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Example Projects

Contributions to Science

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Insulin resistance is a major risk factor for Type 2 diabetes and also plays a role in cardiovascular disease and dementia risk.  Dietary patterns influence insulin resistance in both healthy people and those with diabetes.  Our research showed that dietary fat – particularly saturated and trans fats – increases insulin resistance in healthy adults and those with obesity.  

  • Lovejoy JC, Champagne CM, Smith SR, et al.  (2001) Relationship of dietary fat and serum cholesterol ester and phospholipid fatty acids to markers of insulin resistance in men and women with a range of glucose tolerance.  Metabolism 50: 86-92.

  • Lovejoy JC, Smith SR, Champagne CM, et al.  (2002) Effects of diets enriched in saturated (palmitic), monounsaturated (oleic), or trans (elaidic) fatty acids on insulin sensitivity and substrate oxidation in healthy adults.  Diabetes Care 25(8): 1283-1288.

  • Bray GA, Lovejoy JC, Smith SR, et al.  (2002) The influence of different fats and fatty acids on obesity, insulin resistance and in inflammation.   J. Nutrition 132: 2488-2491.


I helped to design the Intensive Lifestyle Intervention for the seminal Diabetes Prevention Program (DPP) clinical trial.  The outcomes of this study showed that an intensive lifestyle intervention and weight loss could reduce new diabetes onset by 58%.  More recently, we demonstrated that telephone coaching based on the interventions originally developed for DPP could be cost-effectively scaled and delivered via phone and web coaching while still driving good clinical outcomes.

  • Knowler WC, et al, 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.

  • Mayer-Davis EJ, Sparks KC, Hirst K, Costacou T, Lovejoy JC, Regensteiner JG, Hoskins M, Kriska A, Bray GA.  (2004) Dietary intake in the Diabetes Prevention Program cohort: Baseline and 1-year post-randomization. Ann. Epidemiol.14(10):763-72.

  • Carpenter KC, Lovejoy JC, Lange JM, Hapgood JE, Zbikowski SM.  (2014) Outcomes and utilization of a low intensity workplace weight loss program.  J Obesity 2014;414987.

  • Carpenter KM, Vickerman KA, Salmon EE, Javitz HS, Epel ES, Lovejoy JC. A Randomized Pilot Study of a Phone-Based Mindfulness and Weight Loss Program. Behav Med. 2019;45(4):271-281.

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Image by Jonathan Borba

Although it had long been recognized that menopause often results in significant weight gain and can mark a transition to obesity, there had been little clinical research to understand how female sex hormones impact appetite, weight gain, fat distribution and metabolic risk.  Through our NIH-funded Healthy Transitions research project, we followed women in their 40s and 50s longitudinally through the menopausal transition looking at changes in body composition, health risk factors, hormones and menopausal symptoms.  Our research showed that both resting metabolism and physical activity decline at menopause, resulting in a positive energy balance that can lead to weight gain.  We also observed race differences in the effect of menopause on body fat distribution.


  • Lovejoy JC, Smith SR, Rood JC.  (2001)  Comparison of regional fat distribution and health risk factors in perimenopausal Caucasian and African-American women: The Healthy Transitions Study.  Obesity Research 9: 10-16.

  • Lovejoy JC, Champagne C, DeJonge L, Xie H, Smith SR. (2008) Decreased physical activity and increased visceral fat during the menopausal transition.  Int J Obesity 32(6):949-58. 

  • Lovejoy JC, Sainsbury A, the Stock 2008 Working Group.  (2009) Sex Differences in Obesity and the Regulation of Energy Homeostasis.  Obesity Reviews 10: 154

  • Marlatt KL, Redman LM, Beyl RA, Smith SR, Champagne, CM, Yi F, Lovejoy JC. (2020) Caucasian women have greater gains in abdominal adiposity in the years preceding menopause compared to African-American women: a prospective, observational study.  Amer J Obstet Gynecol 222(4): 365.e1-365.e18.


In 2014, I had the opportunity to combine my research on behavioral and lifestyle interventions with collection of multi-omic data (genetics, metabolomic, proteomic, clinical labs, behavioral data) in a pilot study of 108 individuals.  We subsequently expanded this approach combining genotyping with longitudinal deep phenotyping and personalized behavioral coaching to a larger real-world cohort, creating a unique database of over 5000 individuals that is laying the groundwork for new scientific developments in personalized medicine.

  • Hood L, Lovejoy JC, Price ND (2015).  Integrating big data and actionable health coaching to optimize wellness.  BMC Medicine, 2015 Jan 9;13:4.

  • Price ND, Magis AT, Earls JC, Glusman G, Levy R, Lausted C, McDonald DT, Kusebauch U, Moss CL, Zhou Y, Qin S, Moritz RL, Brogaard K, Omenn GS, Lovejoy JC, Hood L. (2017) A wellness study of 108 individuals using personal, dense, dynamic data clouds.  Nat Biotechnol. 2017 Aug;35(8):747-756.

  • Zubair N, Conomos MP, Hood L, Omenn GS, Price ND, Spring BJ, Magis AT, Lovejoy JC.  Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program.  Sci Rep. 2019;9(1):6805.

  • Earls JC, Rappaport N, Heath L, Wilmanski T, Magis AT, Schork N, Omenn GS, Lovejoy J, Hood L, Price ND. (2019) Multi-omic biological age estimation and its correlation with wellness and disease phenotypes: A longitudinal study of 3558 individuals.  J Gerontol A Biol Sci Med Sci.;74(Suppl. 1): S52-S60

  • Manor O, Dai CL, Kornilov SA, Smith B, Price ND, Lovejoy JC, Gibbons SM, Magis AT. Health and disease markers correlate with gut microbiome composition across thousands of people. Nat Commun. 2020; 15;11(1):5206.

Image by Sangharsh Lohakare
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