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Aims/hypothesisMetabolomics technologies have identified numerous blood biomarkers for type 2 diabetes risk in case−control studies of middle-aged and older individuals. We aimed to validate existing and identify novel metabolic biomarkers predictive of future diabetes in large cohorts of young adults.MethodsNMR metabolomics was used to quantify 229 circulating metabolic measures in 11,896 individuals from four Finnish observational cohorts (baseline age 24–45 years). Associations between baseline metabolites and risk of developing diabetes during 8–15 years of follow-up (392 incident cases) were adjusted for sex, age, BMI and fasting glucose. Prospective metabolite associations were also tested with fasting glucose, 2 h glucose and HOMA-IR at follow-up.ResultsOut of 229 metabolic measures, 113 were associated with incident type 2 diabetes in meta-analysis of the four cohorts (ORs per 1 SD: 0.59–1.50; p< 0.0009). Among the strongest biomarkers of diabetes risk were branched-chain and aromatic amino acids (OR 1.31–1.33) and triacylglycerol within VLDL particles (OR 1.33–1.50), as well as linoleic n-6 fatty acid (OR 0.75) and non-esterified cholesterol in large HDL particles (OR 0.59). The metabolic biomarkers were more strongly associated with deterioration in post-load glucose and insulin resistance than with future fasting hyperglycaemia. A multi-metabolite score comprised of phenylalanine, non-esterified cholesterol in large HDL and the ratio of cholesteryl ester to total lipid in large VLDL was associated with future diabetes risk (OR 10.1 comparing individuals in upper vs lower fifth of the multi-metabolite score) in one of the cohorts (mean age 31 years).Conclusions/interpretationMetabolic biomarkers across multiple molecular pathways are already predictive of the long-term risk of diabetes in young adults. Comprehensive metabolic profiling may help to target preventive interventions for young asymptomatic individuals at increased risk.

Background Insulin resistance (IR) is predictive for type 2 diabetes and associated with various metabolic abnormalities in fasting conditions. However, limited data are available on how IR affects metabolic responses in a non-fasting setting, yet this is the state people are mostly exposed to during waking hours in the modern society. Here, we aim to comprehensively characterise the metabolic changes in response to an oral glucose test (OGTT) and assess the associations of these changes with IR. Methods Blood samples were obtained at 0 (fasting baseline, right before glucose ingestion), 30, 60, and 120 min during the OGTT. Seventy-eight metabolic measures were analysed at each time point for a discovery cohort of 4745 middle-aged Finnish individuals and a replication cohort of 595 senior Finnish participants. We assessed the metabolic changes in response to glucose ingestion (percentage change in relative to fasting baseline) across the four time points and further compared the response profile between five groups with different levels of IR and glucose intolerance. Further, the differences were tested for covariate adjustment, including gender, body mass index, systolic blood pressure, fasting, and 2-h glucose levels. The groups were defined as insulin sensitive with normal glucose (IS-NGT), insulin resistant with normal glucose (IR-NGT), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and new diabetes (NDM). IS-NGT and IR-NGT were defined as the first and fourth quartile of fasting insulin in NGT individuals. Results Glucose ingestion induced multiple metabolic responses, including increased glycolysis intermediates and decreased branched-chain amino acids, ketone bodies, glycerol, and triglycerides. The IR-NGT subgroup showed smaller responses for these measures (mean + 23%, interquartile 9–34% at 120 min) compared to IS-NGT (34%, 23–44%, P  < 0.0006 for difference, corrected for multiple testing). Notably, the three groups with glucose abnormality (IFG, IGT, and NDM) showed similar metabolic dysregulations as those of IR-NGT. The difference between the IS-NGT and the other subgroups was largely explained by fasting insulin, but not fasting or 2 h glucose. The findings were consistent after covariate adjustment and between the discovery and replication cohort. Conclusions Insulin-resistant non-diabetic individuals are exposed to a similar adverse postprandial metabolic milieu, and analogous cardiometabolic risk, as those with type 2 diabetes. The wide range of metabolic abnormalities associated with IR highlights the necessity of diabetes diagnostics and clinical care beyond glucose management.

