetes; A Adiposity; E Ethnicity.

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect. Low = no statistically sigificant

effect

24

Primary Prevention - Part 2 December 2001

What simple measures can be used to determine body fat distribution?

Waist circumference (>100 cm for European men and >90 cm for European women) should

be used in addition to body weight and body mass index (BMI) to identify individuals who

should seek and be offered weight management programs.

• The waist circumference has major advantages over the WHR in its simplicity of

measurement and its relation to body weight and fat distribution

• Threshold values for waist circumference may be lower in older adults

• Threshold values for waist circumference are population specific

# Studies assesed using the non-intervention assesment

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Primary Prevention - Part 2 December 2001

Simple anthropometric measures can be used to identify individuals at increased risk of Type

2 diabetes due to abdominal fat accumulation. Waist circumference and the waist to hip

circumference ratio (WHR) are commonly used anthropometric measures for this purpose

(Han et al, 1995; Carey et al, 1997; Ledoux et al, 1997; Han et al, 1998). Over the last decade,

a high WHR (WHR > 1.0 in men and > 0.85 in women) has become accepted as the clinical

method for identifying patients with abdominal fat accumulation (James, 1996). More recent

evidence, however, suggests that waist circumference alone - measured at the midpoint

between the lower border of the rib cage and the iliac crest - may provide a better correlate of

abdominal adiposity and diabetes risk (Lean et al, 1995; Seidell, 1995; James, 1996).

These thresholds for waist circumference may be lower in European men and women over the

age of 40 years. These threshold values for waist circumference are not directly applicable to

people from other ethnic backgrounds although the same principles apply.

The advantage of using waist circumference as an indicator of impaired health and diabetes

risk was demonstrated by Lean and co-workers in a cross-sectional study of 5,887 men and

7,018 women aged 20-59 years recruited from the general population of two cities in the

Netherlands (Lean et al, 1998). After adjustment for age and lifestyle, risk of Type 2 diabetes

was found to be increased in men who had a waist circumference greater than 102 cm [OR 4.5

(CI 2.5-7.8)]. The risk of Type 2 diabetes was similarly found to be increased in women with

a waist circumference greater than 88 cm [OR 3.8 (CI 1.9-7.3)].

Arguments advanced in favour of the waist circumference include its simplicity of

measurement and its relation to body weight and fat distribution (Lean et al, 1995). In

comparison with the WHR, the waist circumference appears be a better indicator of adiposity

in women. In a study of 96 women aged 16-80 years, body fat reference measures were

obtained by Dual Energy X-ray Absorptiometry (DXA) (Taylor et al, 1998). In comparing the

results of anthropometric measures with DXA, a positive reference test was defined as a result

categorises only 58% of subjects, misclassifying 28 whereas the waist circumference

percentile, 86.9 cm accurately classified 83% of subjects, misclassifying only eight (p<0.05).

In another study of 24 older men with cardiac disease where estimation of body fat by

circumferences was compared with hydrostatic weighing, use of circumferences was found to

be accurate, easy to administer and had low potential for error (Young et al, 1998).

Lemieux and co-workers examined the relationship of anthropometric variables to visceral

adipose tissue accumulation (by computed tomography (CT)) in 213 men and 190 women and

determined how these relationships were affected by sex, age, menopausal status, and the

degree of obesity (Lemieux et al, 1996). The threshold for visceral adipose tissue

26

Primary Prevention - Part 2 December 2001

women, substantial alterations in plasma glucose-insulin and lipoprotein profiles are observed

(Despres and Lamarche, 1993).

The relationship of anthropometric variables to visceral adipose tissue accumulation also

appeared age-specific. In both sexes, waist circumferences corresponding to the critical

threshold of visceral adipose tissue were lower in older subjects (≥40 years old) than in

younger individuals. Thus in men older than 40 years, a waist circumference of 90.9 cm

than 40 years, a waist circumference of 98.9 cm corresponded to a visceral adipose tissue area

Another similar smaller study in 71 Dutch men and 34 Dutch women has also found that

intra-abdominal fat area by CT was higher in older subjects for a given waist circumference

(Han et al, 1997).

In the study by Lemieux and co-workers, it was found from computed regression equations,

that a waist circumference of 95 cm in men or women, or a WHR of 0.94 in men and of 0.88

this study, threshold values of waist circumference were similar in normal and overweight

men and women, whereas threshold values of WHR were higher in normal than in overweight

subjects (Lemieux et al, 1996). The waist circumference was argued to be a stronger and more

convenient anthropometric correlate of visceral adipose tissue than the WHR since threshold

values of the waist circumference appeared to be independent of the degree of obesity

(Lemieux et al, 1996).

The effect of change in visceral adipose tissue on waist circumference has also been

examined. In a study in 842 Swedes aged 37-60 years, it was found that over a 2-year period,

for a given change in BMI, the change in visceral adipose tissue was 6 times greater in men

than it was in women (Sjostrom et al, 1997). Despite this difference, the change in both the

waist circumference and the WHR was similar in both sexes. The low change in visceral

adipose tissue in women found in this study may have reflected the relatively high proportion

who were pre-menopausal and had gynoid rather than android obesity.

A community derived random sample of men and women was studied in North Glasgow, UK,

to test the hypothesis that waist circumference might be used as a single measurement to

identify overweight people with android obesity (Lean et al, 1995). A group of 1,014 women

and 904 men, aged 25-74 years, was recruited from the general population of North Glasgow.

From this population thresholds were determined for waist circumference that identified

subjects with high BMI or with high WHR. These threshold levels were then validated on a

separately recruited group of 86 men and 202 women. These people were slightly younger

than the first group, but exhibited a similar range of anthropometric measurements. Results

indicated that a waist circumference ≥ 94 cm for men and ≥ 80 cm for women identified both

for men, ≥ 0.80 women) with a sensitivity of 96% and specificity 97.5%. A waist

circumference ≥102 cm for men or ≥88 cm for women identified subjects with BMI ≥30 and

those with lower BMI but high WHR with a sensitivity of 96% and specificity 98%. In this

case only 2% of the sample was misclassified. From these data, Lean and co-workers

recommended the following cut-off values: at a waist circumference of ≥ 94 cm in men and ≥

80 cm in women, subjects should gain no further weight; at a waist circumference of ≥ 102

cm in men and ≥ 88 cm in women, subjects should reduce their weight. The waist

circumference could therefore be used to identify individuals who should seek and be offered

27

Primary Prevention - Part 2 December 2001

weight management programs. These threshold values identifying men at increased risk are

very similar to those obtained by Lemieux and coworkers (Lemieux et al, 1996), however, the

values for women were considerably lower. This may be due to the fact that the relationship

of the waist circumference to visceral adipose tissue is influenced by age (Lemieux et al,

1996).

A study was carried out to determine whether waist circumference could accurately identify

android obesity in 1,513 Hong Kong Chinese men and women (Ko et al, 1996). It was found

that a waist circumference ≥ 94 cm for men and ≥ 80 cm for women identified subjects with

women) with a sensitivity of 31% and specificity of 100%. Decreasing the waist

circumference cut-off to 85 cm for men and 75 cm for women increased the sensitivity to

79.2% in men and 56.4% in women. It was concluded that a single waist circumference

measurement did not allow sensitive identification of Chinese people at health risk from being

overweight or from having central obesity (Ko et al, 1996). This study indicates threshold

values for waist circumference may be population specific.

• There is moderate to strong evidence, from studies applicable to Australia, that body mass

index (BMI), waist to hip circumference ratio (WHR), and waist circumference are

powerful independent predictors of Type 2 diabetes

• There is moderate evidence to indicate that the simplicity of measurement and its relation

to body weight and fat distribution are major advantages for waist circumference over

BMI and WHR

• There is strong evidence that waist circumference identifies normal-weight and

overweight subjects with excess visceral adipose tissue which increases their risk of Type

2 diabetes

• There is strong evidence to suggest that in both men and women, threshold values of waist

circumference corresponding to critical amounts of visceral adipose tissue are lower in

those over 40 years than in younger individuals

• It has been suggested on the basis of experimental studies that in European populations,

men with a waist circumference ≥ 94 cm and women with a waist circumference ≥ 80 cm

should gain no further weight. In addition, men with a waist circumference ≥ 102 cm and

women with a waist circumference ≥ 88 cm should reduce their weight

• These values could be changed to the closest mark on the tape measure to increase the

ease of recording them for the health professional. The measurements that indicate the

need to maintain weight in the European populations would be >95 cm for men and >80

cm for women and to reduce weight at >100 cm for men and >90 cm for women

• There is moderately strong evidence to suggest that threshold values for the waist

circumference are population specific

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Primary Prevention - Part 2 December 2001

(Adult women – US) II# Cohort Low HighB+ ; W+ High

(Adult men - US) II# Cohort Low High B+; W+ High

(Adult women – Scotland,

UK; The Netherlands)

III # Cross-sectional Low High W+ High

(Adults - Hong Kong) III# Cross-sectional Medium High W+ Medium

(Adults – Scotland, UK) III# Cross-sectional Medium High W+ High

(Adults - The Netherlands) III# Cross-sectional Medium High W+ High

(Adults - Canada) III# Cross-sectional Medium High W+; B+; WHR+ High

(Adults - Canada) III# Cross-sectional Medium High W+ High

(Adults - Sweden) II# Cohort Medium High C+ High

(Adults – New Zealand) III# Cross-sectional Medium High W+; B+; High

(Adult men - US) III# Cross-sectional Medium Medium W+ High

# Studies assesed using the non-intervention assesment

+ The measure of body fat distribution is associated with an increased risk of Type 2 diabetes; B BMI; W Waist Circumference; WHR Waist –

hip ratio; C Computed tomography.

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect.

29

Primary Prevention - Part 2 December 2001

What is the evidence that physical activity can decrease, and sedentary behaviour increase, the

risk of Type 2 diabetes?

Regular physical activity is recommended to reduce the risk of Type 2 diabetes.

• In both men and women, physical activity reduces the risk of Type 2 diabetes

• In adults, the risk of Type 2 diabetes declines as frequency of exercise increases to 3-5

times per week

• In men, the risk of Type 2 diabetes declines with increased energy expended

• Men with low cardiovascular fitness have greater risk of IGT and Type 2 diabetes than

men with high cardiovascular fitness

• Physical activity reduces the risk of Type 2 diabetes in older men just as well as it does in

younger men

• Physical activity reduces the risk of Type 2 diabetes more in overweight and obese men

than it does in non-obese men

• Exercise programs can slow the progression from IGT to Type 2 diabetes

• Exercise can reduce diabetes-related mortality

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Primary Prevention - Part 2 December 2001

As societies become more sedentary with westernisation, the prevalence of Type 2 diabetes

increases (Collins et al, 1994). Accordingly, as reviewed below, individuals who maintain a

physically active lifestyle appear to be at less risk of developing IGT and Type 2 diabetes

(Ivy, 1997).

The importance of physical activity as part of a lifestyle that protects against diabetes has only

been recognised relatively recently. In 1983, Horton suggested that preventive measures to

reduce the prevalence of Type 2 diabetes should not only focus on eliminating or reducing

factors such as obesity that were associated with the development of insulin resistance, but

should also encourage physical activity that enhanced insulin sensitivity (Horton, 1983). Since

that time, increasing attention has been given to the importance of physical activity in diabetes

prevention (Hardman, 1996; Ivy, 1997).

Greater levels of physical activity have been found to be inversely related to the presence of

Type 2 diabetes in 14 of 16 cross-sectional studies (James et al, 1998). Moreover, findings in

cross-sectional studies appear to be consistent across different ethnic groups, including Fijian

Indians (Taylor et al, 1984), Micronesians (King et al, 1984), Polynesians (Taylor et al, 1983),

Mauritians (Dowse et al, 1991) and Swedes (Cederholm and Wibell, 1985). Prospective

cohort studies as outlined below, have now greatly strengthened these findings.

Increased physical activity reduces the risk of Type 2 diabetes in men. In the Physicians’

Health Study, where 21,271 American men were followed for five years, men who reported

exercising at least once a week had an age-adjusted risk of Type 2 diabetes of 0.64 (CI 0.51-

0.82, p < 0.003) relative to men who exercised infrequently (Manson et al, 1992). This

association was little attenuated by adjustment for BMI, indicating that physical activity is an

independent risk factor for diabetes. A multicentre study of 7,735 British men has also shown

that increased physical activity reduces diabetes risk. In this study, after adjustment for age

and BMI, the risk of diabetes for men who reported that they engaged in moderate levels of

physical activity was reduced to 0.4 (CI 0.2-0.7) relative to men who were inactive (Perry et

al, 1995).

While most studies that examine the relationship between diabetes risk and physical activity

have relied on self-reported exercise patterns, one recent study has examined the relationship

between measured cardiovascular fitness and diabetes risk (Wei et al, 1999). In this study,

which was carried out in 8,633 American men, cardiovascular fitness was estimated by a

maximal exercise test on a treadmill. After a follow-up of 6 years, it was found that men who

were least fit at baseline (20% of the cohort) had a 3.7 times increased risk of Type 2 diabetes

(CI 2.4-5.8) compared with those with greatest cardiovascular fitness at baseline (40% of the

cohort).

The protective effect of physical activity appears to be most pronounced in the heaviest men.

In a study of male alumni it was demonstrated that each 2-unit increase in BMI was

associated with a 21% increase in the risk of Type 2 diabetes (Helmrich et al, 1991). A greater

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Primary Prevention - Part 2 December 2001

protective effect of exercise in overweight or obese men was also reported in the Physicians’

Health Study (Manson et al, 1992).

The age at which men are exercising does not appear to affect the protective influence of

increased physical activity. In the Honolulu Heart Study, the effect of physical activity on

diabetes risk was found to be similar in both middle-aged and older men. Thus for men aged

45-54 at baseline the OR was 0.52 (CI 0.32-0.85) while for men aged 55-68 at baseline the

OR was 0.45 (CI 0.25-0.80) (Burchfiel et al, 1995).

The association between physical activity and the risk of diabetes has been much less

frequently studied in women than in men. In the Nurses Health Study however, 87,253

American women were followed for 8 years. It was found that women who reported that they

engaged in vigorous activity once or more times per week had a reduced age-adjusted relative

risk of diabetes of 0.67 (CI 0.60-0.75) compared with women who did not exercise (Manson

et al, 1991).

The effect of physical activity on diabetes risk is also evident from cohort studies in different

ethnic groups. Protective effects were evident in Japanese American men enrolled in the

Honolulu Heart program (Burchfiel et al, 1995), in African-American men and women

participating in the Pitt County Study (James et al, 1998) and in Mexican American men

(Monterrosa et al, 1995). A case-control study in Japanese military men (men with IGT,

n=38; men with normal glucose tolerance, n=60) has also indicated the protective value of a

high level of fitness at age 30-39 on the risk of impaired glucose tolerance 20 years later

(Takemura et al, 1999). In this study, physical fitness was indicated by the shortest time in

which the men had run 1,500 m when they were aged 30-39 years. The reduced risk of

impaired glucose tolerance with high physical fitness at this age was 0.31 (CI 0.11-0.86,

p<0.05).

A recent randomised control trial carried out in the city of Da Qing, China has shown that an

exercise intervention program can reduce the rate of conversion from IGT to Type 2 diabetes

(Pan et al, 1997). In this study, men and women with IGT (n=577) were randomised to a

control group and intervention groups including a group treated with exercise. After 6 years,

the incidence of diabetes was 68% (CI 60-75%) in the control group but only 41% (CI 33-

49%) in the exercise group (p<0.05). By a proportional hazards analysis, and adjusting for

differences in baseline BMI and fasting glucose, the exercise intervention was associated with

a 46% reduction (p<0.005) in the risk of developing diabetes. It should be noted that the

cohort treated in this Chinese study was both relatively lean and physically active. The data

may not therefore be directly extrapolated to people with IGT in western societies on whom

further research of this nature is needed.

Increased physical activity may reduce diabetes-related mortality. In the Malmö Preventive

Trial, it was found after 12 years follow-up that men with IGT (n=288) who participated in a

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Primary Prevention - Part 2 December 2001

diet and exercise program, exhibited similar mortality to men (n=6,389) with normal glucose

tolerance (6.5 vs 6.2 deaths /1,000 person years, respectively). In marked contrast, 135 men

with IGT who received only conventional treatment had 14.0 deaths/1,000 person years,

p=0.009 (Eriksson and Lindgarde, 1998).

