Depressive Symptoms and Plasma Markers of Alzheimer's Disease and Neurodegeneration: A Coordinated Meta-Analysis of 8 Cohort Studies.

BACKGROUND: Depressive symptoms are associated with an increased risk of Alzheimer's disease (AD). There has been a recent emergence in plasma biomarkers for AD pathophysiology, such as amyloid-beta (Aß) and phosphorylated tau (p-tau), as well as for axonal damage (neurofilament light, NfL) and astrocytic activation (glial fibrillary acidic protein, GFAP). Hypothesizing that depressive symptoms may occur along the AD process, we investigated associations between plasma biomarkers of AD with depressive symptoms in individuals without dementia. METHODS: A two-stage meta-analysis was performed on 2 clinic-based and 6 population-based cohorts (N = 7210) as part of the Netherlands Consortium of Dementia Cohorts. Plasma markers (Aß42/40, p-tau181, NfL, and GFAP) were measured using Single Molecular Array (Simoa; Quanterix) assays. Depressive symptoms were measured with validated questionnaires. We estimated the cross-sectional association of each standardized plasma marker (determinants) with standardized depressive symptoms (outcome) using linear regressions, correcting for age, sex, education, and APOE ε4 allele presence, as well as subgrouping by sex and APOE ε4 allele. Effect estimates were entered into a random-effects meta-analysis. RESULTS: Mean age of participants was 71 years. The prevalence of clinically relevant depressive symptoms ranged from 1% to 22%. None of the plasma markers were associated with depressive symptoms in the meta-analyses. However, NfL was associated with depressive symptoms only in APOE ε4 carriers (ß 0.11; 95% CI: 0.05-0.17). CONCLUSIONS: Late-life depressive symptoms did not show an association to plasma biomarkers of AD pathology. However, in APOE ε4 allele carriers, a more profound role of neurodegeneration was suggested with depressive symptoms.

Drug Utilisation Patterns of Alternatives to Ranitidine-Containing Medicines in Patients Treated with Ranitidine: A Network Analysis of Data from Six European National Databases.

INTRODUCTION: Ranitidine, a histamine H2-receptor antagonist (H2RA), is indicated in the management of gastric acid-related disorders. In 2020, the European Medicines Agency (EMA) recommended suspension of all ranitidine-containing medicines in the European Union (EU) due to the presence of N-nitrosodimethylamine (NDMA) impurities, which were considered to be carcinogenic. The aim of this study was to investigate the impact of regulatory intervention on use patterns of ranitidine-containing medicines and their therapeutic alternatives. OBJECTIVES: The aim was to study drug utilisation patterns of ranitidine and report discernible trends in treatment discontinuation and switching to alternative medications. METHODS: This retrospective, population-based cohort study was conducted using primary care records from six European countries between 2017 and 2023. To explore drug utilisation patterns, we calculated (1) incident use of ranitidine, other H2RAs, and other alternative drugs for the treatment of gastric ulcer and/or gastric bleeding; (2) ranitidine discontinuation; and (3) switching from ranitidine to alternative drugs (H2RAs, proton-pump inhibitors [PPIs], and other medicinal products for acid-related disorders). RESULTS: During the study period, 385,273 new ranitidine users were observed, with most users being female and aged 18-74 years. Ranitidine was the most commonly prescribed H2RA in the pre-referral period (September 2017-August 2019), with incidence rates between 0.8 and 9.0/1000 person years (PY). A steep decline to 0.3-3.8/1000 PY was observed in the referral period (September 2019-March 2020), eventually dropping to 0.0-0.4/1000 PY in the post-referral period (April 2020-March 2022). Switching from ranitidine to alternative drugs increased in the post-referral period, with the majority of patients switching to PPIs. Discontinuation of ranitidine use ranged from 270 to 380/1000 users in 2017 and decreased over time. CONCLUSIONS: Ranitidine was commonly used prior to referral, but it was subsequently discontinued and replaced primarily with PPIs.

Risk of cardiovascular events after an exacerbation of chronic obstructive pulmonary disease: results from the EXACOS-CV cohort study using the PHARMO Data Network in the Netherlands.