Type 2 diabetes rates vary significantly across geographic regions. These differences are sometimes assumed to be entirely driven by differential distribution of environmental triggers, including obesity and insufficient physical activity (IPA). In this review, we discuss data which conflicts with this supposition. We carried out a secondary analysis of publicly available data to unravel the relative contribution of obesity and IPA towards diabetes risk across different populations. We used sex-specific, age-standardized estimates from Non-Communicable Disease Risk Factor Collaboration (NCD-RisC) on diabetes (1980–2014) and obesity (1975–2016) rates, in 200 countries, and from WHO on IPA rates in 168 countries in the year 2016. NCD-RisC and WHO organized countries into nine super-regions. All analyses were region- and sex-specific. Although obesity has been increasing since 1975 in every part of the world, this was not reflected in a proportional increase in diabetes rates in several regions, including Central and Eastern Europe, and High-income western countries region. Similarly, the association of physical inactivity with diabetes is not homogeneous across regions. Countries from different regions across the world could have very similar rates of diabetes, despite falling on opposite ends of IPA rate spectrum. The combined effect of obesity and IPA on diabetes risk was analyzed at the worldwide and country level. The overall findings highlighted the larger impact of obesity on disease risk; low IPA rates do not seem to be protective of diabetes, when obesity rates are high. Despite that, some countries deviate from this overall observation. Sex differences were observed across all our analyses. Overall, data presented in this review indicate that different populations, while experiencing similar environmental shifts, are apparently differentially subject to diabetes risk. Sex-related differences observed suggest that males and females are either subject to different risk factor exposures or have different responses to them.

Antenatal corticosteroid therapy (ACT) is used clinically to prepare the fetal lung for impending preterm birth, but animal and human studies link corticosteroids to smaller birth size. Whether ACT is associated with birth size is debated; therefore, we assessed differences in birth size in treated versus untreated pregnancies. This observational register-based study used data from the Finnish Medical Birth Register (FMBR) covering all births in Finland (January 1, 2006-December 31, 2010). We used unadjusted and adjusted regression analyses as well as propensity score matching (PSM) to analyze whether birth size differed by ACT exposure. PSM provides a stringent comparison, as subsamples were created matched on baseline and medical characteristics between treated and untreated women. All analyses were stratified by timing of birth. The primary study outcome was birth size: birth weight (BWT), birth length (BL), ponderal index (PI), and head circumference (HC) measured immediately after birth and recorded in the FMBR. Additional analyses explored indicators of neonatal health in relation to ACT exposure and birth size. A total of 278,508 live-born singleton births with ≥24 gestational completed weeks were registered in the FMBR during the 5-year study period. Over 4% of infants were born preterm, and 4,887 women were treated with ACT (1.75%). More than 44% of the exposed infants (n = 2,173) were born at term. First, results of unadjusted regression analyses using the entire sample showed the greatest reductions in BWT as compared to the other analytic methods: very preterm -61.26 g (±SE 24.12, P < 0.01), preterm -232.90 g (±SE 17.24, P < .001), near term -171.50 g (±SE 17.52, P < .001), and at term -101.95 g (±SE 10.89, P < .001). Second, using the entire sample, regression analyses adjusted for baseline and medical conditions showed significant differences in BWT between exposed and unexposed infants: very preterm -61.54 g (±SE 28.62, P < .03), preterm -222.78 g (±SE 19.64, P < .001), near term -159.25 g (±SE 19.14, P < .001), and at term -91.62 g (±SE 11.86, P < .03). Third, using the stringent PSM analyses based on matched subsamples, infants exposed to ACT weighed less at birth: -220.18 g (±SE 21.43, P < .001), -140.68 g (±SE 23.09, P < .001), and -89.38 g (±SE 14.16, P < .001), born preterm, near term, and at term, respectively. Similarly, significant reductions in BL and HC were also observed using the three analytic methods. There were no differences among postterm infants regardless of analytic method. Likewise, we observed no differences with respect to PI. Additional analyses showed that exposed and unexposed infants had generally similar Apgar scores at birth, yet the ACT-treated infants received greater medical care during the first 7 days of life and beyond. Our study is mainly limited by lack of data in FMBR specifying the interval between treatment and birth as well as other potential confounders that could not be tested. In this study, ACT was consistently associated with reduction in birth size for infants born preterm, near term, or at term. Further investigation is warranted alongside reevaluation of guidelines. Efforts need to be made to correctly identify and target patients who will deliver preterm. Reduced growth should be considered when deliberating early care decisions.