The recently published Diabetes Prevention Study (Tuomilehto et al, 2001) conducted in

Finland with a 3.2 year follow-up showed that individual diet and exercise intervention

conducted by a dietitian could reduce the risk of diabetes by 58% (p<0.001) in a high risk

population. This was achieved with 7 sessions in the first year and regular 3 monthly sessions

after that during which goals were set on how to lose weight (>5%), decrease total fat intake

to <30% of energy, decrease saturated fat intake to <10% of energy, increase fibre intake to

>15 g/1000 kcal consumed and to exercise for at least 30 min per day. The groups were

individually given guidance to increase their exercise levels (30 min per day) with a

combination of endurance exercise and supervised resistance training. The control group were

set goals and given general written and oral information about diet and exercise initially and

then annually but no specific individualized programs were offered. The incidence of diabetes

in the intervention group was significantly lower than the control group (11% vs 23%).

Diabetes did not develop in any of the groups who achieved 4 or 5 goals, however

significantly more people were able to achieve these goals in the intervention group (49 in the

intervention group vs 15 in the control group, p< 0.001 for each of the goals). In those who

did not achieve any of the goals the incidence of diabetes was 38% vs 31% respectively

(Tuomilehto et al, 2001).

The Diabetes Prevention Program was a major clinical trial conducted in the United States

over 3 years in a group with people with IGT and therefore at high risk of developing Type 2

diabetes. The intensive lifestyle intervention included instructed on a low fat diet, exercising

for 150 minutes per week and behaviour modification skills. The results show on average that

this group had a 7% weight loss in the first year and sustained a 5% loss for the study’s

duration and maintained 30 minutes of exercise per day. A 58% reduction in the risk of

developing Type 2 diabetes was found compared to the control group who received only basic

diet and exercise advice. This study also included another group which was treated with

Metformin which was effective in redudcing the risk of developing diabetes by approximately

30% (National Institutes of Diabetes and Digestive and Kidney Diseases, 2001).

In men there appears to be a clear dose-response relationship between total energy

expenditure in physical activity and the prevention of Type 2 diabetes. For example, in a

study, which followed 5,990 American men for 14 years, with leisure-time physical activity

determined by questionnaire, physical activity was measured as megajoules (MJ) of energy

used. It was calculated that for every 2.1 MJ increment in weekly energy expenditure, there

was a 6% decrease in diabetes risk (Helmrich et al, 1991). In agreement with this finding a

study of 6,815 Japanese-American men enrolled in the Honolulu Heart program found lower

risk in men who were taking the most exercise (Burchfiel et al, 1995). The age-adjusted odds

ratio for the lowest versus the highest quintile of self-reported physical activity was 0.55 (CI

0.41-0.75). Similarly, it has been reported that the age adjusted risk of diabetes in American

men who played vigorous sport was lower than in men who played moderate sport only; while

diabetes risk was also lower in men who walked furthest each day (Helmrich et al, 1991). In

addition, men who have lowest cardiovascular fitness were found to have greater risk of IGT

or Type 2 diabetes than men who had moderate or high cardiovascular fitness as assessed by a

33

Primary Prevention - Part 2 December 2001

maximal exercise test on an electronic treadmill with varying speed and elevation (Wei et al,

1999). In 1,500 Finnish men, followed for a period of 10 years, the relative risk of diabetes

was 1.63 (CI 0.92-2.88) in men who engaged in vigorous activity less than once a week

relative to men who were vigorously active more than once a week (Haapanen et al, 1997).

Although the above studies all suggest the most vigorous activity is protective, some studies

have indicated that for Type 2 diabetes prevention, there may be little additional benefit in

exceeding moderate intensity activity. These studies determined a physical activity score

based on the frequency and intensity of the activities reported by the subjects. In a study of

British men the risk of diabetes decreased progressively as intensity of physical activity

increased from light to moderate, but risk was not decreased any further in men who regularly

performed vigorous physical activity (Perry et al, 1995). Similarly, the age-adjusted risk of

dying or developing cardiovascular disease or diabetes was lower in men who engaged in

moderate physical activity than in men who engaged in light physical activity [RR 0.53 (CI

0.44-0.64) and 0.66 (CI 0.56-0.78), respectively]. Risk was not, however, reduced any further

in men who engaged in moderately vigorous or vigorous activity (Wannamethee et al, 1998).

In striking comparison to men, there is a paucity of data on the frequency and intensity of

exercise in relation to risk of diabetes in women. The Nurses’ Health Study reported no clear

dose-response gradient in relation to frequency of exercise. Women who exercised only once

a week had an age adjusted relative risk of Type 2 diabetes of 0.74 (CI 0.6-0.91) relative to

sedentary women, while those who exercised four or more times a week had an age-adjusted

relative risk of 0.63 (CI 0.53-0.75) (Manson et al, 1991). Another study followed 1,340

Finnish women for a period of 10 years (Haapanen et al, 1997). In this study, both a higher

total amount of leisure time activity, and vigorous activity undertaken more than once a week

were protective against diabetes. The study population was divided into three equal groups

(each called a tertile) according to the amount of leisure time physical activity reported. The

tertile of women who were least active had an age-adjusted relative risk of 2.64 (CI 1.28-5.44)

relative to the tertile of women who were most active. In addition, the tertile of women who

were moderately active had an age-adjusted relative risk of 1.17 (CI 0.50-2.70) relative to the

tertile who were most active.

There is also evidence that exercise needs to be taken regularly for Preventive effect. In the

Physicians Health Study, the age-adjusted relative risk for diabetes gradually fell from 0.77

(CI 0.55-1.07) in men who exercised only once a week to 0.58 (CI 0.40-0.84) in men who

exercised 5 or more times per week (p, trend =0.0002) (Manson et al, 1992).

All studies reviewed here demonstrate the risk of developing diabetes associated with some

level of habitual physical activity compared to inactivity. In addition, data supports the very

encouraging public health message that the greatest health benefits of increased physical

activity may accrue to those who are initially most inactive (Manson et al, 1991; Perry et al,

1995; Haapanen et al, 1997; Wannamethee et al, 1998).

The majority of studies, reviewed here, rely upon self-reported estimates of physical activity.

Just as the advent of doubly labelled water has shown that the obese tend to under-report food

intake (Price et al, 1997), subjects may also over-report physical activity (Richman et al,

1996). If this is the case, there may be an even stronger inverse relationship between physical

activity and diabetes than studies now indicate.

34

Primary Prevention - Part 2 December 2001

Obesity is known to be a primary risk factor for Type 2 diabetes (Ferrannini and Camastra,

1998) and android obesity also has an important influence on risk (Despres, 1998). As levels

of physical activity are inversely correlated with obesity and can directly reduce abdominal

obesity (Tremblay et al, 1990), it might be expected that physical activity would influence

progression of diabetes by reduction of obesity, and specifically, reduction of abdominal fat.

Adjustments for body mass index (BMI) have however, in the main, shown physical activity

to be an independent risk factor. Wei and coworkers also found that the association remains

after controlling for waist circumference (Wei et al, 1999).

While the evidence that increased physical activity reduces the risk of Type 2 diabetes is

strong, less is known about the type of exercise that is most protective. Indeed, some forms of

exercise may be contra-indicated: resistance training and high intensity exercise may be

inappropriate for hypertensive individuals (Wallberg-Henriksson et al, 1998). The American

College of Sports Medicine recommended that asymptomatic, apparently healthy individuals

of any age who have two or more risk factors for coronary artery disease, should undergo

exercise testing before participating in vigorous exercise programs (Gordon et al., 1992).

Nevertheless, vigorous sports activity (expending at least 42 KJ/min) appears protective

against Type 2 diabetes (Helmrich et al., 1991). Due to the greater glucose disposal that

results from the use of large muscle groups, activities such as walking, swimming, or cycling

appear to be preferable to exercises isolating small muscle groups such as archery, shooting,

some callisthenics or some forms of weight lifting. Compliance with an exercise program is

also important. Therefore flexible programs, based on accumulated lifestyle oriented

activities, may be more conducive to improvements in long term exercise adherence and to

improvements in metabolic health than a structured program (Jakicic et al, 1995). To this end,

the recently released National Physical Activity Guidelines for Australians (Egger et al, 1999)

represent an appropriate public health approach to Type 2 diabetes prevention, given that

greatest attention needs to be given to those at increased risk.

35

Primary Prevention - Part 2 December 2001

• There is strong evidence from well designed longitudinal studies, highly applicable to

Australia, that physical activity decreases the risk of Type 2 diabetes

• There is strong evidence in men that high cardiovascular fitness protects against the

development of Type 2 diabetes

• The protective effect of physical activity appears to be more pronounced in men who are

obese but is as effective in older as in younger men

• There is strong evidence from a study in Chinese men and women that an exercise

program will decrease the progression from IGT to Type 2 diabetes but it is not clear,

however, how applicable this data will be to Australian populations

• There is strong evidence from a European study that an exercise intervention will reduce

diabetes-related deaths

• There is some strong evidence that protection against Type 2 diabetes increases in a dose

response relationship with physical activity but other studies indicate that moderate

activity can be as beneficial as vigorous activity and further studies are required on this

question, particularly in women

36

Primary Prevention - Part 2 December 2001

(Adult men - US:

Japanese)

III-2 Cohort High High + Low

(Adult men – Sweden) II RCT Medium High + High

(Adults – Finland) III-2 Cohort High Medium + High

(Adult men - US) III-2 Cohort Low High + High

(Adults - US: African

American)

III-2 Cohort High High + Low

(Adult women – US) III-2 Cohort High High + High

(Adult men - US) III-2 Cohort High High + High

(Adults - US: Mexican

Americans)

III-2 Cohort High High + Low

(Adults – US) II RCT High High + High

(Adults - China) II RCT High High + Medium

(Adult men - UK) III-2 Cohort Low High + High

(Adult men – Japan) III-2 Case-control High High + Low

(Adults – Finland) II RCT High High + High

(Adult men - UK) III-2 Cohort High High + High

(Adult men - US) III-2 Cohort High High + High

+ Physical activity reduces the risk of developing Type 2 diabetes.

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect.

37

Primary Prevention - Part 2 December 2001

What simple measures can be used to determine physical activity and inactivity?

Physical activity should be measured in free-living subjects by:

· movement recorders, particularly the pedometer

· questionnaires focussing on leisure-time activities

· heart rate monitoring

Functional aerobic capacity should be measured from predictive equations based on gender,

age, self-reported physical activity and body composition or BMI.

• Physical activity can be measured by the use of pedometers or accelerometers. At least 5

to 6 days recording that includes both weekdays and weekend days are required if intra-individual

variance is to be kept below 5%

• Questionnaires have been developed and validated for reliably measuring leisure-time

physical activity

• Detailed activity diaries can estimate energy expenditure accurately for populations but

their inherent variability makes them less suitable for individual estimates

• Heart rate monitoring can estimate total energy expenditure accurately if recording

continues for at least 3 days

• Functional aerobic capacity can be estimated without exercise testing by a predictive

model based on gender, age, self-reported physical activity and either percentage body fat

from skinfolds or BMI

• Nomograms have been developed that provide an estimate of exercise capacity with age

# Studies assesed using the non-intervention assesment

38

Primary Prevention - Part 2 December 2001

The most accurate method available for the assessment of daily energy expenditure and

physical activity in free-living subjects is the doubly labelled water method (DLW) (Schoeller

and van Santen, 1982). Free-living is a term used to indicate that for the duration of the study

these subjects are able to continue their normal activities outside the laboratory environment.

physical activity patterns may be continued while wash-out kinetics are determined for both

isotopes in saliva samples as their concentrations in body water decline exponentially back

or respiratory quotient (Prentice, 1990). Although the DLW method has the very great

advantage that it does not interfere with the patterns of everyday life, it is expensive and has a

demanding methodology suitable only for specialised research (Bratteby et al, 1997). Other

direct measures for estimating physical activity include questionnaires that can be filled out

by the individual or by an interviewer (Jacobs et al, 1993), diaries to record activity (Bratteby

et al, 1997), or motion sensors including pedometers (Gretebeck & Montoye, 1992),

surveillance cameras or remote-reading telemetric devices (Montoye & Taylor, 1984).

Indirect methods of assessing physical activity include assessment of usual energy intake by

dietary measurements, measurement of body composition, physiological indicators such as

pulse rate, muscular strength or fitness testing, survey of sports and recreational participation

or recording of occupation (Paffenbarger et al, 1992).

Physical activity in free-living subjects can be determined by a number of movement

recorders. These include pedometers, which measure either distance travelled or number of

force in which movement is directed vertically, forwards and backwards or laterally. The

simplest pedometers estimate only the number of steps taken, whereas more sophisticated

pedometers estimate both distance and energy used, once the stride length is entered

(Rowlands et al, 1997). A pedometer worn securely on the waist tends to be more accurate

than one worn at the ankle since jolts to the ankle can cause the pedometer to count irregularly

(Saris and Binkhorst, 1977). A study of the accuracy of five brands of electronic pedometers

speeds (Bassett et al, 1996). Walking surface did not affect accuracy. The pedometer has the

advantage of convenience of use, compatibility with most daily activities, objectivity and the

possibility of repeated use. It also can be used in population studies where different ethnic

groups may experience difficulty with self-report of physical activity using English and may

be particularly applicable also for use in children (Rowlands et al, 1997).

movement, age, gender, height and body weight). They have been reported to underestimate

energy expenditure during slow walking, and overestimate energy expenditure during fast

39

Primary Prevention - Part 2 December 2001

A study was carried out in 30 healthy American men engaged in a wide variety of

occupations, to determine how many days subjects would need to be monitored by

pedometers or accelerometers to provide an estimate of habitual physical activity (Gretebeck

& Montoye, 1992). The men were monitored during their waking hours for 7 continuous days,

repeated measures ANOVA showed that although daily measurements did not differ

significantly when weekdays were compared, there were significant differences with the

0.05). At least 5 or 6 days, including both weekdays and weekend days, were required to

minimise intra-individual variance (to < 5%) in making physical activity determinations by

these methods.

Many different questionnaires have been developed to determine habitual or recent physical

activity profiles. These range from a few questions, to very detailed inventories of activity

patterns and they vary greatly in known reliability and validity. Ten of the most commonly

used physical activity questionnaires have been evaluated in 78 American men and women

with varying physical activity habits for the National Institutes of Health (Jacobs et al, 1993).

Validity was studied by comparison with treadmill exercise performance, vital capacity, body

fatness, the average of 14 four-week physical activity histories and the average of 14 two-day

accelerometer readings. Interestingly, while most questionnaires related well to the presence

of heavy intensity physical activity, fewer questionnaires related to the performance of light

or moderate activity. The CARDIA physical activity and the Baecke physical activity

questionnaires were found to have the highest correlation coefficient of r= 0.83 and r= 0.71,

respectively. It was suggested that there is a need for future questionnaires to address this

area as well as examining length of sleep, and patterns of occupational activity and household

chores. While many questionnaires consider overall or habitual physical activity, others focus

more specifically on leisure-time physical activity (LTPA), recognising that in western

societies, LTPA makes the dominant contribution to overall physical activity. In particular,

leisure-time is more susceptible to changes from health promotion or intervention programs

than work-related activity (Montoye & Taylor, 1984).

A British group has developed an interview questionnaire specifically targeted at leisure-time

rather than occupational physical activity (Lamb & Brodie, 1991). This questionnaire was

validated in 77 British men and 41 British women from sedentary occupations. Measurements

included a sit-and-reach test for lower-back flexibility, assessment of muscular strength via

hand-grip dynamometry, leg power via a vertical jump test, and physical work capacity

(PWC) via a submaximal cycle ergometer protocol. The LTPA questionnaire was shown to be

stable following test-retest administration after a period of one month (r=0.86, p< 0.0001).

The total LTPA determined by questionnaire was significantly related to PWC (r= 0.48, P <

0.0001). In addition, ‘very heavy’ LTPA, and ‘heavy’ LTPA were significantly related to

PWC (r= 0.55 and r=0.28, respectively, both p< 0.0001), but there was no significant

relationship between ‘moderate’ LTPA and PWC. Another group has assessed the validity

and reliability of the MOSP-Q questionnaire used in the WHO-MONICA study (Roeykens et

al, 1998). This questionnaire measures the average weekly time and energy expenditure spent

40

Primary Prevention - Part 2 December 2001

not only on LTPA but also during occupational work, on transport-related activities and on

household chores. Subjects were 167 Belgian male and female university graduates aged 34-

64 years. Test-retest reliability for the MOSPA-Q was found to be greater over the short-term

than the long term. Over 38 days, test-retest reliability ranged from 0.57-0.91 whereas over

240 days reliability ranged from 0.45-0.85. Reliability for household chores and transportrelated

activity were found to be lower than that for LTPA or occupation-related physical

activity. Validation studies indicated a strong correlation between calculated total energy

The activity diary method uses a structured form to record activity levels on a scale of 1-9 for

96 periods each 15 min in length, for a day and a night. The categories range from 1: sleeping

or resting in bed, to 9: maximal intensity sports activities such as competitive running. When

the subject was uncertain how to code a given activity, they made an alphabetically coded

note describing this activity in their diary (Bratteby et al, 1997). The method has been

reported as simple and not difficult for subjects to learn and use (Bratteby et al, 1997).

