Study on Lifestyle Intervention and Impaired Glucose Tolerance Maastricht (SLIM): preliminary results after one year

Department of Human Biology, Maastricht University, Maastricht, The Netherlands.
International Journal of Obesity (Impact Factor: 5). 03/2003; 27(3):377-84. DOI: 10.1038/sj.ijo.0802249
Source: PubMed


Important risk factors for the progression from impaired glucose tolerance to type II diabetes mellitus are obesity, diet and physical inactivity. The aim of this study is to evaluate the effect of a lifestyle-intervention programme on glucose tolerance in Dutch subjects with impaired glucose tolerance (IGT).
A total of 102 subjects were studied, randomised into two groups. Subjects in the intervention group received regular dietary advice, and were stimulated to lose weight and to increase their physical activity. The control group received only brief information about the beneficial effects of a healthy diet and increased physical activity. Before and after the first year, glucose tolerance was measured and several other measurements were done.
Body weight loss after 1 y was higher in the intervention group. The 2-h blood glucose concentration decreased 0.8+/-0.3 mmol/l in the intervention group and increased 0.2+/-0.3 mmol/l in the control group (P<0.05). Body weight loss and increased physical fitness were the most important determinants of improved glucose tolerance and insulin sensitivity.
A lifestyle-intervention programme according to general recommendations is effective and induces beneficial changes in lifestyle, which improve glucose tolerance in subjects with IGT. Body weight loss and increased physical fitness were the most important determinants of improved glucose tolerance and insulin sensitivity.

