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The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes

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Abstract

Aims/hypothesis

Diets with higher inflammatory and insulinaemic potential have been associated with an increased risk of type 2 diabetes. However, it remains unknown whether plasma metabolomic profiles related to proinflammatory/hyperinsulinaemic diets and to inflammatory/insulin biomarkers are associated with type 2 diabetes risk.

Methods

We analysed 6840 participants from the Nurses’ Health Study and Health Professionals Follow-up Study to identify the plasma metabolome related to empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), four circulating inflammatory biomarkers and C-peptide. Dietary intakes were assessed using validated food frequency questionnaires. Plasma metabolomic profiling was conducted by LC-MS/MS. Metabolomic signatures were derived using elastic net regression. Multivariable Cox regression was used to examine associations of the metabolomic profiles with type 2 diabetes risk.

Results

We identified 27 metabolites commonly associated with both EDIP and inflammatory biomarker z score and 21 commonly associated with both EDIH and C-peptide. Higher metabolomic dietary inflammatory potential (MDIP), reflecting higher metabolic potential of both an inflammatory dietary pattern and circulating inflammatory biomarkers, was associated with higher type 2 diabetes risk. The HR comparing highest vs lowest quartiles of MDIP was 3.26 (95% CI 2.39, 4.44). We observed a strong positive association with type 2 diabetes risk for the metabolomic signature associated with EDIP-only (HR 3.75; 95% CI 2.71, 5.17) or inflammatory biomarkers-only (HR 4.07; 95% CI 2.91, 5.69). In addition, higher metabolomic dietary index for hyperinsulinaemia (MDIH), reflecting higher metabolic potential of both an insulinaemic dietary pattern and circulating C-peptide, was associated with greater type 2 diabetes risk (HR 3.00; 95% CI 2.22, 4.06); further associations with type 2 diabetes were HR 2.79 (95% CI 2.07, 3.76) for EDIH-only signature and HR 3.89 (95% CI 2.82, 5.35) for C-peptide-only signature. The diet scores were significantly associated with risk, although adjustment for the corresponding metabolomic signature scores attenuated the associations with type 2 diabetes, these remained significant.

Conclusions/interpretation

The metabolomic signatures reflecting proinflammatory or hyperinsulinaemic diets and related biomarkers were positively associated with type 2 diabetes risk, supporting that these dietary patterns may influence type 2 diabetes risk via the regulation of metabolism.

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Abbreviations

BIOM-only:

Metabolomic profile score comprised of metabolites associated mainly with inflammatory biomarkers, not considering their associations with EDIP

CPEP-only:

Metabolomic profile score comprised of metabolites associated mainly with C-peptide, not considering their associations with EDIH

CRP:

C-reactive protein

EDIH:

Empirical dietary index for hyperinsulinemia

EDIP:

Empirical dietary inflammatory pattern

FDR:

False discovery rate

FFQ:

Food frequency questionnaire

HPFS:

Health Professionals Follow-up Study

MDIH:

Metabolomic dietary index for hyperinsulinemia

MDIP:

Metabolomic dietary inflammatory potential

NHS:

Nurses’ Health Study

PC:

Phosphatidylcholine

PE:

Phosphatidylethanolamine

TAG:

Triacylglycerol

TNF-α-R2:

TNF-α receptor 2

WHI:

Women’s Health Initiative

References

  1. Harmon BE, Boushey CJ, Shvetsov YB et al (2015) Associations of key diet-quality indexes with mortality in the Multiethnic Cohort: the Dietary Patterns Methods Project. Am J Clin Nutr 101(3):587–597. https://doi.org/10.3945/ajcn.114.090688

