Search Results (384)

Background and Objectives: Emerging evidence indicates that individuals exposed to adverse childhood experiences (ACEs) face elevated risks for various chronic illnesses. However, the association between ACEs and osteoporosis risk remains underexplored, particularly regarding potential modifications by genetic susceptibility. This prospective cohort study aims to examine the relationship of ACEs with incident osteoporosis and investigate interactions with polygenic risk score (PRS). Materials and Methods: This study analyzed 124,789 UK Biobank participants initially free of osteoporosis. Cumulative ACE burden (emotional neglect, emotional abuse, physical neglect, physical abuse, sexual abuse) was ascertained through validated questionnaires. Multivariable-adjusted Cox proportional hazards models assessed osteoporosis risk during a median follow-up of 12.8 years. Moderation analysis examined genetic susceptibility interactions using a standardized PRS incorporating osteoporosis-related SNPs. Results: Among 2474 incident osteoporosis cases, cumulative ACEs showed dose–response associations with osteoporosis risk (adjusted hazard ratio [HR]_{per one-unit increase} = 1.07, 95% confidence interval [CI] 1.04–1.11; high ACEs [≥3 types] vs. none: HR = 1.26, 1.10–1.43). Specifically, emotional neglect (HR = 1.14, 1.04–1.25), emotional abuse (HR = 1.14, 1.03–1.27), physical abuse (HR = 1.17, 1.05–1.30), and sexual abuse (HR = 1.15, 1.01–1.31) demonstrated comparable effect sizes. Sex-stratified analysis revealed stronger associations in women. Joint exposure to high ACEs/high PRS tripled osteoporosis risk (HR = 3.04, 2.46–3.76 vs. low ACEs/low PRS) although G × E interaction was nonsignificant (P-interaction = 0.10). Conclusions: These results suggest that ACEs conferred incremental osteoporosis risk independent of genetic predisposition. These findings support the inclusion of ACE screening in osteoporosis prevention strategies and highlight the need for targeted bone health interventions for youth exposed to ACEs. Full article

Brain morphological abnormalities are common in patients with neurodevelopmental disorders (NDDs) and other neuropsychiatric disorders, often reflecting abnormal brain development and function. Genetic studies have found common genetic factors in NDDs and other neuropsychiatric disorders, although the etiology of brain structural changes in these disorders remains poorly understood. In this study, we analyzed magnetic resonance imaging (MRI) and genetic data from more than 30K individuals from the UK Biobank to evaluate whether NDD-risk copy number variants (CNVs) are also associated with neuroanatomical changes in both patients and neurotypical individuals. We found that the size differences in brain regions such as corpus callosum and cerebellum were associated with the deletions of specific areas of the human genome, and that specific neuroanatomical changes confer a risk of neuropsychiatric disorders. Furthermore, we observed that gene sets located in these genomic regions were enriched for pathways crucial for brain development and for phenotypes commonly observed in patients with NDDs. These findings highlight the link between CNVs, brain structure abnormalities, and the shared pathophysiology of NDDs and other neuropsychiatric disorders, providing new insights into the underlying mechanisms of these disorders and the identification of potential biomarkers for better diagnosis. Full article

Objectives: Observational studies have shown that chronic obstructive pulmonary disease (COPD) is associated with an increased risk of hearing impairment. However, causality remains unclear, including with respect to lung function. This study aimed to investigate the associations of lung function and COPD with hearing impairment in the UK Biobank and confirm potential causalities using Mendelian randomization (MR). Methods: Cross-sectional analyses were performed using logistic regression models in a subsample of the UK Biobank. Two-sample MR analyses were performed on summary statistics for forced expiratory volume in one second (FEV1), forced vital capacity (FVC), COPD, and sensorineural hearing loss. Results: FEV1 and FVC were negatively associated with hearing impairment, with odds ratios (95% confidence intervals) of 0.80 (0.77, 0.84) and 0.80 (0.76, 0.83), respectively. COPD was positively associated with hearing impairment, with an odds ratio (95% confidence interval) of 1.10 (1.02, 1.18). In the MR analyses, a negative association was found between FVC and sensorineural hearing loss, with an odds ratio (95% confidence interval) of 0.91 (0.83, 0.99). For FVE1 and COPD, no significant associations were found. Conclusions: The results of this study showed that FVC was causally associated with hearing impairment, suggesting a potential protective effect of FVC on hearing impairment. Full article