Context:Polycystic ovary syndrome (PCOS) is associated with increased psychological distress, obesity and hyperandrogenism being suggested as key promoters.Objectives:To investigate the prevalence of anxiety/depression and their coexistence in women with PCOS/PCOS-related symptoms at ages 31 and 46. The roles of obesity, hyperandrogenism, and awareness of PCOS on psychological distress were also assessed.Design:Population-based follow-up.Setting:Northern Finland Birth Cohort 1966 with 15-year follow-up.Participants:At age 31, a questionnaire-based screening for oligoamenorrhea (OA) and hirsutism (H): 2188 asymptomatic (controls), 331 OA, 323 H, and 125 OA plus H (PCOS). Follow-up at age 46: 1576 controls, 239 OA, 231 H, and 85 PCOS.Interventions:Questionnaire-based screening for anxiety and depression symptoms (Hopkins Symptom Checklist-25) and previously diagnosed/treated depression at ages 31 and 46. Body mass index (BMI), serum testosterone/free androgen index, and awareness of polycystic ovaries/PCOS on psychological distress were also assessed.Main Outcomes:Population-based prevalence of anxiety and/or depression in women with PCOS/PCOS-related symptoms at ages 31 and 46.Results:Anxiety and/or depression symptoms, their coexistence, and rate of depression were increased at ages 31 and 46 in women with PCOS or isolated H compared with controls. High BMI or hyperandrogenism did not associate with increased anxiety or depression symptoms. The awareness of PCOS was associated with increased anxiety.Conclusions:Women with PCOS or isolated H present more often with anxiety and/or depression symptoms and their coexistence compared with controls. High BMI or hyperandrogenism did not provoke psychological distress in PCOS. The awareness of PCOS increased anxiety but did not associate with severe anxiety or depression.We studied psychological distress in women with PCOS symptoms and found high anxiety and depression rates in women with PCOS or isolated hirsutism lasting until perimenopausal age.

The role of metabolic syndrome (MetS) as a preceding metabolic state for type 2 diabetes and cardiovascular disease is widely recognised. To accumulate knowledge of the pathological mechanisms behind the condition at the methylation level, we conducted an epigenome-wide association study (EWAS) of MetS and its components, testing 1187 individuals of European ancestry for approximately 470 000 methylation sites throughout the genome. Methylation site cg19693031 in gene TXNIP —previously associated with type 2 diabetes, glucose and lipid metabolism, associated with fasting glucose level ( P  = 1.80 × 10 −8 ). Cg06500161 in gene ABCG1 associated both with serum triglycerides ( P  = 5.36 × 10 −9 ) and waist circumference ( P  = 5.21 × 10 −9 ). The previously identified type 2 diabetes–associated locus cg08309687 in chromosome 21 associated with waist circumference for the first time ( P  = 2.24 × 10 −7 ). Furthermore, a novel HDL association with cg17901584 in chromosome 1 was identified ( P  = 7.81 × 10 −8 ). Our study supports previous genetic studies of MetS, finding that lipid metabolism plays a key role in pathology of the syndrome. We provide evidence regarding a close interplay with glucose metabolism. Finally, we suggest that in attempts to identify methylation loci linking separate MetS components, cg19693031 appears to represent a strong candidate.

Copy number variants (CNVs) are an important class of genomic variation known to be important for human physiology and diseases. Here we present genome-wide metabolomic signatures for CNVs in two Finnish cohorts—The Northern Finland Birth Cohort 1966 (NFBC 1966) and NFBC 1986. We have analysed and reported CNVs in over 9,300 individuals and characterised their dosage effect (CNV-metabolomic QTL) on 228 plasma lipoproteins and metabolites. We have reported reference (normal physiology) metabolomic signatures for up to ~ 2.6 million COVID-19 GWAS results from the National Institutes of Health (NIH) GRASP database, including for outcomes related to COVID-19 death, severity, and hospitalisation. Furthermore, by analysing two exemplar genes for COVID-19 severity namely LZTFL1 and OAS1, we have reported here two additional candidate genes for COVID-19 severity biology, (1) NFIX, a gene related to viral (adenovirus) replication and hematopoietic stem cells and (2) ACSL1, a known candidate gene for sepsis and bacterial inflammation. Based on our results and current literature we hypothesise that (1) charge imbalance across the cellular membrane between cations (Fe 2+ , Mg 2+ etc.) and anions (e.g. ROS, hydroxide ion from cellular Fenton reactions, superoxide etc.), (2) iron trafficking within and between different cell types e.g., macrophages and (3) systemic oxidative stress response (e.g. lipid peroxidation mediated inflammation), together could be of relevance in severe COVID-19 cases. To conclude, our unique atlas of univariate and multivariate metabolomic signatures for CNVs (~ 7.2 million signatures) with deep annotations of various multi-omics data sets provide an important reference knowledge base for human metabolism and diseases.

