Background Information on long-term alcohol consumption is relevant for medical and public health research, disease therapy, and other areas. Recently, DNA methylation-based inference of alcohol... Show moreBackground Information on long-term alcohol consumption is relevant for medical and public health research, disease therapy, and other areas. Recently, DNA methylation-based inference of alcohol consumption from blood was reported with high accuracy, but these results were based on employing the same dataset for model training and testing, which can lead to accuracy overestimation. Moreover, only subsets of alcohol consumption categories were used, which makes it impossible to extrapolate such models to the general population. By using data from eight population-based European cohorts (N = 4677), we internally and externally validated the previously reported biomarkers and models for epigenetic inference of alcohol consumption from blood and developed new models comprising all data from all categories. Results By employing data from six European cohorts (N = 2883), we empirically tested the reproducibility of the previously suggested biomarkers and prediction models via ten-fold internal cross-validation. In contrast to previous findings, all seven models based on 144-CpGs yielded lower mean AUCs compared to the models with less CpGs. For instance, the 144-CpG heavy versus non-drinkers model gave an AUC of 0.78 +/- 0.06, while the 5 and 23 CpG models achieved 0.83 +/- 0.05, respectively. The transportability of the models was empirically tested via external validation in three independent European cohorts (N = 1794), revealing high AUC variance between datasets within models. For instance, the 144-CpG heavy versus non-drinkers model yielded AUCs ranging from 0.60 to 0.84 between datasets. The newly developed models that considered data from all categories showed low AUCs but gave low AUC variation in the external validation. For instance, the 144-CpG heavy and at-risk versus light and non-drinkers model achieved AUCs of 0.67 +/- 0.02 in the internal cross-validation and 0.61-0.66 in the external validation datasets. Conclusions The outcomes of our internal and external validation demonstrate that the previously reported prediction models suffer from both overfitting and accuracy overestimation. Our results show that the previously proposed biomarkers are not yet sufficient for accurate and robust inference of alcohol consumption from blood. Overall, our findings imply that DNA methylation prediction biomarkers and models need to be improved considerably before epigenetic inference of alcohol consumption from blood can be considered for practical applications. Show less
Ritter, R. de; Sep, S.J.S.; Kallen, C.J.H. van der; Greevenbroek, M.M.J. van; Jong, M. de; Vos, R.C.; ... ; Stehouwer, C.D.A. 2021
Background Women with type 2 diabetes are disproportionally affected by macrovascular complications; we here investigated whether this is also the case for microvascular complications and retinal... Show moreBackground Women with type 2 diabetes are disproportionally affected by macrovascular complications; we here investigated whether this is also the case for microvascular complications and retinal microvascular measures. Methods In a population-based cohort study of individuals aged 40-75 years (n = 3410; 49% women, 29% type 2 diabetes (oversampled by design)), we estimated sex-specific associations, and differences therein, of (pre)diabetes (reference: normal glucose metabolism), and of continuous measures of glycemia with microvascular complications and retinal measures (nephropathy, sensory neuropathy, and retinal arteriolar and venular diameters and dilatation). Sex differences were analyzed using regression models with interaction terms (i.e. sex-by- (pre)diabetes and sex-by-glycemia) and were adjusted for potential confounders. Results Men with type 2 diabetes (but not those with prediabetes) compared to men with normal glucose metabolism, (and men with higher levels of glycemia), had significantly higher prevalences of nephropathy (odds ratio: 1.58 95% CI (1.01;2.46)) and sensory neuropathy (odds ratio: 2.46 (1.67;3.63)), larger retinal arteriolar diameters (difference: 4.29 mu m (1.22;7.36)) and less retinal arteriolar dilatation (difference: - 0.74% (- 1.22; - 0.25)). In women, these associations were numerically in the same direction, but generally not statistically significant (odds ratios: 1.71 (0.90;3.25) and 1.22 (0.75;1.98); differences: 0.29 mu m (- 3.50;4.07) and: - 0.52% (- 1.11;0.08), respectively). Interaction analyses revealed no consistent pattern of sex differences in the associations of either prediabetes or type 2 diabetes or glycemia with microvascular complications or retinal measures. The prevalence of advanced-stage complications was too low for evaluation. Conclusions Our findings show that women with type 2 diabetes are not disproportionately affected by early microvascular complications. Show less
Jong, M. de; Peters, S.A.E.; Ritter, R. de; Kallen, C.J.H. van der; Sep, S.J.S.; Woodward, M.; ... ; Vos, R.C. 2021
