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.

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.

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.

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.

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.

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.

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.

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.

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.

Whole exome sequencing analyses reveal novel genes in telomere length and their biomedical implications.

Telomere length is a putative biomarker of aging and is associated with multiple age-related diseases. There are limited data on the landscape of rare genetic variations in telomere length. Here, we systematically characterize the rare variant associations with leukocyte telomere length (LTL) through exome-wide association study (ExWAS) among 390,231 individuals in the UK Biobank. We identified 18 robust rare-variant genes for LTL, most of which estimated effects on LTL were significant (> 0.2 standard deviation per allele). The biological functions of the rare-variant genes were associated with telomere maintenance and capping and several genes were specifically expressed in the testis. Three novel genes (ASXL1, CFAP58, and TET2) associated with LTL were identified. Phenotypic association analyses indicated significant associations of ASXL1 and TET2 with cancers, age-related diseases, blood assays, and cardiovascular traits. Survival analyses suggested that carriers of ASXL1 or TET2 variants were at increased risk for cancers; diseases of the circulatory, respiratory, and genitourinary systems; and all-cause and cause-specific deaths. The CFAP58 carriers were at elevated risk of deaths due to cancers. Collectively, the present whole exome sequencing study provides novel insights into the genetic landscape of LTL, identifying novel genes associated with LTL and their implications on human health and facilitating a better understanding of aging, thus pinpointing the genetic relevance of LTL with clonal hematopoiesis, biomedical traits, and health-related outcomes.

DDAH-1 maintains endoplasmic reticulum-mitochondria contacts and protects dopaminergic neurons in Parkinson's disease.

The loss of dopaminergic neurons in the substantia nigra is a hallmark of pathology in Parkinson's disease (PD). Dimethylarginine dimethylaminohydrolase-1 (DDAH-1) is the critical enzyme responsible for the degradation of asymmetric dimethylarginine (ADMA) which inhibits nitric oxide (NO) synthase and has been implicated in neurodegeneration. Mitochondrial dysfunction, particularly in the mitochondria-associated endoplasmic reticulum membrane (MAM), plays a critical role in this process, although the specific molecular target has not yet been determined. This study aims to examine the involvement of DDAH-1 in the nigrostriatal dopaminergic pathway and PD pathogenesis. The distribution of DDAH-1 in the brain and its colocalization with dopaminergic neurons were observed. The loss of dopaminergic neurons and aggravated locomotor disability after rotenone (ROT) injection were showed in the DDAH-1 knockout rat. L-arginine (ARG) and NO donors were employed to elucidate the role of NO respectively. In vitro, we investigated the effects of DDAH-1 knockdown or overexpression on cell viability and mitochondrial functions, as well as modulation of ADMA/NO levels using ADMA or ARG. MAM formation was assessed by the Mitofusin2 oligomerization and the mitochondrial ubiquitin ligase (MITOL) phosphorylation. We found that DDAH-1 downregulation resulted in enhanced cell death and mitochondrial dysfunctions, accompanied by elevated ADMA and reduced NO levels. However, the recovered NO level after the ARG supplement failed to exhibit a protective effect on mitochondrial functions and partially restored cell viability. DDAH-1 overexpression prevented ROT toxicity, while ADMA treatment attenuated these protective effects. The declines of MAM formation in ROT-treated cells were exacerbated by DDAH-1 downregulation via reduced MITOL phosphorylation, which was reversed by DDAH-1 overexpression. Together, the abundant expression of DDAH-1 in nigral dopaminergic neurons may exert neuroprotective effects by maintaining MAM formation and mitochondrial function probably via ADMA, indicating the therapeutic potential of targeting DDAH-1 for PD.

The Etiology of Rapidly Progressive Dementia: A 3-Year Retrospective Study in a Tertiary Hospital in China.

Background: Rapidly progressive dementia (RPD), characterized by a rapid cognitive decline leading to dementia, comprises a diverse range of disorders. Despite advancements in diagnosis and treatment, research on RPD primarily focuses on Western populations. Objective: This study aims to explore the etiology and demographics of RPD in Chinese patients. Methods: We retrospectively analyzed 323 RPD inpatients at Huashan Hospital from May 2019 to March 2023. Data on sociodemographic factors, epidemiology, clinical presentation, and etiology were collected and analyzed. Results: The median onset age of RPD patients was 60.7 years. Two-thirds received a diagnosis within 6 months of symptom onset. Memory impairment was the most common initial symptom, followed by behavioral changes. Neurodegenerative diseases accounted for 47.4% of cases, with central nervous system inflammatory diseases at 30.96%. Autoimmune encephalitis was the leading cause (16.7%), followed by Alzheimer's disease (16.1%), neurosyphilis (11.8%), and Creutzfeldt-Jakob disease (9.0%). Alzheimer's disease, Creutzfeldt-Jakob disease, and frontotemporal dementia were the primary neurodegenerative causes, while autoimmune encephalitis, neurosyphilis, and vascular cognitive impairment were the main non-neurodegenerative causes. Conclusions: The etiology of RPD in Chinese patients is complex, with neurodegenerative and non-neurodegenerative diseases equally prevalent. Recognizing treatable conditions like autoimmune encephalitis and neurosyphilis requires careful consideration and differentiation.

Genetic architecture of brain morphology and overlap with neuropsychiatric traits.

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.

Physical frailty, genetic predisposition, and incident dementia: a large prospective cohort study.

