Identifying modifiable factors and their joint effect on frailty: a large population-based prospective cohort study.

A thorough understanding and identification of potential determinants leading to frailty are imperative for the development of targeted interventions aimed at its prevention or mitigation. We investigated the potential determinants of frailty in a cohort of 469,301 UK Biobank participants. The evaluation of frailty was performed using the Fried index, which encompasses measurements of handgrip strength, gait speed, levels of physical activity, unintentional weight loss, and self-reported exhaustion. EWAS including 276 factors were first conducted. Factors associated with frailty in EWAS were further combined to generate composite scores for different domains, and joint associations with frailty were evaluated in a multivariate logistic model. The potential impact on frailty when eliminating unfavorable profiles of risk domains was evaluated by PAFs. A total of 21,020 (4.4%) participants were considered frailty, 192,183 (41.0%) pre-frailty, and 256,098 (54.6%) robust. The largest EWAS identified 90 modifiable factors for frailty across ten domains, each of which independently increased the risk of frailty. Among these factors, 67 have the potential to negatively impact health, while 23 have been found to have a protective effect. When shifting all unfavorable profiles to intermediate and favorable ones, overall adjusted PAF for potentially modifiable frailty risk factors was 85.9%, which increases to 86.6% if all factors are transformed into favorable tertiles. Health and medical history, psychosocial factors, and physical activity were the most significant contributors, accounting for 11.9%, 10.4%, and 10.1% respectively. This study offers valuable insights for developing population-level strategies aimed at preventing frailty.

Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults.

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential interventions. Leveraging data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline, we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident depression. Linear regression was then used to assess associations between depression-related proteins and brain structures, genetic factors, and stress-related events. Our analysis identified 157 proteins associated with incident depression (P <1.71 × 10-5), including novel associations with proteins such as GAST, PLAUR, LRRN1, BCAN, and ITGA11. Notably, higher expression levels of GDF15 (P = 6.18 × 10-26) and PLAUR (P = 2.88 × 10-14) were linked to an increased risk of depression, whereas higher levels of LRRN1 (P = 4.28 × 10-11) and ITGA11 (P = 3.68 × 10-9) were associated with a decreased risk. Dysregulation of the 157 proteins is correlated with brain regions implicated in depression, including the hippocampus and middle temporal gyrus. Additionally, these protein alterations were strongly correlated with stress-related events, including self-harm events, adult, and childhood trauma. Biological pathway enrichment analysis highlighted the critical roles of the immune response. EGFR and TNF emerged as key proteins in the protein-protein interaction network. BTN3A2, newly linked to incident depression (P = 4.35 × 10-10), was confirmed as a causal factor through Mendelian randomization analysis. In summary, our research identified the proteomic signatures associated with the onset of depression, highlighting its potential for early intervention and tailored therapeutic avenues.

Identifying modifiable factors associated with neuroimaging markers of brain health.

AIMS: Brain structural alterations begin long before the presentation of brain disorders; therefore, we aimed to systematically investigate a wide range of influencing factors on neuroimaging markers of brain health. METHODS: Utilizing data from 30,651 participants from the UK Biobank, we explored associations between 218 modifiable factors and neuroimaging markers of brain health. We conducted an exposome-wide association study using the least absolute shrinkage and selection operator (LASSO) technique. Restricted cubic splines (RCS) were further employed to estimate potential nonlinear correlations. Weighted standardized scores for neuroimaging markers were computed based on the estimates for individual factors. Finally, stratum-specific analyses were performed to examine differences in factors affecting brain health at different ages. RESULTS: The identified factors related to neuroimaging markers of brain health fell into six domains, including systematic diseases, lifestyle factors, personality traits, social support, anthropometric indicators, and biochemical markers. The explained variance percentage of neuroimaging markers by weighted standardized scores ranged from 0.5% to 7%. Notably, associations between systematic diseases and neuroimaging markers were stronger in older individuals than in younger ones. CONCLUSION: This study identified a series of factors related to neuroimaging markers of brain health. Targeting the identified factors might help in formulating effective strategies for maintaining brain health.