Background Insight in sex disparities in the detection of cardiovascular risk factors and diabetes-related complications may improve diabetes care. The aim of this systematic review is to study... Show moreBackground Insight in sex disparities in the detection of cardiovascular risk factors and diabetes-related complications may improve diabetes care. The aim of this systematic review is to study whether sex disparities exist in the assessment of cardiovascular risk factors and screening for diabetes-related complications.MethodsPubMed was systematically searched up to April 2020, followed by manual reference screening and citations checks (snowballing) using Google Scholar. Observational studies were included if they reported on the assessment of cardiovascular risk factors (HbA1c, lipids, blood pressure, smoking status, or BMI) and/or screening for nephropathy, retinopathy, or performance of feet examinations, in men and women with diabetes separately. Studies adjusting their analyses for at least age, or when age was considered as a covariable but left out from the final analyses for various reasons (i.e. backward selection), were included for qualitative analyses. No meta-analyses were planned because substantial heterogeneity between studies was expected. A modified Newcastle-Ottawa Quality Assessment Scale for cohort studies was used to assess risk of bias.ResultsOverall, 81 studies were included. The majority of the included studies were from Europe or North America (84%).The number of individuals per study ranged from 200 to 3,135,019 and data were extracted from various data sources in a variety of settings. Screening rates varied considerably across studies. For example, screening rates for retinopathy ranged from 13% to 90%, with half the studies reporting screening rates less than 50%. Mixed findings were found regarding the presence, magnitude, and direction of sex disparities with regard to the assessment of cardiovascular risk factors and screening for diabetes-related complications, with some evidence suggesting that women, compared with men, may be more likely to receive retinopathy screening and less likely to receive foot exams.ConclusionOverall, no consistent pattern favoring men or women was found with regard to the assessment of cardiovascular risk factors and screening for diabetes-related complications, and screening rates can be improved for both sexes. Show less
Akker, E.B. van den; Trompet, S.; Wolf, J.J.H.B.; Beekman, M.; Suchiman, H.E.D.; Deelen, J.; ... ; Slagboom, P.E. 2020
BACKGROUND: The blood metabolome incorporates cues from the environment and the host's genetic background, potentially offering a holistic view of an individual's health status.METHODS: We have... Show moreBACKGROUND: The blood metabolome incorporates cues from the environment and the host's genetic background, potentially offering a holistic view of an individual's health status.METHODS: We have compiled a vast resource of proton nuclear magnetic resonance metabolomics and phenotypic data encompassing over 25 000 samples derived from 26 community and hospital-based cohorts.RESULTS: Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared with one's chronological age, confers an increased risk on future cardiovascular disease, mortality, and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bmri.nl/samples-images-data.CONCLUSIONS: In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardiometabolic health. Show less
Context: There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease.Objective: To investigate associations between plasma metabolites and kidney... Show moreContext: There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease.Objective: To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D).Design: 3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts.Main outcome measures: Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing.Results: In total, 125 metabolites were significantly associated (P-FDR = 1.5x10(-32) - 0.046; beta = -11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (P-FDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (P-FDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (P-Tyr = 0.003; P-HDLrelated < 0.05), but not after.Conclusions: This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation. Show less
Pool, R.; Hagenbeek, F.A.; Hendriks, A.M.; Dongen, J. van; Willemsen, G.; Geus, E. de; ... ; Duijn, C.M. van 2020
Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term 'metabolomics' refers to the comprehensive study of these molecules. The... Show moreMetabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term 'metabolomics' refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented. Show less
Pool, R.; Hagenbeek, F.A.; Hendriks, A.M.; Dongen, J. van; Willemsen, G.; Geus, E. de; ... ; BBMRI Metabol Consortium 2020
Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term 'metabolomics' refers to the comprehensive study of these molecules. The... Show moreMetabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term 'metabolomics' refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented. Show less
Hagenbeek, F.A.; Pool, R.; Dongen, J. van; Draisma, H.M.; Hottenga, J.J.; Willemsen, G.; ... ; Abdel Abdellaoui 2020
