Abstract
Background: Advances in metabolomics have allowed high-throughput metabolic profiling of large population samples. We aimed to identify circulating lipids and metabolites predictive of the risk for type 2 diabetes in young adults. Methods: Nuclear magnetic resonance metabolomics was used to quantify 229 metabolic measures in 10,938 individuals from four Finnish cohorts (mean age 35 years, range 24-45). Associations between baseline metabolites and diabetes onset during 7-15 years of follow-up (330 incident cases) were assessed by logistic regression adjusted for sex, baseline age and glucose. Findings: Out of 229 metabolic measures, 174 were associated with risk for incident diabetes in meta-analysis of the four cohorts (P<0.001; range of odds ratios (OR) per 1-SD: 0.41-1.85). Among the strongest biomarkers were increased concentrations of branched-chained and aromatic amino acids (OR: 1.54-1.74) and triglycerides in very-low-density lipoproteins (VLDL; OR 1.78), and lower levels of omega-6 fatty acids (OR 0.61) and free cholesterol within large high-density lipoprotein (HDL; OR 0.41). A biomarker score was derived in three of the cohorts, comprised of phenylalanine, free cholesterol in large HDL, and the ratio of cholesterol esters to total lipids in large VLDL. When validated in the fourth cohort, those in the upper quartile of the biomarker score had considerably higher 15-year-risk for diabetes compared to those in the lowest quartile (OR 16.1). Interpretation: Metabolic aberrations across multiple molecular pathways are predictive of the long-term risk of type 2 diabetes in young adults. Comprehensive metabolic profiling may facilitate targeting preventive interventions at young asymptomatic individuals at increased risk for type 2 diabetes. Disclosure A.V. Ahola-Olli: None. L. Mustelin: Employee; Self; Nightingale Health Ltd. M. Kalimeri: Employee; Self; Nightingale Health Oy. J. Kettunen: Other Relationship; Self; Nightingale Ltd. J.J. Jokelainen: None. J. Auvinen: None. K.S. Puukka: None. A.S. Havulinna: None. T. Lehtimäki: None. M. Kähönen: None. V. Salomaa: Other Relationship; Self; Novo Nordisk Inc.. M. Perola: None. M. Jarvelin: None. M. Ala-Korpela: None. P. Wurtz: Employee; Self; Nightingale Health. Stock/Shareholder; Self; Nightingale Health.
Highlights
The global prevalence of type 2 diabetes is increasing rapidly, in low- and middle-income countries [1]
The ORs of 104 selected metabolic measures with incident type 2 diabetes are shown in Figs 1 and 2; results for the remaining 125 metabolic measures assayed are found in electronic supplementary material (ESM) Fig. 2
In meta-analysis of all four cohorts, 113 out of the 229 metabolic measures were robustly associated with incident type 2 diabetes (p< 0.0009) when adjusting for sex, baseline age, BMI and fasting glucose
Summary
The global prevalence of type 2 diabetes is increasing rapidly, in low- and middle-income countries [1]. The risk for developing type 2 diabetes is, to some extent, reflected in current measures of hyperglycaemia and dyslipidaemia; these markers are ineffective for identifying high-risk individuals [6]. This has spurred interest in metabolite profiling technologies, known as metabolomics, to identify biochemical changes occurring before the onset of diabetes to elucidate the pathophysiology and potentially aid risk prediction for better targeted prevention [7, 8]. Multiple case−control studies have identified circulating lipids and metabolites associated with the risk for type 2 diabetes using a range of technological assays, based on MS or NMR [7, 9, 10]. Previous metabolomics studies have commonly involved a modest number of participants in nested case−control settings and have almost exclusively been conducted in middle-aged and older individuals
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