OBJECTIVE We evaluated insulin sensitivity and insulin secretion across the entire range of fasting (FPG) and 2-h plasma glucose (PG), and we investigated the differences in insulin sensitivity and insulin release in different glucose tolerance categories. RESEARCH DESIGN AND METHODS A total of 6,414 Finnish men (aged 57 ± 7 years, BMI 27.0 ± 3.9 kg/m²) from our ongoing population-based METSIM (Metabolic Syndrome in Men) study were included. Of these subjects, 2,168 had normal glucose tolerance, 2,859 isolated impaired fasting glucose (IFG), 217 isolated impaired glucose tolerance (IGT), 701 a combination of IFG and IGT, and 469 newly diagnosed type 2 diabetes. RESULTS The Matsuda index of insulin sensitivity decreased substantially within the normal range of FPG (−17%) and 2-h PG (−37%) and was approximately −65 and −53% in the diabetic range of FPG and 2-h PG, respectively, compared with the reference range (FPG and 2-h PG <5.0 mmol/l). Early-phase insulin release declined by only approximately −5% within the normal range of FPG and 2-h PG but decreased significantly in the diabetic range of FPG (by 32–70%) and 2-h PG (by 33–51%). Changes in insulin sensitivity and insulin secretion in relation to hyperglycemia were independent of obesity. The predominant feature of isolated IGT was impaired peripheral insulin sensitivity. Isolated IFG was characterized by impaired early and total insulin release. CONCLUSIONS Peripheral insulin sensitivity was already decreased substantially at low PG levels within the normoglycemic range, whereas impairment in insulin secretion was observed mainly in the diabetic range of FPG and 2-h PG. Obesity did not affect changes in insulin sensitivity or insulin secretion in relation to hyperglycemia.

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism^1–7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases^8–11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study. We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbiota and fasting serum levels of a number of metabolites, including fatty acids, amino acids, lipids, and glucose. In particular, we detect associations with fasting plasma trimethylamine N-oxide (TMAO) levels, a gut microbiota-dependent metabolite associated with coronary artery disease and stroke. We further investigate the gut microbiota composition and microbiota–metabolite relationships in subjects with different body mass index and individuals with normal or altered oral glucose tolerance. Finally, we perform microbiota co-occurrence network analysis, which shows that certain metabolites strongly correlate with microbial community structure and that some of these correlations are specific for the pre-diabetic state. Our study identifies novel relationships between the composition of the gut microbiota and circulating metabolites and provides a resource for future studies to understand host–gut microbiota relationships.