Background/Objectives: Epilepsy and sleep disturbances frequently co-occur, yet the causal nature of this relationship remains uncertain, particularly in relation to epilepsy subtypes and status epilepticus. We investigated potential bidirectional causal associations between sleep-related traits and epilepsy, including subtypes and status epilepticus, using Mendelian randomization (MR). Methods: We conducted two-sample MR using genome-wide association study (GWAS) summary statistics from European ancestry cohorts. Epilepsy, its subtypes, and status epilepticus were analyzed using data from the International League Against Epilepsy Consortium on Complex Epilepsies (ILAE) and the FinnGen study. Nine self-reported sleep-related traits were derived from the UK Biobank-based GWAS. Causal estimates were primarily obtained using inverse variance weighted models with additional MR analysis methods. Pleiotropy and heterogeneity were assessed to enhance the robustness of the finding. Results: Several subtype-specific associations were identified, with direction and statistical significance varying across cohorts and subtypes. After correction for multiple testing and filtering for tests with ≥10 instrumental variables to ensure robust and reliable MR estimates, several consistent and potentially mutually reinforcing associations emerged. In the ILAE cohort, focal epilepsy with hippocampal sclerosis was associated with an increased risk of insomnia, and juvenile myoclonic epilepsy with reduced sleep duration. In the FinnGen cohort, overall epilepsy was associated with increased risk of both insomnia and daytime sleepiness. In reverse MR, daytime sleepiness and napping were associated with increased risk of epilepsy, while daytime napping and frequent insomnia symptoms were linked to elevated risk of status epilepticus. Conclusions: Our findings reveal subtype-specific and bidirectional causal links between epilepsy and sleep-related traits. These results highlight the biological interplay between epileptic networks and sleep regulation and underscore the need for further clinical and mechanistic studies. Full article

The coronavirus disease 2019 (COVID-19) pandemic has led to substantial health and financial burdens worldwide, and vaccines provide hope for reducing the burden of this pandemic. However, vaccinated people remain at risk for SARS-CoV-2 infection. Genome-wide association studies (GWASs) may identify potential genetic factors involved in the development of COVID-19 breakthrough infections (BIs); however, very few or no GWASs have been conducted for COVID-19 BI thus far. We conducted a GWAS and detailed bioinformatics analysis on COVID-19 BIs in a European population via the UK Biobank (UKBB). We conducted a series of analyses at different levels, including SNP-based, gene-based, pathway, and transcriptome-wide association analyses, to investigate genetic factors associated with COVID-19 BIs and hospitalized infections. The polygenic risk score (PRS) and Hoeffding’s test were performed to reveal the genetic relationships between BIs and other medical conditions. Two independent loci (LD-clumped at r² = 0.01) reached genome-wide significance (p < 5 × 10⁻⁸), including rs36170929, which mapped to LOC102725191/VWDE, and rs28645263, which mapped to RETREG1. A pathway enrichment analysis highlighted pathways such as viral myocarditis, Rho-selective guanine exchange factor AKAP13 signaling, and lipid metabolism. The PRS analyses revealed significant genetic overlap between COVID-19 BIs and heart failure and between HbA1c and type 1 diabetes. Genetic dependence was also observed between COVID-19 BIs and asthma, lung abnormalities, schizophrenia, and type 1 diabetes on the basis of Hoeffding’s test. This GWAS revealed two significant loci that may be associated with COVID-19 BIs and a number of genes and pathways that may be involved in BIs. Genetic overlap with other diseases was identified. Further studies are warranted to replicate these findings and elucidate the mechanisms involved. Full article