Whole exome sequencing identified six novel genes for depressive symptoms.

Previous genome-wide association studies of depression have primarily focused on common variants, limiting our comprehensive understanding of the genetic architecture. In contrast, whole-exome sequencing can capture rare coding variants, helping to explore the phenotypic consequences of altering protein-coding genes. Here, we conducted a large-scale exome-wide association study on 296,199 participants from the UK Biobank, assessing their depressive symptom scores through the Patient Health Questionnaire-4. We identified 22 genes associated with depressive symptoms, including 6 newly discovered genes (TRIM27, UBD, SVOP, ADGRB2, IRF2BPL, and ANKRD12). Both ontology enrichment analysis and plasma proteomics association analysis consistently revealed that the identified genes were associated with immune responses. Furthermore, we identified associations between these genes and brain regions related to depression, such as anterior cingulate cortex and orbitofrontal cortex. Additionally, phenome-wide association analysis demonstrated that TRIM27 and UBD were associated with neuropsychiatric, cognitive, biochemistry, and inflammatory traits. Our findings offer new insights into the potential mechanisms and genetic architecture of depressive symptoms.

Whole exome sequencing analyses identified novel genes for Alzheimer's disease and related dementia.

INTRODUCTION: The heritability of Alzheimer's disease (AD) is estimated to be 58%-79%. However, known genes can only partially explain the heritability. METHODS: Here, we conducted gene-based exome-wide association study (ExWAS) of rare variants and single-variant ExWAS of common variants, utilizing data of 54,569 clinically diagnosed/proxy AD and related dementia (ADRD) and 295,421 controls from the UK Biobank. RESULTS: Gene-based ExWAS identified 11 genes predicting a higher ADRD risk, including five novel ones, namely FRMD8, DDX1, DNMT3L, MORC1, and TGM2, along with six previously reported ones, SORL1, GRN, PSEN1, ABCA7, GBA, and ADAM10. Single-variant ExWAS identified two ADRD-associated novel genes, SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-ß process pathways, microglia, and brain regions like hippocampus. The druggability evidence suggests that DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets. DISCUSSION: Our study contributes to the current body of evidence on the genetic etiology of ADRD. HIGHLIGHTS: Gene-based analyses of rare variants identified five novel genes for Alzheimer's disease and related dementia (ADRD), including FRMD8, DDX1, DNMT3L, MORC1, and TGM2. Single-variant analyses of common variants identified two novel genes for ADRD, including SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-ß process pathways, microglia, and brain regions like hippocampus. DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.

Contribution of copy number variations to education, socioeconomic status and cognition from a genome-wide study of 305,401 subjects.

Educational attainment (EA), socioeconomic status (SES) and cognition are phenotypically and genetically linked to health outcomes. However, the role of copy number variations (CNVs) in influencing EA/SES/cognition remains unclear. Using a large-scale (n = 305,401) genome-wide CNV-level association analysis, we discovered 33 CNV loci significantly associated with EA/SES/cognition, 20 of which were novel (deletions at 2p22.2, 2p16.2, 2p12, 3p25.3, 4p15.2, 5p15.33, 5q21.1, 8p21.3, 9p21.1, 11p14.3, 13q12.13, 17q21.31, and 20q13.33, as well as duplications at 3q12.2, 3q23, 7p22.3, 8p23.1, 8p23.2, 17q12 (105 kb), and 19q13.32). The genes identified in gene-level tests were enriched in biological pathways such as neurodegeneration, telomere maintenance and axon guidance. Phenome-wide association studies further identified novel associations of EA/SES/cognition-associated CNVs with mental and physical diseases, such as 6q27 duplication with upper respiratory disease and 17q12 (105 kb) duplication with mood disorders. Our findings provide a genome-wide CNV profile for EA/SES/cognition and bridge their connections to health. The expanded candidate CNVs database and the residing genes would be a valuable resource for future studies aimed at uncovering the biological mechanisms underlying cognitive function and related clinical phenotypes.