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Liese AD, Krebs-Smith SM, Subar AF et al (2015) The Dietary Patterns Methods Project: synthesis of findings across cohorts and relevance to dietary guidance. J Nutr 145(3):393–402. https://doi.org/10.3945/jn.114.205336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Cespedes EM, Hu FB, Tinker L et al (2016) (2016) Multiple healthful dietary patterns and type 2 diabetes in the Women’s Health Initiative. Am J Epidemiol 183:622–633. https://doi.org/10.1093/aje/kwv241

    Article  PubMed  PubMed Central  Google Scholar 

  4. World Cancer Research Fund/American Institute for Cancer Research (2018) Diet, nutrition, physical activity and cancer: a global perspective. Continuous Update Project Expert Report 2018. Available from www.wcrf.org/wp-content/uploads/2021/02/Summary-of-Third-Expert-Report-2018.pdf

  5. Chiuve SE, Fung TT, Rimm EB et al (2012) Alternative dietary indices both strongly predict risk of chronic disease. J Nutr 142(6):1009–1018. https://doi.org/10.3945/jn.111.157222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Schulze MB, Hoffmann K, Kroke A, Boeing H (2003) An approach to construct simplified measures of dietary patterns from exploratory factor analysis. Br J Nutr 89(3):409–418. https://doi.org/10.1079/BJN2002778

    Article  CAS  PubMed  Google Scholar 

  7. Varraso R, Garcia-Aymerich J, Monier F et al (2012) Assessment of dietary patterns in nutritional epidemiology: principal component analysis compared with confirmatory factor analysis. Am J Clin Nutr 96(5):1079–1092. https://doi.org/10.3945/ajcn.112.038109

    Article  CAS  PubMed  Google Scholar 

  8. Tabung FK, Giovannucci EL, Giulianini F et al (2018) An empirical dietary inflammatory pattern score is associated with circulating inflammatory biomarkers in a multi-ethnic population of postmenopausal women in the United States. J Nutr 148(5):771–780. https://doi.org/10.1093/jn/nxy031

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tabung FK, Smith-Warner SA, Chavarro JE et al (2017) An empirical dietary inflammatory pattern score enhances prediction of circulating inflammatory biomarkers in adults. J Nutr 147(8):1567–1577. https://doi.org/10.3945/jn.117.248377

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tabung FK, Smith-Warner SA, Chavarro JE et al (2016) Development and validation of an empirical Dietary Inflammatory Index. J Nutr 146(8):1560–1570. https://doi.org/10.3945/jn.115.228718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tabung FK, Wang W, Fung TT et al (2016) Development and validation of empirical indices to assess the insulinaemic potential of diet and lifestyle. Br J Nutr 116(10):1787–1798. https://doi.org/10.1017/S0007114516003755

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Liu L, Nishihara R, Qian ZR et al (2017) Association between inflammatory diet pattern and risk of colorectal carcinoma subtypes classified by immune responses to tumor. Gastroenterology 153(6):1517–1530. https://doi.org/10.1053/j.gastro.2017.08.045. (e1514)

    Article  PubMed  Google Scholar 

  13. Tabung FK, Liu L, Wang W et al (2018) Association of dietary inflammatory potential with colorectal cancer risk in men and women. JAMA Oncol 4(3):366–373. https://doi.org/10.1001/jamaoncol.2017.4844

    Article  PubMed  PubMed Central  Google Scholar 

  14. Tabung FK, Wang W, Fung TT et al (2018) Association of dietary insulinemic potential and colorectal cancer risk in men and women. Am J Clin Nutr 108(2):363–370. https://doi.org/10.1093/ajcn/nqy093

    Article  PubMed  PubMed Central  Google Scholar 

  15. Li J, Lee DH, Hu J et al (2020) Dietary inflammatory potential and risk of cardiovascular disease among men and women in the U.S. J Am Coll Cardiol 76(19):2181–2193. https://doi.org/10.1016/j.jacc.2020.09.535

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lee DH, Li J, Li Y et al (2020) Dietary inflammatory and insulinemic potential and risk of type 2 diabetes: results from three prospective US cohort studies. Diabetes Care 43(11):2675–2683. https://doi.org/10.2337/dc20-0815