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Available from: Marco Mensink, Jul 15, 2014
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    • "Australia 56 0.56 NA 30.1 <30% TF, <10% SF moderate intensity, 30 min/S, most days/wk 12 Bo et al. 2007 [31] Italy 56 0.58 MS 30.0 reduced TF and SF intake moderate intensity (i. e. brisk walking), ~150 min/wk 13 Arciero et al. 2006 [32] USA 43 0.48 NA 27.8 high protein (40%) and low fat (20%) diet resistance and cardiovascular training, 20 min/S, 4–6 S/wk 14 Brekke et al. 2005 [33] Sweden 42 0.37 NA 26.1 <30% TF intake, <10% SF intake walking or more intensive exercise, 30 min/S, 4–5 S/wk 15 Watkins et al. 2003 [34] USA 50 0.50 NA 33.7 500 kcal/d restriction, <20% TF cycle ergometry and jogging, or walking, ~60 min/S, 3–4 S/wk 16 Lindstrom et al. 2003 [35] Finland 55 0.66 IGT 31.3 200 kcal/d restriction, <30% TF, <10% SF endurance exercise & resistance training, >30 min/S 17 Esposito et al. 2003 [36] Italy 35 1.00 NA 34.5 1400 kcal/d, 55% carbohydrate, 30% TF, <10% SF aerobic exercise (walking and swimming) 18 Mensink et al. 2003 [37] Netherlands 56 0.43 IGT 29.5 >55% carbohydrate, <30% TF, <10% SF moderate physical activity, >30 min/S, 5 S/wk 19 McAuley et al. 2002 [38] New Zealand 46 0.71 IR 34.5 400 kcal/d restriction, 27% TF, 9% SF Moderate exercise plus resistance training, >20 min/S, 5 S/wk 20 Miller et al. 2002 [39] USA 54 0.62 NA 33.7 500 kcal/d restriction, 27% TF, 6% SF aerobic (brisk walking and biking), 30–45 min/S, 3 S/wk 21 Reseland et al. 2001 [40] Norway 45 0.00 MS 27.5 400 kcal/d restriction, <30% TF endurance exercise, 1 h/S, 3 S/wk 22 Oldroyd et al. 2001 [41] UK 58 0.40 IGT 30.2 <30% TF intake, ~50% carbohydrate aerobic exercise, 20–30 min/S, 2–3 S/wk 23 Kuller et al. 2001 [42] USA 47 1.00 NA 25.0 Calorie restriction upto 1300 kcal, 25% TF, 7% SF increasing physical activity to 1250 kcal expended weekly 24 Ornish et al. 1998 [43] USA 60 0.09 NA 26.9 10%-fat vegetarian diet moderate-intensity aerobic, 1 h/S, 5 S/wk 25 Stefanick et al. 1998 (female) [16] USA 57 1.00 NA 25.6 <30% TF intake, <7% SF intake aerobic (jogging and brisk walking), 60 min/S, 3 S/wk 26 Stefanick et al. 1998 (male) [16] USA 48 1.00 NA 27.8 <30% TF intake, <7% SF intake aerobic (jogging and brisk walking), 60 min/S, 3 S/wk variance τ 2 . If τ 2 = 0, homogeneity is implied among true effects across individual studies such that μ=θ. "
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    ABSTRACT: Background and aims Fasting insulin (FI), fasting glucose (FG), systolic blood pressure (SBP), high density lipoproteins (HDL), triacylglycerides (TAG), and body mass index (BMI) are well-known risk factors for type 2 diabetes. Reliable estimates of lifestyle intervention effects on these factors allow diabetes risk to be predicted accurately. The present meta-analyses were conducted to quantitatively summarize effects of diet and exercise intervention programs on FI, FG, SBP, HDL, TAG and BMI in adults without diabetes. Materials and methods MEDLINE and EMBASE were searched to find studies involving diet plus exercise interventions. Studies were required to use adults not diagnosed with type 2 diabetes, involve both dietary and exercise counseling, and include changes in diabetes risk factors as outcome measures. Data from 18, 24, 23, 30, 29 and 29 studies were used for the analyses of FI, FG, SBP, HDL, TAG and BMI, respectively. About 60% of the studies included exclusively overweight or obese adults. Mean age and BMI of participants at baseline were 48 years and 30.1 kg/m2. Heterogeneity of intervention effects was first estimated using random-effect models and explained further with mixed-effects models. Results Adults receiving diet and exercise education for approximately one year experienced significant (P <0.001) reductions in FI (-2.56 ± 0.58 mU/L), FG (-0.18 ± 0.04 mmol/L), SBP (-2.77 ± 0.56 mm Hg), TAG (-0.258 ± 0.037 mmol/L) and BMI (-1.61 ± 0.13 kg/m2). These risk factor changes were related to a mean calorie intake reduction of 273 kcal/d, a mean total fat intake reduction of 6.3%, and 40 minutes of moderate intensity aerobic exercise four times a week. Lifestyle intervention did not have an impact on HDL. More than 99% of total variability in the intervention effects was due to heterogeneity. Variability in calorie and fat intake restrictions, exercise type and duration, length of the intervention period, and the presence or absence of glucose, insulin, or lipid abnormalities explained 23-63% of the heterogeneity. Conclusions Calorie and total fat intake restrictions coupled with moderate intensity aerobic exercises significantly improved diabetes risk factors in healthy normoglycemic adults although normoglycemic adults with glucose, insulin, and lipid abnormalities appear to benefit more.
    Diabetology and Metabolic Syndrome 11/2014; 6(1). DOI:10.1186/1758-5996-6-127 · 2.17 Impact Factor
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    • "The sample size calculation for this study is estimated based on changes in fasting insulin, observed in SLIM after one year [22]. In the SLIM study, mean difference in fasting insulin between groups was 2.9 mU/l with a standard deviation of 5.3 mU/l [22]. "
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    ABSTRACT: Background Implementation of interventions in real-life settings requires a comprehensive evaluation approach. The aim of this article is to describe the evaluation design of the SLIMMER diabetes prevention intervention in a Dutch real-life setting. Methods/Design The SLIMMER study is a randomised, controlled intervention study including subjects aged 40 through 70 years with impaired fasting glucose or high risk of diabetes. The 10-month SLIMMER intervention involves a dietary and physical activity intervention, including case management and a maintenance programme. The control group receives usual health care and written information about a healthy lifestyle. A logic model of change is composed to link intervention activities with intervention outcomes in a logical order. Primary outcome is fasting insulin. Measurements are performed at baseline and after 12 and 18 months and cover quality of life, cardio-metabolic risk factors (e.g. glucose tolerance, serum lipids, body fatness, and blood pressure), eating and physical activity behaviour, and behavioural determinants. A process evaluation gives insight in how the intervention was delivered and received by participants and health care professionals. The economic evaluation consists of a cost-effectiveness analysis and a cost-utility analysis. Costs are assessed from both a societal and health care perspective. Discussion This study is expected to provide insight in the effectiveness, including its cost-effectiveness, and delivery of the SLIMMER diabetes prevention intervention conducted in Dutch primary health care. Results of this study provide valuable information for primary health care professionals, researchers, and policy makers. Trial registration The SLIMMER study is registered with (NCT02094911) since March 19, 2014.
    BMC Public Health 06/2014; 14(1):602. DOI:10.1186/1471-2458-14-602 · 2.26 Impact Factor
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    • "A number of efficacy and replication trials have not provided any information on enrolment rates amongst eligible participants [4,6,12,18-23]. In other trials the proportion of eligible participants who agreed to enroll has varied widely from a third to 100 percent [3,5,7,10,11,13-16]. Even less is known about factors influencing enrolment in such programs. "
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    ABSTRACT: Background The effectiveness of lifestyle interventions in reducing diabetes incidence has been well established. Little is known, however, about factors influencing the reach of diabetes prevention programs. This study examines the predictors of enrolment in the Sydney Diabetes Prevention Program (SDPP), a community-based diabetes prevention program conducted in general practice, New South Wales, Australia from 2008–2011. Methods SDPP was an effectiveness trial. Participating general practitioners (GPs) from three Divisions of General Practice invited individuals aged 50–65 years without known diabetes to complete the Australian Type 2 Diabetes Risk Assessment tool. Individuals at high risk of diabetes were invited to participate in a lifestyle modification program. A multivariate model using generalized estimating equations to control for clustering of enrolment outcomes by GPs was used to examine independent predictors of enrolment in the program. Predictors included age, gender, indigenous status, region of birth, socio-economic status, family history of diabetes, history of high glucose, use of anti-hypertensive medication, smoking status, fruit and vegetable intake, physical activity level and waist measurement. Results Of the 1821 eligible people identified as high risk, one third chose not to enrol in the lifestyle program. In multivariant analysis, physically inactive individuals (OR: 1.48, P = 0.004) and those with a family history of diabetes (OR: 1.67, P = 0.000) and history of high blood glucose levels (OR: 1.48, P = 0.001) were significantly more likely to enrol in the program. However, high risk individuals who smoked (OR: 0.52, P = 0.000), were born in a country with high diabetes risk (OR: 0.52, P = 0.000), were taking blood pressure lowering medications (OR: 0.80, P = 0.040) and consumed little fruit and vegetables (OR: 0.76, P = 0.047) were significantly less likely to take up the program. Conclusions Targeted strategies are likely to be needed to engage groups such as smokers and high risk ethnic groups. Further research is required to better understand factors influencing enrolment in diabetes prevention programs in the primary health care setting, both at the GP and individual level.
    BMC Public Health 09/2012; 12(1). DOI:10.1186/1471-2458-12-822 · 2.26 Impact Factor
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