Records are simply processed by summing the number of 15 min periods that fall into each

categorical value. The total for each category is then multiplied by the physical activity rating

determined for this category of activity and by the basal metabolic rate (BMR) of the

individual subject as predicted from age, sex and body weight by the Schofield equation

(Schofield, 1985).

A study of 50 randomly selected 15 year old Swedish adolescents has been used to assess the

use of physical activity diaries (Bratteby et al, 1997). The participants kept activity diaries for

seven days and the total energy expenditure (TEE) and the ratio of TEE:BMR estimated from

these diaries was compared with the results of DLW and indirect calorimetry measurements

taken during the same period. Results indicated that the diary method provided a close

estimate of TEE with the mean difference compared to the DLW method of 1.2%. The limits

of agreement (mean difference 2 SD) were –3.47 and 3.77 MJ/day, the equivalent of a

coefficient of variation (CV) of 15%. A close relationship and only a small bias was seen

between the two methods but the variation was fairly large. This implies that the activity diary

is suitable for providing a dose estimate of TEE in a population but is not suitable for use in

making an individual estimate (Bratteby et al, 1997). Another study has compared the diary

method with DLW in nine British men (Davidson et al, 1997). A detailed activity diary was

kept for 9 days. Ten predefined coded activities ranging from 1, in bed to 10 walking briskly

were recorded in this study in 5 min blocks. Additional codes were provided for different

kinds of sports or other activities. BMR was determined with a ventilated hood respirometer

and the energy cost of defined activities was measured in a calorimeter chamber and by

Oxylog during leisure exercise. In this study the diary method was found to provide data 12.1

± 4.0 % (mean ± SEM) lower than the DLW method.

Heart rate (HR) monitoring provides another alternative to whole body calorimetry or DLW

for the measure of TEE. HR monitors are inexpensive, acceptable to subjects and are now

storing minute-by-minute HR data for periods of more than 24h (Davidson et al, 1997). The

appropriate use of HR monitoring, however, demands continuous monitoring for a period of

at least three days (Gretebeck & Montoye, 1992). In addition, a calibration of the relationship

between HR and energy expenditure at light, moderate and heavy work loads, together with a

41

Primary Prevention - Part 2 December 2001

determination of resting HR and assessment or prediction of BMR must be carried out for

each individual subject (Bratteby et al, 1997). It has also been recommended that HR

monitoring should be used primarily for assessing moderate to vigorous activity at heart rates

> 120 bpm (Riddoch & Boreham, 1995).

A British study has compared the assessment of free-living energy expenditure in nine men,

by continuous heart rate monitoring and by DLW (Davidson et al, 1997). Although all the

subjects had a largely sedentary lifestyle, they had different levels of leisure time physical

activity. Continuous HR monitoring was carried out over nine consecutive days. In addition,

the HR - energy expenditure (EE) relationship was individually obtained for each subject

using 30 min average values of HR and EE obtained during 24h whole-body calorimetry with

a defined exercise protocol. Additional data points for individual leisure activities were also

measured with an Oxylog portable oxygen consumption meter. After data processing, the HRderived

EE for these men was (mean ± SEM) 6.0 ± 4.2 % higher than that estimated by DLW.

Similar results have been reported in earlier studies (Schulz et al, 1989; Livingstone et al,

1990; Heini et al, 1991). One study showed no significant difference between the two

methods (Lovelady et al, 1993).

As with the diary method, while HR monitoring provides accurate information on populations

it is less accurate than DLW for assessment of EE in individuals. In the study carried out by

Davidson and coworkers, the day-to-day CV in the estimation of TEE from HR monitoring

ranged from 6.6 - 21.5% (Davidson et al, 1997).

functional aerobic capacity. It can be measured by exercise testing with indirect calorimetry,

by the HR response at submaximal power output, or from the elapsed time to voluntary

exhaustion with treadmill exercise (Jackson et al, 1990). The Rockport walking test provides

to complete a 2.2 km walk (Kline et al, 1987). These equations have been validated for use in

women over the age of 65 (Fenstermaker et al, 1992).

Maximum oxygen uptake, either estimated or measured from the workload achieved for a

given exercise protocol, may also be translated into a unitless measure, the MET, by dividing

by 3.5. One MET is the quantity of oxygen consumed by the body under basal conditions. On

average, this is equal to 3.5 ml oxygen/kg/min (Jette et al, 1990).

limitations in a clinical setting of applying these techniques. A study of 2,009 American

subjects (mainly men) were randomly divided into validation (n=1543) and cross-validation

(n= 466) groups (Jackson et al, 1990). The validation group was used to develop two

% body fat from skinfolds (model 1) or gender, age, self-reported physical activity and BMI

by the Bruce treadmill protocol (Bruce et al, 1973). The non-exercise models were found to

were therefore deemed appropriate for use for about 96% of the adult population.

42

Primary Prevention - Part 2 December 2001

Morris and coworkers have developed a nomogram based on METs and age, for assessing a

subject’s ability to perform exercise (Morris et al, 1993). The exercise test results of 1,388

male patients, aged 21-89 years, without apparent heart disease, were retrospectively

reviewed. This referral group, plus subgroups of active (n=346) and sedentary (n=253) men

were analysed to determine norms for age and for age by decades to exercise responses,

including METs, maximal HR, and maximal systolic blood pressure. These data were used to

generate regression equations from which a nomogram for calculating the degree of exercise

capacity from age and METs was generated. It was argued that a nomogram providing an

estimate of exercise capacity to age has clinical utility, since it can be used to quantify

disability, and to communicate to patients their cardiopulmonary status. It can also be used to

encourage improvement in exercise capacity.

• Physical activity can be measured by the use of pedometers or accelerometers. At least 5

to 6 days recording that includes both weekdays and weekend days are required if intraindividual

variance is to be kept below 5%

• The use of pedometers are the most practical and objective methods for determining

energy expenditure

• Questionnaires have been developed and validated for reliably measuring both total

physical activity and leisure-time physical activity

• Physical activity can also be estimated by the use of detailed activity diaries. The method

is not difficult for subjects to learn and use. However, while the activity diary can estimate

total energy expenditure accurately for populations, the diaries have an inherent variability

that makes them less suitable for making individual estimates

• Total energy expenditure can be estimated by heart rate monitoring. Recording should

continue for at least 3 days. Heart rate monitoring may provide an estimate of energy

expenditure that is higher than that obtained by the doubly labelled water method. Heart

rate monitoring appears accurate for populations but is less accurate than doubly labelled

water for individual estimates

• Functional aerobic capacity can be estimated without exercise testing by a predictive

model based on gender, age, self-reported physical activity and either percentage body fat

from skinfolds or BMI

• Nomograms have been developed that provide an estimate of exercise capacity with age

43

Primary Prevention - Part 2 December 2001

(Adults – US) III# Cross-sectional Low High P+ High

(Adolescents – Sweden) III# Cohort High High D+ High

(Adults - US) III# Cross-sectional Medium High F+ High

(Adult men - UK) III# Cohort Medium High H+ High

(Adult women – US) III# Cross-sectional Medium Medium F- High

(Adult men - US) III# Cross-sectional Medium High H+ High

(Adult women – Gambia) III# Case-control Medium High H+ Low

(Adults - US) III# Cross-sectional Medium Medium F+ High

(Adults – US) III# Cohort Medium Medium Q+ High

(Adults - US) III# Cross-sectional Medium High F+ High

(Adults - UK) III# Cross-sectional High High Q+ High

(Adults – UK) III# Cross-sectional Medium High H+ High

(Adult women – US) III# Cross-sectional Medium Low High

(Adults – US) III# Cross-sectional Medium High P+ High

(Adult men – US) III# Cohort High High F+ High

(Adults – Belgium) III# Cohort Medium High Q+ High

(Adult men and children) III# Cross-sectional Medium High P+ High

(Adults – Germany) III# Cohort Medium High H+ High

# Studies assesed using the non-intervention assesment

+ The method assessed is a good estimate of energy expenditure; P Pedometer or accelerometer; D Activity diary; F Functional aerobic

capacity; H Heart rate monitor; Q+Questionaires.

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect. Low= no statistically significant

effect

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Primary Prevention - Part 2 December 2001

Do high-fat diets increase the risk of Type 2 diabetes and how is this affected by fat type?

Individuals at risk of developing Type 2 diabetes should have dietary intake assessed and

should receive individualised dietary advice and continued dietetic support.

Individuals at risk should consume a diet with <30% energy as fat with <10% energy as

saturated fat.

• People with a high intake of dietary fat are at increased risk of insulin resistance and Type

2 diabetes

• People with a high intake of saturated fat intake are at increased risk of insulin resistance

and Type 2 diabetes

• Reduction in dietary fat to <30% with restriction of saturated fat to <10% of energy intake

reduces the risk of Type 2 diabetes.

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Primary Prevention - Part 2 December 2001

There is evidence from mechanistic studies that dietary fat may play a part in insulin

resistance and thereby predispose individuals to Type 2 diabetes and gestational diabetes

(GDM) (Storlien et al, 1996). In this model fats are not only seen as fuel sources, but also as

important components of membranes, precursors to powerful metabolites such as the

eicosanoids and as potent gene regulators. The development of Type 2 diabetes and GDM is

therefore likely to result from the interaction between genetic predisposition and

environmental factors, such as diet, exercise and the development of obesity.

The evidence obtained from cross-sectional and cohort studies concerning the relationship

between dietary fat and the development of Type 2 diabetes and GDM is summarised in Table

1. It includes six cross-sectional and five cohort studies.

The cross sectional studies compared normal, impaired glucose tolerant and diabetic subjects

in male and female populations, with different ethnic backgrounds. One of the largest and

strongest, the San Luis Valley Study (n=1317) compared the nutrient intakes of men and

women from different ethnic backgrounds who had normal, IGT or diabetic profiles (Marshall

et al, 1991). The adjusted odds ratios relating to a 40 g/day increase in fat intake to

development of Type 2 diabetes and IGT were 1.51 (CI 0.85-2.67) and 1.62 (CI 1.33-5.36),

respectively. While dietary intakes did not differ significantly between normal and diabetic

subjects, the group of subjects with IGT consumed significantly more fat, both as g/day and as

a percentage of total energy, than the normal group (p <0.05). These results are in agreement

with results from the California based Kaiser Permanente Women Twins Study (N=544)

which showed that total dietary fat intake was weakly associated with fasting insulin

concentrations, although not if activity were taken into account (Mayer et al, 1993). In

addition, higher intakes of each subtype of dietary fat were positively related to fasting insulin

values, although only saturated fat was significantly related to 2h post-glucose load insulin

values. This later relationship with saturated fat was not seen after adjustment for obesity

(Mayer et al, 1993). Dietary fat may also be important in the development of GDM. In a

study (n =35), in women with GDM from an area of NSW, it was shown by three-day diet

record that women who had a recurrence of GDM consumed significantly more (p< 0.001)

energy as fat than those who did not (Moses et al, 1997).

The San Luis Valley study was also analysed on a cohort basis (1984-1988), examining the

progression to disease in both the non-diabetic population and the population with IGT. In the

non-diabetic sample (n=1069), total fat and saturated fatty acids resulted in a significantly

higher insulin response after adjusting for age, gender, ethnicity, vigorous activity, BMI, waist

circumference and total energy. No effect was seen with unsaturated fat (Marshall et al,

1997). In the smaller IGT cohort (n=134), those who developed diabetes consumed

significantly more fat (p=0.02) and monounsaturated fat (p=0.03) at baseline than those who

did not (Marshall et al, 1994). The effect for saturated fat was only borderline (p=0.06), while

no effect was seen for polyunsaturated fat (p=0.74). The small numbers in this study did not

allow for an assessment of any mediating effect by activity, obesity or sex.

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Primary Prevention - Part 2 December 2001

Adler 1994 cross

sectional

Alaskan, normal,

IGT, diabetic

660 OGTT 25 item FFQ food items Protective effect against IGT of seal oil,

salmon consumption

Gittelsohn

1998

cross

sectional

Native

Canadians

Normal, IGT,

diabetic

721 OGTT 34 item FFQ food groups Increased risk of diabetes with ‘junk

foods’, bread and butter

Marshall

1991

cross

sectional

San Luis Valley

population,

normal, IGT,

diabetic

1317 OGTT 24 hr recall Nutrients Precentage energy total fat increased odds

for NIDDM and IGT

Mayer 1993 crosssectional

Women twins in

California, nondiabetic

544 Fasting

insulins

FFQ Nutrients Total dietary fat associated with fasting

insulin but mediated by obesity

Mayer-

Davies 1997

Crosssectional

Adults in

California, Texas

and Colorado

1173 OGTT FFQ Nutrients Percentage energy from total fat associated

with fasting insulin

Moses 1997 crosssectional

Women with

GDM +

recurrence of

GDM, NSW

35 OGTT DHx and FR Nutrients Women with recurring GDM consumed

more total dietary fat than non-recurrers

Colditz

1992

cohort Women, nondiabetic

84,360 self report 61 item FFQ Nutrients, fat

source

Vegetable fat consumption inversely

associated with risk of NIDDM, attenuated

by obesity

Feskens

1990

cohort Men

Non-diabetic

394 OGTT,

insulin

under the

curve

DHx nutrients and

foods

Saturated fat detrimental to glucose

tolerance, vegetable fat negatively assoc

with IAUC

Feskens

1995

cohort Men

non-diabetic

338 IAUC DHx nutrients and

food

Total fat and saturated fat associated with

development of diabetes, but fish may have

a protective effect

Marshall

1997

cohort San Luis Valley

non-diabetic

population

1069 OGTT 24hr recall Nutrients Total and saturated fat associated with IGT

Marshall

1994

cohort San Luis Valley

IGT population

134 OGTT 24 hr recall Nutrients Total fat consumption associated with

development of Type 2 diabetes

Pan 1997 RCT IGT population

China

577 OGTT Not measured

during the study

Not measured

during the study

When educated to consume less fat people

were less likely to develop Type 2 diabetes

Tuomilehto

2001

RCT Finland IGT

population

522 OGTT 3 day Food diary Nutrients Those in the intervention group were less

likely to develop Type 2 diabetes.

47

Primary Prevention - Part 2 December 2001

These studies suggest that there is an effect of dietary fat on the development of Type 2

diabetes and GDM. This effect may be mediated by obesity and possibly also by physical

activity levels. This finding has been confirmed in a cross-sectional study of 1,173 nondiabetic

North American adults in the Insulin Resistance Atherosclerosis study which found

that total fat intake was inversely related to insulin sensitivity, but that this was not significant

when adjusted for BMI and WHR (Mayer-Davis et al, 1997). Further work on the effect of

dietary fat subtypes on insulin resistance was suggested.

Evidence on the effects of fatty acid sub-types seem to clearly implicate saturated fat in the

development of Type 2 diabetes, and there is some indication that polyunsaturated fats may

be protective. The former finding was confirmed in a cross sectional study (n=4,304)

involving the analysis of phospholipid fatty acid levels as indicators of dietary intakes.

Fasting serum insulin was positively associated with the percentage of saturated fatty acids in

the plasma phospholipid fraction (Folsom et al, 1996). The situation with monounsaturated

fats is less clear, although it may relate to the food sources of these fats.

Two cross-sectional studies of indigenous peoples have examined the impact of foods rather

than nutrients on diabetes risk. In the Native Canadian Study (n=721), a high consumption of

‘junk foods’ (chips, hamburgers, pizza), bread and butter, or fatty methods of food

preparation were associated with increased risk of diabetes [OR 2.40 (CI 1.13-5.10); OR 2.22

(CI 1.22-4.41); OR 2.58 (CI 1.11-6.02), respectively], (Gittelsohn et al, 1998). In contrast, a

study on American Indians from Alaska found that none of the subjects who ate seal oil or

salmon on a daily basis developed IGT or diabetes, whereas those who consumed neither of

these products had a prevalence of IGT or diabetes of 9.9% (Adler et al, 1994).

Studies involving either men or women suggested further interesting patterns concerning

foods as well as nutrients. The Nurses Health Study cohort (n=84,360) found intakes of

(Colditz et al, 1992). This effect was adjusted for BMI, but physical activity was not assessed.

Analysis of data on men from a number of Finnish and Dutch cohorts in the Seven Countries

Study (n=338) found that a high intake of total fat especially saturated fat increases the risk of

glucose intolerance and Type 2 diabetes (Feskens et al, 1995). Increased consumption of fish

during the 20-year follow up was inversely related to 2 h glucose (p<0.05). An analysis of

data from the Zutphen cohort alone (n=394) found that changes in saturated fat were

positively associated with IAUC (p < 0.05) independent of subscapular skinfolds indicating

that saturated fat intake may be detrimental to glucose tolerance (Feskens & Kromhout,

1990). The effect for polyunsaturated fat appeared to be the reverse (Table 2).

