BACKGROUND:Gait is related to weight factors and regulated by the nervous system. Emerging evidence indicates that adiposity impacts brain health. This study explores the relationship between anthropometric indices and gait performance, and investigates the role of brain parameters in the relationship. METHODS:This study included 710 community-dwelling older adults from the Taizhou Imaging Study. Nine anthropometric indices were calculated to assess body weight using standard formulas based on measured height, weight, waist circumference (WC), and hip circumference. Gait assessment includes Timed-Up-and-Go tests, Tinetti tests, and quantitative gait assessment conducted with wearable insole devices. 16 quantitative parameters were summarized into five independent gait domains (rhythm, symmetry, phase, pace and variability) using factor analysis. Quantitative susceptibility mapping was used to measure iron levels in cortical regions. RESULTS:Overweight/obesity group (body mass index, BMI ≥ 24 kg/m²) had significantly poorer performance in the phase domain (standardized β = -0.172, P = 0.023), specifically manifested in the percentage of the double support time (standardized β = 0.783, P = 0.028) and stance time (standardized β = 0.422, P = 0.019). They also exhibited higher iron levels of the inferior temporal gyrus (ITG) (standardized β = 0.641, P = 0.021). The metabolic-related anthropometric indices have stronger associations with gait performance and indices of abdominal assessment more closely correlated with iron levels. The iron levels of ITG mediated the association of anthropometric indices (BMI, WC, Waist-to-Height Ratio, abdominal volume index and body roundness index) with the phase domain, with mediation proportions ranging from 8.95% to 13.45%. CONCLUSION:Increased anthropometric indices are associated with relative prolongation of the stance phase and the iron levels of ITG mediates the associations. Our research offers valuable insights into the neuropathological mechanisms underlying of gait abnormalities in the community-dwelling older adults with overweight/obesity.

Osteoporosis is influenced by both genetic and environmental factors, yet the relative contribution of the exposome remains unclear. This study aimed to systematically identify non-genetic exposures related to osteoporosis and develop an exposome risk score (ERS) to evaluate individual osteoporosis susceptibility. We conducted an exposome-wide analysis of 477,792 UK Biobank participants to identify key exposures associated with osteoporosis. The selected exposures were combined into a weighted Meta-ERS and validated in the Scotland/Wales cohort. The Meta-ERS was further compared with polygenic risk scores (PRS) and linked to plasma proteomics to explore underlying biological pathways. We identified 41 independent non-genetic exposures spanning socioeconomic status, mental health, sleep, diet, smoking, physical activity, environment, and marital status, with socioeconomic status and mental health emerging as the most significant drivers. Based on the identified exposures, we constructed eight domain-specific exposure risk scores and integrated them into a weighted Meta-ERS. The Meta-ERS (R2 = 5.1

Sleep regularity may represent a modifiable risk factor affecting osteoporosis susceptibility, but epidemiological evidence remains scarce. This research sought to analyze the link between sleep regularity parameters and incident osteoporosis, its interaction with genetic risk, and the potential for improved sleep regularity to mitigate risk in individuals with different genetic predispositions within a population-based cohort. A longitudinal analysis was conducted using data from the UK Biobank, which included 87,231 participants without osteoporosis at the time of accelerometer data collection in 2013-2015, with follow-up until June 30, 2023. Sleep regularity parameters were determined by calculating the within-person standard deviation (SD) of 7-day accelerometer-tracked sleep parameters (including sleep duration, onset time, wake-up time, and midpoint). We investigated the association between these four sleep regularity parameters and osteoporosis risk, and assessed the potential reduction of osteoporosis occurrence by enhancing sleep regularity. Additionally, subgroup and sensitivity analyses were executed. Across a median follow-up period of 8.6 years, 2035 new osteoporosis cases were recorded. Participants in the top quartile of SD for sleep duration, onset time, and midpoint had higher osteoporosis risk compared to those in the bottom quartile (fully adjusted HRs ranged from 1.17 to 1.21). Sleep duration SD showed the highest population attributable fraction (PAF). Moreover, a statistically significant additive gene-sleep interaction was identified. When considering both sleep regularity and osteoporosis polygenic risk score (PRS), the group with the highest risk nearly doubled their osteoporosis risk compared to the group with the lowest risk (fully adjusted HRs ranged from 2.22 to 2.24). Importantly, improving sleep regularity mitigated the PRS effect on osteoporosis, with the greatest absolute risk reduction observed in individuals with intermediate PRS-1.6 to 1.7 times that of those with high PRS. Irregular sleep patterns were associated with an increased risk of developing osteoporosis, with exploratory analyses suggesting potential variation across levels of genetic susceptibility. These findings underscore the potential importance of maintaining stable sleep patterns for bone health and suggest that sleep regularity may represent a modifiable behavioral factor for osteoporosis prevention, warranting further investigation.

