[go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

Distinct genetic subtypes of adiposity and glycemic changes in response to weight-loss diet intervention: the POUNDS Lost trial

  • Original Contribution
  • Published:
European Journal of Nutrition Aims and scope Submit manuscript

Abstract

Purpose

Obesity is a heterogeneous condition and distinct adiposity subtypes may differentially affect type 2 diabetes risk. We assessed relations between genetically determined subtypes of adiposity and changes in glycemic traits in a dietary intervention trial.

Methods

The four genetic subtypes of adiposity including waist–hip ratio-increase only (WHRonly+), body mass index-increase only (BMIonly+), WHR-increase and BMI-increase (BMI+WHR+), and WHR-decrease and BMI-increase (BMI+WHR−) were assessed by polygenetic scores (PGSs), calculated based on 159 single nucleotide polymorphisms related to BMI and/or WHR. We examined the associations between the four PGSs and changes in fasting glucose, insulin, β-cell function (HOMA-B) and insulin resistance (HOMA-IR) in 692 overweight participants (84% white Americans) who were randomly assigned to one of four weight-loss diets in a 2-year intervention trial.

Results

Higher BMI+WHR−PGS was associated with a greater decrease in 2-year changes in waist circumference in white participants (P = 0.002). We also found significant interactions between WHRonly+PGS and dietary protein in 2-year changes in fasting glucose and HOMA-B (P = 0.0007 and < 0.0001, respectively). When consuming an average-protein diet, participants with higher WHRonly+PGS showed less increased fasting glucose (β = − 0.46, P = 0.006) and less reduction in HOMA-B (β = 0.02, P = 0.005) compared with lower WHRonly+PGS. Conversely, eating high-protein diet was associated with less decreased HOMA-B among individuals with lower than higher WHRonly+PGS (β = − 0.02, P = 0.006).

Conclusions

Distinct genetically determined adiposity subtypes may differentially modify the effects of weight-loss diets on improving glucose metabolism in white Americans. This trial was registered at clinicaltrials.gov as NCT00072995.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Kopelman PG (2000) Obesity as a medical problem. Nature 404:635–643. https://doi.org/10.1038/35007508

    Article  CAS  PubMed  Google Scholar 

  2. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS et al (2003) Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 289(1):76–79. https://doi.org/10.1001/jama.289.1.76

    Article  PubMed  Google Scholar 

  3. Wang T, Zhang R, Ma X, Wang S, He Z, Huang Y et al (2018) Causal association of overall obesity and abdominal obesity with type 2 diabetes: a Mendelian randomization analysis. Obesity 26(5):934–942. https://doi.org/10.1002/oby.22167

    Article  CAS  PubMed  Google Scholar 

  4. Sacks FM, Bray GA, Carey VJ, Smith SR, Ryan DH (2009) Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N Engl J Med 360(9):859–873. https://doi.org/10.1056/NEJMoa1411087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346(6):393–403. https://doi.org/10.1056/NEJMoa012512

    Article  CAS  Google Scholar 

  6. Santilli F, Simeone PG, Guagnano MT, Leo M, Maccarone MT, Di CA et al (2017) Effects of liraglutide on weight loss, fat distribution, and b-cell function in obese subjects with prediabetes or early type 2 diabetes. Diabetes Care 40(11):1556–1564. https://doi.org/10.2337/dc17-0589

    Article  CAS  PubMed  Google Scholar 

  7. Phillips CM (2016) Metabolically healthy obesity: personalised and public health implications. Trends Endocrinol Metab 27(4):189–191. https://doi.org/10.1016/j.tem.2016.02.001

    Article  CAS  PubMed  Google Scholar 

  8. Phillips CM (2013) Metabolically healthy obesity: definitions, determinants and clinical implications. Rev Endocr Metab Disord 14(3):219–227. https://doi.org/10.1007/s11154-013-9252-x

    Article  CAS  PubMed  Google Scholar 

  9. Heianza Y, Qi L (2017) Gene-diet interaction and precision nutrition in obesity. Int J Mol Sci 18(4):787. https://doi.org/10.3390/ijms18040787

    Article  CAS  PubMed Central  Google Scholar 

  10. Qi L (2014) Personalized nutrition and obesity. Ann Med 46(5):247–252. https://doi.org/10.1007/s10741-014-9462-7.Natural

    Article  PubMed  PubMed Central  Google Scholar 

  11. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR et al (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518(7538):197–206. https://doi.org/10.1038/nature14177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Magi R et al (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518(7538):187–196. https://doi.org/10.1038/nature14132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Akiyama M, Okada Y, Kanai M, Takahashi A, Momozawa Y, Ikeda M et al (2017) Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat Genet 49(10):1458–1467. https://doi.org/10.1038/ng.3951

