The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes.
Dietary pattern
Hyperinsulinaemia
Inflammation
Metabolomics
Type 2 diabetes
Journal
Diabetologia
ISSN: 1432-0428
Titre abrégé: Diabetologia
Pays: Germany
ID NLM: 0006777
Informations de publication
Date de publication:
Jan 2024
Jan 2024
Historique:
received:
08
04
2023
accepted:
31
08
2023
pubmed:
11
10
2023
medline:
11
10
2023
entrez:
10
10
2023
Statut:
ppublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 37816982
doi: 10.1007/s00125-023-06021-3
pii: 10.1007/s00125-023-06021-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
88-101Subventions
Organisme : NIH HHS
ID : U01 CA176726
Pays : United States
Organisme : NIH HHS
ID : UM1 CA186107
Pays : United States
Organisme : NIH HHS
ID : U01 CA167552
Pays : United States
Organisme : NIH HHS
ID : P01 CA87969
Pays : United States
Organisme : NIH HHS
ID : R00 CA207736
Pays : United States
Organisme : NCI NIH HHS
ID : R00 CA207736
Pays : United States
Organisme : NIH HHS
ID : R01 CA50385
Pays : United States
Organisme : NIH HHS
ID : P01 CA87969
Pays : United States
Organisme : NIH HHS
ID : R00 CA207736
Pays : United States
Organisme : NIH HHS
ID : R01 CA50385
Pays : United States
Organisme : NIH HHS
ID : U01 CA167552
Pays : United States
Organisme : NIH HHS
ID : U01 CA176726
Pays : United States
Organisme : NIH HHS
ID : UM1 CA186107
Pays : United States
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
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
doi: 10.3945/ajcn.114.090688
pubmed: 25733644
pmcid: 4340063
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
doi: 10.3945/jn.114.205336
pubmed: 25733454
pmcid: 4336525
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
doi: 10.1093/aje/kwv241
pubmed: 26940115
pmcid: 4801136
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
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
doi: 10.3945/jn.111.157222
pubmed: 22513989
pmcid: 3738221
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
doi: 10.1079/BJN2002778
pubmed: 12628035
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
doi: 10.3945/ajcn.112.038109
pubmed: 23034967
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
doi: 10.1093/jn/nxy031
pubmed: 29897561
pmcid: 5972616
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
doi: 10.3945/jn.117.248377
pubmed: 28659407
pmcid: 5525108
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
doi: 10.3945/jn.115.228718
pubmed: 27358416
pmcid: 4958288
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
doi: 10.1017/S0007114516003755
pubmed: 27821188
pmcid: 5623113
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)
doi: 10.1053/j.gastro.2017.08.045
pubmed: 28865736
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
doi: 10.1001/jamaoncol.2017.4844
pubmed: 29346484
pmcid: 5844836
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
doi: 10.1093/ajcn/nqy093
pubmed: 29901698
pmcid: 6454497
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
doi: 10.1016/j.jacc.2020.09.535
pubmed: 33153576
pmcid: 7745775
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
doi: 10.2337/dc20-0815
pubmed: 32873589
pmcid: 7576428
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
doi: 10.1373/clinchem.2017.272344
pubmed: 29038146
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
doi: 10.1002/mnfr.201701064
pubmed: 29688616
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
doi: 10.1038/nrm.2016.25
pubmed: 26979502
pmcid: 5729912
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
doi: 10.1089/jwh.1997.6.49
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
doi: 10.1016/0140-6736(91)90542-W
pubmed: 1678444
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
doi: 10.1093/jnci/87.17.1297
pubmed: 7658481
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
doi: 10.1016/j.ebiom.2021.103799
pubmed: 34979341
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
doi: 10.1016/0002-8223(93)91754-E
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
doi: 10.1093/oxfordjournals.aje.a116211
pubmed: 1632423
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
doi: 10.1093/oxfordjournals.aje.a114086
pubmed: 4014201
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
doi: 10.1093/aje/kwx328
pubmed: 29036411
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
doi: 10.1093/aje/kww104
pubmed: 28338828
pmcid: 5859994
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
doi: 10.1093/oxfordjournals.aje.a009849
pubmed: 10084242
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
doi: 10.1056/NEJMoa040967
pubmed: 15602020
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
doi: 10.1038/ncomms7791
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
doi: 10.1038/nm.3868
pubmed: 26005855
pmcid: 4476246
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
doi: 10.1172/JCI95995
pubmed: 29083323
pmcid: 5707166
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
doi: 10.1161/CIRCULATIONAHA.117.029468
pubmed: 29459470
pmcid: 5854187
Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33(1):1–22
doi: 10.18637/jss.v033.i01
pubmed: 20808728
pmcid: 2929880
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
doi: 10.1093/eurheartj/ehaa209
pubmed: 32406924
pmcid: 7377580
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
doi: 10.1007/s00125-022-05692-8
pubmed: 35391539
pmcid: 9810389
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
doi: 10.3390/metabo13060744
pubmed: 37367904
pmcid: 10304271
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
doi: 10.1111/j.2517-6161.1995.tb02031.x
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
doi: 10.1016/j.clnu.2019.06.010
pubmed: 31255351
pmcid: 6918009
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
doi: 10.1038/s41387-018-0017-1
pubmed: 29549241
pmcid: 5856836
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
doi: 10.2337/db08-1078
pubmed: 19073774
pmcid: 2646053
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
doi: 10.1074/jbc.272.6.3324
pubmed: 9013572
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
doi: 10.2337/dc20-0800
pubmed: 32788283
pmcid: 7510042
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
doi: 10.1016/S2213-8587(18)30037-8
pubmed: 29433995
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
doi: 10.1373/clinchem.2012.199133
pubmed: 23897902
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
doi: 10.2337/dc20-2216
pubmed: 33419931
pmcid: 7896263
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
doi: 10.3389/fnut.2021.690428
pubmed: 34616762
pmcid: 8488136