BACKGROUND AND AIMS:Caloric restriction (CR) has demonstrated benefits in improving individual biomarkers and longevity, but its organ-specific systemic effects remain unclear. We aimed to quantify the effects of long-term CR on longitudinal changes in organ-specific biological age across multiple physiological systems. METHODS:In the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy Phase 2 randomized controlled trial, a subset of 185 participants (120 CR, 65 ad libitum) with available organ-specific biomarkers at baseline and at least one follow-up assessment at 12 or 24 months was analyzed. Participants were assigned to 2 years of sustained CR or an ad libitum diet. Five organ-specific biological ages (cardiovascular, immune, kidney, liver, metabolic) and the whole body age were assessed at both time points. Intention-to-treat, dose-response, and treatment-on-the-treated analyses were performed to evaluate changes in these biological age measures over time. RESULTS:CR mitigated organ-specific increases in biological age relative to the ad libitum diet, with the most robust effects in metabolic system (-0.54 years at 12 months, P = 1.26 × 10-5; -0.63 years at 24 months, P = 3.02 × 10-7) and cardiovascular system (-0.82, P = 9.55 × 10-5; -1.00, P = 1.96 × 10-6), followed by whole body (-1.00, P = 2.53 × 10-3; -1.27, P = 1.20 × 10-4) and immune system (-0.65, P = 1.83 × 10-3; -0.62, P = 2.92 × 10-3); liver age increase was modestly slowed only at 24 months (-0.54, P = 8.30 × 10-3), while kidney age remained unaffected. Participants with a higher dose of CR (≥12.4%) showed a more pronounced attenuation of increases in metabolic and whole body age. Adherence analysis further showed that achieving the 20% CR target led to significant declines in multiple biological ages. CONCLUSION:CR exerts heterogeneous effects on biological aging across organ systems, with the most pronounced responses in metabolic, cardiovascular, immune, and whole body systems. These findings support organ-specific biological ages as sensitive surrogate endpoints for detecting early responses to anti-aging interventions and as practical tools for monitoring or targeting organ-specific aging in future efforts. CLINICAL TRIAL REGISTRATION:ClinicalTrials.gov identifier NCT00427193 (registered January 25, 2007; URL: https://clinicaltrials.gov/study/NCT00427193).

Red cell distribution width (RDW) has been reported to be associated with many diseases, but there is no conclusive evidence for any causal relationship. This study aims to explore to determine the causal effects of high RDW on health with the help of phenome-wide association study (PheWAS), genome-wide association study (GWAS), and Mendelian randomization (MR). The data was derived from the UK Biobank and the population was selected to include white British with complete RDW data at baseline and similar genetic ancestry. The eligible subjects were divided into two non-overlapping groups using random sampling in a ratio of 8:2, with the former for PheWAS and the latter for GWAS. MR analysis was performed to determine whether there was a causal association between RDW and disease outcomes. After multiple corrections, 222 phenotypes associated with RDW were identified with PheWAS. According to GWAS, RDW was associated with 29 independent genetic signals. MR analysis confirmed that RDW had a statistically significant causal association with rheumatoid arthritis and congestive heart failure. Our study provides new evidence that sheds light on the controversial question of whether RDW is associated with disease risk.

