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Early Response to Preventive Strategies in the Diabetes Prevention Program

Journal of General Internal Medicine Aims and scope Submit manuscript



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.

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  1. Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.

    Article  Google Scholar 

  2. Knowler WC, Fowler SE, Hamman RF, et al. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. 2009;374(9702):1677–86. Epub 2009/11/03.

    Article  PubMed  Google Scholar 

  3. Hamman RF, Wing RR, Edelstein SL, et al. Effect of weight loss with lifestyle intervention on risk of diabetes. Diabetes Care. 2006;29(9):2102–7. Epub 2006/08/29.

    Article  PubMed  Google Scholar 

  4. Lachin JM, Christophi CA, Edelstein SL, et al. Factors associated with diabetes onset during metformin versus placebo therapy in the Diabetes Prevention Program. Diabetes. 2007;56(4):1153–9. Epub 2007/03/31.

    Article  PubMed  CAS  Google Scholar 

  5. Standards of medical care in diabetes—2012. Diabetes Care. 2012;35(Suppl 1):S11–63. Epub 2012/01/04.

    Google Scholar 

  6. The Diabetes Prevention Program. Design and methods for a clinical trial in the prevention of type 2 diabetes. Diabetes Care. 1999;22(4):623–34. Epub 1999/04/06.

    Article  Google Scholar 

  7. American Diabetes Association. Standards of medical care in diabetes—2011. Diabetes Care. 2011;34(Supplement 1):S11–61.

    Article  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.

  10. Wing RR, Hamman RF, Bray GA, et al. Achieving weight and activity goals among Diabetes Prevention Program lifestyle participants. Obes Res. 2004;12(9):1426–34. Epub 2004/10/16.

    Article  PubMed  Google Scholar 

  11. Svetkey LP, Stevens VJ, Brantley PJ, et al. Comparison of strategies for sustaining weight loss: the weight loss maintenance randomized controlled trial. JAMA. 2008;299(10):1139–48.

    Article  PubMed  CAS  Google Scholar 

  12. Sarwer DB, von Sydow Green A, Vetter ML, Wadden TA. Behavior therapy for obesity: where are we now? Curr Opin Endocrinol Diabetes Obes. 2009;16(5):347–52. Epub 2009/07/23.

    Article  PubMed  Google Scholar 

  13. Perreault L, Kahn SE, Christophi CA, Knowler WC, Hamman RF. Regression from pre-diabetes to normal glucose regulation in the Diabetes Prevention Program. Diabetes Care. 2009;32(9):1583–8. Epub 2009/07/10.

    Article  PubMed  Google Scholar 

  14. Tuomilehto J, Lindstrom J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343–50.

    Article  PubMed  CAS  Google Scholar 

  15. Pan XR, Li GW, Hu YH, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20(Journal Article):537–44.

    Article  PubMed  CAS  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.

    Article  PubMed  CAS  Google Scholar 

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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.

Author Contributions

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.

Prior Presentations


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.

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Correspondence to Nisa M. Maruthur MD, MHS.

Additional information

Clinical Trials Registration: NCT00004992

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(DOC 59 kb)

Appendix Table 1

Distribution of Baseline Characteristics Based on Inclusion in Study Sample (DOC 67 kb)

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Maruthur, N.M., Ma, Y., Delahanty, L.M. et al. Early Response to Preventive Strategies in the Diabetes Prevention Program. J GEN INTERN MED 28, 1629–1636 (2013).

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