Osteoarthritis, osteoarthritis treatment and risk of incident dementia: a prospective cohort study based on UK Biobank.

BACKGROUND: We aimed to investigate the association between OA and treatment with dementia risk and structural brain abnormalities. METHODS: We recruited a total of 466,460 individuals from the UK Biobank to investigate the impact of OA on the incidence of dementia. Among the total population, there were 63,081 participants diagnosed with OA. We subsequently categorised the OA patients into medication and surgery groups based on treatment routes. Cox regression models explored the associations between OA/OA treatment and dementia risk, with the results represented as hazard ratios (HRs) and 95% confidence intervals (95% CI). Linear regression models assessed the associations of OA/OA therapy with alterations in cortical structure. RESULTS: During an average of 11.90 (± 1.01) years of follow-up, 5,627 individuals were diagnosed with all-cause dementia (ACD), including 2,438 AD (Alzheimer's disease), and 1,312 VaD (vascular dementia) cases. Results revealed that OA was associated with the elevated risk of ACD (HR: 1.116; 95% CI: 1.039-1.199) and AD (HR: 1.127; 95% CI: 1.013-1.254). OA therapy lowered the risk of dementia in both medication group (HR: 0.746; 95% CI: 0.652-0.854) and surgery group (HR: 0.841; 95% CI: 0.736-0.960). OA was negatively associated with cortical area, especially precentral, postcentral and temporal regions. CONCLUSIONS: Osteoarthritis increased the likelihood of developing dementia, and had an association with regional brain atrophy. OA treatment lowered the dementia risk. OA is a promising modifiable risk factor for dementia.

Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer's disease.

Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.

Genome-wide meta-analysis identifies ancestry-specific loci for Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) is a devastating neurological disease with complex genetic etiology. Yet most known loci have only identified from the late-onset type AD in populations of European ancestry. METHODS: We performed a two-stage genome-wide association study (GWAS) of AD totaling 6878 Chinese and 63,926 European individuals. RESULTS: In addition to the apolipoprotein E (APOE) locus, our GWAS of two independent Chinese samples uncovered three novel AD susceptibility loci (KIAA2013, SLC52A3, and TCN2) and a novel ancestry-specific variant within EGFR (rs1815157). More replicated variants were observed in the Chinese (31%) than in the European samples (15%). In combining genome-wide associations and functional annotations, EGFR and TCN2 were prioritized as two of the most biologically significant genes. Phenome-wide Mendelian randomization suggests that high mean corpuscular hemoglobin concentration might protect against AD. DISCUSSION: The current study reveals novel AD susceptibility loci, emphasizes the importance of diverse populations in AD genetic research, and advances our understanding of disease etiology. HIGHLIGHTS: Loci KIAA2013, SLC52A3, and TCN2 were associated with Alzheimer's disease (AD) in Chinese populations. rs1815157 within the EGFR locus was associated with AD in Chinese populations. The genetic architecture of AD varied between Chinese and European populations. EGFR and TCN2 were prioritized as two of the most biologically significant genes. High mean corpuscular hemoglobin concentrations might have protective effects against AD.

Expanding the targeting scope of CRISPR/Cas9-mediated genome editing by Cas9 variants in Brassica.