Saturated fat (g) 49.7 13.1 0.13** -0.05

Monounsaturated fat (g) 51.9 12.8 0.10* -0.02

Polyunsaturated fat (g) 20.2 7.4 0.03 0.04

* p<0.01

** p<0.001

48

Primary Prevention - Part 2 December 2001

In some countries, monounsaturated fats are predominantly consumed from animal sources

(meat and milk), foods which also contain substantial amounts of saturated fats. In other

countries for examples, those around the Mediterranean, monounsaturated fats are consumed

predominantly from vegetable sources such as olive oil (Feskens et al, 1994). In the Nurses

Health Study, vegetable fat was found to be inversely related to the risk of diabetes, so it may

be that the effect of monounsaturates needs to be considered in the light of dietary source

(Colditz et al, 1992). Further studies are required to clarify this.

Fish has become another recognised food source linked with reduction in risk of diabetes and

whilst the links with polyunsaturated fat, notably n-3 polyunsaturates is attractive, this finding

also needs to be explored further.

Three intervention studies have examined the effect of dietary fat reduction in preventing the

development of Type 2 diabetes in people with IGT. The Da Qing study (Pan et al, 1997), a 6

year study in China, compared the effect of diet modification, increased physical activity or a

combination of both, on the development of diabetes in people with IGT. The study showed a

cumulative incidence of diabetes in the control group of 67.7% (written instruction) compared

with 43.8% in the diet group, 41.1% in the exercise group and 46.0% in the combined diet

and exercise group which all had an individual assessment followed by regular small group

education sessions (weekly for 1 month, monthly for 3 months and every 3 months after that).

25-30% of total energy intake, achieve a consumption of 55-65% carbohydrate, 10-15%

protein and consuming more vegetables.

The potential effect of diet on the prevention of Type 2 diabetes was shown from the

preliminary results of the Finnish Diabetes Prevention Study (Eriksson et al, 1999). In the first

year of this multicentre study of overweight subjects with IGT (n=523) significant weight loss

occurred in the first 212 subjects (4.7 ± 5.5 vs 0.9 ± 4.1 kg in the intervention vs control

groups, p < 0.001). In addition, plasma glucose concentrations were significantly lower after

intervention (fasting 5.9 ± 0.7 vs 6.4 ± 0.8 mmol/L, p<0.001; and 2-h 7.8 ± 1.8 vs 8.5 ± 2.3

mmol/L, p < 0.05). The intervention involved both intensive dietetic management (<10%

saturated fat, 20% monounsaturated fat and polyunsaturated fat, or up to 25% if the surplus is

from monounsaturated fat; 50% carbohydrate and 1g /kg body weight protein) plus an

individualised exercise prescription. The control group received general advice on diet and

exercise (Eriksson et al, 1999).

The recently published final results of the Finnish Diabetes Prevention Study confirm the

preliminary findings (Tuomilehto et al, 2001). After a 3.2 year follow-up the individual diet

and exercise intervention conducted by a dietitian reduced the risk of diabetes by 58%

(p<0.001) in a high risk population. This was achieved with 7 sessions in the first year and

regular 3 monthly sessions during which goals were set on how to lose weight (>5%),

decrease total fat intake to <30% of energy, decrease saturated fat intake to <10% of energy,

increase fibre intake to >15 g/1000 kcal consumed and to exercise for at least 30 min per day.

They were individually given guidance to increase their exercise levels (30 min per day) with

a combination of endurance exercise and supervised resistance training. The control group

were set goals and given written and oral information about diet (including the target for

reduction in fat intake to less than 30% of daily intake) and exercise initially and then

annually. The incidence of diabetes in the intervention group was significantly lower than the

49

Primary Prevention - Part 2 December 2001

control group (11% vs 23%). Diabetes did not develop in any of the groups who achieved 4 or

5 goals, however significantly more people were able to achieve these goals in the

intervention group (49 in the intervention group vs 15 in the control group, p< 0.001 for each

of the goals). In those who did not achieve any of the goals the incidence of diabetes was 38%

vs 31% in the interventin and control groups respectively (Tuomilehto et al, 2001).

The Diabetes Prevention Program was a major clinical trial conducted in the United States

over 3 years in a group of people with IGT and therefore at high risk of developing Type 2

diabetes. The intensive lifestyle intervention included instructed on a low fat diet, exercising

for 150 minutes per week and behaviour modification skills. The results show on average that

this group had a 7% weight loss in the first year and sustained a 5% loss for the study’s

duration and maintained 30 minutes of exercise per day. A 58% reduction in the risk of

developing Type 2 diabetes was found compared to the control group who received only basic

diet and exercise advice (National Institutes of Diabetes and Digestive and Kidney Diseases,

2001).

• There is moderately strong evidence from well-designed studies applicable to Australia

that dietary fat has an effect on the development of Type 2 diabetes mellitus. More

research is required to determine if this is mediated by obesity

• Dietary fat may have an effect on the recurrence of gestational diabetes mellitus although

more research is required in this area

• There is moderately strong evidence from well designed studies applicable to Australia that

dietary saturated fat has an effect on the development of Type 2 diabetes mellitus

• There is good evidence from well designed studies applicable to Australia that unsaturated

fat of vegetable origin may have a protective or neutral effect on the development of Type

2 diabetes mellitus

• More research is required to determine the effect of monounsaturated fat on the

development of Type 2 diabetes, taking into account whether it comes primarily from

animal or vegetable origin

• There is some evidence that fish consumption has a protective effect on the development of

Type 2 diabetes mellitus

50

Primary Prevention - Part 2 December 2001

(Adult - US: Yup’ik Eskimo

& Athabaskin Indians) III-2 Cross-sectional Medium High +

Low

(Adult women – US) III-2 Cohort Medium Medium + High

(Adults - Finland) II RCT High High + High

(Adult men – The

Netherlands) III-2 Cohort Medium High + High

(Adult men – The

Netherlands & Finland) III-2 Cohort Medium High + High

(Adults & adolescents –

Canada: Native Canadians) III-2 Cross-sectional Medium High + Low

(Adults - US: Hispanic &

Non-Hispanic) III-2 Case-control Low Medium+ High

(Adults - US: Hispanic &

Non-Hispanic) III-2 Cohort Medium High + High

(Adults - US: Hispanic &

Non-Hispanic) III-2 Cohort Medium Medium + High

(Adult women – US: Twins) III-2 Cross-sectional High High + High

(Adults – US) III-2 Cross-sectional Medium High + High

(Adult women – Australia:

GDM) III-2 Case-control Medium High + High

(Adults – US) II RCT High High + High

(Adults - China) II RCT High High + Medium

(Adults – Finland) II RCT High High + High

+ Dietary fat increases the risk of Type 2 diabetes

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect.

51

Primary Prevention - Part 2 December 2001

Does gestational diabetes mellitus increase the subsequent risk of Type 2 diabetes in mothers

and infants?

Identification of women with GDM would allow:

• postnatal clinical interventions in those with diabetes persisting after delivery

• the option to use preventive methods to reduce the risk of Type 2 diabetes

• GDM is associated with increased risk of future Type 2 diabetes in the mother

• GDM may be associated with increased risk of Type 2 diabetes in the offspring

52

Primary Prevention - Part 2 December 2001

Gestational diabetes mellitus (GDM), now defined as diabetes (or impaired glucose tolerance)

first identified in pregnancy, was originally introduced as a means of identifying women at

risk of future Type 2 diabetes (O'Sullivan, 1964). The criteria adopted were subsequently

found to predict perinatal mortality and morbidity (O'Sullivan, 1973). Since these studies,

obstetric care and maternal and perinatal outcomes have improved, the methods for measuring

blood glucose have changed and a number of methods for diagnosing GDM have been

introduced. There remains no global agreement on the criteria for the diagnosis of GDM

making international epidemiological comparisons and interpretations difficult. Australia

currently uses the Australasian Diabetes in Pregnancy (ADIPS) criteria for GDM (Hoffman et

al, 1998). The recent change in criteria for Type 2 diabetes (ADA 1997; Alberti et al, 1998)

are also likely to impact on estimates of conversion rates.

A further difficulty exists with the interpretation of the data, particularly among non-

European ethnic groups: the proportion of women with previously undiagnosed Type 2

diabetes is unknown and related to the structure of health care delivery. The contributions

from genetic, intra-uterine and environmental factors to the development of Type 2 diabetes

in a given individual and population are likely to vary substantially and change over time. As

to whether the GDM itself increases the risk of future Type 2 diabetes in the mother has not

been established. The issue is complicated as increasing parity may (Kritz-Silverstein 1989;

Simmons 1992) or may not (Collins et al, 1991; Manson et al, 1992) be associated with an

increased risk of Type 2 diabetes. Practical issues relating to studies including loss to follow

up and need for long follow up periods also may impact on the estimates of progression to

Type 2 diabetes and generation of data.

Investigating the possible impact of in utero exposure to maternal hyperglycaemia has been

the subject of two follow-up studies. As GDM predicts future Type 2 diabetes in a high

proportion of women and Type 2 diabetes is associated with increased rates of Type 2

diabetes in the offspring, GDM might be expected to be associated with an increased risk of

Type 2 diabetes in the offspring. Whether anti-hyperglycaemic intervention during GDM

reduces the risk of obesity and Type 2 diabetes in the offspring remains unknown although

insulin therapy was associated with reduced adiposity by 2.8 years in one study among

Polynesians and South Asians (Simmons and Robinson, 1997).

There are a large number of cohort studies indicating an increased risk of Type 2 diabetes in

women who had GDM. Recent studies using the 75g oral glucose tolerance test on follow up

are shown on Table 3.

Kjos (1995) followed up 671 Latino women for up to 6 years who had experienced past GDM

but without Type 2 diabetes immediately after delivery. New cases developed at a rate of 11.1

(CI 9.3-12.9)/100 person years. The development of Type 2 diabetes was associated with the

area under the oral glucose tolerance test (OGTT) curve at 4-16 weeks post-partum as well as

the area under the OGTT curve during pregnancy, the gestational age at which GDM was

diagnosed and the highest fasting glucose achieved during pregnancy. The authors

53

Primary Prevention - Part 2 December 2001

Navajo Indians 111 11 53 %* (ND) Steinhart, 1997

Copenhagen 345 5.9 13 % 0 % Damm, 1989

Sweden 145 3-4 3.4 % 0 % Persson, 1991

Australia 881 17 40 %* 10 % Henry, 1991

US Latinos 671 6 55 % (ND) Kjos, 1995

Trinidad 60 3.5-6.5 62 % (ND) Ali, 1990

Zuni Indians 94 6 36 %* 10 % Benjamin, 1993

* Life Table, ND: not done

suggested that a history of GDM imparts a risk for Type 2 diabetes greater than that observed

when patients are not pregnant.

The mechanisms for increased risk of GDM for Type 2 diabetes have been investigated.

Damm and coworkers (1995) studied 91 women with past GDM and 33 controls using an

OGTT and demonstrated reduced insulin secretion among women with past gestational

diabetes. Others demonstrated a delayed insulin peak response (Catalano et al, 1986; Persson

et al; 1991) while another normal insulin secretion (Efendic et al, 1987). Buchanan and

coworkers (1998) studied insulin sensitivity among 122 Latino women in the third trimester.

There was no association with subsequent diabetes or impaired glucose tolerance within 6

months post-partum. Defects in insulin sensitivity after adjusting for obesity have not yet been

shown among women with past GDM.

Peters and coworkers (1996) followed up 666 Latino women with GDM in a past pregnancy,

of which 87 subsequently became pregnant again. The additional single pregnancy among

Latino women increased the risk of developing Type 2 diabetes within the subsequent 7.5

years by a rate ratio of 3.34 (CI 1.80-6.19).

The 1,064 offspring of Pima women with diabetes in pregnancy were more likely to have

diabetes by the age of 20-24 years than offspring of women developing diabetes after

pregnancy or not at all (45% versus 8.6% versus 1.4%) (Pettitt et al, 1988). This was

independent of paternal diabetes status.

Pettitt and coworkers (1983) undertook a review of the follow-up studies among 2,638 Pimas.

The study attempted no new analyses but repeated the same analyses on later expanded data

sets. The study demonstrated that the higher the glucose antenatally, the more hyperglycaemic

the offspring by the age of 19 years. Similar findings have been shown in animal experiments

and in the other long-term follow up study (Silverman et al, 1995). In a cross sectional study

of 86 European and 432 Polynesian women, women with past diabetes in pregnancy were

2.05 fold more likely to have diabetic offspring than diabetic women without known past

diabetes in pregnancy (Simmons 1995).

54

Primary Prevention - Part 2 December 2001

• GDM is associated with an increased risk of current and future Type 2 diabetes in women

and probably obesity and Type 2 diabetes in the offspring

• Whether this is causal or part of an underlying diabetogenic process is unknown

• Additional pregnancies complicated by GDM might increase the risk of Type 2 diabetes

in women with past GDM

• There is also good experimental and cohort evidence to support the hypothesis that

intrauterine exposure to hyperglycaemia increases the risk of Type 2 diabetes in the

offspring

• Whether the hyperglycaemia in GDM (besides that from undiagnosed Type 2 diabetes) is

sufficient to be causal is also unknown

• Whether anti-hyperglycaemic treatment for GDM would reduce the risk of future Type 2

diabetes in the mother or offspring is unknown

55

Primary Prevention - Part 2 December 2001

(Adult women – Trinidad) III-2 Cohort High High+ Low

(Adult women - US: Zuni

Indians)

III-2 Case-control High High+ Low

(Adult women - US:

Hispanic)

III-2 Cohort Medium High+ Low

(Adult women - US) III-2 Case-control Medium High+ High

(Adult women - Denmark) III-2 Case-control High High+ High

(Adult women - Denmark) III-2 Case-control High High+ High

(Adult women - Sweden) III-2 Case-control Low High+ High

(Adult women – Australia) III-2 Case-control High Medium+ High

(Adult women – US:

Latino) III-2 Cohort High High+ Low

(Adult women - Sweden) III-2 Case-control High High+ High

(Adult women - US:

Latino) III-2 Cohort High High+ Low

(Adolescents - US: Pima

Indians) III-2 Cohort Medium High+ Low

(Adolescents - US: Pima

Indian) III-2 Cohort Medium High+ Low

(Adolescents - US) III-2 Case-control Medium High+ High

(Adults & adolescents -

New Zealand: Europeans,

Maori, Pacific Island)

III-2 Crosssectional

Medium Medium+ High

(Adult women - US:

Navajo Indian) III-2 Cohort High High+ Low

+ Mothers with GDM and there infants are at increased risk of Type 2 diabetes

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect.

56

Primary Prevention - Part 2 December 2001

How does birth weight affect later risk of Type 2 diabetes?

In view of the association between low birth weight and the later development of diabetes,

studies are required to evaluate interventions aimed at reducing low birth weight and the

impact this has on the development of Type 2 diabetes

• Low birth weight is associated with increased risk of Type 2 diabetes in populations of

European descent

• Low birth weight may be associated with increased risk of Type 2 diabetes in some

populations not of European descent

• High birth weight is not related to future Type 2 diabetes in populations of European

descent

57

Primary Prevention - Part 2 December 2001

The debate over the relative contributions of environment and genes in the aetiology of Type

2 diabetes has been added to by the hypothesis that exposure to an adverse intrauterine

environment leads to permanent alterations in metabolism ("programming"). Two intrauterine

mechanisms have been discussed, the "thrifty phenotype hypothesis" (Hales & Barker, 1992)

and the "fuel mediated teratogenesis hypothesis" (Freinkel, 1980). These two hypotheses

postulate that fetal intra-uterine under- and over-nutrition respectively lead to Type 2 diabetes

in adult life. The putative manifestations of these two phenomena are a relatively low and

high birth weight respectively. An alternative hypothesis for a small baby to be associated

with Type 2 diabetes later on in life has been that this is a manifestation of the "thrifty

genotype". That is, the fetus able to survive in an adverse intrauterine environment is

genetically endowed with an ability to survive as an adult in times of poor food supply, but

develops obesity and Type 2 diabetes when food is plentiful (Hattersley & Tooke, 1999).

There is a large range of confounders in prospective studies of birth weight. There is often

substantial loss to follow up, in one study this being as high as 94.2% (Fall et al, 1998). The

characteristics of those not followed up are unknown. The use of birth weight as a measure is

fraught with difficulties relating to gestational age at delivery, impact of smoking, maternal

comorbidity (eg renal disease and hypertension) and the family history of diabetes in the

parents. Birth weight itself is a composite of skeletal and non-skeletal components and it may

be more appropriate to use placental weight and/or other neonatal anthropometric

measurements as the key indicators (eg caliper measurements, length, head circumference,

ponderal index). To determine the ponderal index the birth weight is divided by the cube of

the birth length.