Background: The associations between physical activity (PA) and all-cause mortality remain under-investigated among individuals with impaired lung function. Methods: With 201,596 participants from the UK Biobank cohort, baseline pre-bronchodilation lung function tests and a modified International Physical Activity Questionnaire were used to assess lung function status (normal, restricted, obstructed) and PA attributes (volume, intensity, duration). All-cause mortality was determined through linkage to the National Health Services Register. Cox proportional hazard regression was applied to characterize the associations between PA metrics and all-cause mortality among people with different lung function statuses. Dose–response relationships between PA metrics and all-cause mortality risks were examined using restricted cubic splines (number of knots = 4). Results: Over a 11.81-year median follow-up, 5.24% of participants died. All-cause mortality risk declined with increasing total PA volume, plateauing at 1800 MET-min/week without further reduction in individuals with and without impaired lung function. Similar trends were observed for PA intensity and duration, with both factors demonstrating reduced mortality risk that plateaued after reaching a specific threshold. Notably, 24.1% (95% CI: 16.7%, 30.8%) and 43.1% (95% CI: 36.1%, 49.7%) lower mortality risk was observed among individuals with and without impaired lung function for PA with 1201–1800 MET-min/wk. Conclusions: PA was associated with a decreased risk of all-cause mortality among individuals with and without impaired lung function, suggesting that those with impaired lung function might also benefit from PA. Full article

Background: The role of dietary patterns in the development of chronic obstructive pulmonary disease (COPD), particularly under varying levels of ambient air pollution, remains insufficiently understood. Aims: We aimed to investigate the association between adherence to multiple established dietary patterns and the risk of incident COPD, and to assess potential effect modification by exposure to ambient air pollutants. Methods: We conducted a prospective study including 206,463 participants from the UK Biobank free of COPD at baseline. Individual-level residential air pollution exposure was estimated for the year 2010. Nine dietary indices were derived from 24 h dietary recalls. Associations with incident COPD were assessed using Cox proportional hazards models. Effect modification was examined using smoking-specific tertiles of nitrogen oxides (NO, NO₂, and NO_x) and particulate matter (PM_2.5, PM_2.5–10, and PM₁₀). Results: Greater adherence to healthy dietary patterns was associated with a 14% to 34% reduced risk of COPD (highest vs. lowest quintile). In contrast, high adherence to the Unhealthful plant-based diet index (PDI) was associated with a 34% increased risk (HR = 1.34, 95% CI: 1.16–1.54). Notably, the protective associations of the AHA, EAT-Lancet, and MIND dietary patterns were most pronounced in settings with relatively high air pollution, as evidenced by elevated levels in at least four air quality indicators (p for interaction < 0.05). Conclusions: Adherence to AHA, EAT-Lancet, and MIND dietary patterns is associated with a reduced risk of incident COPD, with potentially amplified benefits observed in areas with higher ambient air pollution. Full article

Background: Obesity significantly impacts disease burden, with waist-to-hip ratio (WHR) as a key obesity indicator, but the genetic and biological pathways underlying WHR, particularly its sex-specific differences, remain poorly understood. Methods: This study explored WHR’s sexual dimorphism and its links to complex traits using cross-sectional surveys and genetic data from Giant and UK Biobank (UKB). We analyzed WHR heritability, performed tissue-specific transcriptome-wide association studies (TWAS) using FUSION, and conducted genetic correlation analyses with linkage disequilibrium score regression (LDSC) and Local Analysis of [co]Variant Association (LAVA). Polygenic scores (PGS) for WHR were constructed using the clumping and thresholding method (CT), and associations with complex traits were assessed via logistic or linear models. Results: The genetic analysis showed sex-specific heritability for WHR, with TWAS identifying female-specific (e.g., CCDC92) and male-specific (e.g., UQCC1) genes. Global genetic correlation analysis revealed sex-specific associations between WHR and 23 traits, while local analysis identified eight sex-specific loci across five diseases. Regression analysis highlighted sex-specific associations for 70 traits with WHR and 45 traits with WHR PGS, with stronger effects in females. Predictive models also performed better in females. Conclusions: This study underscores WHR’s sexual dimorphism and its distinct associations with complex traits, offering insights into sex-specific biological differences, health management, and clinical advancements. Full article