CRISPR/Cas9, presently the most widely used genome editing technology, has provided great potential for functional studies and plant breeding. However, the strict requirement for a protospacer adjacent motif (PAM) has hindered the application of the CRISPR/Cas9 system because the number of targetable genomic sites is limited. Recently, the engineered variants Cas9-NG, SpG, and SpRY, which recognize non-canonical PAMs, have been successfully tested in plants (mainly in rice, a monocot). In this study, we evaluated the targeted mutagenesis capabilities of these Cas9 variants in two important Brassica vegetables, Chinese cabbage (Brassica rapa spp. pekinensis) and cabbage (Brassica oleracea var. capitata). Both Cas9-NG and SpG induced efficient mutagenesis at NGN PAMs, while SpG outperformed Cas9-NG at NGC and NGT PAMs. SpRY achieved efficient editing at almost all PAMs (NRN > NYN), albeit with some self-targeting activity at transfer (T)-DNA sequences. And SpRY-induced mutants were detected in cabbage plants in a PAM-less fashion. Moreover, an adenine base editor was developed using SpRY and TadA8e deaminase that induced A-to-G conversions within target sites using non-canonical PAMs. Together, the toolboxes developed here induced successful genome editing in Chinese cabbage and cabbage. Our work further expands the targeting scope of genome editing and paves the way for future basic research and genetic improvement in Brassica. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-024-00155-7.

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures.

BACKGROUND AND OBJECTIVES: Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables. METHODS: Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB. RESULTS: A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD. DISCUSSION: Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

Whole-exome sequencing identifies protein-coding variants associated with brain iron in 29,828 individuals.

Iron plays a fundamental role in multiple brain disorders. However, the genetic underpinnings of brain iron and its implications for these disorders are still lacking. Here, we conduct an exome-wide association analysis of brain iron, measured by quantitative susceptibility mapping technique, across 26 brain regions among 26,789 UK Biobank participants. We find 36 genes linked to brain iron, with 29 not being previously reported, and 16 of them can be replicated in an independent dataset with 3,039 subjects. Many of these genes are involved in iron transport and homeostasis, such as FTH1 and MLX. Several genes, while not previously connected to brain iron, are associated with iron-related brain disorders like Parkinson's (STAB1, KCNA10), Alzheimer's (SHANK1), and depression (GFAP). Mendelian randomization analysis reveals six causal relationships from regional brain iron to brain disorders, such as from the hippocampus to depression and from the substantia nigra to Parkinson's. These insights advance our understanding of the genetic architecture of brain iron and offer potential therapeutic targets for brain disorders.

Exome sequencing identifies novel genetic variants associated with varicose veins.

BACKGROUND: Varicose veins (VV) are one of the common human diseases, but the role of genetics in its development is not fully understood. METHODS: We conducted an exome-wide association study of VV using whole-exome sequencing data from the UK Biobank, and focused on common and rare variants using single-variant association analysis and gene-level collapsing analysis. FINDINGS: A total of 13,823,269 autosomal genetic variants were obtained after quality control. We identified 36 VV-related independent common variants mapping to 34 genes by single-variant analysis and three rare variant genes (PIEZO1, ECE1, FBLN7) by collapsing analysis, and most associations between genes and VV were replicated in FinnGen. PIEZO1 was the closest gene associated with VV (P = 5.05 × 10-31), and it was found to reach exome-wide significance in both single-variant and collapsing analyses. Two novel rare variant genes (ECE1 and METTL21A) associated with VV were identified, of which METTL21A was associated only with females. The pleiotropic effects of VV-related genes suggested that body size, inflammation, and pulmonary function are strongly associated with the development of VV. CONCLUSIONS: Our findings highlight the importance of causal genes for VV and provide new directions for treatment.

Whole exome sequencing analysis identifies genes for alcohol consumption.

Alcohol consumption is a heritable behavior seriously endangers human health. However, genetic studies on alcohol consumption primarily focuses on common variants, while insights from rare coding variants are lacking. Here we leverage whole exome sequencing data across 304,119 white British individuals from UK Biobank to identify protein-coding variants associated with alcohol consumption. Twenty-five variants are associated with alcohol consumption through single variant analysis and thirteen genes through gene-based analysis, ten of which have not been reported previously. Notably, the two unreported alcohol consumption-related genes GIGYF1 and ANKRD12 show enrichment in brain function-related pathways including glial cell differentiation and are strongly expressed in the cerebellum. Phenome-wide association analyses reveal that alcohol consumption-related genes are associated with brain white matter integrity and risk of digestive and neuropsychiatric diseases. In summary, this study enhances the comprehension of the genetic architecture of alcohol consumption and implies biological mechanisms underlying alcohol-related adverse outcomes.

