Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

Author Correction: L1 drives IFN in senescent cells and promotes age-associated inflammation.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

L1 drives IFN in senescent cells and promotes age-associated inflammation.

Retrotransposable elements are deleterious at many levels, and the failure of host surveillance systems for these elements can thus have negative consequences. However, the contribution of retrotransposon activity to ageing and age-associated diseases is not known. Here we show that during cellular senescence, L1 (also known as LINE-1) retrotransposable elements become transcriptionally derepressed and activate a type-I interferon (IFN-I) response. The IFN-I response is a phenotype of late senescence and contributes to the maintenance of the senescence-associated secretory phenotype. The IFN-I response is triggered by cytoplasmic L1 cDNA, and is antagonized by inhibitors of the L1 reverse transcriptase. Treatment of aged mice with the nucleoside reverse transcriptase inhibitor lamivudine downregulated IFN-I activation and age-associated inflammation (inflammaging) in several tissues. We propose that the activation of retrotransposons is an important component of sterile inflammation that is a hallmark of ageing, and that L1 reverse transcriptase is a relevant target for the treatment of age-associated disorders.

Fat metabolism is associated with telomere length in six population-based studies.

Telomeres are repetitive DNA sequences located at the end of chromosomes, which are associated to biological aging, cardiovascular disease, cancer and mortality. Lipid and fatty acid metabolism have been associated with telomere shortening. We have conducted an in-depth study investigating the association of metabolic biomarkers with telomere length (LTL). We performed an association analysis of 226 metabolic biomarkers with LTL using data from 11 775 individuals from six independent population-based cohorts (BBMRI-NL consortium). Metabolic biomarkers include lipoprotein lipids and subclasses, fatty acids, amino acids, glycolysis measures and ketone bodies. LTL was measured by quantitative polymerase chain reaction or FlowFISH. Linear regression analysis was performed adjusting for age, sex, lipid-lowering medication and cohort-specific covariates (model 1) and additionally for body mass index (BMI) and smoking (model 2), followed by inverse variance-weighted meta-analyses (significance threshold Pmeta = 6.5 × 10-4). We identified four metabolic biomarkers positively associated with LTL, including two cholesterol to lipid ratios in small VLDL (S-VLDL-C % and S-VLDL-CE %) and two omega-6 fatty acid ratios (FAw6/FA and LA/FA). After additionally adjusting for BMI and smoking, these metabolic biomarkers remained associated with LTL with similar effect estimates. In addition, cholesterol esters in very small VLDL (XS-VLDL-CE) became significantly associated with LTL (P = 3.6 × 10-4). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (P = 1.9 × 10-4). To conclude, we identified multiple metabolic biomarkers involved in lipid and fatty acid metabolism that may be involved in LTL biology. Longitudinal studies are needed to exclude reversed causation.

Late reproduction is associated with extended female survival but not with familial longevity.

RESEARCH QUESTION: Are age at last childbirth and number of children, as facets of female reproductive health, related to individual lifespan or familial longevity? DESIGN: This observational study included 10,255 female participants from a multigenerational historical cohort, the LINKing System for historical family reconstruction (LINKS), and 1258 female participants from 651 long-lived families in the Leiden Longevity Study (LLS). Age at last childbirth and number of children, as outcomes of reproductive success, were compared with individual and familial longevity using the LINKS dataset. In addition, the genetic predisposition in the form of a polygenic risk score (PRS) for age at menopause was studied in relation to familial longevity using the LLS dataset. RESULTS: For each year increase in the age of the birth of the last child, a woman's lifespan increased by 0.06 years (22 days; P = 0.002). The yearly risk for having a last child was 9% lower in women who survived to the oldest 10% of their birth cohort (hazard ratio 0.91, 95% CI 0.86-0.95). Women who came from long-living families did not have a higher mean age of last childbirth. There was no significant association between familial longevity and genetic predisposition to age at menopause. CONCLUSIONS: Female reproductive health associates with a longer lifespan. Familial longevity does not associate to extended reproductive health. Other factors in somatic maintenance that support a longer lifespan are likely to have an impact on reproductive health.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data.

