Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While... Show moreAging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the “AccelerAge” framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual’s survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual’s aging status. Show less
Andreu-Sanchez, S.; Ahmad, S.; Kurilshikov, A.; Beekman, M.; Ghanbari, M.; Faassen, M. van; ... ; Vojinovic, D. 2024
Trimethylamine N-oxide (TMAO) is a circulating microbiome-derived metabolite implicated in the development of atherosclerosis and cardiovascular disease (CVD). We investigated whether plasma levels... Show moreTrimethylamine N-oxide (TMAO) is a circulating microbiome-derived metabolite implicated in the development of atherosclerosis and cardiovascular disease (CVD). We investigated whether plasma levels of TMAO, its precursors (betaine, carnitine, deoxycarnitine, choline), and TMAO-to-precursor ratios are associated with clinical outcomes, including CVD and mortality. This was followed by an in-depth analysis of their genetic, gut microbial, and dietary determinants. The analyses were conducted in five Dutch prospective cohort studies including 7834 individuals. To further investigate association results, Mendelian Randomization (MR) was also explored. We found only plasma choline levels (hazard ratio [HR] 1.17, [95% CI 1.07; 1.28]) and not TMAO to be associated with CVD risk. Our association analyses uncovered 10 genome-wide significant loci, including novel genomic regions for betaine (6p21.1, 6q25.3), choline (2q34, 5q31.1), and deoxycarnitine (10q21.2, 11p14.2) comprising several metabolic gene associations, for example, CPS1 or PEMT. Furthermore, our analyses uncovered 68 gut microbiota associations, mainly related to TMAO-to-precursors ratios and the Ruminococcaceae family, and 16 associations of food groups and metabolites including fish-TMAO, meat-carnitine, and plant-based food-betaine associations. No significant association was identified by the MR approach. Our analyses provide novel insights into the TMAO pathway, its determinants, and pathophysiological impact on the general population. Show less
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1,2,3,4,5,6,7. This detailed knowledge of the genetic... Show moreGenome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1,2,3,4,5,6,7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8,9,10,11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases. Show less
Bizzarri, D.; Reinders, M.J.T.; Beekman, M.; Slagboom, P.E.; Akker, E.B. van den; BbmriNl 2023
H-1-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of H-1-NMR metabolomics, has recently updated the quantification strategy to further... Show moreH-1-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of H-1-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (similar to 28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean rho > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (rho < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued. Show less
Kuiper, L.M.; Polinder-Bos, H.A.; Bizzarri, D.; Vojinovic, D.; Vallerga, C.L.; Beekman, M.; ... ; Meurs, J.B.J. van 2023
Biological age captures a person’s age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers... Show moreBiological age captures a person’s age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment. Show less
Berg, N. van den; Rodríguez-Girondo, M.; Dijk, I.K. van; Slagboom, P.E.; Beekman, M. 2023
Globally, the lifespan of populations increases but the healthspan is lagging behind. Previous research showed that survival into extreme ages (longevity) clusters in families as illustrated by... Show moreGlobally, the lifespan of populations increases but the healthspan is lagging behind. Previous research showed that survival into extreme ages (longevity) clusters in families as illustrated by the increasing lifespan of study participants with each additional long-lived family member. Here we investigate whether the healthspan in such families follows a similar quantitative pattern using three-generational data from two databases, LLS (Netherlands), and SEDD (Sweden). We study healthspan in 2143 families containing index persons with 26 follow-up years and two ancestral generations, comprising 17,539 persons. Our results provide strong evidence that an increasing number of long-lived ancestors associates with up to a decade of healthspan extension. Further evidence indicates that members of long-lived families have a delayed onset of medication use, multimorbidity and, in mid-life, healthier metabolomic profiles than their partners. We conclude that both lifespan and healthspan are quantitatively linked to ancestral longevity, making family data invaluable to identify protective mechanisms of multimorbidity. Show less
Bogaards, F.A.; Gehrmann, T.; Beekman, M.; Akker, E. ben van den; Rest, O. van de; Hangelbroek, R.W.J.; ... ; Slagboom, P.E. 2022
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... Show moreThe 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 H-1-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. Show less
Morwani Mangnani, J.; Giannos, P.; Belzer, C.; Beekman, M.; Slagboom, P.E.; Prokopidis, K. 2022