Correction to: Nature Communicationshttps://doi.org/10.1038/s41467-019-13770-6, published online 7 January 2020.The original version of the Supplementary Information associated with this Article... Show moreCorrection to: Nature Communicationshttps://doi.org/10.1038/s41467-019-13770-6, published online 7 January 2020.The original version of the Supplementary Information associated with this Article included an incorrect Supplementary Data 1 file, in which additional delimiters were included in the first column for a number of rows, resulting in column shifts for some of these rows. The HTML has been updated to include a corrected version of Supplementary Data 1; the original incorrect version of Supplementary Data 1 can be found as Supplementary Information associated with this Correction. Show less
Background Recent evidence indicates that insulin resistance (IR) in obesity may develop independently in different organs, representing different etiologies toward type 2 diabetes and other... Show moreBackground Recent evidence indicates that insulin resistance (IR) in obesity may develop independently in different organs, representing different etiologies toward type 2 diabetes and other cardiometabolic diseases. The aim of this study was to investigate whether IR in the liver and IR in skeletal muscle are associated with distinct metabolic profiles. Methods This study includes baseline data from 634 adults with overweight or obesity (BMI >= 27 kg/m(2)) (<= 65 years; 63% women) without diabetes of the European Diogenes Study. Hepatic insulin resistance index (HIRI) and muscle insulin sensitivity index (MISI), were derived from a five-point OGTT. At baseline 17 serum metabolites were identified and quantified by nuclear-magnetic-resonance spectroscopy. Linear mixed model analyses (adjusting for center, sex, body mass index (BMI), waist-to-hip ratio) were used to associate HIRI and MISI with these metabolites. In an independent sample of 540 participants without diabetes (BMI >= 27 kg/m(2); 40-65 years; 46% women) of the Maastricht Study, an observational prospective population-based cohort study, 11 plasma metabolites and a seven-point OGTT were available for validation. Results Both HIRI and MISI were associated with higher levels of valine, isoleucine, oxo-isovaleric acid, alanine, lactate, and triglycerides, and lower levels of glycine (all p < 0.05). HIRI was also associated with higher levels of leucine, hydroxyisobutyrate, tyrosine, proline, creatine, and n-acetyl and lower levels of acetoacetate and 3-OH-butyrate (all p < 0.05). Except for valine, these results were replicated for all available metabolites in the Maastricht Study. Conclusions In persons with obesity without diabetes, both liver and muscle IR show a circulating metabolic profile of elevated (branched-chain) amino acids, lactate, and triglycerides, and lower glycine levels, but only liver IR associates with lower ketone body levels and elevated ketogenic amino acids in circulation, suggestive of decreased ketogenesis. This knowledge might enhance developments of more targeted tissue-specific interventions to prevent progression to more severe disease stages. Show less
BACKGROUND: Depression has been associated with metabolic alterations, which adversely impact cardiometabolic health. Here, a comprehensive set of metabolic markers, predominantly lipids, was... Show moreBACKGROUND: Depression has been associated with metabolic alterations, which adversely impact cardiometabolic health. Here, a comprehensive set of metabolic markers, predominantly lipids, was compared between depressed and nondepressed persons.METHODS: Nine Dutch cohorts were included, comprising 10,145 control subjects and 5283 persons with depression, established with diagnostic interviews or questionnaires. A proton nuclear magnetic resonance metabolomics platform provided 230 metabolite measures: 51 lipids, fatty acids, and low-molecular-weight metabolites; 98 lipid composition and particle concentration measures of lipoprotein subclasses; and 81 lipid and fatty acids ratios. For each metabolite measure, logistic regression analyses adjusted for gender, age, smoking, fasting status, and lipid-modifying medication were performed within cohort, followed by random-effects meta-analyses.RESULTS: Of the 51 lipids, fatty acids, and low-molecular-weight metabolites, 21 were significantly related to depression (false discovery rate q < .05). Higher levels of apolipoprotein B, very-low-density lipoprotein cholesterol, triglycerides, diglycerides, total and monounsaturated fatty acids, fatty acid chain length, glycoprotein acetyls, tyrosine, and isoleucine and lower levels of high-density lipoprotein cholesterol, acetate, and apolipoprotein Al were associated with increased odds of depression. Analyses of lipid composition indicators confirmed a shift toward less high-density lipoprotein and more very-low-density lipoprotein and triglyceride particles in depression. Associations appeared generally consistent across gender, age, and body mass index strata and across cohorts with depressive diagnoses versus symptoms.CONCLUSIONS: This large-scale meta-analysis indicates a clear distinctive profile of circulating lipid metabolites associated with depression, potentially opening new prevention or treatment avenues for depression and its associated cardiometabolic comorbidity. Show less