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Guasch-Ferre M, Bhupathiraju SN, Hu FB (2018) Use of metabolomics in improving assessment of dietary intake. Clin Chem 64(1):82–98. https://doi.org/10.1373/clinchem.2017.272344

    Article  CAS  PubMed  Google Scholar 

  18. Brennan L, Hu FB (2019) Metabolomics-based dietary biomarkers in nutritional epidemiology—current status and future opportunities. Mol Nutr Food Res 63(1):e1701064. https://doi.org/10.1002/mnfr.201701064

    Article  CAS  PubMed  Google Scholar 

  19. Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17(7):451. https://doi.org/10.1038/nrm.2016.25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Colditz GA, Manson JE, Hankinson SE (1997) The Nurses’ Health Study: 20-year contribution to the understanding of health among women. J Women’s Health 6(1):49–62. https://doi.org/10.1089/jwh.1997.6.49

    Article  CAS  Google Scholar 

  21. Rimm EB, Giovannucci EL, Willett WC et al (1991) Prospective study of alcohol consumption and risk of coronary disease in men. Lancet 338(8765):464–468. https://doi.org/10.1016/0140-6736(91)90542-W

    Article  CAS  PubMed  Google Scholar 

  22. Hankinson SE, Willett WC, Manson JE et al (1995) Alcohol, height, and adiposity in relation to estrogen and prolactin levels in postmenopausal women. J Natl Cancer Inst 87(17):1297–1302. https://doi.org/10.1093/jnci/87.17.1297

    Article  CAS  PubMed  Google Scholar 

  23. Wittenbecher C, Guasch-Ferre M, Haslam DE et al (2022) Changes in metabolomics profiles over ten years and subsequent risk of developing type 2 diabetes: results from the Nurses’ Health Study. EBioMedicine 75:103799. https://doi.org/10.1016/j.ebiom.2021.103799

    Article  CAS  PubMed  Google Scholar 

  24. Feskanich D, Rimm EB, Giovannucci EL et al (1993) Reproducibility and validity of food intake measurements from a semiquantitative food frequency questionnaire. J Am Dietetic Assoc 93(7):790–796. https://doi.org/10.1016/0002-8223(93)91754-E

    Article  CAS  Google Scholar 

  25. Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC (1992) Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol 135(10):1114–1126. https://doi.org/10.1093/oxfordjournals.aje.a116211

    Article  CAS  PubMed  Google Scholar 

  26. Willett WC, Sampson L, Stampfer MJ et al (1985) Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 122(1):51–65. https://doi.org/10.1093/oxfordjournals.aje.a114086

    Article  CAS  PubMed  Google Scholar 

  27. Yuan C, Spiegelman D, Rimm EB et al (2018) Relative validity of nutrient intakes assessed by questionnaire, 24-hour recalls, and diet records as compared with urinary recovery and plasma concentration biomarkers: findings for women. Am J Epidemiol 187(5):1051–1063. https://doi.org/10.1093/aje/kwx328

    Article  PubMed  Google Scholar 

  28. Yuan C, Spiegelman D, Rimm EB et al (2017) Validity of a dietary questionnaire assessed by comparison with multiple weighed dietary records or 24-hour recalls. Am J Epidemiol 185(7):570–584. https://doi.org/10.1093/aje/kww104

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hu FB, Stampfer MJ, Rimm E et al (1999) Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements. Am J Epidemiol 149(6):531–540. https://doi.org/10.1093/oxfordjournals.aje.a009849

    Article  CAS  PubMed  Google Scholar 

  30. Pai JK, Pischon T, Ma J et al (2004) Inflammatory markers and the risk of coronary heart disease in men and women. N Engl J Med 351(25):2599–2610. https://doi.org/10.1056/NEJMoa040967