BACKGROUND: There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆. METHODS: We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. RESULTS: The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. CONCLUSIONS: Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

Facing up to the global challenges of ageing.

Longer human lives have led to a global burden of late-life disease. However, some older people experience little ill health, a trait that should be extended to the general population. Interventions into lifestyle, including increased exercise and reduction in food intake and obesity, can help to maintain healthspan. Altered gut microbiota, removal of senescent cells, blood factors obtained from young individuals and drugs can all improve late-life health in animals. Application to humans will require better biomarkers of disease risk and responses to interventions, closer alignment of work in animals and humans, and increased use of electronic health records, biobank resources and cohort studies.

Senescent human melanocytes drive skin ageing via paracrine telomere dysfunction.

Cellular senescence has been shown to contribute to skin ageing. However, the role of melanocytes in the process is understudied. Our data show that melanocytes are the only epidermal cell type to express the senescence marker p16INK4A during human skin ageing. Aged melanocytes also display additional markers of senescence such as reduced HMGB1 and dysfunctional telomeres, without detectable telomere shortening. Additionally, senescent melanocyte SASP induces telomere dysfunction in paracrine manner and limits proliferation of surrounding cells via activation of CXCR3-dependent mitochondrial ROS. Finally, senescent melanocytes impair basal keratinocyte proliferation and contribute to epidermal atrophy in vitro using 3D human epidermal equivalents. Crucially, clearance of senescent melanocytes using the senolytic drug ABT737 or treatment with mitochondria-targeted antioxidant MitoQ suppressed this effect. In conclusion, our study provides proof-of-concept evidence that senescent melanocytes affect keratinocyte function and act as drivers of human skin ageing.

PLIS: A metabolomic response monitor to a lifestyle intervention study in older adults.

The response to lifestyle intervention studies is often heterogeneous, especially in older adults. Subtle responses that may represent a health gain for individuals are not always detected by classical health variables, stressing the need for novel biomarkers that detect intermediate changes in metabolic, inflammatory, and immunity-related health. Here, our aim was to develop and validate a molecular multivariate biomarker maximally sensitive to the individual effect of a lifestyle intervention; the Personalized Lifestyle Intervention Status (PLIS). We used 1 H-NMR fasting blood metabolite measurements from before and after the 13-week combined physical and nutritional Growing Old TOgether (GOTO) lifestyle intervention study in combination with a fivefold cross-validation and a bootstrapping method to train a separate PLIS score for men and women. The PLIS scores consisted of 14 and four metabolites for females and males, respectively. Performance of the PLIS score in tracking health gain was illustrated by association of the sex-specific PLIS scores with several classical metabolic health markers, such as BMI, trunk fat%, fasting HDL cholesterol, and fasting insulin, the primary outcome of the GOTO study. We also showed that the baseline PLIS score indicated which participants respond positively to the intervention. Finally, we explored PLIS in an independent physical activity lifestyle intervention study, showing similar, albeit remarkably weaker, associations of PLIS with classical metabolic health markers. To conclude, we found that the sex-specific PLIS score was able to track the individual short-term metabolic health gain of the GOTO lifestyle intervention study. The methodology used to train the PLIS score potentially provides a useful instrument to track personal responses and predict the participant's health benefit in lifestyle interventions similar to the GOTO study.

Pooled analysis of epigenome-wide association studies of food consumption in KORA, TwinsUK and LLS.

PURPOSE: Examining epigenetic patterns is a crucial step in identifying molecular changes of disease pathophysiology, with DNA methylation as the most accessible epigenetic measure. Diet is suggested to affect metabolism and health via epigenetic modifications. Thus, our aim was to explore the association between food consumption and DNA methylation. METHODS: Epigenome-wide association studies were conducted in three cohorts: KORA FF4, TwinsUK, and Leiden Longevity Study, and 37 dietary exposures were evaluated. Food group definition was harmonized across the three cohorts. DNA methylation was measured using Infinium MethylationEPIC BeadChip in KORA and Infinium HumanMethylation450 BeadChip in the Leiden study and the TwinsUK study. Overall, data from 2293 middle-aged men and women were included. A fixed-effects meta-analysis pooled study-specific estimates. The significance threshold was set at 0.05 for false-discovery rate-adjusted p values per food group. RESULTS: We identified significant associations between the methylation level of CpG sites and the consumption of onions and garlic (2), nuts and seeds (18), milk (1), cream (11), plant oils (4), butter (13), and alcoholic beverages (27). The signals targeted genes of metabolic health relevance, for example, GLI1, RPTOR, and DIO1, among others. CONCLUSION: This EWAS is unique with its focus on food groups that are part of a Western diet. Significant findings were mostly related to food groups with a high-fat content.