Insights into individual differences in gene expression and its heritability (h(2)) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52... Show moreInsights into individual differences in gene expression and its heritability (h(2)) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52,844 genes measured in whole blood in the largest twin RNA-Seq sample to date (1497 individuals including 459 monozygotic twin pairs and 150 dizygotic twin pairs) from classical twin modeling and identity-by-state-based approaches. We estimated for each gene h(total)(2), composed of cis-heritability (h(cis)(2), the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h(res)(2), the residual variance explained by all other genome-wide variants). Mean h(total)(2) was 0.26, which was significantly higher than heritability estimates earlier found in a microarray-based study using largely overlapping (>60%) RNA samples (mean h(2) = 0.14, p = 6.15 x 10(-258)). Mean h(cis)(2) was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, rho = 0.76, p < 10(-308)) and with estimates from earlier RNA-Seq-based studies. Mean h(res)(2) was 0.20 and correlated with the beta of the corresponding trans-eQTL (rho = 0.04, p < 1.89 x 10(-3)) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 x 10(-15)), many other immune system pathways, and genes identified in genome-wide association studies for various traits including behavioral disorders and cancer. This study provides a thorough characterization of cis- and trans-h(2) estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies. Show less
Background & Aims Plasma soluble E-selectin (sE-selectin) is a frequently used biomarker of systemic endothelial dysfunction. The present study explored the relationship between nonalcoholic... Show moreBackground & Aims Plasma soluble E-selectin (sE-selectin) is a frequently used biomarker of systemic endothelial dysfunction. The present study explored the relationship between nonalcoholic fatty liver disease (NAFLD) and plasma sE-selectin levels. Methods Expression of E-selectin in liver, visceral adipose tissue (VAT) and muscle was studied in relation to plasma sE-selectin in severely obese individuals (n = 74). The course of hepatic E-selectin expression in relation to hepatic steatosis and inflammation was examined in C57BL/6J LDLR-/- mice on a Western-type diet. The relationship between biomarkers of NAFLD, that is, plasma aminotransferase (ALT) and NAFLD susceptibility genes (rs738409 [PNPLA3] and rs1260326 [GCKR]), and plasma sE-selectin was studied in the combined CODAM (n = 571) and Hoorn (n = 694) studies. Results E-selectin expression in liver, not VAT or muscle, was associated with plasma sE-selectin in severely obese individuals (beta = 0.26; 95% CI: 0.05-0.47). NAFLD severity was associated with hepatic E-selectin expression (P = .02) and plasma sE-selectin (P = .003). LDLR-/- mice on a Western-type diet displayed increased hepatic E-selectin expression that followed the same course as hepatic inflammation, but not steatosis. In the CODAM study, plasma ALT was associated with plasma sE-selectin, independent of potential confounders (beta = 0.25; 95% CI: 0.16-0.34). Both rs738409 and rs1260326 were associated with higher plasma sE-selectin in the combined CODAM and Hoorn studies (P = .01 and P = .004 respectively). Conclusions NAFLD and related markers are associated with higher expression of hepatic E-selectin and higher levels of plasma sE-selectin. Further studies are required to investigate the role of E-selectin in the pathogenesis of NAFLD and the applicability of sE-selectin as a plasma biomarker of NAFLD/NASH. Show less
Hagenbeek, F.A.; Pool, R.; Dongen, J. van; Draisma, H.H.M.; Hottenga, J.J.; Willemsen, G.; ... ; BBMRI Metabolomics Consortium 2020
Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability... Show moreMetabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify > 800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h(total)(2)), and the proportion of heritability captured by known metabolite loci (h(Metabolite-hits)(2)) for 309 lipids and 52 organic acids. Our study reveals significant differences in h(Metabolite-hits)(2) among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h(Metabolite-hits)(2) estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes. Show less
Ritter , R. de; Jong , M. de; Vos, R.C.; Kallen, C.J.H. van der; Sep, S.J.S.; Woodward, M.; ... ; Peters, S.A.E. 2020
Diabetes is a strong risk factor for vascular disease. There is compelling evidence that the relative risk of vascular disease associated with diabetes is substantially higher in women than men.... Show moreDiabetes is a strong risk factor for vascular disease. There is compelling evidence that the relative risk of vascular disease associated with diabetes is substantially higher in women than men. The mechanisms that explain the sex difference have not been identified. However, this excess risk could be due to certain underlying biological differences between women and men. In addition to other cardiometabolic pathways, sex differences in body anthropometry and patterns of storage of adipose tissue may be of particular importance in explaining the sex differences in the relative risk of diabetes-associated vascular diseases. Besides biological factors, differences in the uptake and provision of health care could also play a role in women's greater excess risk of diabetic vascular complications. In this review, we will discuss the current knowledge regarding sex differences in both biological factors, with a specific focus on sex differences adipose tissue, and in health care provided for the prevention, management, and treatment of diabetes and its vascular complications. While progress has been made towards understanding the underlying mechanisms of women's higher relative risk of diabetic vascular complications, many uncertainties remain. Future research to understanding these mechanisms could contribute to more awareness of the sex-specific risk factors and could eventually lead to more personalized diabetes care. This will ensure that women are not affected by diabetes to a greater extent and will help to diminish the burden in both women and men. Show less