BACKGROUND: People living with chronic obstructive pulmonary disease (COPD) have an increased risk of experiencing cardiovascular (CV) events, particularly after an exacerbation. Such CV burden is not yet known for incident COPD patients. We examined the risk of severe CV events in incident COPD patients in periods following either moderate and/or severe exacerbations. METHODS: Persons aged ≥ 40 years with an incident COPD diagnosis from the PHARMO Data Network were included. Exposed time periods included 1-7, 8-14, 15-30, 31-180 and 181-365 days following an exacerbation. Moderate exacerbations were defined as those managed in outpatient settings; severe exacerbations as those requiring hospitalisation. The outcome was a composite of time to first severe CV event (acute coronary syndrome, heart failure decompensation, cerebral ischaemia, or arrhythmia) or death. Hazard ratios (HR) were estimated for association between each exposed period and outcome. RESULTS: 8020 patients with newly diagnosed COPD were identified. 2234 patients (28%) had ≥ 1 exacerbation, 631 patients (8%) had a non-fatal CV event, and 461 patients (5%) died during a median follow-up of 36 months. The risk of experiencing the composite outcome was increased following a moderate/severe exacerbation as compared to time periods of stable disease [range of HR: from 15.3 (95% confidence interval 11.8-20.0) in days 1-7 to 1.3 (1.0-1.8) in days 181-365]. After a moderate exacerbation, the risk was increased over the first 180 days [HR 2.5 (1.3-4.8) in days 1-7 to 1.6 (1.3-2.1) in days 31-180]. After a severe exacerbation, the risk increased substantially and remained higher over the year following the exacerbation [HR 48.6 (36.9-64.0) in days 1-7 down to 1.6 (1.0-2.6) in days 181-365]. Increase in risk concerned all categories of severe CV events. CONCLUSIONS: Among incident COPD patients, we observed a substantial risk increase of severe CV events or all-cause death following either a moderate or severe exacerbation of COPD. Increase in risk was highest in the initial period following an exacerbation. These findings highlight the significant cardiopulmonary burden among people living with COPD even with a new diagnosis.

Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci.

Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10-8) and suggestive (p < 1 × 10-6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.

Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk.

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. 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.

Metabolomic profiles predict individual multidisease outcomes.

Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.

Gene-mapping study of extremes of cerebral small vessel disease reveals TRIM47 as a strong candidate.

Cerebral small vessel disease is a leading cause of stroke and a major contributor to cognitive decline and dementia, but our understanding of specific genes underlying the cause of sporadic cerebral small vessel disease is limited. We report a genome-wide association study and a whole-exome association study on a composite extreme phenotype of cerebral small vessel disease derived from its most common MRI features: white matter hyperintensities and lacunes. Seventeen population-based cohorts of older persons with MRI measurements and genome-wide genotyping (n = 41 326), whole-exome sequencing (n = 15 965), or exome chip (n = 5249) data contributed 13 776 and 7079 extreme small vessel disease samples for the genome-wide association study and whole-exome association study, respectively. The genome-wide association study identified significant association of common variants in 11 loci with extreme small vessel disease, of which the chr12q24.11 locus was not previously reported to be associated with any MRI marker of cerebral small vessel disease. The whole-exome association study identified significant associations of extreme small vessel disease with common variants in the 5' UTR region of EFEMP1 (chr2p16.1) and one probably damaging common missense variant in TRIM47 (chr17q25.1). Mendelian randomization supports the causal association of extensive small vessel disease severity with increased risk of stroke and Alzheimer's disease. Combined evidence from summary-based Mendelian randomization studies and profiling of human loss-of-function allele carriers showed an inverse relation between TRIM47 expression in the brain and blood vessels and extensive small vessel disease severity. We observed significant enrichment of Trim47 in isolated brain vessel preparations compared to total brain fraction in mice, in line with the literature showing Trim47 enrichment in brain endothelial cells at single cell level. Functional evaluation of TRIM47 by small interfering RNAs-mediated knockdown in human brain endothelial cells showed increased endothelial permeability, an important hallmark of cerebral small vessel disease pathology. Overall, our comprehensive gene-mapping study and preliminary functional evaluation suggests a putative role of TRIM47 in the pathophysiology of cerebral small vessel disease, making it an important candidate for extensive in vivo explorations and future translational work.

Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits.

Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from 5 ancestry groups. In the combined meta-analyses of Stages 1 and 2, we identified 59 loci (p value <5e-8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A, PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5, CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations.

Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure.

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups in two stages using 2 degree of freedom (df) joint test followed by 1df test of interaction effects. Primary multi-ancestry analysis in 62,969 individuals in stage 1 identified three novel gene by sleep interactions that were replicated in an additional 59,296 individuals in stage 2 (stage 1 + 2 Pjoint < 5 × 10-8), including rs7955964 (FIGNL2/ANKRD33) that increases BP among long sleepers, and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) that increase BP among short sleepers (Pint < 5 × 10-8). Secondary ancestry-specific analysis identified another novel gene by long sleep interaction at rs111887471 (TRPC3/KIAA1109) in individuals of African ancestry (Pint = 2 × 10-6). Combined stage 1 and 2 analyses additionally identified significant gene by long sleep interactions at 10 loci including MKLN1 and RGL3/ELAVL3 previously associated with BP, and significant gene by short sleep interactions at 10 loci including C2orf43 previously associated with BP (Pint < 10-3). 2df test also identified novel loci for BP after modeling sleep that has known functions in sleep-wake regulation, nervous and cardiometabolic systems. This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.

Gene-educational attainment interactions in a multi-ancestry genome-wide meta-analysis identify novel blood pressure loci.

Educational attainment is widely used as a surrogate for socioeconomic status (SES). Low SES is a risk factor for hypertension and high blood pressure (BP). To identify novel BP loci, we performed multi-ancestry meta-analyses accounting for gene-educational attainment interactions using two variables, "Some College" (yes/no) and "Graduated College" (yes/no). Interactions were evaluated using both a 1 degree of freedom (DF) interaction term and a 2DF joint test of genetic and interaction effects. Analyses were performed for systolic BP, diastolic BP, mean arterial pressure, and pulse pressure. We pursued genome-wide interrogation in Stage 1 studies (N = 117 438) and follow-up on promising variants in Stage 2 studies (N = 293 787) in five ancestry groups. Through combined meta-analyses of Stages 1 and 2, we identified 84 known and 18 novel BP loci at genome-wide significance level (P < 5 × 10-8). Two novel loci were identified based on the 1DF test of interaction with educational attainment, while the remaining 16 loci were identified through the 2DF joint test of genetic and interaction effects. Ten novel loci were identified in individuals of African ancestry. Several novel loci show strong biological plausibility since they involve physiologic systems implicated in BP regulation. They include genes involved in the central nervous system-adrenal signaling axis (ZDHHC17, CADPS, PIK3C2G), vascular structure and function (GNB3, CDON), and renal function (HAS2 and HAS2-AS1, SLIT3). Collectively, these findings suggest a role of educational attainment or SES in further dissection of the genetic architecture of BP.

Microbiomics, Metabolomics, Predicted Metagenomics, and Hepatic Steatosis in a Population-Based Study of 1,355 Adults.

BACKGROUND AND AIMS: Previous small studies have appraised the gut microbiome (GM) in steatosis, but large-scale studies are lacking. We studied the association of the GM diversity and composition, plasma metabolites, predicted functional metagenomics, and steatosis. APPROACH AND RESULTS: This is a cross-sectional analysis of the prospective population-based Rotterdam Study. We used 16S ribosomal RNA gene sequencing and determined taxonomy using the SILVA reference database. Alpha diversity and beta diversity were calculated using the Shannon diversity index and Bray-Curtis dissimilarities. Differences were tested across steatosis using permutational multivariate analysis of variance. Hepatic steatosis was diagnosed by ultrasonography. We subsequently selected genera using regularized regression. The functional metagenome was predicted based on the GM using Kyoto Encyclopedia of Genes and Genomes pathways. Serum metabolomics were assessed using high-throughput proton nuclear magnetic resonance. All analyses were adjusted for age, sex, body mass index, alcohol, diet, and proton-pump inhibitors. We included 1,355 participants, of whom 472 had steatosis. Alpha diversity was lower in steatosis (P = 1.1∙10-9 ), and beta diversity varied across steatosis strata (P = 0.001). Lasso selected 37 genera of which three remained significantly associated after adjustment (Coprococcus3: ß = -65; Ruminococcus Gauvreauiigroup: ß = 62; and Ruminococcus Gnavusgroup: ß = 45, Q-value = 0.037). Predicted metagenome analyses revealed that pathways of secondary bile-acid synthesis and biotin metabolism were present, and D-alanine metabolism was absent in steatosis. Metabolic profiles showed positive associations for aromatic and branched chain amino acids and glycoprotein acetyls with steatosis and R. Gnavusgroup, whereas these metabolites were inversely associated with alpha diversity and Coprococcus3. CONCLUSIONS: We confirmed, on a large-scale, the lower microbial diversity and association of Coprococcus and Ruminococcus Gnavus with steatosis. We additionally showed that steatosis and alpha diversity share opposite metabolic profiles.