Physical frailty and genetic factors are both risk factors for increased dementia; nevertheless, the joint effect remains unclear. This study aimed to investigated the long-term relationship between physical frailty, genetic risk, and dementia incidence. A total of 274,194 participants from the UK Biobank were included. We applied Cox proportional hazards regression models to estimate the association between physical frailty and genetic and dementia risks. Among the participants (146,574 females [53.45%]; mean age, 57.24 years), 3,353 (1.22%) new-onset dementia events were recorded. Compared to non-frailty, the hazard ratio (HR) for dementia incidence in prefrailty and frailty was 1.396 (95% confidence interval [CI], 1.294-1.506, P < 0.001) and 2.304 (95% CI, 2.030-2.616, P < 0.001), respectively. Compared to non-frailty and low polygenic risk score (PRS), the HR for dementia risk was 3.908 (95% CI, 3.051-5.006, P < 0.001) for frailty and high PRS. Furthermore, among the participants, slow walking speed (HR, 1.817; 95% CI, 1.640-2.014, P < 0.001), low physical activity (HR, 1.719; 95% CI, 1.545-1.912, P < 0.001), exhaustion (HR, 1.670; 95% CI, 1.502-1.856, P < 0.001), low grip strength (HR, 1.606; 95% CI, 1.479-1.744, P < 0.001), and weight loss (HR, 1.464; 95% CI, 1.328-1.615, P < 0.001) were independently associated with dementia risk compared to non-frailty. Particularly, precise modulation for different dementia genetic risk populations can also be identified due to differences in dementia risk resulting from the constitutive pattern of frailty in different genetic risk populations. In conclusion, both physical frailty and high genetic risk are significantly associated with higher dementia risk. Early intervention to modify frailty is beneficial for achieving primary and precise prevention of dementia, especially in those at high genetic risk.

CAIDE Score, Alzheimer's Disease Pathology, and Cognition in Cognitively Normal Adults: The CABLE Study.

Background: Cardiovascular Risk Factors, Ageing and Dementia (CAIDE) risk score serves as a credible predictor of an individual's risk of dementia. However, studies on the link of the CAIDE score to Alzheimer's disease (AD) pathology are scarce. Objective: To explore the links of CAIDE score to cerebrospinal fluid (CSF) biomarkers of AD as well as to cognitive performance. Methods: In the Chinese Alzheimer's Biomarker and LifestylE (CABLE) study, we recruited 600 cognitively normal participants. Correlations between the CAIDE score and CSF biomarkers of AD as well as cognitive performance were probed through multiple linear regression models. Whether the correlation between CAIDE score and cognitive performance was mediated by AD pathology was researched by means of mediation analyses. Results: Linear regression analyses illustrated that CAIDE score was positively associated with tau-related biomarkers, including pTau (p < 0.001), tTau (p < 0.001), as well as tTau/Aß42 (p = 0.008), while it was in negative association with cognitive scores, consisting of MMSE score (p < 0.001) as well as MoCA score (p < 0.001). The correlation from CAIDE score to cognitive scores was in part mediated by tau pathology, with a mediation rate varying from 3.2% to 13.2%. Conclusions: A higher CAIDE score, as demonstrated in our study, was linked to more severe tau pathology and poorer cognitive performance, and tau pathology mediated the link of CAIDE score to cognitive performance. Increased dementia risk will lead to cognitive decline through aggravating neurodegeneration.

Clinical trajectories preceding incident dementia up to 15 years before diagnosis: a large prospective cohort study.

BACKGROUND: Dementia has a long prodromal stage with various pathophysiological manifestations; however, the progression of pre-diagnostic changes remains unclear. We aimed to determine the evolutional trajectories of multiple-domain clinical assessments and health conditions up to 15 years before the diagnosis of dementia. METHODS: Data was extracted from the UK-Biobank, a longitudinal cohort that recruited over 500,000 participants from March 2006 to October 2010. Each demented subject was matched with 10 healthy controls. We performed logistic regressions on 400 predictors covering a comprehensive range of clinical assessments or health conditions. Their evolutional trajectories were quantified using adjusted odds ratios (ORs) and FDR-corrected p-values under consecutive timeframes preceding the diagnosis of dementia. FINDINGS: During a median follow-up of 13.7 [Interquartile range, IQR 12.9-14.2] years until July 2022, 7620 subjects were diagnosed with dementia. In general, upon approaching the diagnosis, demented subjects witnessed worse functional assessments and a higher prevalence of health conditions. Associations up to 15 years preceding the diagnosis comprised declined physical strength (hand grip strength, OR 0.65 [0.63-0.67]), lung dysfunction (peak expiratory flow, OR 0.78 [0.76-0.81]) and kidney dysfunction (cystatin C, OR 1.13 [1.11-1.16]), comorbidities of coronary heart disease (OR 1.78 [1.67-1.91]), stroke (OR 2.34 [2.1-1.37]), diabetes (OR 2.03 [1.89-2.18]) and a series of mental disorders. Cognitive functions in multiple tests also demonstrate decline over a decade before the diagnosis. Inadequate activity (3-5 year, overall time of activity, OR 0.82 [0.73-0.92]), drowsiness (3-5 year, sleep duration, OR 1.13 [1.04-1.24]) and weight loss (0-5 year, weight, OR 0.9 [0.83-0.98]) only exhibited associations within five years before the diagnosis. In addition, serum biomarkers of enriched endocrine, dysregulations of ketones, deficiency of brand-chain amino acids and polyunsaturated fatty acids were found in a similar prodromal time window and can be witnessed as the last pre-symptomatic conditions before the diagnosis. INTERPRETATION: Our findings present a comprehensive temporal-diagnostic landscape preceding incident dementia, which could improve selection for preventive and early disease-modifying treatment trials.

Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults.

While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene-phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.