Vascular cognitive impairment: Advances in clinical research and management.

ABSTRACT: Vascular cognitive impairment (VCI) encompasses a wide spectrum of cognitive disorders, ranging from mild cognitive impairment to vascular dementia. Its diagnosis relies on thorough clinical evaluations and neuroimaging. VCI predominately arises from vascular risk factors (VRFs) and cerebrovascular disease, either independently or in conjunction with neurodegeneration. Growing evidence underscores the prevalence of VRFs, highlighting their potential for early prediction of cognitive impairment and dementia in later life. The precise mechanisms linking vascular pathologies to cognitive deficits remain elusive. Chronic cerebrovascular pathology is the most common neuropathological feature of VCI, often interacting synergistically with neurodegenerative processes. Current research efforts are focused on developing and validating reliable biomarkers to unravel the etiology of vascular brain changes in VCI. The collaborative integration of these biomarkers into clinical practice, alongside routine incorporation into neuropathological assessments, presents a promising strategy for predicting and stratifying VCI. The cornerstone of VCI prevention remains the control of VRFs, which includes multi-domain lifestyle modifications. Identifying appropriate pharmacological approaches is also of paramount importance. In this review, we synthesize recent advancements in the field of VCI, including its definition, determinants of vascular risk, pathophysiology, neuroimaging and fluid-correlated biomarkers, predictive methodologies, and current intervention strategies. Increasingly evident is the notion that more rigorous research for VCI, which arises from a complex interplay of physiological events, is still needed to pave the way for better clinical outcomes and enhanced quality of life for affected individuals.

Large-scale whole-exome sequencing analyses identified protein-coding variants associated with immune-mediated diseases in 350,770 adults.

The genetic contribution of protein-coding variants to immune-mediated diseases (IMDs) remains underexplored. Through whole exome sequencing of 40 IMDs in 350,770 UK Biobank participants, we identified 162 unique genes in 35 IMDs, among which 124 were novel genes. Several genes, including FLG which is associated with atopic dermatitis and asthma, showed converging evidence from both rare and common variants. 91 genes exerted significant effects on longitudinal outcomes (interquartile range of Hazard Ratio: 1.12-5.89). Mendelian randomization identified five causal genes, of which four were approved drug targets (CDSN, DDR1, LTA, and IL18BP). Proteomic analysis indicated that mutations associated with specific IMDs might also affect protein expression in other IMDs. For example, DXO (celiac disease-related gene) and PSMB9 (alopecia areata-related gene) could modulate CDSN (autoimmune hypothyroidism-, psoriasis-, asthma-, and Graves' disease-related gene) expression. Identified genes predominantly impact immune and biochemical processes, and can be clustered into pathways of immune-related, urate metabolism, and antigen processing. Our findings identified protein-coding variants which are the key to IMDs pathogenesis and provided new insights into tailored innovative therapies.

Novel diagnostic and prognostic approach for rapidly progressive dementias: Indicators based on amyloid/tau/neurodegeneration (ATN) framework.