Circulating metabolites are associated with brain atrophy and white matter hyperintensities.

INTRODUCTION: Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH). METHODS: We studied associations of 143 plasma-based metabolites with MRI measures of brain and hippocampal atrophy and WMH in three independent cohorts (n = 3962). We meta-analyzed the results of linear regression analyses to determine the association of metabolites with MRI measures. RESULTS: Higher glucose levels and lower levels of three small high density lipoprotein (HDL) particles were associated with brain atrophy. Higher glucose levels were associated with WMH. DISCUSSION: Glucose levels were associated with brain atrophy and WMH, and small HDL particle levels were associated with brain atrophy. Circulating metabolites may aid in developing future intervention trials.

Association of circulating metabolites in plasma or serum and risk of stroke: Meta-analysis from seven prospective cohorts.

OBJECTIVE: To conduct a comprehensive analysis of circulating metabolites and incident stroke in large prospective population-based settings. METHODS: We investigated the association of metabolites with risk of stroke in seven prospective cohort studies including 1,791 incident stroke events among 38,797 participants in whom circulating metabolites were measured by Nuclear Magnetic Resonance (1H-NMR) technology. The relationship between metabolites and stroke was assessed using Cox proportional hazards regression models. The analyses were performed considering all incident stroke events and ischemic and hemorrhagic events separately. RESULTS: The analyses revealed ten significant metabolite associations. Amino acid histidine (hazard ratio (HR) per standard deviation (SD) = 0.90, 95% confidence interval (CI): 0.85, 0.94; P = 4.45×10-5), glycolysis-related metabolite pyruvate (HR per SD = 1.09, 95% CI: 1.04, 1.14; P = 7.45×10-4), acute phase reaction marker glycoprotein acetyls (HR per SD = 1.09, 95% CI: 1.03, 1.15; P = 1.27×10-3), cholesterol in high-density lipoprotein (HDL) 2 and several other lipoprotein particles were associated with risk of stroke. When focusing on incident ischemic stroke, a significant association was observed with phenylalanine (HR per SD = 1.12, 95% CI: 1.05, 1.19; P = 4.13×10-4) and total and free cholesterol in large HDL particles. CONCLUSIONS: We found association of amino acids, glycolysis-related metabolites, acute phase reaction markers, and several lipoprotein subfractions with the risk of stroke. These findings support the potential of metabolomics to provide new insights into the metabolic changes preceding stroke.

The Prognostic Value of Metabolic Profiling in Older Patients With a Proximal Femoral Fracture.

