Wide-scale profiling technologies including metabolomics broaden the possibility of novel discoveries related to the pathogenesis of type 2 diabetes (T2D). By applying non-targeted metabolomics approach, we investigated here whether serum metabolite profile predicts T2D in a well-characterized study population with impaired glucose tolerance by examining two groups of individuals who took part in the Finnish Diabetes Prevention Study (DPS); those who either early developed T2D (n = 96) or did not convert to T2D within the 15-year follow-up (n = 104). Several novel metabolites were associated with lower likelihood of developing T2D, including indole and lipid related metabolites. Higher indolepropionic acid was associated with reduced likelihood of T2D in the DPS. Interestingly, in those who remained free of T2D, indolepropionic acid and various lipid species were associated with better insulin secretion and sensitivity, respectively. Furthermore, these metabolites were negatively correlated with low-grade inflammation. We replicated the association between indolepropionic acid and T2D risk in one Finnish and one Swedish population. We suggest that indolepropionic acid, a gut microbiota-produced metabolite, is a potential biomarker for the development of T2D that may mediate its protective effect by preservation of β-cell function. Novel lipid metabolites associated with T2D may exert their effects partly through enhancing insulin sensitivity.

We investigated the association of glycemia and 43 genetic risk variants for hyperglycemia/type 2 diabetes with amino acid levels in the population-based Metabolic Syndrome in Men (METSIM) Study, including 9,369 nondiabetic or newly diagnosed type 2 diabetic Finnish men. Plasma levels of eight amino acids were measured with proton nuclear magnetic resonance spectroscopy. Increasing fasting and 2-h plasma glucose levels were associated with increasing levels of several amino acids and decreasing levels of histidine and glutamine. Alanine, leucine, isoleucine, tyrosine, and glutamine predicted incident type 2 diabetes in a 4.7-year follow-up of the METSIM Study, and their effects were largely mediated by insulin resistance (except for glutamine). We also found significant correlations between insulin sensitivity (Matsuda insulin sensitivity index) and mRNA expression of genes regulating amino acid degradation in 200 subcutaneous adipose tissue samples. Only 1 of 43 risk single nucleotide polymorphisms for type 2 diabetes or hyperglycemia, the glucose-increasing major C allele of rs780094 of GCKR, was significantly associated with decreased levels of alanine and isoleucine and elevated levels of glutamine. In conclusion, the levels of branched-chain, aromatic amino acids and alanine increased and the levels of glutamine and histidine decreased with increasing glycemia, reflecting, at least in part, insulin resistance. Only one single nucleotide polymorphism regulating hyperglycemia was significantly associated with amino acid levels.

Background: LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matter the reason for the missing values in the data, coherent and complete data matrix is always a pre-requisite for accurate and reliable statistical analysis. Therefore, there is a need for proper imputation strategies that account for the missingness and reduce the bias in the statistical analysis. Results: Here we present our results after evaluating nine imputation methods in four different percentages of missing values of different origin. The performance of each imputation method was analyzed by Normalized Root Mean Squared Error (NRMSE). We demonstrated that random forest (RF) had the lowest NRMSE in the estimation of missing values for Missing at Random (MAR) and Missing Completely at Random (MCAR). In case of absent values due to Missing Not at Random (MNAR), the left truncated data was best imputed with minimum value imputation. We also tested the different imputation methods for datasets containing missing data of various origin, and RF was the most accurate method in all cases. The results were obtained by repeating the evaluation process 100 times with the use of metabolomics datasets where the missing values were introduced to represent absent data of different origin. Conclusion: Type and rate of missingness affects the performance and suitability of imputation methods. RF-based imputation method performs best in most of the tested scenarios, including combinations of different types and rates of missingness. Therefore, we recommend using random forest-based imputation for imputing missing metabolomics data, and especially in situations where the types of missingness are not known in advance.