Association of Torquetenovirus Viremia with Physical Frailty and Cognitive Impairment in Three Independent European Cohorts.

INTRODUCTION: Immunosenescence and inflammaging have been implicated in the pathophysiology of frailty. Torquetenovirus (TTV), a single-stranded DNA anellovirus, the major component of the human blood virome, shows an increased replication rate with advancing age. An elevated TTV viremia has been associated with an impaired immune function and an increased risk of mortality in the older population. The objective of this study was to analyze the relation between TTV viremia, physical frailty, and cognitive impairment. METHODS: TTV viremia was measured in 1,131 nonfrail, 45 physically frail, and 113 cognitively impaired older adults recruited in the MARK-AGE study (overall mean age 64.7 ± 5.9 years), and then the results were checked in two other independent cohorts from Spain and Portugal, including 126 frail, 252 prefrail, and 141 nonfrail individuals (overall mean age: 77.5 ± 8.3 years). RESULTS: TTV viremia ≥4log was associated with physical frailty (OR: 4.69; 95% CI: 2.06-10.67, p < 0.0001) and cognitive impairment (OR: 3.49, 95% CI: 2.14-5.69, p < 0.0001) in the MARK-AGE population. The association between TTV DNA load and frailty status was confirmed in the Spanish cohort, while a slight association with cognitive impairment was observed (OR: 1.33; 95% CI: 1.000-1.773), only in the unadjusted model. No association between TTV load and frailty or cognitive impairment was found in the Portuguese sample, although a negative association between TTV viremia and MMSE score was observed in Spanish and Portuguese females. CONCLUSIONS: These findings demonstrate an association between TTV viremia and physical frailty, while the association with cognitive impairment was observed only in the younger population from the MARK-AGE study. Further research is necessary to clarify TTV's clinical relevance in the onset and progression of frailty and cognitive decline in older individuals.

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies.

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

Timing of objectively-collected physical activity in relation to body weight and metabolic health in sedentary older people: a cross-sectional and prospective analysis.

BACKGROUND: Little is known about the impact of timing as opposed to frequency and intensity of daily physical activity on metabolic health. Therefore, we assessed the association between accelerometery-based daily timing of physical activity and measures of metabolic health in sedentary older people. METHODS: Hourly mean physical activity derived from wrist-worn accelerometers over a 6-day period was collected at baseline and after 3 months in sedentary participants from the Active and Healthy Ageing study. A principal component analysis (PCA) was performed to reduce the number of dimensions (e.g. define periods instead of separate hours) of hourly physical activity at baseline and change during follow-up. Cross-sectionally, a multivariable-adjusted linear regression analysis was used to associate the principal components, particularly correlated with increased physical activity in data-driven periods during the day, with body mass index (BMI), fasting glucose and insulin, HbA1c and the homeostatic model assessment for insulin resistance (HOMA-IR). For the longitudinal analyses, we calculated the hourly changes in physical activity and change in metabolic health after follow-up. RESULTS: We included 207 individuals (61.4% male, mean age: 64.8 [SD 2.9], mean BMI: 28.9 [4.7]). Higher physical activity in the early morning was associated with lower fasting glucose (-2.22%, 95% CI: -4.19, -0.40), fasting insulin (-13.54%, 95%CI: -23.49, -4.39), and HOMA-IR (-16.07%, 95%CI: -27.63, -5.65). Higher physical activity in the late afternoon to evening was associated with lower BMI (-2.84%, 95% CI: -4.92, -0.70). Higher physical activity at night was associated with higher BMI (2.86%, 95% CI: 0.90, 4.78), fasting glucose (2.57%, 95% CI: 0.70, 4.30), and HbA1c (2.37%, 95% CI: 1.00, 3.82). Similar results were present in the prospective analysis. CONCLUSION: Specific physical activity timing patterns were associated with more beneficial metabolic health, suggesting particular time-dependent physical activity interventions might maximise health benefits.