    Article  CAS  PubMed  Google Scholar 

  14. Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Ssteinthorsdottir V et al (2010) Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 42(11):949–960. https://doi.org/10.1038/ng.685

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, Overvad K et al (2008) General and abdominal adiposity and risk of death in Europe. N Engl J Med 359(13):2105–2120. https://doi.org/10.1056/NEJMoa0801891

    Article  CAS  PubMed  Google Scholar 

  16. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ et al (2017) Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. JAMA 317(6):626–634. https://doi.org/10.1001/jama.2016.21042

    Article  PubMed  PubMed Central  Google Scholar 

  17. Dale CE, Fatemifar G, Palmer TM, White J, Prieto-Merino D (2017) Causal associations of adiposity and body fat distribution with coronary heart disease, stroke subtypes, and type 2 diabetes mellitus: a Mendelian randomization analysis. Circulation 135:2373–2388. https://doi.org/10.1161/CIRCULATIONAHA.116.026560

    Article  PubMed  PubMed Central  Google Scholar 

  18. Yaghootkar H, Lotta LA, Tyrrell J, Smit RAJ, Jones SE, Donnelly L et al (2016) Genetic evidence for a link between favorable adiposity and lower risk of type 2 diabetes, hypertension, and heart disease. Diabetes 65(August):2448–2460. https://doi.org/10.2337/db15-1671

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Winkler TW, Günther F, Höllerer S, Zimmermann M, Loos RJ, Kutalik Z et al (2018) A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications. Nat Commun 9:1946. https://doi.org/10.1038/s41467-018-04124-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huang T, Qi Q, Zheng Y, Ley SH, Manson JAE, Hu FB et al (2015) Genetic predisposition to central obesity and risk of type 2 diabetes: two independent cohort studies. Diabetes Care 38(7):1306–1311. https://doi.org/10.2337/dc14-3084

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Qi Q, Chu AY, Kang JH, Huang J, Rose LM, Jensen MK et al (2014) Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies. BMJ 348:g1610. https://doi.org/10.1136/bmj.g1610

    Article  PubMed  PubMed Central  Google Scholar 

  22. Qi Q, Bray GA, Smith SR, Hu FB, Sacks FM, Qi L (2011) Insulin receptor substrate 1 gene variation modifies insulin resistance response to weight-loss diets in a 2-year randomized trial the preventing overweight using novel dietary strategies (POUNDS LOST) trial. Circulation 124(5):563–571. https://doi.org/10.1161/CIRCULATIONAHA.111.025767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hwang YC, Hayashi T, Fujimoto WY, Kahn SE, Leonetti DL, McNeely MJ et al (2015) Visceral abdominal fat accumulation predicts the conversion of metabolically healthy obese subjects to an unhealthy phenotype. Int J Obes 39(9):1365–1370. https://doi.org/10.1038/ijo.2015.75

    Article  CAS  Google Scholar 

  24. Song Q-Y, Meng X-R, Hinney A, Song J-Y, Huang T, Ma J et al (2018) Waist-hip ratio related genetic loci are associated with risk of impaired fasting glucose in Chinese children: a case control study. Nutr Metab 15:34. https://doi.org/10.1186/s12986-018-0270-2

    Article  CAS  Google Scholar 

  25. Aschard H, Vilhjálmsson BJ, Joshi AD, Price AL, Kraft P (2015) Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am J Hum Genet 96(2):329–339. https://doi.org/10.1016/j.ajhg.2014.12.021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Rietman A, Schwarz J, Tomé D, Kok FJ, Mensink M (2014) High dietary protein intake, reducing or eliciting insulin resistance? Eur J Clin Nutr 68(9):973–979. https://doi.org/10.1038/ejcn.2014.123

    Article  CAS  PubMed  Google Scholar 

  27. Liu X, Gan W, Gao C, Qi X, Liao X, Lin J et al (2018) The independent associations of protein consumption with body fat and glycaemic control in adult Chinese. Eur J Nutr 58(5):1981–1990. https://doi.org/10.1007/s00394-018-1751-9

    Article  CAS  PubMed  Google Scholar 

  28. Kitabchi AE, Mcdaniel KA, Wan JY, Tylavsky FA, Jacovino CA, Sands CW et al (2013) Effects of high-protein versus high-carbohydrate diets on markers of β-cell function, oxidative stress, lipid peroxidation, proinflammatory cytokines, and adipokines in obese, premenopausal women without diabetes: a randomized controlled trial. Diabetes Care 36(7):1919–1925. https://doi.org/10.2337/dc12-1912