Background: Different healthy food patterns may modify cardiometabolic risk. We investigated the effects of an isocaloric healthy Nordic diet on insulin sensitivity, lipid profile, blood pressure and inflammatory markers in people with metabolic syndrome.Methods: We conducted a randomized dietary study lasting for 18–24 weeks in individuals with features of metabolic syndrome (mean age 55 years, BMI 31.6 kg m⁻², 67% women). Altogether 309 individuals were screened, 200 started the intervention after 4‐week run‐in period, and 96 (proportion of dropouts 7.9%) and 70 individuals (dropouts 27%) completed the study, in the Healthy diet and Control diet groups, respectively. Healthy diet included whole‐grain products, berries, fruits and vegetables, rapeseed oil, three fish meals per week and low‐fat dairy products. An average Nordic diet served as a Control diet. Compliance was monitored by repeated 4‐day food diaries and fatty acid composition of serum phospholipids.Results: Body weight remained stable, and no significant changes were observed in insulin sensitivity or blood pressure. Significant changes between the groups were found in non‐HDL cholesterol (−0.18, mmol L⁻¹ 95% CI −0.35; −0.01, P = 0.04), LDL to HDL cholesterol (−0.15, −0.28; −0.00, P = 0.046) and apolipoprotein B to apolipoprotein A1 ratios (−0.04, −0.07; −0.00, P = 0.025) favouring the Healthy diet. IL‐1 Ra increased during the Control diet (difference −84, −133; −37 ng L⁻¹, P = 0.00053). Intakes of saturated fats (E%, beta estimate 4.28, 0.02; 8.53, P = 0.049) and magnesium (mg, −0.23, −0.41; −0.05, P = 0.012) were associated with IL‐1 Ra.Conclusions: Healthy Nordic diet improved lipid profile and had a beneficial effect on low‐grade inflammation.

OBJECTIVE: We investigated the effects of 18 confirmed type 2 diabetes risk single nucleotide polymorphisms (SNPs) on insulin sensitivity, insulin secretion, and conversion of proinsulin to insulin. RESEARCH DESIGN AND METHODS: A total of 5,327 nondiabetic men (age 58 ± 7 years, BMI 27.0 ± 3.8 kg/m2) from a large population-based cohort were included. Oral glucose tolerance tests and genotyping of SNPs in or near PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, LOC387761, CDKN2B, IGF2BP2, CDKAL1, HNF1B, WFS1, JAZF1, CDC123, TSPAN8, THADA, ADAMTS9, NOTCH2, KCNQ1, and MTNR1B were performed. HNF1B rs757210 was excluded because of failure to achieve Hardy-Weinberg equilibrium. RESULTS: Six SNPs (TCF7L2, SLC30A8, HHEX, CDKN2B, CDKAL1, and MTNR1B) were significantly (P < 6.9 × 10−4) and two SNPs (KCNJ11 and IGF2BP2) were nominally (P < 0.05) associated with early-phase insulin release (InsAUC0–30/GluAUC0–30), adjusted for age, BMI, and insulin sensitivity (Matsuda ISI). Combined effects of these eight SNPs reached −32% reduction in InsAUC0–30/GluAUC0–30 in carriers of ≥11 vs. ≤3 weighted risk alleles. Four SNPs (SLC30A8, HHEX, CDKAL1, and TCF7L2) were significantly or nominally associated with indexes of proinsulin conversion. Three SNPs (KCNJ11, HHEX, and TSPAN8) were nominally associated with Matsuda ISI (adjusted for age and BMI). The effect of HHEX on Matsuda ISI became significant after additional adjustment for InsAUC0–30/GluAUC0–30. Nine SNPs did not show any associations with examined traits. CONCLUSIONS: Eight type 2 diabetes–related loci were significantly or nominally associated with impaired early-phase insulin release. Effects of SLC30A8, HHEX, CDKAL1, and TCF7L2 on insulin release could be partially explained by impaired proinsulin conversion. HHEX might influence both insulin release and insulin sensitivity.