Attention-deficit/hyperactivity disorder (ADHD) often co-occurs with obesity, however, the potential causality between the traits remains unclear. We examined both genetic and prenatal evidence for causality using Mendelian Randomisation (MR) and polygenic risk scores (PRS). We conducted bi-directional MR on ADHD liability and six obesity-related traits using summary statistics from the largest available meta-analyses of genome-wide association studies. We also examined the shared genetic aetiology between ADHD symptoms (inattention and hyperactivity) and body mass index (BMI) by PRS association analysis using longitudinal data from Northern Finland Birth Cohort 1986 (NFBC1986, n = 2984). Lastly, we examined the impact of the prenatal environment by association analysis of maternal pre-pregnancy BMI and offspring ADHD symptoms, adjusted for PRS of both traits, in NFBC1986 dataset. Through MR analyses, we found evidence for bidirectional causality between ADHD liability and obesity-related traits. PRS association analyses showed evidence for genetic overlap between ADHD symptoms and BMI. We found no evidence for a difference between inattention and hyperactivity symptoms, suggesting that neither symptom subtype is driving the association. We found evidence for association between maternal pre-pregnancy BMI and offspring ADHD symptoms after adjusting for both BMI and ADHD PRS (association p-value = 0.027 for inattention, p = 0.008 for hyperactivity). These results are consistent with the hypothesis that the co-occurrence between ADHD and obesity has both genetic and prenatal environmental origins.

Metabolite associations with insulin resistance were studied in 7,098 young Finns (age 31 ± 3 years; 52% women) to elucidate underlying metabolic pathways. Insulin resistance was assessed by the homeostasis model (HOMA-IR) and circulating metabolites quantified by high-throughput nuclear magnetic resonance spectroscopy in two population-based cohorts. Associations were analyzed using regression models adjusted for age, waist, and standard lipids. Branched-chain and aromatic amino acids, gluconeogenesis intermediates, ketone bodies, and fatty acid composition and saturation were associated with HOMA-IR (P < 0.0005 for 20 metabolite measures). Leu, Ile, Val, and Tyr displayed sex- and obesity-dependent interactions, with associations being significant for women only if they were abdominally obese. Origins of fasting metabolite levels were studied with dietary and physical activity data. Here, protein energy intake was associated with Val, Phe, Tyr, and Gln but not insulin resistance index. We further tested if 12 genetic variants regulating the metabolites also contributed to insulin resistance. The genetic determinants of metabolite levels were not associated with HOMA-IR, with the exception of a variant in GCKR associated with 12 metabolites, including amino acids (P < 0.0005). Nonetheless, metabolic signatures extending beyond obesity and lipid abnormalities reflected the degree of insulin resistance evidenced in young, normoglycemic adults with sex-specific fingerprints.

Background/objectiveChildren BMI is a longitudinal phenotype, developing through interplays between genetic and environmental factors. Whilst childhood obesity is escalating, we require a better understanding of its early origins and variation across generations to prevent it.Subjects/methodsWe designed a cross-cohort study including 12,040 Finnish children from the Northern Finland Birth Cohorts 1966 and 1986 (NFBC1966 and NFBC1986) born before or at the start of the obesity epidemic. We used group-based trajectory modelling to identify BMI trajectories from 2 to 20 years. We subsequently tested their associations with early determinants (mother and child) and the possible difference between generations, adjusted for relevant biological and socioeconomic confounders.ResultsWe identified four BMI trajectories, ‘stable-low’ (34.8%), ‘normal’ (44.0%), ‘stable-high’ (17.5%) and ‘early-increase’ (3.7%). The ‘early-increase’ trajectory represented the highest risk for obesity. We analysed a dose-response association of maternal pre-pregnancy BMI and smoking with BMI trajectories. The directions of effect were consistent across generations and the effect sizes tended to increase from earlier generation to later. Respectively for NFBC1966 and NFBC1986, the adjusted risk ratios of being in the early-increase group were 1.08 (1.06–1.10) and 1.12 (1.09–1.15) per unit of pre-pregnancy BMI and 1.44 (1.05–1.96) and 1.48 (1.17–1.87) in offspring of smoking mothers compared to non-smokers. We observed similar relations with infant factors including birthweight for gestational age and peak weight velocity. In contrast, the age at adiposity peak in infancy was associated with the BMI trajectories in NFBC1966 but did not replicate in NFBC1986.ConclusionsExposures to adverse maternal predictors were associated with a higher risk obesity trajectory and were consistent across generations. However, we found a discordant association for the timing of adiposity peak over a 20-year period. This suggests the role of residual environmental factors, such as nutrition, and warrants additional research to understand the underlying gene–environment interplay.