Association between fat-soluble vitamins and self-reported health status: a cross-sectional analysis of the MARK-AGE cohort.

Self-rated health (SRH) is associated with higher risk of death. Since low plasma levels of fat-soluble vitamins are related to mortality, we aimed to assess whether plasma concentrations of vitamins A, D and E were associated with SRH in the MARK-AGE study. We included 3158 participants (52 % female) aged between 35 and 75 years. Cross-sectional data were collected via questionnaires. An enzyme immunoassay quantified 25-hydroxyvitamin D and HPLC determined α-tocopherol and retinol plasma concentrations. The median 25-hydroxyvitamin D and retinol concentrations differed significantly (P < 0·001) between SRH categories and were lower in the combined fair/poor category v. the excellent, very good and good categories (25-hydroxvitamin D: 40·8 v. 51·9, 49·3, 46·7 nmol/l, respectively; retinol: 1·67 v. 1·75, 1·74, 1·70 µmol/l, respectively). Both vitamin D and retinol status were independently associated with fair/poor SRH in multiple regression analyses: adjusted OR (95 % CI) for the vitamin D insufficiency, deficiency and severe deficiency categories were 1·33 (1·06-1·68), 1·50 (1·17-1·93) and 1·83 (1·34-2·50), respectively; P = 0·015, P = 0·001 and P < 0·001, and for the second/third/fourth retinol quartiles: 1·44 (1·18-1·75), 1·57 (1·28-1·93) and 1·49 (1·20-1·84); all P < 0·001. No significant associations were reported for α-tocopherol quartiles. Lower vitamin A and D status emerged as independent markers for fair/poor SRH. Further insights into the long-term implications of these modifiable nutrients on health status are warranted.

Associations of Cytomegalovirus Infection With All-Cause and Cardiovascular Mortality in Multiple Observational Cohort Studies of Older Adults.

BACKGROUND: Whether latent cytomegalovirus (CMV) infection in older adults has any substantial health consequences is unclear. Here, we sought associations between CMV-seropositivity and IgG titer with all-cause and cardiovascular mortality in 5 longitudinal cohorts. METHODS: Leiden Longevity Study, Prospective Study of Pravastatin in the Elderly at Risk, Longitudinal Study of Aging Danish Twins, and Leiden 85-plus Study were assessed at median (2.8-11.4 years) follow-up . Cox regression and random effects meta-analysis were used to estimate mortality risk dependent on CMV serostatus and/or IgG antibody titer, in quartiles after adjusting for confounders. RESULTS: CMV-seropositivity was seen in 47%-79% of 10 122 white community-dwelling adults aged 59-93 years. Of these, 3519 had died on follow-up (579 from cardiovascular disease). CMV seropositivity was not associated with all-cause (hazard ratio [HR], 1.05; 95% confidence interval [CI], .97-1.14) or cardiovascular mortality (HR, 0.97; 95% CI, .83-1.13). Subjects in the highest CMV IgG quartile group had increased all-cause mortality relative to CMV-seronegatives (HR, 1.16; 95% CI, 1.04-1.29) but this association lost significance after adjustment for confounders (HR, 1.13; 95% CI, .99-1.29). The lack of increased mortality risk was confirmed in subanalyses. CONCLUSIONS: CMV infection is not associated with all-cause or cardiovascular mortality in white community-dwelling older adults.

Correction for both common and rare cell types in blood is important to identify genes that correlate with age.

BACKGROUND: Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. RESULTS: Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10-6). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. CONCLUSIONS: We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.

Higher thyrotropin leads to unfavorable lipid profile and somewhat higher cardiovascular disease risk: evidence from multi-cohort Mendelian randomization and metabolomic profiling.