A low birth weight, but not a high birth weight has been associated with an increased risk of

Type 2 diabetes on follow-up in a number of studies of populations of European decent.

(Hales et al, 1991; Phipps et al, 1993; Fall et al, 1995; Curhan et al, 1996; Lithell et al, 1996;

Olah et al, 1996; Rich-Edwards et al, 1999). In populations that are not of European decent a

relationship between low birth weight and future Type 2 diabetes has been shown among

Mexican Americans (Valdez et al, 1994), but not South Asians (Fall et al, 1998) or African

Americans (Hulman et al, 1998). In a Danish twin study, the lighter twin was found to be

more hyperglycaemic than the larger twin (Poulsen et al, 1997). Among Pima Indians, a Ushaped

curve was found with high rates of Type 2 diabetes among those with the highest and

lowest birth weights after 25-34 years (Pettitt and Knowler, 1998). In Indians, those who were

short at birth but with a relatively high birth weight were most likely to have developed

diabetes after 39-60 years (Fall et al, 1998).

While an association between fetal size and risk of future Type 2 diabetes is consistent among

people of European descent, the cause for the reduced fetal size remains unclear in humans

with the evidence for fetal under-nutrition being causal not being supported (Stanner et al,

1997; Mathews et al, 1999). In a prospective cohort study of 693 nulliparous women (those

who have never given birth to a viable infant) in England, placental and birth weight were

58

Primary Prevention - Part 2 December 2001

unrelated to intake of any macronutrient (Mathews et al, 1999). Each milligrams increase in

Vitamin C was associated with a 50.8 (4.6-97.0)g increase in birth weight and 3.2(0.4-6.1)%

increase in placental weight. In a follow-up study of those exposed in utero to profound

calorie restriction during the siege of Leningrad, there was no significant increase in the

prevalence of the insulin resistance syndrome (Stanner et al, 1997).

The use of Ponderal Index rather than birth weight was associated with a stronger relationship

with future Type 2 diabetes among Swedes (Lithell et al, 1996; McKeigue et al, 1998). A

relationship was also found between insulin sensitivity as measured by euglycaemic clamp

and birth weight (McKeigue et al, 1998). Among European populations, those with a low

birth weight, and found to be overweight on follow up were most likely to have developed

Type 2 diabetes (Lithell et al, 1996; McKeigue et al, 1998; Yarbrough et al, 1998).

• Among populations of European decent, the data shows a relationship between reduced

fetal size at birth and increased Type 2 diabetes in adult life

• The mechanism behind this relationship remains speculative ranging from thrifty

genotype survival to epiphenomenon to fetal undernutrition

• The evidence for fetal under-nutrition being causal is not supported

• Among populations not of European decent, the data is not consistently supportive of a

relationship between low birth weight and future Type 2 diabetes, and more research

should be done in this area

• The evidence relating high birth weight with future Type 2 diabetes is weak

59

Primary Prevention - Part 2 December 2001

(Adult men – US) III-2 Cohort Medium High+ High

(Adult women – UK) III-2 Cohort Medium High+ High

(Adults – India) III-2 Cohort Medium Medium+ Medium

(Adult men – UK) III-2 Cohort High High+ High

(Adults – US: African

Americans)

III-2 Cohort Medium Low Low

(Adult men – Sweden) III-2 Cohort High Medium+ High

(Adult women – UK) III-2 Cohort High Low High

(Adult men – Sweden) III-2 Cohort High High+ High

(Adult women – UK) III-2 Cohort High High+ High

Pettitt DJ (1998)

(Adult women & children

– US: Pima Indians)

III-2 Cohort High High+ Low

(Adults – UK) III-2 Cohort High High+ High

(Adults – Denmark) III-2 Cohort High High+ High

(Adult women – US) III-2 Cohort Medium High+ High

(Adults – Russia) III-2 Cross-sectional Medium Low High

(Adults – US: Mexican

American, Non-Hispanic

white)

III-2 Cohort Medium Low Medium

(Adult women – US) III-2 Cohort Medium High+ High

+ Those with a low birth weight have an increased risk of developing Type 2 diabetes.

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect. Low = no statistically sigificant

effect

60

Primary Prevention - Part 2 December 2001

Do low glycaemic index (GI) diets reduce the risk of Type 2 diabetes?

Diets of low energy density and containing a wide range of carbohydrate foods rich in dietary

fibre and of low glycaemic index (cereals, vegetables, legumes and fruits) are recommended

to reduce the risk of Type 2 diabetes.

• Diets of high glycaemic index may increase the risk of Type 2 diabetes in both men and

women particularly when associated with low cereal fibre intake

61

Primary Prevention - Part 2 December 2001

The glycaemic index (GI) has been used to classify carbohydrate foods for various

applications, including for use in diets designed for the treatment of Type 2 diabetes (Brand et

al, 1991; Fontvieille et al, 1992; Frost et al, 1994; Miller, 1994; Jarvi et al, 1999). The GI

classifies foods based on their potential to raise blood glucose concentrations. It is defined as:

A standardised protocol has been developed for determination of GI, which includes repeated

testing in each individual subject (FAO/WHO expert consultation on carbohydrates in human

nutrition, 1998). Either white bread or glucose is generally used as the standard food. A list of

GI values for almost 600 individual foods has now been published (Foster-Powell & Miller,

1995), and GI can be predicted from total dietary fibre content for foods with no published

value for GI (Nishimune et al, 1991). The GI can be applied to mixed meals (Hollenbeck &

Coulston, 1991) and to whole diets by calculation of the weighted GI value of the meal or

diet. When calculating the GI for a whole diet, an adjustment can be made for the amount of

carbohydrate present in each meal (Wolever & Bolognesi, 1996).

The GI can be used, together with information about energy density, macronutrient and

dietary fibre content, to guide food choices. The GI can be used to develop lists of low GI

foods such as legumes, bulgur, whole grain cereals, whole grain pumpernickel breads, and

pasta. Information is available for particular local and ethnic foods (Mani et al, 1990; Indar-

Brown et al, 1992; Ayuo & Ettyang, 1996) and different types of rice (Miller et al, 1992). It

is most important that food composition is used as well as GI when making food choices.

Foods high in fat have low GI but would not be recommended for inclusion in diets for

individuals at risk of Type 2 diabetes, as they are energy dense. Chocolate (GI=49), icecream

(GI= 61), potato crisps (GI= 54) and shortbread (GI=48) are examples of such inappropriate

low GI foods. On the other hand, some high GI foods may be a good choice, for example a

dietary staple such as rice, or foods with low energy density and high nutrient content, such as

some vegetables (eg, carrots) (FAO/WHO Expert Consultation on carbohydrates in human

nutrition, 1998). Sucrose raises blood glucose levels less than some refined starchy foods such

as mashed potatoes (Wolever & Miller, 1995) but provides energy without nutrients.

Nevertheless, the addition of a small amount of sucrose may be useful to improve the taste

and palatability of a low GI food such as baked beans (Voster et al, 1987).

Few longitudinal studies have examined the relationship between the glycaemic index of the

diet and the risk of Type 2 diabetes. It was examined, however, in 42,759 American men

without diagnosed diabetes or heart disease, who were aged 40-75 years in 1986 (Salmeron et

al, 1997a). The diet at baseline was assessed by a validated semi-quantitative food frequency

questionnaire. During six years of follow-up in this study, 523 cases of Type 2 diabetes were

reported. The average dietary glycaemic index value was determined for each participant as

was the dietary glycaemic load which was used as an indicator of glucose response or insulin

demand. This was determined by averaging the GI of the foods consumed. Data were adjusted

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Primary Prevention - Part 2 December 2001

for age, BMI, smoking, physical activity, family history of diabetes, total energy intake (but

not fat intake) and consumption of alcohol and cereal fibre. After these adjustments, the GI of

the diet was found to be related to diabetes risk. Comparing the lowest and the highest

quintiles for GI, the relative risk of Type 2 diabetes was 1.37 (CI 1.02-1.83, p trend= 0.03).

The combination of a high glycaemic load and a low intake of cereal fibre further increased

the risk of diabetes [2.17 (CI 1.04-4.54)].

A similar study has been carried out in American women (Salmeron et al, 1997b). A total of

65,173 American women, free of known diabetes, heart disease or cancer, and aged 40-65

years completed a detailed dietary questionnaire in 1986. After six years of follow-up there

were 915 reported cases of Type 2 diabetes. After adjustment, as in the study in men above,

the dietary GI was again found to relate to the risk of Type 2 diabetes. When the highest

quintile for GI was compared to the lowest quintile, the relative risk was 1.37 (CI 1.09-1.71).

Again, the combination of a high glycemic load and a low intake of cereal fibre further

increased the risk of Type 2 diabetes [2.50 (CI 1.14-5.51)]. There was no association with

fruit or vegetable fibre. Significant positive associations were seen with cola beverages, white

bread, white rice, french fries, and cooked potatoes.

In these studies the GI of the diet (and thereby the glycaemic load) was determined by

averaging the GI of the individual foods as reported in the food frequency questionnaires. A

concern of this approach is that it does not take into account other factors that may affect the

GI of the food. For example fat and certain types of fibre delay gastric emptying which

reduces the GI of the meal (Frost & Dornhorst, 2000). Furthermore, foods consumed at

breakfast give a lower GI than the same foods consumed at lunch, the ‘second meal effect’

(Frost & Dornhorst, 2000). However although these other factors may influence the

glycaemic response, the relative response of one carbohydrate to another remain the same

(Frost & Dornhorst, 2000). Further prospective studies are required in this area before the full

significance of the GI can be determined.

• There is some evidence from large studies of a population similar to that of Australia, that

a diet of high glycaemic index may contribute to an increased risk of Type 2 diabetes

• There is also evidence in these studies that the risk of Type 2 diabetes is further increased

by the combination of a high glycaemic load and a low intake of cereal fibre

• Further longitudinal studies are needed in both men and women that are not based on

dietary self-reporting including the use of food frequency questionnaires

• Further studies examining the influence of particular types of dietary fibre on the

prevention of Type 2 diabetes should also be done

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(Adult men – US) III-2 Cohort Medium High+ High

(Adult women – US) III-2 Cohort Medium High+ High

+ Low glycaemic diets decrease the risk of developing Type 2 diabetes.

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect.

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Does psychosocial stress increase the risk of Type 2 diabetes?

More research is required to assess the impact of psychosocial stress or a major depressive

episode in affecting the risk of Type 2 diabetes.

• Individuals with newly diagnosed diabetes may report a history of psychosocial stress

• Exposure to undesirable life events may increase the risk of diabetes in later life

# Studies assesed using the non-intervention assesment

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Stress has been considered as a possible risk factor for the development of Type 2 diabetes.

Although human studies on the role of stress in the onset of diabetes are limited, there is

evidence from animal studies that stress consistently produces hyperglycemia (Surwit, 1996).

In humans, three categories of stress can be distinguished: chronic ‘background’ stress

relating to the pressures of western lifestyles, acute ‘occasional’ or unexpected stress and

stress associated with individual temperament (Ionescu-Tirgoviste et al, 1987). Reaction

patterns to stress have been separated into ‘defense’ reactions and ‘defeat’ reactions (Henry,

1993). The ‘defense’ reaction, leading to control, is characterised by increased noradrenaline,

gonadotrophins and, in men, testosterone. The ‘defeat’ reaction, when control is lost, is

associated with activation of the hypothalamo-pituitary adrenal (HPA) axis, increase in

ACTH and cortisol and decrease in gonadotrophins and peripheral sex hormones (Bjorntorp,

1997). Stress-related cortisol secretion has been found, in middle-aged men, to be strongly

related to BMI, WHR, fasting insulin, glucose, blood lipids, systolic and diastolic blood

pressure and heart rate (Rosmond et al, 1998). Both chronic and acute stress also alter the

Kawakami et al, 1989). Indigenous populations subjected to social environmental stress

associated with cultural breakdown, discrimination, loss of land, poverty and lack of control

which is an interesting observation due to the very high prevalence of Type 2 diabetes

reported for many indigenous populations once they are subjected to urbanisation.

Japanese men aged 30-60 years, employed by an electrical company (Kawakami et al, 1995).

Six moods were assessed by the Profile of Mood States (POMS) questionnaire (McNair et al,

1971). Anger-hostility, but not tension-anxiety, depression, fatigue, vigour or confusion was

was still evident after controlling for the number of cigarettes smoked per day (p< 0.05).

(Kawakami et al, 1995).

The effect of the psychosocial work environment on diabetes was examined in a crosssectional

study of an occupational cohort in France (Niedhammer et al, 1998). Volunteers

(n=13,226) employed by an electrical company were given three psychosocial work

environment social scores to assess psychological demands, decision latitude and social

support at work. Although psychosocial work factors were found to be associated with the

presence of hypertension or hyperlipidemia within the preceding 12 months, no association

was found with diabetes.

Another Japanese study examined the effects of social support, stress and the effect of mood

states on the presence of glucose intolerance (Fukunishi et al, 1998). Subjects were 600

Japanese aged 28 to 72 who had presented for primary health care screening and agreed to

further hospital-based tests. All were given a 75g oral glucose tolerance test and placed into 3

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groups on the basis of the level of impaired glucose tolerance. The subjects also took Japanese

versions of three psychological questionnaires: the Stress and Coping Inventory (SCI) (Rahe

and Veache, 1995), the Dealing with Illness (DWI) test (Namir et al, 1987) and the Profile of

Mood States (POMS) test (McNair et al., 1971). Subjects with the greatest degree of glucose

intolerance were found in the SCI to use less social support either from social groups or from

individuals.

A further indication of the potential importance of emotional support from others in

mitigating the effects of stress on diabetes risk was found in an American study (Krause,

1995). In this study, non-institutionalised Americans over the age of 65 years, from all states

except Alaska and Hawaii were contacted using the Health Care Finance Administration

(HCFA) Medicine Beneficiary Eligibility List. This list identifies all older US residents who

have a social security number. Interviews were completed with 1,068 participants in which

the presence of diabetes was determined by self-report, stressful life events were recorded

through a 49-item checklist and social support was measured with a four item index. Logistic

regression models were then developed to determine if diabetes could be predicted by

combined stressful life events, social support, and the interaction between stress and social

support in addition to age, sex, education and obesity. It was found that, at the lowest

observed level of emotional support, an increase of one undesirable stressful life event

increased the risk of diabetes by around 30% [OR 1.307 (CI 1.151-1.464)]. In contrast, at the

highest observed level of emotional support, an increase of one undesirable stressful event did

not increase diabetes risk [OR 0.972 (CI 0.865-1.080)]. Risk of diabetes increased with the

number of stressors experienced. Thus the impact of two stressful events for those receiving

the lowest level of emotional support was OR= 1.709 (CI not reported). In this study,

participants were also asked to identify 3 of 8 social roles with which they identified most

strongly. Stressors occurring in any of these three roles identified as important were ‘salient

stressors’. The risk of having diabetes in older adults with little social support who experience

two salient stressors was 2.406 (CI not reported).

The role of an acute stress in the development of Type 2 diabetes has been examined in

relation to a severe earthquake in Bucharest, Romania on 4 March, 1977 (Ionescu-Tirgoviste

et al, 1987). This earthquake had a magnitude of 7.2 on the Richter scale and lasted 75 sec.

The number of patients newly registered with diabetes at a single Bucharest clinic was

compared for the month of May one year previously, 2 months after or 1 year after the

earthquake. In May, 2 months after the earthquake (156 cases) were registered, a significantly

higher number of patients than in May one year before the earthquake (98 cases) or in May

one year after the earthquake (101 case), p<0.001. In 24 cases, the onset of diabetes occurred

within days after the earthquake.

To examine the role of chronic and subacute stress as a factor present at diabetes onset, 300

newly diagnosed Romanian patients aged 36-76, with newly diagnosed diabetes were given a

detailed stress-related questionnaire (Ionescu-Tirgoviste et al, 1987). Past chronic or subacute

psychosocial stress was recorded in 44% of cases, and the incidence was higher in women

(54%) than in men (39%). In 44% of cases, the stress levels experienced were rated ‘extreme’,

in 37% stress levels were rated ‘severe’ and in 19% stress levels were rated ‘moderate’

according to WHO criteria.

