Global Diabetes Prevention Interventions: A Systematic Review and Network Meta-analysis of the Real-World Impact on Incidence, Weight, and Glucose

Abstract

Objective: Understanding the real-world impacts of lifestyle modification (LSM) for diabetes prevention is imperative to inform resource allocation. The purpose of this study was to synthetize global evidence on the impact of LSM strategies on diabetes incidence and risk factors in one parsimonious model.

Research design and methods: PubMed, Embase, Cochrane Library, and ClinicalTrials.gov were searched for studies published between January 1990 and April 2015. Effectiveness/translation studies of any design testing LSM strategies, targeting high-risk populations (with prediabetes or diabetes risk factors), and reporting diabetes incidence, weight, or glucose outcomes were included. We extracted number of diabetes cases/incidence rates and mean changes in weight (kg), fasting blood glucose (FBG, mmol/L), 2-h postload glucose (mmol/L), and hemoglobin A_1c (%). Pairwise random-effects and frequentist random-effects network meta-analyses were used to obtain pooled effects.

Results: Sixty-three studies were pooled in the meta-analysis (n = 17,272, mean age 49.7 years, 28.8% male, 60.8% white/European). In analyses restricted to controlled studies (n = 7), diabetes cumulative incidence was 9% among intervention participants and 12% among control participants (absolute risk reduction 3%; relative risk 0.71 [95% CI 0.58, 0.88]). In analyses combining controlled and uncontrolled studies (n = 14), participants receiving group education by health care professionals had 33% lower diabetes odds than control participants (odds ratio 0.67 [0.49, 0.92]). Intervention participants lost 1.5 kg more weight [-2.2, -0.8] and achieved a 0.09 mmol/L greater FBG decrease [-0.15, -0.03] than control participants. Every additional kilogram lost by participants was associated with 43% lower diabetes odds (β = 0.57 [0.41, 0.78]).

Conclusions: Real-world LSM strategies can reduce diabetes risk, even with small weight reductions.

Figure 1

Network structure of intervention trials assessed in the arm-based network meta-analysis for all outcomes. The figure shows the eligible comparisons available in the star-shaped network for all outcomes (diabetes incidence, weight, FBG, PPG, and A1C) of the direct and indirect meta-analysis. Each node in the network is depicted; the lines show the comparisons that contributed to the analysis. All treatment arms were compared directly or indirectly against control arms. Direct comparisons were those conducted as part of a study, whereas indirect comparisons were those conducted in the network meta-analysis. Although some intervention strategies were compared directly in some studies, these were not indirectly compared in the present meta-analysis. COM, community member; HP, health care professional; TEC, technology.

Figure 2

A: Meta-regression exploring the association between weight loss and odds of having diabetes at the end of the intervention among intervention participants (N = 15 studies). The meta-regression showed the independent effect of weight loss on diabetes risk; every additional kilogram participants lost was associated with 43% lower odds of developing diabetes than among control participants (β = 0.57 [95% CI 0.41, 0.78]). B: Meta-regression exploring the association between number of intervention sessions received and odds of having diabetes at the end of the intervention among intervention participants (N = 10 studies). The meta-regression showed the independent effect of number of intervention sessions received on diabetes risk; every additional session participants received was associated with 18% lower odds of developing diabetes than among control participants (β = 0.82 [0.74, 0.90]). In both panels, the middle line represents the regression line and the two dotted lines the 95% CIs for the regression line.