BACKGROUND: Observational studies suggest interconnections between thyroid status, metabolism, and risk of coronary artery disease (CAD), but causality remains to be proven. The present study aimed to investigate the potential causal relationship between thyroid status and cardiovascular disease and to characterize the metabolomic profile associated with thyroid status. METHODS: Multi-cohort two-sample Mendelian randomization (MR) was performed utilizing genome-wide significant variants as instruments for standardized thyrotropin (TSH) and free thyroxine (fT4) within the reference range. Associations between TSH and fT4 and metabolic profile were investigated in a two-stage manner: associations between TSH and fT4 and the full panel of 161 metabolomic markers were first assessed hypothesis-free, then directional consistency was assessed through Mendelian randomization, another metabolic profile platform, and in individuals with biochemically defined thyroid dysfunction. RESULTS: Circulating TSH was associated with 52/161 metabolomic markers, and fT4 levels were associated with 21/161 metabolomic markers among 9432 euthyroid individuals (median age varied from 23.0 to 75.4 years, 54.5% women). Positive associations between circulating TSH levels and concentrations of very low-density lipoprotein subclasses and components, triglycerides, and triglyceride content of lipoproteins were directionally consistent across the multivariable regression, MR, metabolomic platforms, and for individuals with hypo- and hyperthyroidism. Associations with fT4 levels inversely reflected those observed with TSH. Among 91,810 CAD cases and 656,091 controls of European ancestry, per 1-SD increase of genetically determined TSH concentration risk of CAD increased slightly, but not significantly, with an OR of 1.03 (95% CI 0.99-1.07; p value 0.16), whereas higher genetically determined fT4 levels were not associated with CAD risk (OR 1.00 per SD increase of fT4; 95% CI 0.96-1.04; p value 0.59). CONCLUSIONS: Lower thyroid status leads to an unfavorable lipid profile and a somewhat increased cardiovascular disease risk.

Investigating the relationships between unfavourable habitual sleep and metabolomic traits: evidence from multi-cohort multivariable regression and Mendelian randomization analyses.

BACKGROUND: Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease. METHODS: We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions. RESULTS: We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (- 0.08 standard deviation (SD)[95% confidence interval (CI) - 0.12, - 0.03] in AMV and - 0.03SD [- 0.07, - 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (- 0.04SD [- 0.08, 0.00] in AMV and - 0.05SD [- 0.09, - 0.02] in MR), and lower phospholipids in very large HDL particles (- 0.04SD [- 0.08, 0.002] in AMV and - 0.05SD [- 0.08, - 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures. CONCLUSIONS: Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.

Differential insulin sensitivity of NMR-based metabolomic measures in a two-step hyperinsulinemic euglycemic clamp study.

BACKGROUND: Insulin is the key regulator of glucose metabolism, but it is difficult to dissect direct insulin from glucose-induced effects. We aimed to investigate the effects of hyperinsulemia on metabolomic measures under euglycemic conditions in nondiabetic participants. METHODS: We assessed concentrations of 151 metabolomic measures throughout a two-step hyperinsulinemic euglycemic clamp procedure. We included 24 participants (50% women, mean age = 62 [s.d. = 4.2] years) and metabolomic measures were assessed under baseline, low-dose (10 mU/m2/min) and high-dose (40 mU/m2/min) insulin conditions. The effects of low- and high-dose insulin infusion on metabolomic measures were analyzed using linear mixed-effect models for repeated measures. RESULTS: After low-dose insulin infusion, 90 metabolomic measures changed in concentration (p < 1.34e-4), among which glycerol (beta [Confidence Interval] = - 1.41 [- 1.54, - 1.27] s.d., p = 1.28e-95) and three-hydroxybutyrate (- 1.22 [- 1.36, - 1.07] s.d., p = 1.44e-61) showed largest effect sizes. After high-dose insulin infusion, 121 metabolomic measures changed in concentration, among which branched-chain amino acids showed the largest additional decrease compared with low-dose insulin infusion (e.g., Leucine, - 1.78 [- 1.88, - 1.69] s.d., P = 2.7e-295). More specifically, after low- and high-dose insulin infusion, the distribution of the lipoproteins shifted towards more LDL-sized particles with decreased mean diameters. CONCLUSION: Metabolomic measures are differentially insulin sensitive and may thus be differentially affected by the development of insulin resistance. Moreover, our data suggests insulin directly affects metabolomic measures previously associated with increased cardiovascular disease risk.