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Sluijs I, Beulens JWJ, Spijkerman AMW, Grobbee DE, Van Der Schouw YT (2010) Dietary intake of total, animal, and vegetable protein and risk of type 2 diabetes in the European prospective. Diabetes Care 33(1):43–48. https://doi.org/10.2337/dc09-132

    Article  CAS  PubMed  Google Scholar 

  30. Li J, Sun C, Liu S, Li Y (2016) Dietary protein intake and type 2 diabetes among women and men in northeast China. Sci Rep 6:37604. https://doi.org/10.1038/srep37604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Schwingshackl L, Hoffmann G (2013) Long-term effects of low-fat diets either low or high in protein on cardiovascular and metabolic risk factors: a systematic review and meta-analysis. Nutr J 12:48. https://doi.org/10.1186/1475-2891-12-48

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. de Biggelaar LJCJ, Eussen SJPM, Sep SJS, Mari A, Ferrannini E, van Greevenbroek MM et al (2018) Prospective associations of dietary carbohydrate, fat, and protein intake with β-cell function in the CODAM study. Eur J Nutr 58(2):597–608. https://doi.org/10.1007/s00394-018-1644-y

    Article  CAS  Google Scholar 

  33. Halton TL, Liu S, Manson JE, Hu FB (2008) Low-carbohydrate-diet score and risk of type 2 diabetes in women. Am J Clin Nutr 87:339–346. https://doi.org/10.3945/ajcn.110.004333.INTRODUCTION

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Goni L, Qi L, Cuervo M, Milagro FI, Saris WH, MacDonald IA et al (2017) Effect of the interaction between diet composition and the PPM1K genetic variant on insulin resistance and β cell function markers during weight loss: results from the nutrient gene interactions in human obesity: implications for dietary guidelines (NUGEN. Am J Clin Nutr 106:902–908. https://doi.org/10.3945/ajcn.117.156281

    Article  CAS  PubMed  Google Scholar 

  35. Rock CL, Flatt SW, Pakiz B, Taylor KS, Leone AF, Brelje K et al (2014) Weight loss, glycemic control, and cardiovascular disease risk factors in response to differential diet composition in a weight loss program in type 2 diabetes: a randomized controlled trial. Diabetes Care 37(6):1573–1580. https://doi.org/10.2337/dc13-2900

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wallace TM, Levy JC, Matthews DR (2004) Use and abuse of HOMA modeling. Diabetes Care 27(6):1487–1495. https://doi.org/10.2337/diacare.27.6.1487

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all of the participants in the trial for their dedication and contribution to the research.

Funding

The work was supported by the National Heart, Lung, and Blood Institute [grant numbers HL071981, HL034594, HL126024]; the National Institute of Diabetes and Digestive and Kidney Diseases [grant numbers DK091718, DK100383, DK078616, DK115679]; the Boston Obesity Nutrition Research Center [grant number DK46200]; and United States – Israel Binational Science Foundation [grant number 2011036]. Dr. Qi was a recipient of the American Heart Association Scientist Development Award (0730094N). Yuhang Chen is a recipient of a scholarship under the China Scholarship Council to pursue her study in the United States of America (201706240060). The funders had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.

Author information

Authors and Affiliations

  1. Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 1724, New Orleans, LA, USA

    Yuhang Chen, Tao Zhou, Dianjianyi Sun, Xiang Li, Hao Ma, Zhaoxia Liang, Yoriko Heianza & Lu Qi

  2. Department of Public Health Laboratory Sciences, West China School of Public Health, Sichuan University, Chengdu, Sichuan, China

    Yuhang Chen & Xiaofang Pei

  3. Obstetrical Department, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China

    Zhaoxia Liang

  4. Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA

    George A. Bray

  5. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Frank M. Sacks

  6. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Lu Qi

  7. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Lu Qi

Authors
  1. Yuhang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dianjianyi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhaoxia Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yoriko Heianza
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaofang Pei
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. George A. Bray
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Frank M. Sacks
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lu Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors’ responsibilities were as follows: YC and LQ contributed to conception and design. YC performed the statistical analyses, interpretation of data and drafted the manuscript. TZ, DS, XL, HM, ZL, YH contributed to analysis and interpretation of data, and manuscript revision. XP contributed to manuscript revision. GAB, FMS and LQ contributed to conception and design, acquisition and interpretation of the data, and manuscript revision. All authors contributed to critical revisions and have read and approved the final manuscript. LQ is the guarantor of this work.

Corresponding author

Correspondence to Lu Qi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 307 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Zhou, T., Sun, D. et al. Distinct genetic subtypes of adiposity and glycemic changes in response to weight-loss diet intervention: the POUNDS Lost trial. Eur J Nutr 60, 249–258 (2021). https://doi.org/10.1007/s00394-020-02244-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00394-020-02244-x

Keywords

Search

Navigation