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Background Whether the delivery of a large-for-gestational-age (LGA) infant predicts future maternal metabolic syndrome (MetS) is not known. To this aim, we investigated the incidence of MetS and its components in women with or without a history of gestational diabetes mellitus (GDM) with a view to the birth weight of the offspring. Methods Eight hundred seventy six women treated for their pregnancies in Kuopio University Hospital in 1989–2009 underwent a follow-up study (mean follow-up time 7.3 (SD 5.1) years), of whom 489 women with GDM and 385 normoglycemic controls. The women were stratified into two groups according to the newborn’s birth weight: 10-90th percentile (appropriate-for-gestational-age; AGA) ( n  = 662) and > 90th percentile (LGA) ( n  = 116). MetS and its components were evaluated in the follow-up study according to the International Diabetes Federation criteria. Results LGA vs. AGA delivery was associated with a higher incidence of MetS at follow-up in women with a background of GDM (54.4% vs. 43.6%), but not in women without GDM. Conclusion An LGA delivery in women with GDM is associated with a higher risk of future MetS and this group is optimal to study preventive measures for MetS. In contrast, an LGA delivery after a normoglycemic pregnancy was not associated with an increased future maternal MetS risk.

Present study examines the relationship between the estimated risk of developing type 2 diabetes (T2D) and health-related quality of life (HRQoL). We quantify the association between Finnish Diabetes Risk Score (FINDRISC) and HRQoL, and examine the potential use of FINDRISC as tool to evaluate HRQoL indirectly. We conducted a cross-sectional study comprising 707 Finnish people without a diagnosis of T2D between the ages of 51 and 75 years. The risk of developing T2D was assessed using the validated and widely used FINDRISC (range 0-26 points), and quality of life was measured using two preference-based HRQoL instruments (15D and SF-6D) and one health profile instrument (SF-36). Effects of the individual FINDRISC items and demographic and clinical characteristics, such as co-morbidities, on HRQoL were studied using multivariable Tobit regression models. Low HRQoL was significantly and directly associated with the estimated risk of developing T2D. An approximate 4-5 point change in FINDRISC score was observed to be associated with clinically noticeable changes in the preference-based instrument HRQoL index scores. The association between HRQoL and the risk of developing T2D was also observed for most dimensions of HRQoL in all applied HRQoL instruments. Overall, old age, lack of physical activity, obesity, and history of high blood glucose were the FINDRISC factors most prominently associated with lower HRQoL. The findings may help the health care professionals to substantiate the possible improvement in glucose metabolism and HRQoL potentially achieved by lifestyle changes, and better convince people at high risk of T2D to take action towards healthier lifestyle habits. FINDRISC may also provide an accurate proxy for HRQoL, and thus by estimating the risk of T2D with the FINDRISC, information about patients' HRQoL may also be obtained indirectly, when it is not feasible to use HRQoL instruments.

We investigated the ability of surrogate markers of tissue-specific insulin resistance (IR, Matsuda IR, Adipocyte IR, Liver IR) to predict deterioration of hyperglycemia, incident type 2 diabetes and cardiovascular events in the Metabolic Syndrome in Men (METSIM) Study. The METSIM Study includes 10,197 Finnish men, aged 45-73 years, and examined in 2005-2010. A total of 558 of 8,749 non-diabetic participants at baseline were diagnosed with new-onset diabetes and 239 with a new CVD event during a 5.9-year follow-up of this cohort (2010-2013). Compared to fasting plasma insulin level, Matsuda IR (IR in skeletal muscle) and Adipocyte IR were significantly better predictors of 2-hour plasma glucose and glucose area under the curve after adjustment for confounding factors. Liver IR was the strongest predictor of both incident type 2 diabetes (hazard ratio = 1.83, 95% confidence interval: 1.68-1.98) and cardiovascular events (hazard ratio = 1.31, 95% confidence interval: 1.15-1.48). Hazard ratios for fasting insulin were 1.37 (95% confidence interval: 1.32-1.42) and 1.11 (95% confidence interval: 1.00-1.24), respectively. Tissue-specific markers of IR, Matsuda IR and Adipocyte IR, were superior to fasting plasma insulin level in predicting worsening of hyperglycemia, and Liver IR was superior to fasting insulin level in predicting incident type 2 diabetes and cardiovascular events.