Existing aging clocks, designed to quantify biological aging, primarily capture systemic changes and may overlook alterations crucial for cardiometabolic diseases (CMDs). In this study, we developed the CardioMetAge model, an aging clock tailored to predict CMD-related outcomes. Trained in the NHANES-III, the model was applied to the continuous NHANES and UK Biobank. Its associations with cardiometabolic mortality, disease incidence, and transitions between disease states were examined, and its performance in predicting 10-year CMD incidence was also evaluated. We further investigated associations of proteomic pathways, lifestyle factors, and socioeconomic status with CardioMetAge, as well as the impact of caloric restriction intervention on its change. The final CardioMetAge was constructed as a linear combination of chronological age and 12 common clinical biomarkers. Its age deviation (CardioMetAgeDev) showed stronger associations with CMD mortality (HR per SD [95

Existing dietary patterns were not specifically designed to target osteoporosis and lack the precision required for effective prevention. We aimed to develop a Bone Health Optimized Diet (BHOD) that is tailored to more precisely reduce the risk of osteoporosis. Using dietary data from 183,092 UK Biobank (UKB) participants, we combined food-wide association analysis with machine learning to develop the BHOD. We identified 10 important osteoporosis-related food groups and integrated them to construct the BHOD. The protective association was observed between BHOD and the risk of osteoporosis, which was validated in both the UKB validation set and the NHANES population. Furthermore, the BHOD showed broad protective associations with other musculoskeletal disorders, such as rheumatoid arthritis and osteoarthritis. Importantly, high adherence to the BHOD mitigated the negative impact of genetic susceptibility on osteoporosis, with particularly pronounced benefits observed among individuals at high genetic risk. The multi-omics analysis identified multiple biomarkers (7 proteins, 66 metabolites, and 5 inflammatory markers) jointly associated with BHOD and osteoporosis risk, highlighting immune-inflammatory regulation and lipid metabolism as key biological pathways linking diet to bone loss.

Given the limited feasibility of population-wide bone mineral density screening and the infrequency of long-term monitoring in healthy individuals, identifying the window for early intervention and the populations to be prioritized for screening is critical. This study aimed to identify intervention windows for osteoporosis and to determine potential high-risk subtypes within the healthy population. Based on proteomic data from 41,408 healthy adults, we conducted the DE-SWAN method to identify change peaks in plasma protein during the pre-diagnostic osteoporosis phase, and employed finite Gaussian mixture model-based clustering to delineate high-risk subtypes of osteoporosis. We identified 122 protein biomarkers significantly associated with osteoporosis risk throughout the follow-up period. Importantly, we identified two critical peaks occurring approximately 10 and 6 years before diagnosis, with the former enriched in immune-related pathways and the latter prominently involving responses to retinoic acid and glucocorticoids. Furthermore, one high-risk subtype for osteoporosis was identified in both males and females, termed the Frailty and Obesity Subtype. This subtype is characterized by a high degree of frailty and obesity, accompanied by a significantly elevated risk of both osteoporosis and fractures. Finally, we developed a predictive model comprising 10 proteins for identifying high-risk subtypes of osteoporosis, which demonstrated better performance than the traditional risk factor model (AUC: 0.743 vs. 0.680). Our findings demonstrate that proteomic profiling can reveal early molecular changes and identify high-risk subtypes years before clinical onset, providing a foundation for screening and precision prevention of osteoporosis.