    Article  CAS  PubMed  Google Scholar 

  31. Cheng S, Larson MG, McCabe EL et al (2015) Distinct metabolomic signatures are associated with longevity in humans. Nat Commun 6(1):1–10. https://doi.org/10.1038/ncomms7791

    Article  CAS  Google Scholar 

  32. Mascanfroni ID, Takenaka MC, Yeste A et al (2015) Metabolic control of type 1 regulatory T cell differentiation by AHR and HIF1-α. Nat Med 21(6):638–646. https://doi.org/10.1038/nm.3868

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. O’Sullivan JF, Morningstar JE, Yang Q et al (2017) Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes. J Clin Investig 127(12):4394–4402. https://doi.org/10.1172/JCI95995

    Article  PubMed  PubMed Central  Google Scholar 

  34. Paynter NP, Balasubramanian R, Giulianini F et al (2018) Metabolic predictors of incident coronary heart disease in women. Circulation 137(8):841–853. https://doi.org/10.1161/CIRCULATIONAHA.117.029468

    Article  PubMed  PubMed Central  Google Scholar 

  35. Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33(1):1–22

    Article  PubMed  PubMed Central  Google Scholar 

  36. Li J, Guasch-Ferré M, Chung W et al (2020) The Mediterranean diet, plasma metabolome, and cardiovascular disease risk. Eur Heart J 41(28):2645–2656. https://doi.org/10.1093/eurheartj/ehaa209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wang F, Baden MY, Guasch-Ferré M et al (2022) Plasma metabolite profiles related to plant-based diets and the risk of type 2 diabetes. Diabetologia 65(7):1119–1132. https://doi.org/10.1007/s00125-022-05692-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lee DH, Jin Q, Shi N et al (2023) Dietary inflammatory and insulinemic potentials, plasma metabolome and risk of colorectal cancer. Metabolites 13(6):744. https://doi.org/10.3390/metabo13060744

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological) 57(1):289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    Article  Google Scholar 

  40. Tabung FK, Liang L, Huang T et al (2019) Identifying metabolomic profiles of inflammatory diets in postmenopausal women. Clin Nutr. https://doi.org/10.1016/j.clnu.2019.06.010

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bene J, Hadzsiev K, Melegh B (2018) Role of carnitine and its derivatives in the development and management of type 2 diabetes. Nutr Diabetes 8(1):8. https://doi.org/10.1038/s41387-018-0017-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bruce CR, Hoy AJ, Turner N et al (2009) Overexpression of carnitine palmitoyltransferase-1 in skeletal muscle is sufficient to enhance fatty acid oxidation and improve high-fat diet-induced insulin resistance. Diabetes 58(3):550–558. https://doi.org/10.2337/db08-1078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Paumen MB, Ishida Y, Muramatsu M, Yamamoto M, Honjo T (1997) Inhibition of carnitine palmitoyltransferase I augments sphingolipid synthesis and palmitate-induced apoptosis. J Biol Chem 272(6):3324–3329. https://doi.org/10.1074/jbc.272.6.3324

    Article  CAS  PubMed  Google Scholar 

  44. Hang D, Zeleznik OA, He X et al (2020) Metabolomic signatures of long-term coffee consumption and risk of type 2 diabetes in women. Diabetes Care 43(10):2588–2596. https://doi.org/10.2337/dc20-0800

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wang DD, Hu FB (2018) Precision nutrition for prevention and management of type 2 diabetes. Lancet Diabetes Endocrinol 6(5):416–426. https://doi.org/10.1016/S2213-8587(18)30037-8

    Article  PubMed  Google Scholar 

  46. Townsend MK, Clish CB, Kraft P et al (2013) Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin Chem 59(11):1657–1667. https://doi.org/10.1373/clinchem.2012.199133