Liu, J.; Lahousse, L.; Nivard, M.G.; Bot, M.; Chen, L.M.; Klinken, J.B. van; ... ; Duijn, C.M. van 2020
Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research... Show moreProgress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/). Show less
Liu, J.; Lahousse, L.; Nivard, M.G.; Bot, M.; Chen, L.M.; Klinken, J.B. van; ... ; Duijn, C.M. van 2020
Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research... Show moreProgress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/). Show less
Inferring a person's smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications.... Show moreInferring a person's smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications. However, a finite DNA methylation marker set and a validated statistical model based on a large dataset are not yet available. Employing 14 epigenome-wide association studies for marker discovery, and using data from six population-based cohorts (N = 3764) for model building, we identified 13 CpGs most suitable for inferring smoking versus non-smoking status from blood with a cumulative Area Under the Curve (AUC) of 0.901. Internal fivefold cross-validation yielded an average AUC of 0.897 +/- 0.137, while external model validation in an independent population-based cohort (N = 1608) achieved an AUC of 0.911. These 13 CpGs also provided accurate inference of current (average AUC(crossvalidation) 0.925 +/- 0.021, AUC(externalvalidation)0.914), former (0.766 +/- 0.023, 0.699) and never smoking (0.830 +/- 0.019, 0.781) status, allowed inferring pack-years in current smokers (10 pack-years 0.800 +/- 0.068, 0.796; 15 pack-years 0.767 +/- 0.102, 0.752) and inferring smoking cessation time in former smokers (5 years 0.774 +/- 0.024, 0.760; 10 years 0.766 +/- 0.033, 0.764; 15 years 0.767 +/- 0.020, 0.754). Model application to children revealed highly accurate inference of the true non- smoking status (6 years of age: accuracy 0.994, N = 355; 10 years: 0.994, N = 309), suggesting prenatal and passive smoking exposure having no impact on model applications in adults. The finite set of DNA methylation markers allow accurate inference of smoking habit, with comparable accuracy as plasma cotinine use, and smoking history from blood, which we envision becoming useful in epidemiology and public health research, and in medical and forensic applications. Show less
Objective To investigate whether adverse differences in levels of cardiovascular risk factors in women than men, already established when comparing individuals with and without diabetes, are also... Show moreObjective To investigate whether adverse differences in levels of cardiovascular risk factors in women than men, already established when comparing individuals with and without diabetes, are also present before type 2 diabetes onset.Research design and methods In a population-based cohort study of individuals aged 40-75 years (n=3410; 49% women, 29% type 2 diabetes (oversampled by design)), we estimated associations with cardiometabolic and lifestyle risk factors of (1) pre-diabetes and type 2 diabetes (reference category: normal glucose metabolism) and (2) among non-diabetic individuals, of continuous levels of hemoglobin A1c (HbA1c). Age-adjusted sex differences were analyzed using linear and logistic regression models with sex interaction terms.Results In pre-diabetes, adverse differences in cardiometabolic risk factors were greater in women than men for systolic blood pressure (difference, 3.02 mm Hg; 95% CI:-0.26 to 6.30), high-density lipoprotein (HDL) cholesterol (difference, -0.10 mmol/L; 95% CI: -0.18 to -0.02), total-to-HDL cholesterol ratio (difference, 0.22; 95% CI: -0.01 to 0.44), triglycerides (ratio: 1.11; 95% CI: 1.01 to 1.22), and inflammation markers Z-score (ratio: 1.18; 95% CI: 0.98 to 1.41). In type 2 diabetes, these sex differences were similar in direction, and of greater magnitude. Additionally, HbA1c among non-diabetic individuals was more strongly associated with several cardiometabolic risk factors in women than men: per one per cent point increase, systolic blood pressure (difference, 3.58 mm Hg; 95% CI: -0.03 to 7.19), diastolic blood pressure (difference, 2.10 mm Hg; 95% CI: -0.02 to 4.23), HDL cholesterol (difference, -0.09 mmol/L; 95% CI: -0.19 to 0.00), and low-density lipoprotein cholesterol (difference, 0.26 mmol/L; 95% CI: 0.05 to 0.47). With regard to lifestyle risk factors, no consistent pattern was observed.Conclusion Our results are consistent with the concept that the more adverse changes in cardiometabolic risk factors in women (than men) arise as a continuous process before the onset of type 2 diabetes. Show less