OBJECTIVE:To evaluate the associations of both baseline serological profiles and serological transitions of Helicobacter pylori infection patterns with the risk of precancerous gastric lesions (PGLs). DESIGN:We analyzed data from 6208 participants in the FuSion cohort who underwent gastroscopy examination, with available H. pylori antibody and pepsinogen measurements at both baseline and follow-up. H. pylori status defined by IgG antibodies (Ab) and pepsinogens (PG), classified participants into four ABC groups. Multivariable logistic regression evaluated associations with PGLs across baseline and transition statuses. Trend tests were performed across the ABC groups and infection transition categories. RESULTS:A significant increasing trend in the PGLs prevalence and severity was observed across the ABC groups (p for trend < 0.05). Analysis of infection transitions revealed graded risk increases for PGLs from consistently negative to remained positive groups. Interestingly, even participants who seroreverted remained at significantly elevated risks of atrophic gastritis (adjusted odds ratio [aOR] = 2.01, 95% CI: 1.67-2.43) and intestinal metaplasia (aOR = 1.72, 95% CI: 1.14-2.51) compared to the persistently negative participants. The sensitivity analyses excluding baseline PG-positive subjects yielded similar results. CONCLUSION:Long-term exposure to H. pylori is associated with an increased risk of PGLs, and this risk may remain elevated even after seroreversion.

Aging-related metabolic dysregulation and vascular vulnerability contribute substantially to stroke susceptibility, yet subtype-specific metabolic signatures remain incompletely characterized. Employing a nested case–control design within the Taizhou Longitudinal Study, we quantified 296 lipoprotein parameters and 54 metabolites in 1208 stroke-control pairs using nuclear magnetic resonance. Logistic regression estimated subtype-specific associations, and machine learning constructed prediction models for ischemic stroke (IS) and intracerebral hemorrhage (ICH). Distinct metabolic profiles were observed across stroke subtypes. Triglyceride-enriched lipoproteins and several low-molecular-weight metabolites were positively associated with both IS and ICH, whereas apolipoprotein A-related components showed inverse associations, with generally stronger effects observed for IS than for ICH. Age-stratified and interaction analyses revealed age-dependent heterogeneity, especially among histidine and lipoprotein composition measures. To further characterize systemic metabolic vulnerability, we constructed a weighted metabolic risk score (MRS), which was associated with age and statistically accounted for part of the age–stroke association (average causal mediation effects: 0.020 for IS; 0.025 for ICH). MRSs were also positively correlated with age and inflammatory markers, particularly for IS (both P < 0.001). Metabolite-based models improved risk discrimination beyond traditional risk factors for both IS and ICH. These findings identify subtype-specific metabolic signatures of stroke and suggest that circulating metabolomic profiles reflect age-associated metabolic alterations relevant to stroke susceptibility beyond traditional cardiometabolic risk factors.