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• There is moderate evidence from a study in Japanese men that anger-hostility levels are

• There is moderate evidence that exposure to stressful events may increase risk of diabetes

and that this increased risk may be mitigated if individuals receive social support. Studies

are needed to fully determine physiological mechanisms for these environmental

influences

• More research is required into the possible links between stress and Type 2 diabetes as

more sensitive methods for measuring markers of accute and chronic stress are developed

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(Adults – US) II# High Cohort Medium High+ High

(Adults – Japan) III# Medium Cross-sectional Medium Medium+ Low

Garvard JA (1993) III# High Case-control Medium High+ High

Ionescu-Tirgoviste C(1987)

(Adults – Romania) III# Medium

Cohort

Cross-sectional

Case-control

Low Medium+ Low

(Adult men – Japan) III# Medium Cross-sectional Medium Medium+ Low

(Adults – US) III# Medium Cross-sectional Medium High+ High

(Adults – US) III# Medium Case-control Medium High+ High

(Adults – France) III# Medium Cross-sectional Medium Low High

(Adults – US) III# Medium Cross-sectional High Medium+ High

(Adults – US) III# Medium Case-control Medium High+ High

# Studies assesed using the non-intervention assesment

= Psychosocial stress or a major depressive disorder is increased in those with diabetes

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect. Low = no statistically sigificant

effect

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Primary Prevention - Part 2 December 2001

What forms of diet and physical activity can be recommended to prevent excessive weight

gain in childhood and throughout adult life?

Programs of diet and exercise education in children should include parental involvement and

use behavioural techniques to reinforce lifestyle change.

Adults who have achieved weight loss require ongoing programs involving diet, exercise and

social support to prevent weight regain, however the effectiveness of specific long term

programs requires further evaluation.

• Family therapy is more effective in preventing obesity in children than conventional

therapy

• Repeated visits to a multi-disciplinary health care program focussing on dietary change

can effectively induce weight loss in children and adolescents. Outcomes are better in

children seen at least every 2 months over a year than in children seen less often

• Intervention programs to increase exercise levels in addition to dietary change can reduce

obesity in children

• Obese children placed on low energy diets exhibit significantly better long- term weight

loss than their obese parent

• Programs in children aimed at reducing sedentary behaviour produce more body fat loss

after a year than programs aimed at increasing exercise activity or programs that combine

both approaches

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• Mass media campaigns have not successfully prevented weight gain in adult communities

• Targeted dietary and exercise programs based on an empowerment model comprising

diabetes awareness sessions, exercise groups and cooking demonstrations have prevented

weight gain in a high-risk population

• Although body weight can be readily lost during active supervised treatment, it is often

gradually regained during the subsequent unsupervised period

• In men taught dietary and exercise strategies, waist circumference may decline during

active supervised treatment and remain reduced for up to 2 years

• Men and women who have successfully maintained long-term weight loss report the

continued consumption of a low energy, low fat diet

• In women, an ad libitum low fat diet is superior to a low energy diet in maintaining weight

after a major weight loss

• Exercise is an important determinant in the success of weight maintenance after a weight

loss program

• A program of combined diet and exercise is more effective in maintaining weight loss

than either diet alone or exercise alone

• Weight gain after a weight loss program is positively associated with hours of weekly

television viewing

• In women, increased general physical activity is as effective as aerobic exercise in

preventing weight regain after a period of weight loss

• In women, the threshold of energy expenditure required for weight maintenance is 80

minutes/day of moderate activity or 35 minutes/day of vigorous activity added to a

sedentary lifestyle

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• In men and women, neither aerobic training nor weight training exercise programs are

able to reverse the decline in resting metabolic rate that occurs after significant weight

loss

• Continued contact with a therapist may aid weight maintenance after a weight loss

program

• Recruitment of overweight people into weight reduction programs together with their

family and friends influences weight loss but not long-term weight maintenance

• Overweight men with impaired glucose tolerance who followed a long-term program of

diet and exercise exhibited reduced all-cause mortality after a period of 12 years

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Primary Prevention - Part 2 December 2001

Obesity, especially adiposity around the abdomen, appears to be the major potentially

modifiable determinant of insulin resistance and the risk of developing Type 2 diabetes

(Ferrannini & Camastra, 1998). Rates of overweight and obesity have increased dramatically

in recent years in Australia, as has happened in most other Westernised countries (Australian

Institute of Health & Welfare, 1998; Hill & Peters, 1998). Such weight gain appears to be the

response to an environment where energy output is insufficient to counteract the effects of

inappropriately high energy intakes resulting from the widespread availability of convenience

and other energy dense foods (Hill & Peters, 1998). Thus it would appear that the best hope of

reducing the prevalence of Type 2 diabetes in the Australian community lies in the early

prevention of excessive weight gain both by changing energy intake and increasing energy

expenditure.

It is important to target programs establishing healthy eating and physical activity patterns to

children so that they can then be maintained life-long (Huon et al, 1999). It has been

suggested that there are three time-periods that appear to be particularly appropriate in which

to concentrate efforts for obesity prevention in adults (Wing, 1995). These periods are young

adulthood, from the ages of 25 to 35 while the subjects are still healthy and the perimenopausal

period in women. Programs are also needed to prevent long-term weight regain in

adults who have successfully completed weight loss programs (Glenny et al, 1997). The best

way to structure interventions for the prevention of obesity still requires much clarification

both at the community and individual level (Wing et al, 1995). To date, health professionals,

the general public and policy makers have been too reluctant to recognise that obesity poses a

serious public health threat (Hill & Peters, 1998).

In a recent systematic review Glenny (1997) identified only one study focussing on the

prevention rather than the treatment of obesity in children. The study examined the prevention

of progression to severe obesity in Swedish school children aged 10-11 years with a body

regular visits to a paediatrician) was compared to conventional therapy plus family therapy

(six sessions over 12 months) and to a no treatment group. Treatment continued for 14-18

respectively, p< 0.02). In contrast, the proportion of children with severe obesity in the

conventionally treated group did not differ significantly from the control group. Based on

these results, Glenny (1997) has argued that the family therapy approach for prevention of

childhood obesity should now be trialed in other age groups and settings and on larger

numbers of children.

A retrospective study carried out in San Paulo, Brazil has evaluated the impact of a multidisciplinary

program on weight control in children and adolescents (Valverde et al, 1998).

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Treatment consisted of individual visits to a paediatrician and psychologist and to a

nutritionist for dietetic counselling at one urban centre. Information was offered to enable

patients to make adequate food choices, avoiding excessive energy or fat intake and

consumption of high energy density foods. This advice was reinforced at regular clinic visits

(visits ranged for 2-20 with a mean interval between visits of 2 months). Over the period of a

year, 198 children and adolescents were counselled (mean age was 9.25 years mean body

calculated for the child to relate actual BMI to BMI values at the 50th percentile for a

reference population of the same age. Progress in weight control was then evaluated as

percentage change in adjusted BMI. For both boys and girls, adjusted BMI declined

significantly between the first and last clinic visit (p<0.0012, p<0.000 respectively).

Significantly better outcomes occurred among children who made six or more clinic visits

(p<0.000). Variables such as percent body fat, body shape at the first visit, family obesity

pattern, length of obesity and pubertal stage did not significantly affect outcomes.

An American study (Epstein et al, 1995b) examined the effects of increasing activity on

weight change in obese children. Obese children from 61 families recruited by newspaper

advertisement were randomised into three treatment groups. In each family at least one parent

was also willing to attend the treatment sessions. The treatment of one group aimed to

decrease sedentary behaviour, the treatment of the second group aimed to increase exercise

activity while the third group reinforced both aims. The study did not include a control group

without intervention. Children in all groups and participating parents were instructed in the

‘Traffic Light Diet’. In this diet Green foods (primarily vegetables) are low energy and

nutritious; Yellow foods, for example skim milk or apples, are higher in energy but contain

important nutrients whereas Red foods, such as potato chips and sweets, are high in energy

but low in nutrients. Children were instructed to limit Red foods to seven or less per week.

After four months on the program, children in the group in which sedentary behaviour had

been targeted now showed a significantly higher preference for high intensity exercise

activities than children in the combined group or in the group in which increased physical

activity had been targeted (p<0.02). After one year on this program, the group in which

sedentary behaviour had been targeted exhibited a greater decrease in percentage body fat as

estimated by bioelectrical impedence, than the combined group or the group in which

increased physical activity had been targeted (p<0.05).

A study by Johnson and coworkers (1997) also used the ‘Traffic Light’ diet to treat childhood

obesity. Thirty-two obese children aged 8 - 16 years were given information alone (control

group), or they were given a dietary program preceded or followed by an exercise program.

While the impact of combined exercise and dietary interventions endured at a five-year

follow-up, children in the control group remained morbidly obese.

Treating childhood obesity appears to be more effective than treating adults. Children aged 8

years and older and an obese parent from 113 families participating in treatment programs

were studied (Epstein et al, 1995a). Both children and adults were placed on similar energyreduced

diets based on the ‘Traffic Light’ system. After 6 months, 5 or 10 years the children

were found to have a significantly greater decrease in percentage body weight than their

parents (p<0.001, p<0.022 and p<0.001 at 6 months, 5 or 10 years, respectively).

Obesity is growing as a health problem in children from many ethnic groups. An intervention

program in Singapore has addressed the problem of obesity in Malay, Chinese and Indian

children (Ray et al, 1994). The study sample comprised 1,128 obese preschool children aged

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Primary Prevention - Part 2 December 2001

3-6 years who either attended 17 Primary Health care clinics with their family or saw a

dietitian for counselling on diet and exercise. After one year, 456 children (40.4%) had

reduced their obesity while 228 (20.2%) had attained a normal weight. Progress varied

significantly with ethnicity (p< 0.05) with Indian children reducing obesity more than Chinese

children, who in turn reduced their obesity more than the children of Malay origin. Pima

Indian children in the United States also have very high rates of obesity. In 1996 a diabetes

primary prevention program called Quest was implemented as a pilot study at one elementary

school for Pima Indian children in kindergarten and grades 1 and 2. The program included

classes in nutrition, increased physical activity and a structured school breakfast and lunch

program. Results however, have not yet been reported (Cook & Hurley, 1998). Although

some other intervention programs have also been recently reviewed, the number of programs

designed explicitly for children remains low (Huon et al, 1999).

Despite the poverty of data on Preventive and treatment programs for obesity in children, it

appears that preventing excessive weight gain in childhood is an achievable goal. Hill &

Peters (1998), point out nevertheless that we still need to know more about how children’s

eating habits develop and how they can be modified to foster a preference for less energy

dense foods. In addition, we need a better understanding of factors affecting physical activity

patterns in children and how these patterns develop and continue into adulthood (Hill &

Peters, 1998). Successful treatment programs for obesity in children use behaviour

modification techniques (Huon et al, 1999) integrated with advice for improved nutrition and

proper eating habits, regular exercise and a more active lifestyle, and are supported by

continued family involvement (Glenny, 1997). Overweight children and their families during

therapy should be treated with sensitivity and compassion, avoiding any emphasis on physical

appearance or relating obesity to lack of willpower (Barlow & Dietz, 1998). In addition, care

needs to be taken that messages targeted to prevent childhood obesity do not unintentionally

promote inappropriate weight loss in underweight adolescent girls dissatisfied with their body

image and size (O’Dea, 1998).

There is also a need for long term follow-up studies to determine the most effective dietary,

exercise and psychological strategies for use in preventing obesity in children. Decreasing

sedentary behaviour appears to be one of the most effective approaches (Epstein et al, 1995b).

Television watching, a sedentary activity much enjoyed by children, may also prompt

increased food intake via food commercials (Jeffery et al, 1982). Television viewing has been

cross-sectionally and prospectively related to obesity (Dietz & Gortmaker, 1985). It has also

been suggested that increased intake of low energy density, fibre-rich foods may aid

prevention of weight gain in overweight children (Kimm, 1995).

The role of parental involvement in weight management of children appears important but

requires clarification in further studies (Glenny, 1997). While there may be benefit in younger

children attending therapy sessions with a parent (Israel et al, 1994), in adolescents outcomes

may be better if the child and parent attend support sessions separately (Glenny, 1997).

Parental involvement in the success of juvenile weight management may also extend much

further than the support role since parental attitudes to their child’s obesity, their belief that

obesity can be modified by lifestyle factors and their readiness to make lifestyle changes may

all influence outcomes (Barlow & Dietz, 1998).

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A systematic review based on one database (Medline 1966-96) which sought large studies

(>50 participants) of long duration (> 2 years) has identified three community based

prospective cohort design studies that aimed to reduce the prevalence of obesity through

education via the mass media (Douketis et al, 1999). In each study, no significant difference

in body weight was evident between control and test communities during a 3 to 7 year followup

period. Each study however, was found to suffer methodological problems, which limited

their conclusions (Douketis et al, 1999). These included the lack of defined criteria to specify

a successful outcome, a high rate of withdrawal, and a reliance on self-reported body weight.

A more recent study (Dyson et al, 1997) has examined the effects of basic (n=116) or

reinforced (n=111) healthy-living advice on body weight in self-referred hyperglycemic but

non-diabetic volunteers at five centers, two in France (Lyon, Toulouse) and three in Britain

(Exeter, Leicester, Oxford). After three months, the mean body weight in both groups showed

a significant reduction of 1.5 kg (p< 0.001) but at follow-up after one year, weight loss was

non-significant in each case.

More success was achieved in another prospective study (Simmons et al, 1998) that assessed

the impact of a 2-year pilot diabetes risk reduction program in a high risk population of

Western Samoans living in Auckland, New Zealand. This involved comparison of two church

congregations of similar socio-economic status led by the same pastor, a control congregation

(n=115) and an intervention congregation (n= 67). The culturally specific intervention

followed an empowerment model and comprised diabetes awareness sessions, exercise groups

and cooking demonstrations. At 2 year follow-up, body weight had remained stable (0 ± 4.8

kg) at the intervention church but had increased by 3.1± 9.8 kg at the control church (p=0.5).

In the intervention church there was an associated increase in the proportion exercising

regularly (+22% versus -8% in the control group, p< 0.05) and consumption of key fatty

foods was also significantly reduced (eg; the proportion cutting the fat off meat had increased,

p< 0.001). In an Indian study (Viswanathan et al, 1997) 262 non-diabetic adults who had one

or more parents with Type 2 diabetes were given individualised dietary and exercise programs

and followed up after four or more years. During this period 26% exhibited no weight change,

31% exhibited a decrease in body weight while 42% became more overweight. Weight gain

occurred mainly among those who had not adhered to dietary and exercise prescriptions and

59% of those with weight gain developed Type 2 diabetes.

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Dietary therapy for obesity, assessed in a recent systematic review (Douketis et al, 1999), is

usually effective during the period of active supervised treatment, but is then followed by a

pattern of gradual weight regain during the subsequent unsupervised follow-up period.

One such example is a New Zealand study where 39 men and women followed an ad libitum

intake, low fat eating program while taking placebo tablets as the control arm of a study on a

weight-loss drug (Carmichael et al, 1998). Mean body weight after 6 months on this program

was significantly lower than baseline (p< 0.0001) but during an additional 3 months

unsupervised follow-up body weight began to increase (mean increase from 6-9 months, 0.7

kg, p=0.06). Another study (Grodstein et al, 1996) examined the efficacy of weight

maintenance in 192 Americans who had participated in a commercial weight loss program

involving 12 weeks on a formula diet. After a follow-up period of about three years, mean

weight (by self-report) was only modestly less than at baseline (102.6 kg and 105.9 kg, final

and initial weight, respectively). Forty percent of subjects gained back more than they had lost

during the diet. Weight regain in the 12 months after a weight loss program has also been

reported by Jeffery and coworkers (1995) in 74 women who lost weight either by dietary

energy or by dietary fat restriction. It was also seen at one-year follow-up in a study of 46

non-diabetic women who had a family history of Type 2 diabetes (Pascale et al, 1995). At a

96-week follow-up of 22 obese women who had undertaken a weight loss program, only 10%

of the initial weight loss was maintained (Weinstock et al, 1998). Also in a study of selfreferred

subjects with hyperglycaemia (n=227), randomised to reinforced or basic healthyliving

advice, although mean body weight reduced by 1.5 kg by 3 months, weight returned to

baseline after 1 year (Dyson et al, 1997). Due to this phenomenon of weight regain after

therapy, it is considered imperative to build effective weight maintenance strategies into any

weight loss program (Glenny et al, 1997).

Shick and coworkers (1998) determined the habitual diet of a group of American who

sustained an average weight loss of 30kg (minimum 13.6 kg) and maintained this for an

average of 5.1 years (minimum 1 year). Both women (n=355, mean age 45 years) and men

(n=83, mean age 50 years) reported continued consumption of a low energy, low fat diet

(mean 23.5 and 24.3% energy from fat, respectively). In another study Toubro and Astrup

(1997) compared the efficacy of an ad libitum low fat diet with a reduced energy diet in

maintaining weight after major weight loss. Subjects were 41 obese Danish women (BMI 27-

40) who had achieved similar weight loss after either 8 weeks on a low energy diet (2

MJ/day) or 17 weeks on a reduced energy diet (5 MJ/day). After one year on the weight

maintenance program, the group randomised to the ad libitum low fat diet had maintained

13.2 kg (CI 8.1-18.3 kg) of an initial 13.5 kg weight loss. The group randomised to a reduced

energy diet had maintained 9.7 kg (CI 6-13.3 kg) of an initial 13.8 kg weight loss. One year

later, at follow-up 65% of the group given the low fat diet versus 40% of the group given the

reduced energy diet had maintained a weight loss greater than 5 kg (p < 0.07). The rate of the

initial weight loss did not influence these long-term outcomes.