Background Psoriasis (PsO): is an immune-mediated inflammatory disease that imposes a significant burden on patients. Many patients experience relapse or inadequate responses, and PsO subtypes also lack effective therapies, highlighting the need for new therapeutic targets. Methods: We performed a proteome-wide Mendelian randomization (MR) to explore potential therapeutic targets for PsO. Protein quantitative trait loci (pQTLs) data were obtained from the Pharma Proteomics Project (54,219 UK Biobank participants, 2923 proteins), and PsO phenotype and subtype data were sourced from FinnGen (10,312 cases; 397,564 controls) for discovery. Replication MR utilized integrated protein data (Iceland and Norfolk) and phenotype data from multiple databases (UK Biobank and GWAS Catalog). Reverse MR and colocalization were used to support causal relationships. Single-cell RNA-seq analysis revealed distinct expression patterns of protein-coding genes across different cell types in PsO biopsy samples and normal skin tissues. Protein-protein interactions (PPI) and molecular docking were used to evaluate druggability. Results: MR analysis identified 13 proteins significantly associated with PsO risk (p < $2.56\times {10}^{-5}$), including 10 proteins associated with PsO subtypes. Decreased levels of eight proteins (IFNLR1, APOF, TDRKH, DDR1, HLA-E, LTA, MOG, and ICAM3) and increased levels of five proteins (IFNGR2, HCG22, IL12B, BTN3A2, and TRIM40) showed protective effects against PsO progression. Robust colocalization ($PP{H}_4$ > 0.9) identified IFNLR1, IFNGR2, APOF, and TDRKH as top candidates. Single-cell RNA sequencing analysis revealed that IFNLR1, IFNGR2, LTA, TDRKH, and DDR1 were specifically expressed in T cells of psoriatic biopsy specimens compared to healthy controls. Molecular docking indicated the druggability of IFNLR1 and IFNGR2. Conclusions: We identified several potential therapeutic targets for PsO, with IFNLR1, IFNGR2, APOF, and TDRKH emerging as promising candidates, particularly IFNLR1 and IFNGR2, which are associated with the IFN family. These findings may provide new perspectives on PsO therapy and pathogenesis. Full article

This study proposes a novel brain-region-level aging assessment paradigm based on Shapley value interpretation, aiming to overcome the interpretability limitations of traditional brain age prediction models. Although deep-learning-based brain age prediction models using neuroimaging data have become crucial tools for evaluating abnormal brain aging, their unidimensional brain age–chronological age discrepancy metric fails to characterize the regional heterogeneity of brain aging. Meanwhile, despite Shapley additive explanations having demonstrated potential for revealing regional heterogeneity, their application in complex deep learning algorithms has been hindered by prohibitive computational complexity. To address this, we innovatively developed a computational framework featuring efficient Shapley value approximation through a novel multi-stage computational strategy that significantly reduces complexity, thereby enabling an interpretable analysis of deep learning models. By establishing a reference system based on standard Shapley values from healthy populations, we constructed an anatomically specific Regional Brain Aging Deviation Index (RBADI) that maintains age-related validity. Experimental validation using UK Biobank data demonstrated that our framework successfully identified the thalamus (THA) and hippocampus (HIP) as core contributors to brain age prediction model decisions, highlighting their close associations with physiological aging. Notably, it revealed significant correlations between the insula (INS) and alcohol consumption, as well as between the inferior frontal gyrus opercular part (IFGoperc) and smoking history. Crucially, the RBADI exhibited superior performance in the tri-class classification of prodromal neurodegenerative diseases (HCs vs. MCI vs. AD: AUC = 0.92; HCs vs. pPD vs. PD: AUC = 0.86). This framework not only enables the practical implementation of Shapley additive explanations in brain age prediction deep learning models but also establishes anatomically interpretable biomarkers. These advancements provide a novel spatial analytical dimension for investigating brain aging mechanisms and demonstrate significant clinical translational value for early neurodegenerative disease screening, ultimately offering a new methodological tool for deciphering the neural mechanisms of aging. Full article