INTRODUCTION: High mortality rates of approximately 20% within 1 year after treatment are observed for patients with proximal femoral fractures. This preliminary study explores the prognostic value of a previously constructed mortality risk score based on a set of 14 metabolites for the survival and functional recovery in patients with proximal femoral fractures. MATERIALS AND METHODS: A prospective observational cohort study was conducted including patients admitted with a proximal femoral fracture. The primary outcome was patient survival, and the recovery of independence in activities of daily living was included as a secondary outcome. The mortality risk score was constructed for each patient and its prognostic value was tested for the whole population. RESULTS: Data was available form 136 patients. The mean age of all patients was 82.1 years, with a median follow-up of 6 months. Within this period, 19.0% of all patients died and 51.1% recovered to their prefracture level of independence. The mortality score was significantly associated with mortality (HR, 2.74; 95% CI, 1.61-4.66; P < 0.001), but showed only a fair prediction accuracy (AUC = 0.68) and a borderline significant comparison of the mortality score tertile groups in survival analyses (P = 0.049). No decisive associations were found in any of the analyses for the functional recovery of patients. DISCUSSION: These findings support the previously determined prognostic value of the mortality risk score. However, the independent prognostic value when adjusted for potential confounding factors is yet to be assessed. Also, a risk score constructed for this specific patient population might achieve higher accuracies for the prediction of survival and functional recovery. CONCLUSIONS: A modest prediction accuracy was observed for the mortality risk score in this population. More elaborate studies are needed to validate these findings and develop a tailored model for clinical purposes in this patient population.

Association of common genetic variants with brain microbleeds: A genome-wide association study.

OBJECTIVE: To identify common genetic variants associated with the presence of brain microbleeds (BMBs). METHODS: We 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 ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs. RESULTS: BMBs 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 × 10-10). APOE ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19-1.50]; p = 1.0 × 10-6) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86-1.25]; p = 0.68). APOE ε2 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. CONCLUSIONS: Genetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE ε4 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.

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities.

BACKGROUND AND PURPOSE: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. METHODS: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. RESULTS: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (NBEAL), 10q23.1 (TSPAN14/FAM231A), and 10q24.33 (SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 (NOS3) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. CONCLUSIONS: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

Exome Sequencing Analysis Identifies Rare Variants in ATM and RPL8 That Are Associated With Shorter Telomere Length.

Telomeres are important for maintaining genomic stability. Telomere length has been associated with aging, disease, and mortality and is highly heritable (∼82%). In this study, we aimed to identify rare genetic variants associated with telomere length using whole-exome sequence data. We studied 1,303 participants of the Erasmus Rucphen Family (ERF) study, 1,259 of the Rotterdam Study (RS), and 674 of the British Heart Foundation Family Heart Study (BHF-FHS). We conducted two analyses, first we analyzed the family-based ERF study and used the RS and BHF-FHS for replication. Second, we combined the summary data of the three studies in a meta-analysis. Telomere length was measured by quantitative polymerase chain reaction in blood. We identified nine rare variants significantly associated with telomere length (p-value < 1.42 × 10-7, minor allele frequency of 0.2-0.5%) in the ERF study. Eight of these variants (in C11orf65, ACAT1, NPAT, ATM, KDELC2, and EXPH5) were located on chromosome 11q22.3 that contains ATM, a gene involved in telomere maintenance. Although we were unable to replicate the variants in the RS and BHF-FHS (p-value ≥ 0.21), segregation analysis showed that all variants segregate with shorter telomere length in a family. In the meta-analysis of all studies, a nominally significant association with LTL was observed with a rare variant in RPL8 (p-value = 1.48 × 10-6), which has previously been associated with age. Additionally, a novel rare variant in the known RTEL1 locus showed suggestive evidence for association (p-value = 1.18 × 10-4) with LTL. To conclude, we identified novel rare variants associated with telomere length. Larger samples size are needed to confirm these findings and to identify additional variants.

Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose.

Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas.

Progress 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/).

Relationship between gut microbiota and circulating metabolites in population-based cohorts.

Gut microbiota has been implicated in major diseases affecting the human population and has also been linked to triglycerides and high-density lipoprotein levels in the circulation. Recent development in metabolomics allows classifying the lipoprotein particles into more details. Here, we examine the impact of gut microbiota on circulating metabolites measured by Nuclear Magnetic Resonance technology in 2309 individuals from the Rotterdam Study and the LifeLines-DEEP cohort. We assess the relationship between gut microbiota and metabolites by linear regression analysis while adjusting for age, sex, body-mass index, technical covariates, medication use, and multiple testing. We report an association of 32 microbial families and genera with very-low-density and high-density subfractions, serum lipid measures, glycolysis-related metabolites, ketone bodies, amino acids, and acute-phase reaction markers. These observations provide insights into the role of microbiota in host metabolism and support the potential of gut microbiota as a target for therapeutic and preventive interventions.