    Article  CAS  PubMed  Google Scholar 

  47. Jin Q, Shi N, Aroke D et al (2021) Insulinemic and inflammatory dietary patterns show enhanced predictive potential for type 2 diabetes risk in postmenopausal women. Diabetes Care 44(3):707–714. https://doi.org/10.2337/dc20-2216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Shi N, Aroke D, Jin Q et al (2021) Proinflammatory and hyperinsulinemic dietary patterns are associated with specific profiles of biomarkers predictive of chronic inflammation, glucose-insulin dysregulation, and dyslipidemia in postmenopausal women. Front Nutr 8:690428. https://doi.org/10.3389/fnut.2021.690428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea

    Dong Hoon Lee & Justin Y. Jeon

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

    Dong Hoon Lee, Fenglei Wang, Frank B. Hu, Mingyang Song, Kana Wu, A. Heather Eliassen, Edward L. Giovannucci, Jun Li & Fred K. Tabung

  3. Department of Exercise and Nutrition Sciences, Moyes College of Education, Weber State University, Ogden, UT, USA

    Qi Jin

  4. Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA

    Qi Jin, Ni Shi, Steven K. Clinton & Fred K. Tabung

  5. Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA

    Qi Jin, Steven K. Clinton & Fred K. Tabung

  6. Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA

    Ni Shi, Steven K. Clinton & Fred K. Tabung

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

    Alaina M. Bever, Liming Liang, Frank B. Hu, Mingyang Song, A. Heather Eliassen, Edward L. Giovannucci & Jun Li

  8. Harvard–MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA

    Alaina M. Bever

  9. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Liming Liang

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

    Frank B. Hu, Oana A. Zeleznik, Xuehong Zhang, Andrew T. Chan & A. Heather Eliassen

  11. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Mingyang Song, Amit Joshi & Andrew T. Chan

  12. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Mingyang Song, Amit Joshi & Andrew T. Chan

  13. Cancer Prevention Center, Yonsei Cancer Center, Seoul, Republic of Korea

    Justin Y. Jeon

  14. Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

    Jeffrey A. Meyerhardt

  15. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Andrew T. Chan & Clary Clish

  16. Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Andrew T. Chan

  17. Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA

    Fred K. Tabung

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  1. Dong Hoon Lee
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  2. Qi Jin
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  3. Ni Shi
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  4. Fenglei Wang
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Corresponding author

Correspondence to Fred K. Tabung.

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Acknowledgements

The authors thank the participants and staff of the NHS and HPFS for their valuable contributions. The graphical abstract was created using BioRender.com.

Data availability

The datasets generated and analysed during the current study are available from the corresponding author on request.

Funding

This work was supported by the National Institutes of Health research grants UM1 CA186107, U01 CA176726, U01 CA167552, P01 CA87969, R01 CA50385 and R00 CA207736. ELG is funded as an American Cancer Society Clinical Research Professor (CRP-23-1014041). This research was also supported by the Yonsei Signature Research Cluster Project (2021-22-0009) and the Yonsei University Research Fund of 2023-22-0159.

Authors’ relationships and activities

JAM has served as an advisor/consultant to Merck Pharmaceutical and COTA Healthcare. KW is currently an employee and stockholder of Vertex Pharmaceuticals. This work was not funded by this commercial entity. The other authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work.

Contribution statement

DHL, ELG, JL and FKT were involved in the study concept and design. FBH, AHE, CC, ELG and FKT participated in acquisition of data. DHL, QJ, NS, FW, LL, OAZ, JL and FKT were involved in statistical analysis. All authors participated in interpretation of data. DHL prepared the first draft of the manuscript. All authors participated in critical revision of the manuscript and approved the final version of the manuscript. The corresponding author (FKT) takes full responsibility for the work and/or the conduct of the study, had access to the data and controlled the decision to publish.

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Jun Li and Fred K. Tabung contributed equally as co-senior authors.

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Lee, D.H., Jin, Q., Shi, N. et al. The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes. Diabetologia 67, 88–101 (2024). https://doi.org/10.1007/s00125-023-06021-3

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