Ineffective treatment and side effects are associated with high burdens for the patient and society. We investigated the application of graph representation learning (GRL) for predicting medication usage based on individual genetic data in the United Kingdom Biobank (UKBB). A graph convolutional network (GCN) was used to integrate interconnected biomedical entities in the form of a knowledge graph as part of a machine learning (ML) prediction model. Data from The Pharmacogenomics Knowledgebase (PharmGKB) was used to construct a biomedical knowledge graph. Individual genetic data (n = 485,754) from the UKBB was obtained and preprocessed to match with pharmacogenetic variants in the PharmGKB. Self-reported medication usage labels were obtained from UKBB data field 20003. We hypothesize that pharmacogenetic variants can predict the impact of medications on individuals. We assume that an individual using a medication on a regular basis experiences a net benefit (vs. side-effects) from the medication. ML models were trained to predict medication usage for 264 medications. The GCN model significantly outperformed both a baseline logistic regression model (p-value: 1.53 × 10⁻⁹) and a deep neural network model (p-value: 8.68 × 10⁻⁸). The GCN model also significantly outperformed a GCN model trained using a random graph (GCN-random) (p-value: 5.44 × 10⁻⁹). A consistent trend of medications with higher sample sizes having better performance was observed, and for several medications, a high relative rank of the medication (among multiple medications) was associated with greater than 2-fold higher odds of usage of the medication. In conclusion, a graph-based ML approach could be useful in advancing precision medicine by prioritizing medications that a patient may need based on their genetic data. However, further research is needed to improve the quality and quantity of genetic data and to validate our approach using more reliable medication labels. Full article

Objectives: Previous studies have confirmed that biological age (BA) acceleration is associated with single cardio-renal–metabolic diseases (CRMDs), typically including type 2 diabetes mellitus, cardiovascular disease, and chronic kidney disease. However, its association with progression to cardio-renal–metabolic multimorbidity (CRMM, coexistence of ≥2 CRMDs) and subsequent mortality remains unexplored. Methods: Using the multi-state model, we analyzed 278,927 UK Biobank participants free of CRMDs at baseline to investigate the association between BA acceleration—measured by phenotypic age (PhenoAge) and Klemera–Doubal method age (KDMAge)—and CRMM progression trajectory, from health to the first CRMD and then to CRMM and death. BA acceleration was the residual from regressing BA on chronological age; positive values indicated a biologically older individual. Results: PhenoAge acceleration showed stronger associations than KDMAge acceleration. Per the 1-SD increase in PhenoAge acceleration; HRs (95% CIs) were observed at 1.18 (1.17–1.19) for baseline to first CRMD; 1.24 (1.22–1.26) for first CRMD to CRMM; 1.25 (1.22–1.27) for baseline to death; 1.13 (1.11–1.15) for first CRMD to death; and 1.09 (1.06–1.12) for CRMM to death. Biologically older individuals by PhenoAge acceleration showed greater reductions in CRMD-free and total life expectancy than those by KDMAge acceleration. Age, socioeconomic status, education, smoking status, alcohol consumption, physical activity, and diet-modified risks for specific transitions. Conclusions: BA acceleration, particularly PhenoAge acceleration, relates to higher CRMM progression risk and shorter life expectancy. Combining BA acceleration with sociodemographic or lifestyle factors improves risk identification for specific transitions. BA acceleration offers the potential to guide CRMM prevention across its entire progression. Full article