We investigated the levels of non-cholesterol sterols as predictors for the development of hyperglycemia (an increase in the glucose area under the curve in an oral glucose tolerance test) and incident type 2 diabetes in a 5-year follow-up study of a population-based cohort of Finnish men (METSIM Study, N = 1,050) having non-cholesterol sterols measured at baseline. Additionally we determined the association of 538,265 single nucleotide polymorphisms (SNP) with non-cholesterol sterol levels in a cross-sectional cohort of non-diabetic offspring of type 2 diabetes (the Kuopio cohort of the EUGENE2 Study, N = 273). We found that in a cross-sectional METSIM Study the levels of sterols indicating cholesterol absorption were reduced as a function of increasing fasting glucose levels, whereas the levels of sterols indicating cholesterol synthesis were increased as a function of increasing 2-hour glucose levels. A cholesterol synthesis marker desmosterol significantly predicted an increase, and two absorption markers (campesterol and avenasterol) a decrease in the risk of hyperglycemia and incident type 2 diabetes in a 5-year follow-up of the METSIM cohort, mainly attributable to insulin sensitivity. A SNP of ABCG8 was associated with fasting plasma glucose levels in a cross-sectional study but did not predict hyperglycemia or incident type 2 diabetes. In conclusion, the levels of some, but not all non-cholesterol sterols are markers of the worsening of hyperglycemia and type 2 diabetes.

To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA S4/F4) were combined to predict HbA1c levels at six year follow-up. Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. Explained variance, discrimination and calibration were assessed using R2, classification tables (comparing highest/lowest 50% HbA1c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM. At baseline, mean HbA1c level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbA1c level was 5.7% (39 mmol/mol). Calibration graphs showed that predicted HbA1c levels were somewhat underestimated in the Inter99 cohort and overestimated in the Hoorn and KORA cohorts, indicating that the model's intercept should be adjusted for each cohort to improve predictions. Sensitivity and specificity (95% CI) were 55.7% (53.9, 57.5) and 56.9% (55.1, 58.7) respectively, for women, and 54.6% (52.7, 56.5) and 54.3% (52.4, 56.2) for men. External validation showed similar performance in the METSIM cohort. In the non-diabetic population, our DIRECT-DETECT prediction model, including readily available predictors, has a relatively low explained variance and moderate discriminative performance, but can help to distinguish between future highest and lowest HbA1c levels. Absolute HbA1c values are cohort-dependent.

Aims/hypothesis The aim of this work was to investigate the mechanisms underlying the risk of type 2 diabetes associated with statin treatment in the population-based Metabolic Syndrome in Men (METSIM) cohort. Methods A total of 8,749 non-diabetic participants, aged 45–73 years, were followed up for 5.9 years. New diabetes was diagnosed in 625 men by means of an OGTT, HbA 1c ≥6.5% (48 mmol/mol) or glucose-lowering medication started during the follow-up. Insulin sensitivity and secretion were evaluated with OGTT-derived indices. Results Participants on statin treatment ( N  = 2,142) had a 46% increased risk of type 2 diabetes (adjusted HR 1.46 [95% CI 1.22, 1.74]). The risk was dose dependent for simvastatin and atorvastatin. Statin treatment significantly increased 2 h glucose (2hPG) and glucose AUC of an OGTT at follow-up, with a nominally significant increase in fasting plasma glucose (FPG). Insulin sensitivity was decreased by 24% and insulin secretion by 12% in individuals on statin treatment (at FPG and 2hPG <5.0 mmol/l) compared with individuals without statin treatment ( p  < 0.01). Decreases in insulin sensitivity and insulin secretion were dose dependent for simvastatin and atorvastatin. Conclusions/interpretation Statin treatment increased the risk of type 2 diabetes by 46%, attributable to decreases in insulin sensitivity and insulin secretion.

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment 1 . The origins of specific compounds are known, including metabolites that are highly heritable 2 , 3 , or those that are influenced by the gut microbiome 4 , by lifestyle choices such as smoking 5 , or by diet 6 . However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts 7 , 8 that were not available to us when we trained the algorithms. We used feature attribution analysis 9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites. The levels of 1,251 metabolites are measured in 475 phenotyped individuals, and machine-learning algorithms reveal that diet and the microbiome are the determinants with the strongest predictive power for the levels of these metabolites.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. Clinical multi-omics data are integrated and analyzed using a generative deep-learning model.