Major hallmarks of functional loss, loss of metabolic and musculoskeletal health and (multi)morbidity with aging are associated with sleep disturbances. With poor sleep shifts in gut microbial... Show moreMajor hallmarks of functional loss, loss of metabolic and musculoskeletal health and (multi)morbidity with aging are associated with sleep disturbances. With poor sleep shifts in gut microbial composition commonly manifest, which could mediate the pro-inflammatory state between sleep disturbances and sarcopenia. This systematic review presents the recent evidence on how sleep disturbances throughout the lifespan associate with and contribute to gut microbial composition changes, proposing a mechanism to understand the etiology of sarcopenia through sleep disturbances. The relationship between disturbed sleep and clinically relevant gut microbiota composition on health aspects of aging is discussed. A search was performed in PubMed, Cochrane Library, Scopus, Web of Science using keywords including (microbio* OR microflora) AND (sleep OR sleep disorder). Six cross-sectional population-based studies and five experimental clinical trials investigating healthy individuals with ages ranging from 4 to 71 were included. The cross-sectional studies reported similarities in associations with sleep disturbance and gut microbial diversity. In older adults, shorter sleep duration is associated with an increase in pro-inflammatory bacteria whereas increasing sleep quality is positively associated with an increase of beneficial Verrucomicrobia and Lentisphaerae phyla. In young adults, the effect of sleep disruption on gut microbiome composition, specifically the ratio of beneficial Firmicutes over Bacteroidetes phyla, remains contradictory and unclear. The findings of this review warrant further research in the modulation of the gut microbiome linking poor sleep with muscle-catabolic consequences throughout the lifespan. Show less
Bizzarri, D.; Reinders, M.J.T.; Beekman, M.; Slagboom, P.E.; Akker, E.B. van den 2022
Motivation: H-1-NMR metabolomics is rapidly becoming a standard resource in large epidemiological studies to acquire metabolic profiles in large numbers of samples in a relatively low-priced and... Show moreMotivation: H-1-NMR metabolomics is rapidly becoming a standard resource in large epidemiological studies to acquire metabolic profiles in large numbers of samples in a relatively low-priced and standardized manner. Concomitantly, metabolomics-based models are increasingly developed that capture disease risk or clinical risk factors. These developments raise the need for user-friendly toolbox to inspect new H-1-NMR metabolomics data and project a wide array of previously established risk models. Results: We present MiMIR (Metabolomics-based Models for Imputing Risk), a graphical user interface that provides an intuitive framework for ad hoc statistical analysis of Nightingale Health's H-1-NMR metabolomics data and allows for the projection and calibration of 24 pre-trained metabolomics-based models, without any pre-required programming knowledge. Availability and implementation: The R-shiny package is available in CRAN or downloadable at , together with an extensive user manual (also available as Supplementary Documents to the article). Show less
Spek, A. van der; Karamujic-Comic, H.; Pool, R.; Bot, M.; Beekman, M.; Garmaeva, S.; ... ; BBMRI Metabolomics Consortium 2022
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... Show moreTelomeres 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 F1owFISH. 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 P me t a = 6.5 x 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 x 10(-4)). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (P = 1.9 x 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. Show less
Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older... Show moreThrough the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being. Show less
Paraschiakos, S.; Sa, C.R. de; Okai, J.; Slagboom, P.E.; Beekman, M.; Knobbe, A. 2022
Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older... Show moreThrough the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being. Show less
Bizzarri, D.; Reinders, M.J.T.; Beekman, M.; Slagboom, P.E.; BBMRI-NL; Akker, E.B. van den 2022
Background Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples,... Show moreBackground Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. `metabolomics', is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies.Methods To this end, we have employed similar to 26,000 H-1-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC(5-Fold CV) = 0.94) and lipid medication usage (AUC(5-Fold CV) = 0.90).Findings Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants.Interpretation To conclude, we provide H-1-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved. Copyright (C) 2021 The Author(s). Published by Elsevier B.V. Show less
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... Show moreBackground 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. Show less
DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >... Show moreDNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs.Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated. Show less
Wang, W.Y.; Dijk, K.W. van; Wijsman, C.A.; Rozing, M.P.; Mooijaart, S.P.; Beekman, M.; ... ; Heemst, D. van 2021
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... Show moreBackground 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/m(2)/min) and high-dose (40 mU/m(2)/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. Show less
Bos, M.M.; Goulding, N.J.; Lee, M.A.; Hofman, A.; Bot, M.; Pool, R.; ... ; Lawlor, D.A. 2021
Background Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase... Show moreBackground 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. Show less
Ahluwalia, T.S.; Prins, B.P.; Abdollahi, M.; Armstrong, N.J.; Aslibekyan, S.; Bain, L.; ... ; CHARGE Inflammation Working Grp 2021
Interleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been... Show moreInterleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67428 (n(discovery)=52654 and n(replication)=14774) individuals of European ancestry. The inverse variance fixed effects based discovery meta-analysis, followed by replication led to the identification of two independent loci, IL1F10/IL1RN rs6734238 on chromosome (Chr) 2q14, (P-combined=1.8x10(-11)), HLA-DRB1/DRB5 rs660895 on Chr6p21 (P-combined=1.5x10(-10)) in the combined meta-analyses of all samples. We also replicated the IL6R rs4537545 locus on Chr1q21 (P-combined=1.2x10(-122)). Our study identifies novel loci for circulating IL-6 levels uncovering new immunological and inflammatory pathways that may influence IL-6 pathobiology. Show less
Rodriguez-Girondo, M.; Berg, N. van den; Hof, M.H.; Beekman, M.; Slagboom, E. 2021
Background: Although human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited... Show moreBackground: Although human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited success is the selection of participants. Studies generally include sporadically long-lived individuals, i.e. individuals with the longevity phenotype but without a genetic predisposition for longevity. The inclusion of these individuals causes phenotype heterogeneity which results in power reduction and bias. A way to avoid sporadically long-lived individuals and reduce sample heterogeneity is to include family history of longevity as selection criterion using a longevity family score. A main challenge when developing family scores are the large differences in family size, because of real differences in sibship sizes or because of missing data.Methods: We discussed the statistical properties of two existing longevity family scores: the Family Longevity Selection Score (FLoSS) and the Longevity Relatives Count (LRC) score and we evaluated their performance dealing with differential family size. We proposed a new longevity family score, the mLRC score, an extension of the LRC based on random effects modeling, which is robust for family size and missing values. The performance of the new mLRC as selection tool was evaluated in an intensive simulation study and illustrated in a large real dataset, the Historical Sample of the Netherlands (HSN).Results: Empirical scores such as the FLOSS and LRC cannot properly deal with differential family size and missing data. Our simulation study showed that mLRC is not affected by family size and provides more accurate selections of long-lived families. The analysis of 1105 sibships of the Historical Sample of the Netherlands showed that the selection of long-lived individuals based on the mLRC score predicts excess survival in the validation set better than the selection based on the LRC score .Conclusions: Model-based score systems such as the mLRC score help to reduce heterogeneity in the selection of long-lived families. The power of future studies into the genetics of longevity can likely be improved and their bias reduced, by selecting long-lived cases using the mLRC. Show less
ObjectiveTo identify common genetic variants associated with the presence of brain microbleeds (BMBs).MethodsWe performed genome-wide association studies in 11 population-based cohort studies and 3... Show moreObjectiveTo identify common genetic variants associated with the presence of brain microbleeds (BMBs).MethodsWe performed genome-wide association studies in 11 population-based cohort studies and 3 case-control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of APOE epsilon 2 and epsilon 4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.ResultsBMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the APOE region reached genome-wide significance for its association with BMB (lead single nucleotide polymorphism rs769449; odds ratio [OR](any BMB) [95% confidence interval (CI)] 1.33 [1.21-1.45]; p = 2.5 x 10(-10)). APOE epsilon 4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19-1.50]; p = 1.0 x 10(-6)) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86-1.25]; p = 0.68). APOE epsilon 2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.ConclusionsGenetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE epsilon 4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers. Show less
