Early Response to Preventive Strategies in the Diabetes Prevention Program
- 1.6k Downloads
Recommendations for diabetes prevention in patients with prediabetes include lifestyle modification and metformin. However, the significance of early weight loss and glucose measurements when monitoring response to these proven interventions is unknown.
To quantify the relationship between early measures of weight and glucose and subsequent diabetes in patients undergoing diabetes prevention interventions.
Analysis of results from a randomized controlled trial in 27 academic medical centers in the United States.
3,041 adults with hyperglycemia randomized to lifestyle (n = 1,018), metformin (n = 1,036), or placebo (n = 987) with complete follow-up in The Diabetes Prevention Program.
Independent variables were weight loss at 6 and 12 months; fasting glucose (FG) at 6 months; hemoglobin A1c (HbA1c) at 6 months; and post-load glucose at 12 months. The main outcome was time to diabetes diagnosis.
After 6 months, 604 participants developed diabetes in the lifestyle (n = 140), metformin (n = 206), and placebo (n = 258) arms over 2.7 years. In the lifestyle arm, 6-month weight loss predicted decreased diabetes risk in a graded fashion: adjusted HR (95 % CI) 0.65 (0.35–1.22), 0.62 (0.33–1.18), 0.46 (0.24–0.87), 0.34 (0.18–0.64), and 0.15 (0.07–0.30) for 0–<3 %, 3–<5 %, 5–<7 %, 7–<10 %, and ≥10 % weight loss, respectively (reference: weight gain). Attainment of optimal 6-month FG and HbA1c and 12-month post-load glucose predicted >60 % lower diabetes risk across arms. We found a significant interaction between 6-month weight loss and FG in the lifestyle arm (P = 0.038).
Weight and glucose at 6 and 12 months strongly predict lower subsequent diabetes risk with a lifestyle intervention; lower FG predicts lower risk even with substantial weight loss. Early reduction in glycemia is a stronger predictor of future diabetes risk than weight loss for metformin. We offer the first evidence to guide clinicians in making interval management decisions for high-risk patients undertaking measures to prevent diabetes.
KEY WORDSdiabetes prevention diabetes risk type 2 diabetes
The Investigators gratefully acknowledge the commitment and dedication of the participants of the DPP. The NIDDK of the NIH provided funding to the clinical centers and the Coordinating Center for the design and conduct of the study and collection, management, analysis, and interpretation of the data. The Southwestern American Indian Centers were supported directly by the NIDDK and the Indian Health Service. The General Clinical Research Center Program, National Center for Research Resources supported data collection at many of the clinical centers. Funding for data collection and participant support was also provided by the Office of Research on Minority Health, the National Institute of Child Health and Human Development, the National Institute on Aging, the Centers for Disease Control and Prevention, the Office of Research on Women’s Health, and the American Diabetes Association. Bristol-Myers Squibb and Parke-Davis provided medication. This research was also supported, in part, by the intramural research program of the NIDDK. LifeScan Inc., Health O Meter, Hoechst Marion Roussel, Inc., Merck-Medco Managed Care, Inc., Merck and Co., Nike Sports Marketing, Slim Fast Foods Co., and Quaker Oats Co. donated materials, equipment, or medicines for concomitant conditions. McKesson BioServices Corp., Matthews Media Group, Inc., and the Henry M. Jackson Foundation provided support services under subcontract with the Coordinating Center. The opinions expressed are those of the investigators and do not necessarily reflect the views of the Indian Health Service or other funding agencies. See the Online Appendix for a complete list of Centers, investigators, and staff.
The authors dedicate this article to Dr. Frederick L. Brancati, the originator of the study question, who passed away on May 14, 2013. The authors are grateful to Dr. Christopher D. Saudek for his contribution to this study, as well as his contribution to the conduct of the DPP
Dr. Maruthur was supported by NIH/NCCR grant 1KL2RR025006-01.
Development/refinement of objectives (NMM, FLB, JMC); study design (NMM, YM, LMD, JAN, VA, DM, FLB, JMC); data collection (LMD, JAN, VA, NHW, DM, FLB, JMC); analysis (YM); interpretation of results (NMM, YM, LMD, JAN, VA, DM, FLB, JMC); drafting of manuscript (NMM); critical review/revision of manuscript (NMM, YM, LMD, JAN, VA, NHW, DM, FLB, JMC). YM had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Conflict of Interest
LD has a financial interest in Omada Health, a company that develops online behavior-change programs, with a focus on diabetes. LD’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. The authors declare that they do not have a conflict of interest.
- 5.Standards of medical care in diabetes—2012. Diabetes Care. 2012;35(Suppl 1):S11–63. Epub 2012/01/04.Google Scholar
- 8.Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care. 1997;20(7):1183–97. Epub 1997/07/01.Google Scholar
- 9.Lin D, Wei L. The robust inference for the cox proportional hazards model. J Am Stat Assoc. 1989;1074–78.Google Scholar
- 16.Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia. 2006;49(2):289–97. Epub 2006/01/05.PubMedCrossRefGoogle Scholar