Cardiometabolic Risk Factor Changes Observed in Diabetes Prevention Programs in US Settings: A Systematic Review and Meta-analysis

Background: The Diabetes Prevention Program (DPP) study showed that weight loss in high-risk adults lowered diabetes incidence and cardiovascular disease risk. No prior analyses have aggregated weight and cardiometabolic risk factor changes observed in studies implementing DPP interventions in nonresearch settings in the United States. Methods and Findings: In this systematic review and meta-analysis, we pooled data from studies in the United States implementing DPP lifestyle modification programs (focused on modest [5%–7%] weight loss through ≥150 min of moderate physical activity per week and restriction of fat intake) in clinical, community, and online settings. We reported aggregated pre- and post-intervention weight and cardiometabolic risk factor changes (fasting blood glucose [FBG], glycosylated hemoglobin [HbA1c], systolic or diastolic blood pressure [SBP/DBP], total [TC] or HDL-cholesterol). We searched the MEDLINE, EMBASE, Cochrane Library, and Clinicaltrials.gov databases from January 1, 2003, to May 1, 2016. Two reviewers independently evaluated article eligibility and extracted data on study designs, populations enrolled, intervention program characteristics (duration, number of core and maintenance sessions), and outcomes. We used a random effects model to calculate summary estimates for each outcome and associated 95% confidence intervals (CI). To examine sources of heterogeneity, results were stratified according to the presence of maintenance sessions, risk level of participants (prediabetes or other), and intervention delivery personnel (lay or professional). Forty-four studies that enrolled 8,995 participants met eligibility criteria. Participants had an average age of 50.8 years and body mass index (BMI) of 34.8 kg/m2, and 25.2% were male. On average, study follow-up was 9.3 mo (median 12.0) with a range of 1.5 to 36 months; programs offered a mean of 12.6 sessions, with mean participant attendance of 11.0 core sessions. Sixty percent of programs offered some form of post-core maintenance (either email or in person). Mean absolute changes observed were: weight -3.77 kg (95% CI: -4.55; -2.99), HbA1c -0.21% (-0.29; -0.13), FBG -2.40 mg/dL (-3.59; -1.21), SBP -4.29 mmHg (-5.73, -2.84), DBP -2.56 mmHg (-3.40, 1.71), HDL +0.85 mg/dL (-0.10, 1.60), and TC -5.34 mg/dL (-9.72, -0.97). Programs with a maintenance component achieved greater reductions in weight (additional -1.66kg) and FBG (additional -3.14 mg/dl). Findings are subject to incomplete reporting and heterogeneity of studies included, and confounding because most included studies used pre-post study designs. Conclusions: DPP lifestyle modification programs achieved clinically meaningful weight and cardiometabolic health improvements. Together, these data suggest that additional value is gained from these programs, reinforcing that they are likely very cost-effective

Weight change and incident metabolic syndrome in Iranian men and women; a 3 year follow-up study

Abstract Background Although the association of weight gain and developing metabolic syndrome (MetS) has been reported in the Western and Asian populations, data on the gender-stratified effects of weight change (including weight loss) on incident MetS and its components in the Middle East Caucasians is still scarce. Methods A total of 1431 men and 2036 women aged ≥ 20 years with BMI > 18.5 kg/m2 were followed over 3 years. Multivariate logistic regression analysis was used to estimate the relative risk (RR) of MetS and its components (the Adult Treatment Panel III definition) associated with gender-stratified quintiles of percent weight change. Subjects with MetS at baseline were excluded for analyzing the RR of MetS. Results There was 20.4% (95% CI, 19.6–21.2) age-adjusted incident MetS (18.4% male vs. 23.1% women). In men, mild weight gain (WG) predicted high waist circumference (WC) and high triglyceride; moderate WG predicted MetS (RR 2.5, 95% CI 1.4–4.3), high WC and high blood pressure (BP); large WG predicted MetS (RR 3.2, 95% CI 1.8–5.7) and its components, except for high fasting plasma glucose. In women, mild WG predicted MetS (RR 2.5, 95% CI 1.4–4.3), high WC and high BP; moderate WG predicted Mets (RR 4.6, 95% CI 2.7–8.0), high WC and high triglyceride; large WG predicted MetS (RR 6.6, 95% CI 3.8–11.3) and its components except for low HDL-cholesterol. Mild weight loss had protective effect on high WC in both genders and MetS in men (RR 0.5, 95% CI 0.26–0.97, P = 0.04). Conclusion Weight change showed different effects on MetS in men and women. In women, mild WG predicted MetS; however, mild weight loss was protective against MetS in men and high WC in both genders.</p

Outcomes stratified by the presence of maintenance.

<p>(Supporting forest plot in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002095#pmed.1002095.s010" target="_blank">S9 Fig</a>.)</p

Outcomes stratified by type of provider for delivery of the intervention.

<p>(Supporting forest plot in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002095#pmed.1002095.s009" target="_blank">S8 Fig</a>.)</p

Flow diagram of study search.

<p>This flow chart describes the number of studies that were involved in each step of the process of study selection, from the initial study search. After the application of inclusion and exclusion criteria, 44 studies met criteria and were included in the final analysis.</p

Mean weight change of study participants.

<p>Analysis of all 48 intervention groups which reported weight change from baseline to end of follow-up period. Data was analyzed with Mix 2.0 using random effects method with the weighting of each study proportional to the inverse of the variance. Study weight is indicated by the size of the box, and horizontal lines indicate the 95% confidence interval. The red line indicates the overall weight change, and summary diamond indicates pooled estimate with reported mean (95% CI).</p

Comparison of outcomes from DPP versus Community Trials.

<p>Comparison of outcomes from DPP versus Community Trials.</p

Baseline characteristics of trials (aggregate) versus DPP.

<p>Baseline characteristics of trials (aggregate) versus DPP.</p