[Objective]To investigate the associations of multiple glycolipid metabolic indicators and their cumulative abnormality burden with incident colorectal cancer risk in a general population-based prospective cohort,and to examine the mediating role of glycolipid metabolic abnormalities in the relationships between smoking,alcohol consumption,physical activity,and colorectal cancer incidence.[Methods]A total of 17 897 eligible participants recruited from the Taizhou Cohort between 2011 and 2014 were included.Cox proportional hazards regression models were used to assess the associations of conventional and derived glycolipid indicators,as well as a glycolipid abnormality index constructed from total cholesterol,triglycerides,high-density lipoprotein cholesterol,low-density lipoprotein cholesterol,fasting plasma glucose,and insulin,with incident colorectal cancer risk.Restricted cubic spline models were applied to evaluate dose-response relationships for major indicators.Receiver operating characteristic curves were generated to compare predictive performance across indicators.Mediation analyses were conducted to assess the mediating effects of the glycolipid abnormality index on the associations of smoking,alcohol consumption,and physical activity with colorectal cancer incidence.[Results]During a median follow-up of 11.2 years,102 incident colorectal cancer cases were identified,with an incidence density of 51.2 per 100 000 person-years.After adjustment for potential confounders,Cox proportional hazards regression analyses showed that decreased high-density lipoprotein cholesterol was associated with a 1.74-fold higher risk of colorectal cancer(HR=1.74,95%CI:1.03-2.95),elevated low-density lipoprotein cholesterol was associated with a 1.78-fold higher risk(HR=1.78,95%CI:1.05-3.02),and abnormal fasting plasma glucose was associated with a 1.86-fold higher risk(HR=1.86,95%CI:1.15-3.02).Triglycerides and fasting plasma glucose showed an approximately linear increasing association with colorectal cancer risk in multivariable restricted cubic spline models.The glycolipid abnormality index showed a clear gradient association with colorectal cancer risk;participants with three or more abnormal indicators had a 3.08-fold higher risk than those without abnormalities(HR=3.08,95%CI:1.60-5.92).The area under the curve was 0.795,higher than that of individual glycolipid indicators and other combined indices,and gender-stratified analyses showed generally consistent patterns.Glycolipid metabolic abnormalities partially mediated the associations of smoking,alcohol consumption,and physical activity with colorectal cancer incidence,with mediation proportions of 8.33%,9.45%,and 9.04%,respectively.[Conclusion]Glycolipid metabolic abnormalities are associated with an increased risk of incident colorectal cancer.The glycolipid abnormality index shows an increasing relationship with colorectal cancer risk and demonstrates better discrimination,and it partially mediates the associations of smoking,alcohol consumption,and physical activity with colorectal cancer incidence in the overall population.

The Taizhou Longitudinal Study (TZL) is a population-based prospective cohort initiated in 2007, recruiting over 201,000 adults aged 20-80 from urban and rural areas of Taizhou, Jiangsu Province, China. The cohort is extensively phenotyped through baseline questionnaire-based interviews, physical examinations, biochemical assays, and longitudinal follow-up using health records and repeated assessments. A wide range of biospecimens, including blood, urine, saliva, and feces, have been collected to enable omics-level profiling. Genome-wide genotyping has been performed for approximately 50,000 participants recruited from 2009 to 2014. Here, we present an integrated overview of the existing and planned genetic and phenotypic resources, describe genotyping and quality control procedures, and assess cryptic relatedness and population structure, followed by genome-wide association analyses of 66 physical and biochemical traits. In total, 533 independent loci reach Bonferroni significance after clumping. These analyses identify 55 previously unreported loci, demonstrating the capacity of the TZL to elucidate the genetic architecture of complex traits in East Asian populations. By integrating high-quality phenotypic and genotypic data, the TZL enables a comprehensive investigation of gene-environment interactions in the Chinese population. With ongoing expansions and development of a controlled-access data-sharing platform, the TZL is positioned as a valuable resource for precision medicine and public health research.

AIMS:Type 2 diabetes exhibits substantial heterogeneity prior to disease onset, posing challenges for early prevention. This study aimed to identify pre-disease subgroups with distinct risk profiles and develop subgroup-specific prediction models to facilitate early detection and precision intervention. MATERIALS AND METHODS:Proteomic data from 41 030 participants without diabetes at baseline in the UK Biobank were analysed. Proteins associated with incident type 2 diabetes were identified using multivariable-adjusted Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Finite Gaussian mixture model-based clustering was applied to define risk subgroups. Heterogeneity among subgroups was characterised according to proteomic patterns, clinical traits and disease risk. Subgroup-specific prediction models were subsequently developed using Cox regression. RESULTS:A total of 113 protein markers were identified, stratifying the population into three subgroups: metabolically healthy group (MHG), mild inflammation group (MIG) and Dyslipidemia with inflammation group (DLIG). Compared with MHG, DLIG showed the highest risk of type 2 diabetes (HR = 2.58, 95% CI: 2.34-2.84), followed by MIG (HR = 1.71, 95% CI: 1.49-1.98). Enrichment analysis indicated dysregulation of immune-inflammatory and lipid metabolism pathways in DLIG and MIG. Clinically, DLIG exhibited higher BMI, waist circumference, triglycerides, and C-reactive protein levels, whereas MIG showed moderately elevated C-reactive protein. Subgroup-specific proteomic models outperformed traditional clinical models in predicting diabetes risk (AUC 0.820-0.889 vs. 0.709-0.784; C-index 0.787-0.847 vs. 0.687-0.747). CONCLUSION:Proteomics-based clustering identified three pre-disease subtypes of type 2 diabetes characterized by inflammation-lipid profiles, improving risk prediction and supporting precision prevention.

