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Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18–109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex ( C -statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality ( C -statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation. Biomarkers that predict mortality are of interest for clinical as well as research applications. Here, the authors analyze metabolomics data from 44,168 individuals and identify key metabolites independently associated with all-cause mortality risk.

The coexistence of asthma and chronic rhinosinusitis with nasal polyposis (CRSwNP) is associated with allergic phenotypes, disease severity and failure of first-line treatment for both asthma and CRSwNP. Recent studies have highlighted shared genetic components for these diseases. To better understand this shared component, we perform genome-wide meta-analyses of asthma (n = 71,481), CRSwNP (n = 9626) and chronic rhinosinusitis without nasal polyposis (CRSsNP, n = 15,448) in FinnGen and UKB (685,602 controls). We detect 131 genomic associations, including 17 novel loci for asthma, 33 novel loci for CRSwNP, and one for CRSsNP. A shared impact on asthma and CRSwNP is observed at 71 loci. A cross-trait meta-analysis using all disorders further implicates 17 loci associated with asthma or asthma and CRSwNP. We also find 17 nonsynonymous associating variants, including a novel TP63 missense variant association with CRSwNP (OR = 1.519 [1.331–1.734]). Gene set analyses confirm enrichment of genes involved with type 2 inflammation, Jak-STAT signaling, and FOXP3 signaling. Our results highlight new shared and separate genetic pathways for CRSwNP and asthma. These provide several avenues of further investigation in functional and epidemiological follow-up, and evidence for immunological and non-immunological mechanisms behind both diseases. Shared heritability between asthma and rhinosinusitis has not been extensively explored. Here the authors find that genetic loci shared between asthma and chronic rhinosinusitis highlight Jak-STAT signaling, and link a Finnish-enriched TP63 variant with nasal polyps.

Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted high-throughput profiling of blood specimens in two large population-based cohorts. 106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18-103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio [HR] 1.67 per 1-standard deviation increment, 95% CI 1.53-1.82, p = 5×10⁻³¹), albumin (HR 0.70, 95% CI 0.65-0.76, p = 2×10⁻¹⁸), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62-0.77, p = 3×10⁻¹²), and citrate (HR 1.33, 95% CI 1.21-1.45, p = 5×10⁻¹⁰). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001). Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention.

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.

Samuli Ripatti and colleagues report a genome-wide association study for human serum metabolites using NMR of serum samples from over 8,000 Finnish individuals. They identify 31 loci associated with at least one of 216 serum metabolic measures. Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations ( P < 2.31 × 10 −10 ) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders.

Introduction The association between preeclampsia (PE) and an elevated risk of cardiovascular disease (CVD) is well documented. Recent genome‐wide association studies of PE have further highlighted their possible common genetic background. We investigated how the history of hypertensive disorders of pregnancy (HDP), including the PE phenotype, and normotensive pregnancy, combined with polygenic risk scores (PRSs) for PE (PE‐PRS), high systolic blood pressure (SBP‐PRS), coronary artery disease (CAD‐PRS) or stroke (stroke‐PRS), affects the risk for CVD. Material and Methods The study was conducted in the FinnGen cohort of 213 942 Finnish women, including 8858 women with PE, 17916 women with any HDP, and 196 026 parous controls. PE women were included in the HDP phenotype. Participants were classified based on their PRSs into three groups: low (<20%), moderate (20–80%) and high (>80%) genetic risk. Women with normotensive pregnancies and moderate PRSs served as controls. Results Women with PE and a high genetic risk for PE, high SBP, CAD, and stroke had a significantly increased risk of CVD compared to women with normotensive pregnancies and a moderate genetic risk. The hazard ratios (HRs) for CVD were 1.87 for PE‐PRS, 2.31 for SBP‐PRS, 1.94 for CAD‐PRS, and 2.07 for stroke‐PRS, all p‐values 2 × 10−16. A similar pattern was observed in women with any HDP. Among women with normotensive pregnancies, a high genetic risk led to only a modest increase in CVD risk. The corresponding HRs were 1.07 for PE‐PRS (p = 5 × 10−5), 1.32 for SBP‐PRS, 1.19 for CAD‐PRS, and 1.16 for stroke‐PRS (all p values 2 × 10−16). Across all four PRSs, the impact of PE and any HDP on CVD risk was greater than that of genetic risk alone. The elevated CVD risk persisted up to the age of 80. Conclusions In women with PE or any HDP, a high genetic risk for PE, high SBP, CAD, and stroke further increases the overall risk of CVD. Among women with normotensive pregnancies, a high genetic risk confers only a modest increase in CVD risk. In evaluating long‐term CVD risk, clinical risk assessment, including obstetric history, appears to outperform genetic risk evaluation using PRSs. FinnGen data of 213 942 women was utilized in order to investigate the risk of cardiovascular disease in women with and without hypertensive disorders of pregnancy and with diverse genetic risks for cardiovascular disease and preeclampsia measured with polygenic risk scores.

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.

The conventional ‘Neolithic package’ comprised animals and plants originally domesticated in the Near East. As farming spread on a generally northwest trajectory across Europe, early pastoralists would have been faced with the challenge of making farming viable in regions in which the organisms were poorly adapted to providing optimal yields or even surviving. Hence, it has long been debated whether Neolithic economies were ever established at the modern limits of agriculture. Here, we examine food residues in pottery, testing a hypothesis that Neolithic farming was practiced beyond the 60th parallel north. Our findings, based on diagnostic biomarker lipids and δ13C values of preserved fatty acids, reveal a transition at ca 2500 BC from the exploitation of aquatic organisms to processing of ruminant products, specifically milk, confirming farming was practiced at high latitudes. Combining this with genetic, environmental and archaeological information, we demonstrate the origins of dairying probably accompanied an incoming, genetically distinct, population successfully establishing this new subsistence ‘package’.

Background/ObjectiveThis observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools.Subjects/MethodsFirstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25–74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24–39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression.ResultsThe four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile.ConclusionsAge-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.

Background Pregnancy triggers well-known alterations in maternal glucose and lipid balance but its overall effects on systemic metabolism remain incompletely understood. Methods Detailed molecular profiles (87 metabolic measures and 37 cytokines) were measured for up to 4260 women (24–49 years, 322 pregnant) from three population-based cohorts in Finland. Circulating molecular concentrations in pregnant women were compared to those in non-pregnant women. Metabolic profiles were also reassessed for 583 women 6 years later to uncover the longitudinal metabolic changes in response to change in the pregnancy status. Results Compared to non-pregnant women, all lipoprotein subclasses and lipids were markedly increased in pregnant women. The most pronounced differences were observed for the intermediate-density, low-density and high-density lipoprotein triglyceride concentrations. Large differences were also seen for many fatty acids and amino acids. Pregnant women also had higher concentrations of low-grade inflammatory marker glycoprotein acetyls, higher concentrations of interleukin-18 and lower concentrations of interleukin-12p70. The changes in metabolic concentrations for women who were not pregnant at baseline but pregnant 6 years later (or vice versa) matched (or were mirror-images of) the cross-sectional association pattern. Cross-sectional results were consistent across the three cohorts and similar longitudinal changes were seen for 653 women in 4-year and 497 women in 10-year follow-up. For multiple metabolic measures, the changes increased in magnitude across the three trimesters. Conclusions Pregnancy initiates substantial metabolic and inflammatory changes in the mothers. Comprehensive characterisation of normal pregnancy is important for gaining understanding of the key nutrients for fetal growth and development. These findings also provide a valuable molecular reference in relation to studies of adverse pregnancy outcomes.