Background/Objectives: Bariatric surgery (BS), drugs approved for type-2-diabetes (T2D), obesity, and liver fibrosis (resmetirom) announce the widespread use of fibrosis tests in patients with metabolic liver disease (MASLD). An unmet need is to reduce the uncertainty of biomarkers for the diagnosis of the early stage of clinically significant fibrosis (eF). This can be achieved if three essential but neglected STARD methods (3M) are used, which have a more sensitive histological score than the standard comparator (five-tiers), the weighted area under the characteristic curve (wAUROC) instead of the binary AUROC, and biopsy length. We applied 3M to FibroTest-T2D to demonstrate this reduction of uncertainty and constructed proxies predicting eF in large populations. Methods: For uncertainty, seven subsets were analyzed, four included biopsies (n = 1903), and to assess eF incidence, three MASLD-populations (n = 299,098). FibroTest-T2D classification rates after BS and in outpatients-T2D (n = 402) were compared with and without 3M. In MASLD, trajectories of proxies and incidence against confounding factors used hazard ratios. Results: After BS (110 biopsies), reversal of eF was observed in 16/29 patients (84%) using seven-tier scores vs. 3/20 patients (47%) using five-tier scores (p = 0.005). When the biopsy length was above the median, FibroTest-T2D wAUROC was 0.90 (SD = 0.01), and the wAUROC was 0.88 (SD = 0.1) when the length was below the median (p < 0.001). For the first time, obesity was associated with eF before T2D (p < 0.001), and perimenopausal age with apoA1 and haptoglobin increases (p < 0.0001). Conclusions: Validations of circulating biomarkers need to assess their uncertainty. FibroTest-T2D predicts fibrosis regression after BS. Applying 3M and adjustments could avoid misinterpretations in MASLD surveillance. Full article

Background/Objectives: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a multifaceted and diverse disorder with an ambiguous etiology. Recent evidence indicates that immune system impairment and inflammatory mechanisms are pivotal to the initiation and advancement of ME/CFS. Nonetheless, the causal relationships among these factors remain inadequately comprehended. Methods: This study investigated the causative contributions of immunological dysfunction and inflammatory variables in ME/CFS utilizing genome-wide association study (GWAS) data. We employed Mendelian randomization (MR) to investigate associations between 91 inflammatory cytokines, 731 immune cell characteristics, and the risk of ME/CFS. Summary statistics for immune cell traits and inflammatory cytokines were sourced from European GWAS cohorts (n = 3757 and n = 14,824, respectively), while ME/CFS data were obtained from the UK Biobank (n = 462,933, including 2076 cases). We predominantly employed the inverse variance weighted (IVW) approach, complemented by MR-Egger, weighted median, BWMR, and MR-RAPS tests to guarantee robust and precise outcomes. Results: The study revealed significant causal links between various inflammatory factors, immune cell characteristics, and the risk of ME/CFS. Increased CXCL5 and CCL20 levels were significantly linked to a higher risk of ME/CFS, while elevated TNF levels were inversely related to ME/CFS risk. Furthermore, 13 immune cell characteristics were identified as having substantial causal associations with the likelihood of ME/CFS. These data are supportive of the causality that immune system dysfunction and inflammatory variables play a pivotal role in the development of ME/CFS. Conclusions: This study provides new insights into the causal role of immune system dysfunction in the development of ME/CFS, contributing to a deeper understanding of its underlying mechanisms. These results offer a foundation for identifying diagnostic biomarkers and developing targeted therapeutic strategies. Future research should validate these findings using multi-center cohort studies and further investigate the mechanisms behind key factors to enable the development of personalized treatment approaches. Full article

Background: Adequate control for confounding is key to many observational study designs. Confounders are often identified based on subject matter knowledge from empirical investigations. Negative confounders, which typically generate type 2 error, i.e., false nulls, can be elusive. Such confounders can be identified comprehensively by using Mendelian randomization (MR) to search the wealth of publicly available data systematically. Here, to demonstrate the concept, we examined whether a common positive confounder, body mass index (BMI), is also a negative confounder of any common physiological exposures on health outcomes, overall and specifically by sex. Methods: We used an MR study, based on the largest overall and sex-specific genome-wide association studies of BMI (i.e., from the Genetic Investigation of ANthropometric Traits and the UK Biobank) and of relevant exposures likely affected by BMI, to assess, overall and sex-specifically, whether BMI is a negative confounder potentially obscuring effects of harmful physiological exposures. Inverse variance weighting was the main method. We assessed sex differences using a z-test. Results: BMI was a potential negative confounder for apolipoprotein B and total testosterone in men, and for both sexes regarding low-density lipoprotein cholesterol, choline, linoleic acid, polyunsaturated fatty acids, and cholesterol. Conclusions: Using BMI as an illustrative example, we demonstrate that negative confounding is an easily overlooked bias. Given negative confounding is not always obvious or known, using MR systematically to identify potential negative confounders in relevant studies may be helpful. Full article