Assessing aging pace through biological age offers a precise perspective and underscores the need for further investigation into organ-level disparities. This observational study utilized multi-scale phenotypes from the Taizhou Imaging Study, encompassing brain imaging, cognitive assessment, blood biochemistry, omics, and physical measures. A total of 904 individuals (403 men and 501 women) aged 55-65 years were included. Age correlations with single and composite phenotypes were assessed, and multi-modal aging clocks were developed, incorporating organ systems, cognition, and the whole body. Here we show that composite phenotypes, such as those of cardiovascular system and bone, alter with age progression and could serve as aging clock features. Despite existing connections among various organs’ aging rates, their low intensity (under 0.25) indicates the variability of aging. Accelerated aging in the brain (mediating 12.46

Few studies take both the volume and location of white matter hyperintensities (WMHs) into account to explore the association between WMH burden within the cholinergic pathways and cognitive impairment. We aimed to investigate associations of cholinergic WMH volume (WMHV) with global cognitive function, cognitive decline, and incident dementia in older adults, which may help us identify a potential imaging biomarker. We assessed non-demented participants (n = 751, mean age 60 years) from the Taizhou Imaging Study with brain MRI at baseline and repeated measures of cognition over 5 years of follow-up. WMHV in the whole brain, the cholinergic pathways, and different tracts in the Montreal Neurologic Institute (MNI) standard space were analyzed. Linear regression, Cox regression, and partial correlation tests were performed to investigate associations between global and regional WMHV and cognitive outcomes. During follow-up, cholinergic WMHV was associated with an annual decline of Mini-Mental State Examination (MMSE) (β coefficient, -0.239; P = 0.004), and incident dementia (HR = 3.54; 95

BACKGROUND:Disruptions in rest-activity circadian rhythms (RAR) have been shown to be associated with an increased risk of osteoporosis. However, there remains a scarcity of prospective studies examining this association. METHODS:This longitudinal cohort study utilized data from the UK Biobank, including 90,029 participants who were initially free of OP and had reliable accelerometer data at baseline, with a median follow-up time of 8.1 years. Participants newly lost to follow-up within the first two years were excluded. We assessed the associations of eleven RAR variables including nonparametric variables relative amplitude (RA), most active 10-h period counts (M10), least active 5-h period counts (L5), interdaily stability (IS), and intradaily variability (IV), and parametric variables amplitude, mesor, pseudo-F statistic, acrophase, alpha, and beta with the risk of OP using Cox models adjusted for multiple confounders. Mediation analyses were conducted to determine whether inflammatory markers mediated the associations between RAR variables and OP incidence. Additionally, two-sample MR analyses were conducted to infer causality. RESULTS:In this study, 1702 new-onset OP cases were documented. Higher RA(adjusted hazard ratio 0.87 [95 % CI 0.83-0.92]) and M10 (0.73 [0.60-0.89]) were associated with a lower risk of osteoporosis, while higher L5 (1.09 [1.04-1.15]) was associated with an increased risk. The associations between these three RAR variables and osteoporosis risk were possibly mediated by leukocyte count and platelet-to-lymphocyte ratio, with mediation proportions ranging from 6.06 % to 13.84 %. Higher alpha of parametric variables (0.92 [0.88-0.97]) were associated with a lower risk of osteoporosis. Two-sample MR analyses suggested potential significant associations between RA, L5, pseudo-F and femoral neck bone mineral density, consistent with observational results. CONCLUSIONS:Our findings suggest that circadian rhythm disruption, as indicated by impaired RAR variables, was associated with higher osteoporosis risk. Circadian rhythm disruption may be a modifiable risk factor that could be targeted for osteoporosis prevention.

