Ahmad, E., Lim, S., Lamptey, R., Webb, D. R. & Davies, M. J. Type 2 diabetes. Lancet 400, 1803–1820 (2022).
Li, J. et al. The mediterranean diet, plasma metabolome, and cardiovascular disease risk. Eur. Heart J. 41, 2645–2656 (2020).
Morze, J. et al. Metabolomics and type 2 diabetes risk: an updated systematic review and meta-analysis of prospective cohort studies. Diabetes Care. 45, 1013–1024 (2022).
Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or Metformin. N. Engl. J. Med. 346, 393–403 (2002).
Tuomilehto, J. et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N. Engl. J. Med. 344, 1343–1350 (2001).
Wishart, D. S. et al. HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res. 50, D622–D631 (2022).
Pallares-Méndez, R., Aguilar-Salinas, C. A. & Cruz-Bautista, I. Del Bosque-Plata, L. Metabolomics in diabetes, a review. Ann. Med. 48, 89–102 (2016).
Guasch-Ferré, M. et al. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care. 39, 833–846 (2016).
de Mello, V. D. et al. Indolepropionic acid and novel lipid metabolites are associated with a lower risk of type 2 diabetes in the Finnish diabetes prevention study. Sci. Rep. 7, 1–12 (2017).
Kivelä, J., Meinilä, J., Uusitupa, M., Tuomilehto, J. & Lindström, J. Longitudinal Branched-Chain amino Acids, lifestyle Intervention, and type 2 diabetes in the Finnish diabetes prevention study. J. Clin. Endocrinol. Metabolism. 107, 2844–2853 (2022).
del Sevilla-Gonzalez, R. M. et al. Metabolomic markers of glucose regulation after a lifestyle intervention in prediabetes. BMJ Open. Diabetes Res. & Care 10 (2022).
Walford, G. A. et al. Metabolite profiles of diabetes incidence and intervention response in the diabetes prevention program. Diabetes 65, 1424–1433 (2016).
Pihlajamäki, J. et al. Digitally supported program for type 2 diabetes risk identification and risk reduction in real-world setting: protocol for the StopDia model and randomized controlled trial. BMC public. Health. 19, 1–13 (2019).
Lakka, T. A. et al. Real-world effectiveness of digital and group-based lifestyle interventions as compared with usual care to reduce type 2 diabetes risk–A stop diabetes pragmatic randomised trial. The Lancet Reg. Health–Europe 24 (2023).
Makrilakis, K. et al. Validation of the Finnish diabetes risk score (FINDRISC) questionnaire for screening for undiagnosed type 2 diabetes, dysglycaemia and the metabolic syndrome in Greece. Diabetes Metab. 37, 144–151 (2011).
Association, A. D. Diagnosis and classification of diabetes mellitus. Diabetes Care. 33, S62–S69 (2010).
Jalkanen, K. et al. Comparison of communication channels for large-scale type 2 diabetes risk screening and intervention recruitment: empirical study. JMIR Diabetes. 6, e21356 (2021).
Lindström, J. et al. Formation and validation of the healthy diet index (HDI) for evaluation of diet quality in healthcare. Int. J. Environ. Res. Public Health. 18, 2362 (2021).
Klåvus, A. et al. Notame: workflow for Non-Targeted LC–MS metabolic profiling. Metabolites 10, 135 (2020).
Lapatto, H. A. et al. Nicotinamide riboside improves muscle mitochondrial biogenesis, satellite cell differentiation, and gut microbiota in a twin study. Sci. Adv. 9, eadd5163 (2023).
Tsugawa, H. et al. MS-DIAL: data-independent MS/MS Deconvolution for comprehensive metabolome analysis. Nat. Methods. 12, 523–526 (2015).
Tsugawa, H. et al. Hydrogen rearrangement rules: computational MS/MS fragmentation and structure Elucidation using MS-FINDER software. Anal. Chem. 88, 7946–7958 (2016).
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).
Vangipurapu, J., Fernandes Silva, L., Kuulasmaa, T., Smith, U. & Laakso, M. Microbiota-related metabolites and the risk of type 2 diabetes. Diabetes Care. 43, 1319–1325 (2020).
Feldman, E. L., Russell, J. W., Sullivan, K. A. & Golovoy, D. New insights into the pathogenesis of diabetic neuropathy. Curr. Opin. Neurol. 12, 553–563 (1999).
Baumgartner, C. et al. Potential role of skeletal muscle Glycerophosphocholine in response to altered fluid balance in humans: an in vivo nuclear magnetic resonance study. Am. J. Physiology-Endocrinology Metabolism. 324, E339–E346 (2023).