Obese subjects are usually already moderately physically active but are limited in their ability

to perform higher intensity exercise. While exercise without dietary change does not seem

effective in promoting weight loss, it has been argued that exercise combined with dietary

change is very important in promoting weight maintenance following a period of weight loss

(Miller et al, 1997). In a systematic review of the literature from 1969-1994, Miller and

coworkers (1997) found evidence that diet plus aerobic exercise was more effective than a

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program of either diet alone or exercise alone in maintaining weight loss at one year followup.

Whereas those on diet and exercise maintained 77% of their initial weight loss at one

year, those on diet alone maintained only 56% and those on exercise alone maintained only

53% of initial weight loss. Grodstein and coworkers (1996) followed up participants in a

commercial weight loss program. Three years after initial weight loss, the strongest predictor

of continued weight loss was exercise frequency. For every hour of exercise per week, there

was a decrease of 2.0 kg (CI-3.0-1.0 kg) after adjustment for months of regular exercise,

hours per week watching television and age. Conversely, television was associated with an

increase in weight. For each hour of television watching per week, there was an increase of

0.3 kg (CI 0.1-0.5 kg). Another study (Andersen et al 1999) has assessed the effects of the

type of exercise on the success of a weight maintenance program. Forty obese women (mean

age 42.9 years) attended a university-based weight management program where they were

given a low energy diet (5 MJ/day) combined with either structured exercise (step aerobics) or

increased physical activity in a moderate lifestyle change (eg increased walking, stairclimbing)

for 16 weeks. After the program, both aerobic and lifestyle groups exhibited similar

mean weight loss (-8.3 and -7.9 kg, respectively). Participants in the aerobic group assessed at

one-year follow-up had regained 1.6 kg while participants in the lifestyle group had regained

0.08 kg (p=0.06). Weight regain was significantly higher in those who reported the lowest

physical activity levels during follow-up (lowest versus highest p<0.001). Wadden and

coworkers (1998) also followed weight change in 77 obese women treated for 48 weeks with

diet alone or diet combined with supervised aerobic exercise, supervised strength training or

both aerobic exercise and strength training. Weight losses were similar in all groups and at

one year follow-up participants had regained 35-55% of their lost weight. Yet those who

reported exercising regularly in the 4 months preceding the follow-up assessment regained

significantly less weight than those who did less exercise (r=- 0.44, p< 0.005).

Exercise has also proved effective in maintaining long-term loss of abdominal fat in the

Australian “GutBuster” program (Egger et al, 1996). These studies focussed on reduction in

waist girth in men rather than on weight loss. Using instructors qualified in exercise or

dietetics, the men were taught strategies to reduce dietary fat, increase dietary fibre, and to

increase low intensity movement (both planned and incidental). In the program they were also

allowed to ‘trade’ additional moderate alcohol intake for a period of increased movement. In

the first study 42 men completed the 6 weeks course and were followed up 2 years later when

waist and hip circumferences, body weight and BMI still showed a significant reduction from

baseline (all p<0.001). Waist circumference had declined in 64% of these men between week

6 and the 2-year follow-up. No change was evident in the waist:hip ratio due to the finding

that both hip and waist circumferences had declined. In a second study, 87 men were followed

up for one year after the initial 6 week course while 37 were followed up after completing

both the initial course and an additional 6 week ‘advanced’ course. In these groups, 39% of

men and 67% of men respectively, achieved a greater waist loss after 12 months than

exhibited at 6 weeks.

A prospective study has been carried out to determine how much exercise is required to

optimise weight maintenance (Schoeller et al, 1997). Thirty-two American women, mean age

38 years, were recruited within three months of achieving a documented mean targeted weight

loss of 23 kg. The women were followed for 12 months while undertaking habitual exercise.

At study entry, total energy expenditure (TEE) was measured with doubly labelled water

while the resting metabolic rate (RMR) and the thermic effect of a meal (TEM) were

measured by respiratory gas exchange. Physical activity was measured by 7-day recall, 3-day

heart rate monitoring and as TEE minus (RMR + TEM). At 3,6,9 and 12 months body weight

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Primary Prevention - Part 2 December 2001

was measured and physical activity was estimated by 7-day recall. For analysis, subjects were

divided into three groups on the basis of physical activity at baseline. After one year,

physically active women (mean TEE:RMR=1.89, n=9) gained 2.5 ± 3.1 kg; less physically

active women (mean TEE:RMR=1.64, n= 15) gained 9.9 ± 10.5 kg while sedentary women

(mean TEE:RMR=1.44, n= 15) gained 7.0 ± 5.9 kg (p<0.01). Retrospective analyses of

weight regain as a function of energy expended in physical activity indicated that there was a

threshold of physical activity required for weight maintenance. In women the apparent

threshold was at 47 KJ per kg body weight per day, corresponding to an expenditure of 80

minutes per day of moderate activity or 35 minutes per day of vigorous activity added to a

sedentary lifestyle.

Mechanisms where by exercise aids weight maintenance have not yet been established. It has

been suggested that exercise might help maintain the resting metabolic weight, which

characteristically falls after weight loss, making it easier to regain weight. Ballor and

coworkers (1996) addressed this issue in a study. Eighteen men and women between the ages

of 55 and 70 years were randomised to aerobic training (by treadmill exercise) or weight

training for a period of 12 weeks following a successful weight loss program (mean loss 9 ± 1

kg). Although the aerobic training group lost significantly more additional weight than the

weight training group (p< 0.05), neither type of training reversed the depression of resting

metabolism that had occurred following weight loss.

Continued contact with the therapist may also aid weight maintenance. This conclusion was

reached by Glenny et al, (1997) following a systematic review of five long term studies that

examined the effects of continued contact. However, since not all studies reviewed showed

the benefit of contact it was also argued that more research is needed to develop new and

more effective weight maintenance programs. A recent study by Wing & Jeffrey (1999) has

also indicated the importance of social support in the success of weight maintenance

programs. Overweight subjects (n=166) were recruited either alone or with three friends or

family members. Each subject was then randomised to receive 16 weeks treatment on a diet

and exercise program with or without an additional social support intervention. A higher

proportion of subjects recruited with friends and receiving social support completed the 10

month study (95%) than in other groups (75-83%, p=0.048). Both recruitment approach and

social support intervention had significant effects on weight maintenance (p< 0.4 and p<

0.009, respectively). For participants who were recruited alone but received social support

during weight loss, 24% maintained their weight loss in full. In contrast, for participants who

were recruited with friends but also received social support during weight loss, 66%

maintained their weight loss in full. Despite these benefits at 10 months, by 16 months no

significant difference remained evident in weight loss between the four different treatment

groups.

Despite the difficulties in adults of maintaining weight loss in the longer term, there is

emerging evidence that participation in diet and exercise programs can be of substantial

benefit. A recent study in Sweden has shown that overweight men with IGT (n= 288) who

followed a long-term intervention program of physical exercise and dietary change exhibited

significantly reduced all-cause mortality after a 12 year follow-up. This change in men with

IGT on an intervention program was relative to men with IGT (n= 135) who had received

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Primary Prevention - Part 2 December 2001

routine treatment (mortality 6.5 versus 14.0 per 1,000 person years at risk respectively,

p=0.009) (Eriksson & Lindgärde, 1998).

Although weight loss through diet or diet and exercise programs can be effective in reducing

obesity, the longer-term maintenance of body weight at the reduced level may be very

difficult. Extensive review of the literature suggests that a weight maintenance program

should be a priority after the initial first six months of weight loss therapy. Such a weight

maintenance program should employ a combination of low-energy diets, increased physical

activity and behavioural therapy and should be continued indefinitely (National Institutes of

Health Expert Committee,1998).

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Primary Prevention - Part 2 December 2001

• Although children are a very important target group, there is an evident paucity of studies

examining the prevention of obesity in childhood and adolescence

• There is evidence that family therapy has efficacy in preventing obesity in prepubertal

children. Further studies are needed to extend this finding to other age groups

• There is good evidence, although in a population group rather dissimilar to Australia, that

weight loss in children is improved by repeated visits (made at least once every two

months) to a health care centre

• There is good evidence that the addition of exercise to a dietary program for children will

aid obesity reduction . Programs aimed at reduction of sedentary behaviours are even

more effective than programs aimed at increasing physical activity

• In adults there is strong evidence that mass media campaigns are unable to prevent

community weight gain

• There is good evidence that dietary and exercise programs using a culturally specific

empowerment model comprising of diabetes awareness sessions, exercise groups and

cooking demonstrations can be successful when targeted at a high-risk population

• There is strong evidence that adults regain weight in the long-term once active supervision

for weight loss ceases

• In men there is very good evidence that a focus on loss of waist circumference will

produce long-term loss of abdominal fat

• Adults who successfully maintain weight loss long-term are those more likely to consume

a low fat diet

• There is very strong evidence that a program of diet combined with exercise is necessary

to maintain weight loss in the long term

• In adults continued contact with the therapist in the longer term plus the support of family

and friends will aid long-term weight maintenance

81

Primary Prevention - Part 2 December 2001

(Adult women - US) II RCT Medium HighD & A+ High

(Adults - US) II RCT Medium MediumD&A+; D&W+ High

(Adults – New Zealand) II RCT Medium HighD+ High

Douketis JD (1999) I Systematic

review Medium Medium D+; P+; S+; B+ High

(Adults – France; UK) II RCT High Low High

(Adult men - Australia) III-2 Cohort Medium High D & B+ High

(Children & Parents – US) III-2 Cohort Medium High B(c)+; B- High

(Children – US) II RCT Medium High B+ High

(Adult men – Sweden) III-2 Cohort Medium High D&A+ High

Glenny AM (1997) I Systematic

review High High B(c)+, B+;D&A+;P+,S+, High

(Adults - US) III-2 Cohort Low Low High

(Children) III-2

Systematic

review of

Case-control

studies

Low Low High

(Children – US) II RCT Medium Low High

(Adult women - US) II RCT Medium Low High

(Children - US) II RCT Medium HighD&A+ High

(Adults) I Systematic

review Medium High D+; D&E+ High

Adult women - US) II RCT Medium MediumD+ High

(Children - Singapore) III-2 Cohort Medium High B+ Medium

(Adult women - US) III-2 Cohort Medium HighA+ High

(Adults - US) III –2 Cohort Medium High D+ High

(Adults – New Zealand) III-2 Cohort Medium High B+ Medium

(Adults – Denmark) II RCT Medium HighD+ High

(Children & adolescents - Brazil) III-2 Cohort Low Medium D&B+ Low

(Adults - India) III-2 Cohort Medium HighD&A+ Medium

(Adult women – US) II RCT Medium MediumD+;A+;W+ High

(Adult women - US) II RCT Low MediumD+;D&A+;D&W+ High

(Adults - US) II RCT Medium HighB+ High

D = diet; A = aerobic training; W = weight training; S = surgery; B = behaviour therapy; P = pharmacological; c = children; a = adults

Magnitude rating: The direction of the effect is by ‘+’ for a positive effect and ‘–‘ for a negative effect. Low = no statistically sigificant

effect

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Psychinfo for Stress information only

Detailed within the table below

Where possible the searches were limited by the English Language and Human Research. The databases were searched for the following years of publication:

Medline 1990 - 99; Embase 1988 - 99; CINAHL 1982 - 99; Cochrane 1993 - 99; PsycINFO 1984 - 99. Unless other year ranges are specified on the table.

Reference lists at the end of review articles of particular relevance were hand searched.

Relevant articles were solicited from expert colleagues and organisations.

Local and international clinical practice guidelines were reviewed for relevant references

The database searched has been indicated next to each set of keywords using the following abbreviations. M= Medline, EM= Embase, CO= Cochrane and CI=

CINAHL. All EMBASE and Medline searches were done using english language En.La and human as a limit. Other abbreviations used were NIDDM= non-insulindependent

diabetes mellitus; di=diagnosis; pc= prevention and control; ep= epidemiology; DM= Diabetes Melitus; BW= body weight; IGT= impaired glucose

tolerence; Type 2= Type 2 diabetes; PSS= Psychosomatic Stress; St= Stress; Dp= depression; Sm&RP= Smoking and related phenomena; Sm= Smoking; Ex=

Exercise; PA= physical activity; BIA= Bioelectrical impedence; DEXA= dual energy X-ray; Wt= weight; Edu= education; Comp= Composition; Gp=group; Gps=

groups; WHR= waist to hip ratio; Dist= distribution; sf= skinfolds; ad= adipose; ob= obesity; BMI= body mass index; Thpy= therapy

Identified = number of articles which matched the mesh terms listed or contained the text terms in each particular database

Relevant = those articles considered relevant to the questions being asked after viewing titles or abstracts

Articles identified by other strategies = articles identified by hand searching, other searches for other questions, or from colleagues

Total for Review = Those articles which were relevant to the question, contained original data or were systematic reviews of original articles and met the following

criteria.

101

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

In assessing the evidence the following criteria were used to determine the suitability of studies:

2. Papers that examine parameters in relation to cardiovascular disease or mortality risk and the risk of Type 2 diabetes

3. Papers that examine a parameter in relation to development of the metabolic syndrome, and impaired glucose tolerance and Type 2 diabetes

4. Studies of longitudinal design, however in some areas where there are few of these studies the cross-sectional studies were also assessed.

5. Population studies in populations similar to the population of Australia eg: small studies on American Indians or Japanese may not have been included.

6. The paper was published in the English Language.

7. Articles based on human studies

8. Articles were obtained from journals able to be accessed within our Library network or ordered through an interlibrary loan.

102

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

life and/or in

childhood increase

the risk of Type 2

diabetes?

(DM/ OR DM.mp OR

NIDDM/OR NIDDM.mp)

AND (ob/ OR ob.mp OR

BW/ OR BW.mp OR wt

loss/) AND (risk/ OR

risk.mp OR risk factors/ OR

prediabetic state/ OR risk

assessment/) limit adult M

(DM/ OR DM.mp OR

NIDDM/ OR NIDDM.mp)

AND (ob/ OR ob.mp OR

BW/ OR BW.mp) AND

(risk/ OR risk.mp) AND

limit child M

(DM/ OR DM.mpOR

NIDDM/ OR NIDDN.mp

AND (ob/ OR ob.mp/ OR

BW/ OR BW.mp) AND

(child/ OR child.mp OR

childhood/) M

(NIDDM/ OR Type 2.mp)

AND (ob/ OR ob.mp) AND

risk.mp AND child.mp CI

(NIDDM/ OR Type 2.mp)

AND (ob/ OR ob.mp) AND

risk.mp CI

(NIDDM/ OR Type 2.mp)

AND (risk/ OR risk.mp)

AND (ob/ OR ob.mp) EM

88-99

(ob.mp AND risk.mp

AND diabetes.mpCO

103

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

distribution an

additional contributor

to total adiposity in

determining the risk

of Type 2 Diabetes

and does this vary

with ethnicity?

Total For Question

(DM/ OR DM.mp OR

NIDDM/ OR NIDDM.mp)

AND (ad tissue/ OR ad

tissue.mp OR body Comp/

OR body Comp.mp) AND

(risk/ OR risk.mp OR ethnic

Gps/ OR ethnic Gp.mp OR

Asian/) M

(DM/ OR DM.mp OR

NIDDM/ OR NIDDM.mp)

AND (waist.mp OR hip/ OR

hip.mp OR ad tissue.mp)

AND (risk/ OR risk.mp) M

(DM/ OR DM.mp OR

NIDDM/ OR NIDDM.mp)

AND (fats/ OR fat.mpOR

body.mp) AND (risk/ OR

risk.mp) AND (supply and

Dist/ OR Dist.mp) M

(NIDDM/ OR Type 2.mp

OR NIDDM.mp) AND (ad

tissue/ OR ad tissue..mp OR

body Comp/ OR body

Comp.mp OR body

constitution/ OR body

constitution.mp Or fat

Dist.mp OR risk factors/ OR

body fat.mp OR

adiposity.mp) AND

measure..mp M Complete

(NIDDM/ OR Type 2.mp)

AND (Body fat/ OR fat

Dist.mp) AND adiposity.mp

AND (risk/ OR risk.mp) EM

94-99

104

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

distribution an

additional contributor

to total adiposity in

determining the risk

of Type 2 Diabetes

and does this vary

with ethnicity?