Existing biological age (BA) models primarily focus on systemic changes, overlooking alterations crucial for cardiometabolic diseases (CMDs). In this study, we developed the CardioMetAge model, a novel aging clock tailored to predict CMD-related outcomes. Trained in the NHANES-III, the model was applied to the continuous NHANES and UK Biobank. We evaluated it on the prediction of cardiometabolic mortality, morbidity, and disease state transitions. Its associations with proteomic pathways, lifestyle factors, and socioeconomic status, as well as the impact of caloric restriction intervention on its change, were also assessed. The final form of CardioMetAge was concise, incorporating chronological age (CA) and 16 common clinical biomarkers. Its age deviation (CardioMetAgeDev) demonstrated stronger associations with CMD-related mortality, morbidity, and multi-morbidity than age deviations of traditional BA models. It also outperformed CA and PhenoAge in predicting 10-year incidence risks for CMDs. Beyond prediction, our findings highlighted the biological determinants of cardiometabolic aging, with proteomic analyses linking CardioMetAgeDev to inflammatory activation and metabolic disorders. Analysis of modifiable factors revealed that lifestyle and socioeconomic status influenced CMD risks, partly through their effect on CardioMetAgeDev, with mediation proportions of 30.3% and 11.1%, respectively. Additionally, two-year caloric restriction slowed the progression of CardioMetAge by 0.92 years (95% CI: 0.27 to 1.57) compared to the ad libitum group. CardioMetAge outperformed existing BA models in simplicity and in predicting CMD outcomes. It provides valuable insights into the mechanisms of cardiometabolic aging and holds potential for clinical monitoring and evaluating the effectiveness of interventions.

Elevated red blood cell distribution width (RDW) is associated with increased risk of rheumatoid arthritis (RA), but the potential interactions of RDW with genetic risk of incident RA remain unclear. This study aimed to investigate the associations between RDW, genetics, and the risk of developing RA. We analysed data from 145,025 healthy participants at baseline in the UK Biobank. The endpoint was diagnosed rheumatoid arthritis (ICD-10 codes M05 and M06). Using previously reported results, we constructed a polygenic risk score for RA to evaluate the joint effects of RDW and RA-related genetic risk. Two-sample mendelian randomization and bayesian colocalization were used to infer the causal relation between them. A total of 675 patients with RA were enrolled and had a median followed up of 5.1 years, with an incidence rate of 0.57/1000 person-years. The hazard ratio of RA was 1.89 (95

Osteoporosis is the most common disease affecting bone health, and previous studies have identified many risk factors for it. However, the diversity and co-occurrence of risk factors were underestimated in those hypothesis-driven studies. Here we collected data of 491,344 participants from the UK Biobank with more than 17 years of follow-up data for 642 modifiable risk factors. First, we performed an exposure wide association study to identify the factors associated with osteoporosis and generated a composite score for each domain. Then multivariate Cox regression was applied to evaluate the joint effects of risk factors. Finally, Mendelian randomization was used to verify the causal effects. The results showed that osteoporosis is associated with exposure factors from many domains. The results of Mendelian randomization study suggested that irritable bowel syndrome (OR 2.869(1.412-5.831), P = 0.0036), standing height (OR 1.163(1.012-1.337), P = 0.0331) and trunk fat-free mass (OR 1.247(1.017-1.530), P = 0.0341) were potential causal risk factors for osteoporosis, and body mass index (BMI) (OR 0.801(0.675-0.949), P = 0.0103) was associated with reduced risk. Joint analysis suggested that favorable physical measures, health and medical history and lifestyle status could significantly reduce the risk of osteoporosis, especially in men. This study provided a valuable reference for osteoporosis prevention and intervention.