AIMS: Apply established cerebrospinal fluid (CSF) and serum biomarkers and novel combined indicators based on the amyloid/tau/neurodegeneration (ATN) framework to improve diagnostic and prognostic power in patients with rapidly progressive dementias (RPDs). METHODS: CSF and serum biomarkers of Alzheimer's disease (AD) common neuropathology including Aß42, Aß40, p-Tau, and t-Tau were measured in cognitively normal (CN) controls (n = 33) and three RPD groups with rapidly progressive AD (rpAD, n = 23), autoimmune encephalitis (AE, n = 25), and Creutzfeldt-Jakob disease (CJD, n = 28). Logistic regression and multiple linear regression were used for producing combined indicators and prognostic assessment, respectively, including A&T, A&N, T&N, A&T&N, etc. RESULTS: Combined diagnostic indicator with A&T&N had the potential for differentiating AE from other types of RPDs, identifying 62.51% and 75% of AE subjects based on CSF and serum samples, respectively, compared to 39.13% and 37.5% when using autoantibodies. CSF t-Tau was associated with survival in the CJD group (adjusted R-Square = 0.16, p = 0.02), and its prognosis value improved when using combined predictors based on the ATN framework (adjusted R-Square = 0.273, p = 0.014). CONCLUSION: Combined indicators based on the ATN framework provide a novel perspective for establishing biomarkers for early recognition of RPDs due to treatment-responsive causes.

Shift work effects on incident neuropsychiatric disorders and shift work tolerance.

BACKGROUND: Shift work is associated with susceptibility to several neuropsychiatric disorders. This study aims to investigate the effect of shift work on the incidence of neuropsychiatric disorders, and highlighting how individual variability may influence the association. METHODS: UK Biobank participants with employment information were included. Cox survival was conducted in main and subgroup analyses. Correlation analyses explored the impact of shift work on brain structures, and mediation analyses were performed to elucidate the shared underlying mechanisms. Shift work tolerance was evaluated through survival analyses contrasting the risks associated with five neuropsychiatric disorders in shift versus non-shift workers across different demographic or occupational strata. RESULTS: The analysis encompassed 254,646 participants. Shift work was associated with higher risk of dementia (HR 1.29, 95 % CI 1.10-1.52), anxiety (1.08, 1.01-1.15), depression (1.29, 1.22-1.36), and sleep disorders (1.18, 1.09-1.28), but not stroke (p = 0.20). Shift work was correlated with decreasing volume of various brain regions, particularly in thalamus, lateral orbitofrontal, and middle temporal. Mediation analysis revealed that increased immune response and glucose levels are common pathways linking shift work to these disorders. We observed diversity in shift work tolerance across different individual characteristics, among which socioeconomic status and length of working hours were the most essential. LIMITATIONS: Self-reported employment information may cause misclassification and recall bias. And since we focused on the middle-aged population, the conclusions may not be representative of younger or older populations. CONCLUSIONS: Our findings indicated the need to monitor shift worker health and provide personalized management to help adapt to shift work.

Identifying behaviour-related and physiological risk factors for suicide attempts in the UK Biobank.

Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case-control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.

Identifying modifiable factors and their joint effect on brain health: an exposome-wide association study.

Considerable uncertainty remains regarding the associations of multiple factors with brain health. We aimed to conduct an exposome-wide association study on neurodegenerative disease and neuropsychiatry disorders using data of participants from the UK Biobank. Multivariable Cox regression models with the least absolute shrinkage and selection operator technique as well as principal component analyses were used to evaluate the exposures in relation to common disorders of central nervous system (CNS). Restricted cubic splines were conducted to explore potential nonlinear correlations. Then, weighted standardized scores were generated based on the coefficients to calculate the joint effects of risk factors. We also estimated the potential impact of eliminating the unfavorable profiles of risk domains on CNS disorders using population attributable fraction (PAF). Finally, sensitivity analyses were performed to reduce the risk of reverse causality. The current study discovered the significantly associated exposures fell into six primary exposome categories. The joint effects of identified risk factors demonstrated higher risks for common disorders of CNS (HR = 1.278 ~ 3.743, p < 2e-16). The PAF varied by exposome categories, with lifestyle and medical history contributing to majority of disease cases. In total, we estimated that up to 3.7 ~ 64.1% of disease cases could be prevented.This study yielded modifiable variables of different categories and assessed their joint effects on common disorders of CNS. Targeting the identified exposures might help formulate effective strategies for maintaining brain health.