Prada, M. et al. Association of the odd-chain fatty acid content in lipid groups with type 2 diabetes risk: A targeted analysis of lipidomics data in the EPIC-Potsdam cohort. Clin. Nutr. 40, 4988–4999 (2021).
Pfeuffer, M. & Jaudszus, A. Pentadecanoic and heptadecanoic acids: multifaceted odd-chain fatty acids. Adv. Nutr. 7, 730–734 (2016).
Farrell, E. K. & Merkler, D. J. Biosynthesis, degradation and Pharmacological importance of the fatty acid amides. Drug Discovery Today. 13, 558–568 (2008).
Lambert, D. M. & Di Marzo, V. The palmitoylethanolamide and oleamide enigmas: are these two fatty acid amides cannabimimetic? Curr. Med. Chem. 6, 757–773 (1999).
Gruden, G., Barutta, F., Kunos, G. & Pacher, P. Role of the endocannabinoid system in diabetes and diabetic complications. Br. J. Pharmacol. 173, 1116–1127 (2016).
Dohnalová, L. et al. A microbiome-dependent gut–brain pathway regulates motivation for exercise. Nature, 1–9 (2022).
Huynh, K. et al. High-throughput plasma lipidomics: detailed mapping of the associations with cardiometabolic risk factors. Cell. Chem. Biology. 26, 71–84 (2019).
Orsavova, J., Misurcova, L., Ambrozova, V., Vicha, J., Mlcek, J. & R. & Fatty acids composition of vegetable oils and its contribution to dietary energy intake and dependence of cardiovascular mortality on dietary intake of fatty acids. Int. J. Mol. Sci. 16, 12871–12890 (2015).
Xia, T. et al. A longitudinal study on associations of moderate-to-vigorous physical activity with plasma monounsaturated fatty acids in pregnancy. Front. Nutr. 9, 983418 (2022).
Zhu, X. et al. Association between fatty acids and the risk of impaired glucose tolerance and type 2 diabetes mellitus in American adults: NHANES 2005 – 2016. Nutr. Diabetes. 13, 8 (2023).
Dudzik, D. et al. Metabolic fingerprint of gestational diabetes mellitus. J. Proteom. 103, 57–71 (2014).
Haikonen, R., Kärkkäinen, O., Koistinen, V. & Hanhineva, K. Diet-and microbiota-related metabolite, 5-aminovaleric acid betaine (5-AVAB), in health and disease. Trends Endocrinol. & Metabolism (2022).
Kärkkäinen, O. et al. Whole grain intake associated molecule 5-aminovaleric acid betaine decreases β-oxidation of fatty acids in mouse cardiomyocytes. Sci. Rep. 8, 13036 (2018).
O’Sullivan, J. F. et al. Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes. J. Clin. Investig. 127, 4394–4402 (2017).
Ottosson, F. et al. Dimethylguanidino valerate: a lifestyle-related metabolite associated with future coronary artery disease and cardiovascular mortality. J. Am. Heart Association. 8, e012846 (2019).
Chen, Y. et al. Associations between serum amino acids and incident type 2 diabetes in Chinese rural adults. Nutr. Metabolism Cardiovasc. Dis. 31, 2416–2425 (2021).
Thomson, S. C. et al. Ornithine decarboxylase, kidney size, and the tubular hypothesis of glomerular hyperfiltration in experimental diabetes. J. Clin. Investig. 107, 217–224 (2001).
Lankinen, M. A. et al. Plasma fatty acids as predictors of glycaemia and type 2 diabetes. Diabetologia 58, 2533–2544 (2015).
Adams, S. H. et al. Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid β-oxidation and altered Tricarboxylic acid cycle activity in type 2 diabetic African-American women. J. Nutr. 139, 1073–1081 (2009).
Veronese, N. et al. Serum dehydroepiandrosterone sulfate and risk for type 2 diabetes in older men and women: the Pro. VA study. Can. J. Diabetes. 40, 158–163 (2016).
Ennour-Idrissi, K., Maunsell, E. & Diorio, C. Effect of physical activity on sex hormones in women: a systematic review and meta-analysis of randomized controlled trials. Breast Cancer Res. 17, 1–11 (2015).
Sánchez-Guijo, A. et al. Role of steroid sulfatase in steroid homeostasis and characterization of the sulfated steroid pathway: evidence from steroid sulfatase deficiency. Mol. Cell. Endocrinol. 437, 142–153 (2016).
Buergel, T. et al. Metabolomic profiles predict individual multidisease outcomes. Nat. Med. 28, 2309–2320 (2022).