(NIDDM/ OR Type 2.mp)

AND (risk/ OR risk.mp)

AND (ob/ OR ob.mp) AND

(ethnic Gp/ OR ethnic.mp)

EM 94-99

(NIDDM/ OR Type 2.mp)

AND (risk/ OR risk.mp)

AND (ob/ OR ob.mp) AND

EM 88-93

(NIDDM/ OR Type 2.mp)

AND (body fat/ OR fat

Dist.mp OR adiposity.mp

OR ob/) AND (risk/ OR

risk.mp) EM 88-93

(NIDDM/ OR Type 2.mp)

AND (risk/ OR risk.mp)

AND (ethnic difference/ OR

ethnic.mp) AND (ob/ OR

ob.mp) EM 88-93

(NIDDM/ OR Type 2.mp)

AND (ob/ OR ob.mp) AND

asians/OR ethnic Gps/ OR

hispanics/ OR minority Gps/

OR ethnic.mp) CI

(Anthropometry.mp OR sf

thickness/ OR WHR.mp OR

ad tissue Dist/ OR sf.mpOR

BMI/) AND (measures.mp

OR body Wts and measures/)

AND (Ad tissue/ OR body

Comp/ OR adiposity.mp OR

body constitution OR fat

Dist.mp) CI

105

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

distribution an

additional contributor

to total adiposity in

determining the risk

of Type 2 Diabetes

and does this vary

with ethnicity?

(body Wts and measures/ OR

measures.mp) AND (Ad

tissue/ OR body Comp/

adiposity.mp OR body

constitution/ OR fat Dist.mp)

AND (tomography, X-ray

computed/ OR computed

tomography.mp OR electric

impedance/ OR BIA.mp OR

DEXA.mp) CI

Body.mp AND fat.mp AND

risk.mp AND diabetes.mp

CO

[(Fat.mp AND body.mp) OR

(body.mp AND comp.mp)

OR (body.mp AND

constitution.mp) OR

(body.mp AND

patterning.mp)] AND

anthropometry.mp CO

Ethnicity.mp AND

diabetes.mp AND risk.mp

CO

[Body comp/OR (Body.mp

AND comp.mp) OR body

constitution/ OR (body AND

constitution)OR body

patterning/ OR (body.mp

AND patterning.mp) OR fat

body/ OR (fat AND body)]

AND [body-weights-andmeasures/

OR (body.mp

AND weights.mp AND

measures.mp)] CO

106

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

measures can be used

for body fat

distribution?

(DM/ OR NIDDM/ OR

NIDDM.mp OR DM.mp)

AND body.mp AND (fat/

OR ob/ OR adiposity.mp OR

BW/) AND (waist.mp OR

hip/ OR hip.mp) M

(DM/ OR DM.mp OR

NIDDM/) AND (body

Comp/ OR body Comp.mp

M

(Ad tissue/ OR body comp/

OR body constitution/) AND

weights and measures/ AND

(BMI/ OR Anthropometry/

OR sf thickness/ OR

WHR.mp) M

(Ad tissue/ OR body comp/

OR body constitution/) AND

weights and measures/ AND

(electric impedance/

tomography, OR x-ray

computed/ OR dual energy

x-ray.mp) M

(NIDDM/ OR Type 2.mp)

AND (ad tissue OR body

Comp OR fat DistOR body

Wts) AND (measuresOR

skinfold thicksness OR sf

OR WHR OR DEXA OR

computed tomography) CI

107

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

measures can be used

for body fat

distribution?

(Anthropometry.mp OR sf

thickness/ OR sf.mp OR ad

tissue Dist/ OR WHR.mp)

AND (body Wts and

measures/ OR measures.mp)

AND (Ad tissue/ OR body

Comp/ OR adiposity.mp OR

body constitution fat

Dist.mp) CI

(body Wts and measures/ OR

measures.mp) AND (Ad

tissue/ OR body Comp/

adiposity.mp OR body

constitution/ OR fat Dist.mp)

AND (electric impedance/

OR tomography, Xray

computed/ computed

tomography.mp OR BIA.mp/

OR DEXA.mp) CI

(NIDDM/ OR Type 2.mp)

AND (Body fat/ OR fat

Dist.mp) AND adiposity.mp

AND (risk/ OR risk.mp) EM

94-Feb 99

(NIDDM/ OR Type 2.mp)

AND (risk/ OR risk.mp)

AND (ob/ OR ob.mp) EM

88-93

(NIDDM/ OR Type 2.mp)

AND(body fat/ OR fat

Dist.mp OR adiposity.mp

OR ob/) AND (risk/ OR

risk.mp) EM 88-93

Body AND fat AND risk

AND diabetes CO

132

97

17

155

125

0

108

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

activity decrease, and

a sedentary life-style

increase, the risk of

Type 2 diabetes?

Total For Question

(DM/ OR DM.mp OR

NIDDM/ OR diabetes.mp)

AND (Ex/ OR Ex.mp OR

sedentary/ OR PA.mp) AND

(risk/ OR risk.mp) M

(NIDDM/ OR Type 2.mp)

AND (Ex/ OR Ex.mp OR

therapeutic Ex/ OR

sedentary.mp OR PA) AND

(risk.mp OR prevent.mp OR

weights and measure/ OR

data collection methods/)

Sept CI

(PA.mp OR PA/) AND

estimate.mp) EM 88-99

(NIDDM/ OR Type 2.mp)

AND (risk/ OR risk.mp)

AND (Ex.mp OR Ex/) EM

88-99

(Diabetes.mp OR DM/)

AND [(physical.mp AND

activity.mp) OR physical

fitness/ OR ex Thpy/ OR

ex/]AND (risk.mp OR risk/

OR risk factors/ OR risk

assessment/) CO

DM/ AND (risk/ OR risk

factors/ OR risk assessment/)

CO

109

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

activity decrease, and

a sedentary life-style

increase, the risk of

Type 2 diabetes?

[Physical fitness/ OR

(physical.mp AND

fitness.mp) OR ex Thpy/ OR

(ex.mp and Thpy.mp) OR

Ex/ OR Ex.mp] AND [body

wts and measures/ OR

(body.mp and weights.mp

and measures.mp) OR

weights and measures/OR

[weights.mp and

measures.mp)] CO

110

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

measures can be used

to determine physical

activity and

sedentariness?

DM/ OR DM.mp OR

NIDDM/ AND (DM/ OR

DM.mp) (Ex/ OR Ex.mp OR

PA.mp) AND (Wts and

measures/ OR measure.mp)

M

NIDDM/ OR Type 2.mp)

AND (risk.mp OR

prevent.mp) AND (Ex/ OR

Ex.mp PA./ OR therapeutic

Ex/) CI

(NIDDM/ OR Type 2.mp)

AND (Ex/ OR sedentary.mp)

AND risk.mp CI

(PA.MPOR PA.mp) AND

estimate.mp EM 88-99

(NIDDM/ OR Type 2.mp)

AND (risk/ OR risk.mp)

AND (Ex.mp OR Ex/) EM

88-93

Diabetes.mp AND

physical.mp AND

activity.mp AND risk.mp

CO

111

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

increase the risk of

Type 2 diabetes

and/or Gestational

Diabetes Mellitus and

how is this affected

by fat type?

Total For Question

DM/ OR DM.mp OR

NIDDM/ AND (DM/ OR

diabetes.mp) AND (dietary

fats/ OR fats/ OR dietary

fat.mpOR fat.mp) AND

intake/ AND (risk/ OR

risk.mp M

Diabetes, gestational/ OR

gestational diabetes.mp)

AND (fats/ OR fat/ OR

fat.mp) AND (diet/ OR

diet.mp) M

(NIDDM/ OR Type 2.mp

OR DM, gestational/ OR

gestational diabetes.mp)

AND (dietary fats OR

fat.mp) AND diabetic diet/

OR diet/ OR diet, fat

restricted/ OR diet, reducing/

OR diet.mp) AND risk.mp

CI

(NIDDM/ OR Type 2.mp)

AND (risk OR risk.mp)

AND (dietary fat/ OR fat

intake/) EM 88-99

(pregnancy DM/ OR

gestational diabetes.mp)

AND (risk OR risk.mp)

AND (dietary fat.mp OR fat

intake/) EM 88-99

(NIDDM/ OR Type 2.mp)

AND (risk OR risk.mp)

AND (fat/ OR fat.mp) EM

88-99

Diet AND Diabetes AND

Risk CO

112

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

Diabetes Mellitus

increase the

subsequent risk of

Type 2 diabetes in

mothers and/or their

infants ?

(Diabetes, gestational/ OR

gestational diabetes.mp OR

GDM.mp) AND (DM/ OR

DM.mp OR NIDDM) AND

(risk/ OR risk.mp) M

NIDDM/ AND (birth Wt/

birthWt.mp OR fetal

macrosomia/) M Complete

NIDDM/ AND (birth Wt/

birthWt.mp OR fetal

macrosomia/) AND risk

factors/ M Complete

(NIDDM/ OR Type 2.mp)

AND (gestational

diabetes.mp OR DM,

gestational/) AND (risk.mp

OR high-risk pregnancy/) CI

NIDDM/ AND Risk factors/

AND gestational.mp. CI

NIDDM/ pc,di and ep and

gestational diabetes/ CI

(pregnancy DM/ OR

gestational diabetes.mp)

AND (NIDDM/ OR Type

2.mp) AND (risk/ OR

risk.mp) EM 88-99

{gestational diabetes/OR

(gestational.mp and

diabetes.mp)] AND

[NIDDM/ OR NIDDM.mp]

CO

414

73

20

19

11

10

77

5

113

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

birth affect later risk

of Type 2 diabetes?

Total For Review

(DM/ OR DM.mp OR

NIDDM.mp) AND (DM/ OR

diabetes.mp) AND (birth Wt/

OR birth Wt.mp OR

birthWt.mp) AND (risk/ OR

risk.mp) M

NIDDM/ AND (birth wt/ OR

birthwt.mp OR fetal

macrosomia/) M Complete

(NIDDM/ OR non-insulindependent

diabetes.mp OR

Type 2.mp) AND (birth

Wt.mp OR birth Wt/) CI

DM/ AND (Birth Wt/ OR

birth Wt.mp OR

macrosomia/) EM 88-93

DM/ pc, di, ep. AND

(birthWt.mp OR birth Wt/

OR macrosomia/) EM 94-99

Type.mp AND 2.mp AND

Diabetes.mp AND Birth.mp

AND Wt.mp CO

NIDDM.mp AND (birth.mp

AND Wt.mp) OR

birthWt.mp OR

macrosomia.mp CO

262

73

3

68

22

0

0

114

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

index diets decrease

the risk of Type 2

diabetes?

(DM/ OR DM.mp OR

NIDDM.mp) AND (DM/

OR diabetes.mp) AND

(glycaemic/ OR abstracting

and indexing OR index.mp

OR glycemic.mp) AND

(risk/ OR risk.mp) M

(DM/ OR DM.mp OR

NIDDM.mp) AND DM/ OR

diabetes.mp AND dietary

carbohydrates/ OR dietary

carbohydrate.mp AND (risk/

OR risk.mp) M

(NIDDM/ OR Type 2.mp)

AND (glycaemic index.mp

OR glycemic index.mp)

AND (risk/ OR risk.mp) CI

(NIDDM/ OR Type 2.mp)

AND (glycaemic index.mp

OR glycemic index.mp OR

glycemic.mp OR

glycaemic.mp) AND (risk/

OR risk.mp) EM 88-93

NIDDM/ OR Type 2.mp

AND (risk/ OR risk.mp)

AND (Glycaemic index.mp

OR glycemic) EM 94-99

Diabetes.mp AND diet,mp

AND risk.mp CO

124

44

1

62

148

152

115

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

stress increase the

risk of Type 2

diabetes?

(DM/ OR DM.mp OR

NIDDM) AND (DM/ OR

diabetes.mp) AND (Sm/ OR

Sm.mp) AND (risk/ OR

risk.mp) M

(DM/ OR DM.mp OR

NIDDM/) AND (DM/ OR

diabetes.mp AND

(psychosocial AND St/OR

St.mpOR Dp/OR Dp.mp OR

anxiety/ OR anxiety.mp)

AND (risk/ OR risk.mp) M

NIDDM/ OR Type 2.mp)

AND (St, psychological/ OR

psychological St.mp OR Dp/

OR Dp.mp) CI

NIDDM/ AND Smoking.mp

CI

(NIDDM/ OR Type 2.mp

OR IGT/ OR IGT.mp OR

‘IGT’.mp) AND (emotional

St OR psychological aspect

OR PSS OR St OR cigarette

Sm OR Dp) EM 94-99

1517

128

25

36

75

116

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

stress increase the

risk of Type 2

diabetes?

(St/ OR PSS.mp OR

emotional St/ OR mental St/

OR St.mp OR Dp/ OR

Dp.mp OR cigarette Sm/ OR

‘Sm&RP’/ OR Sm/ OR

Sm.mp) AND IGT.mp OR

IGT/) EM 88-93

(NIDDM/ OR Type 2.mp

OR NIDDM.mp) AND

(emotional St/ OR PSS.mp

OR mental St/ OR St/ OR

St.mp OR cigarette Sm/ OR

Sm/ OR Sm.mp OR ‘Sm and

Sm related phenomena’/ OR

Dp/ OR Dp.mp) EM 88-93

(NIDDM/ OR Type 2.mp

OR NIDDM.mp) AND

(emotional St/ OR

psychological aspect/ OR

PSS.mp OR St/ OR cigarette

Sm/ OR Sm/ OR Sm.mp OR

Dp/ OR Dp.mp) EM 94-99

117

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

stress increase the

risk of Type 2

diabetes?

(DM/ OR Type 2.mp OR

NIDDM.mp) AND

(psychosomatic disorders/

OR PSS.mp OR tobacco Sm

OR Sm.mp OR social St/ OR

psychological St OR OR St/

OR St.mp PI

NIDDM.mp AND St.mp

AND pyschosomatic St.mp

AND Sm.mp CO

NIDDM.mp AND St CO

NIDDM.mp AND

psychosomatic.mp AND

St.mp CO

Type.mp AND 2.mp AND

stress.mp AND Diabetes.mp

CO

NIDDM/ AND smoking/ CO

NIDDM/ AND (St/ OR Stpsychological/

OR Sm/ OR

Dp/ OR Anxiety/) CO

NIDDM.mp AND (St/ OR

[St.mp-AND

psychological.mp] OR

Sm.mp OR Dp.mp OR

Anxiety.mp) CO

137

6

8

0

0

9

17

55

118

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

and physical activity

are recommended to

prevent excessive

weight gain in

childhood and

throughout adult

life?

(DM/ OR DM.mp OR

NIDDM) AND diabetes.mp

AND (ob/ OR ob.mp OR Ex/

OR Ex.mp) AND (risk/ OR

risk.mp AND Limit (En.La

AND human) M

(DM/ OR DM.mp OR

NIDDM) AND (DM/ OR

diabetes.mp AND Edu.mp

OR Edu/ OR health Edu/OR

health Edu.mp) AND (risk/

OR risk.mp AND (En.La

AND human) M

(Wt gain/ OR Wt gain.mp

OR Wt loss/ OR Wt loss.mp)

AND (diet/ OR diet, fat –

restricted/ OR diet.mp OR

Ex/ OR Ex.mp) AND

(risk.mp OR prevent.mp) CI

(Wt gain/ OR Wt gain.mp

OR ob/ OR ob, morbid/ OR

ob.mp) AND (diet/ OR diet,

fat –restricted/ OR diet,

reducing/ OR restricted diet/

OR diet.mp OR Ex/ OR

Ex.mp OR PA.mp OR

PA.mp) AND (Edu/ OR

health Edu/ OR Nutrition

Edu/ OR patient Edu/ OR

Edu.mp) AND (risk.mp OR

prevent.mp) CI

1614

489

148

49

119

Primary Prevention Guideline Search Strategy and Yield Part 2 December 2001

and physical activity

are recommended to

prevent excessive

weight gain in

childhood and

throughout adult

life?

(diabetic diet/ OR diet/

OR diet.mp OR PA.mp

OR PA/ Ex.mp OR Ex/)

AND (Wt gain/ OR Wt

gain.mp OR morbid ob/

OR ob/ OR ob.mp) AND

prevent.mp EM 88-99

(Primary and Prevention/

OR Risk/ OR Riskassessment/

OR riskfactors/

OR riskmanagemnet/)

AND

(Obesity/ OR obesity-indiabetes/

OR obesitymorbid/

OR weight-gain/

OR weight-loss/) CO

(Physical-fittness/ OR Ex-

Thpy/ OR Ex/ OR

diabetic-diet/ OR diet/ OR

diet-Thpy/ OR diet-fatrestricted/

OR dietreducing/)

AND (obesity/

OR obesity-in-diabetes/

OR obesity-morbid/ OR

weight-gain/ OR weightloss/)

limit review CO

Diabetes.mp AND

diet,mp AND risk.mp CO

257

114

11

152