Clonal haematopoiesis of indeterminate potential (CHIP) and low vitamin D are recognised as independent risk factors for type 2 diabetes mellitus (T2DM); however, their joint effects on T2DM incidence remain unclear. We hypothesised that CHIP mutations and vitamin D deficiency may be jointly associated with the risk of T2DM. This study included 405,095 participants from the UK Biobank without T2DM at baseline. CHIP was identified through whole-exome sequencing of peripheral blood samples, and vitamin D was categorised into quartiles. All the participants' variables were tested at baseline. Cox regression was used to evaluate the joint effects of CHIP and vitamin D on incident T2DM. The mean age of the participants was 56.4 ± 8.1 years, and 54.0% were female. After a median follow-up of 12.9 years, 24,724 (6.1%) participants developed T2DM. Multivariable analyses revealed that higher vitamin D quartiles were associated with a lower risk of incident T2DM (HR [95% CI]: 0.89 [0.86-0.92], 0.78 [0.75-0.81], and 0.70 [0.67-0.73] for the 2nd, 3rd, and 4th quartiles vs. Q1, respectively), with similar associations observed in participants with and without CHIP, and no evidence of interaction. In contrast, the presence of CHIP was not clearly associated with incident T2DM after adjustment (HR 1.03 [0.97-1.09]). These findings suggest that vitamin D is associated with type 2 diabetes risk, independent of clonal haematopoiesis, and may help to contextualise risk heterogeneity among individuals with and without CHIP.

INTRODUCTION AND OBJECTIVES:Tobacco exposure during critical developmental windows may have lasting health effects, but its role in the development of chronic liver disease (CLD) remains unclear. This study aimed to examine the association between early-life tobacco exposure and CLD incidence in adulthood. MATERIALS AND METHODS:We included 429,603 participants without prior liver diseases from the UK Biobank. Information on in utero tobacco exposure and the age of smoking initiation was extracted, categorized as never-smokers, adulthood (≥18 y), adolescence (15-17 y), and childhood (5-14 y). Composite CLD and individual endpoints, including non-alcoholic fatty liver disease (NAFLD), fibrosis/cirrhosis, alcohol-related liver disease (ALD), viral hepatitis, and liver cancer, were ascertained through electronic health records. RESULTS:After covariate adjustment, in utero tobacco exposure was associated with a greater risk of incident CLD (HR 1.27; 95 % CI 1.21, 1.34). A significant dose-response association was observed between the age of smoking initiation and CLD risk; the HRs (95 % CIs) for smoking initiation in adulthood, adolescence, and childhood were 1.45 (1.36, 1.54), 1.48 (1.40, 1.57), and 1.81 (1.68, 1.96), respectively (P trend <0.001). The results were similar for NAFLD, fibrosis/cirrhosis, and ALD. Participants with both in utero tobacco exposure and smoking initiation in childhood had the highest CLD risk (HR 2.22; 95 % CI 1.98, 2.48). Among participants who started smoking in childhood or adolescence, the risk of CLD was substantially reduced in those with smoking cessation in midlife compared to those who continued smoking. The mediation analysis indicated that metabolic traits including obesity-related traits, lipid profile, and liver function partially explained the association between early-life tobacco exposure and CLD incidence. CONCLUSIONS:In utero and childhood/adolescence exposure to tobacco smoke was associated with an increased risk of CLD later in life.

OBJECTIVES:We aimed to investigate the association of the status of iodized salt in terms of consumption of salt type and urinary iodine concentration (UIC) in diabetes, with frailty and examine whether this association could be modified by thyroid function. DESIGN:A population-based cohort study. SETTING AND PARTICIPANTS:We included 850 patients with type 2 diabetes from 11 communities in Shanghai, who completed five-year follow-up. MEASUREMENTS:The type of salt consumed was collected through a standardized questionnaire and UIC was measured by an inductively coupled plasma-mass spectrometer. Frailty was assessed by frailty phenotype. Serum thyroid-stimulating hormone (TSH) and free thyroxine (FT4) were measured by electrochemiluminescence. Modified Poisson regression model with robust variance was used to estimate the relative risks (RRs) with 95% confidence intervals (CIs) for frailty in relation to iodized salt consumption and UIC. RESULTS:In this five-year follow-up study in patients with diabetes, 111 (12.9%) patients progressed to frailty. Patients who consumed non-iodized salt (RR: 1.09, 95% CI: 1.01-1.18) had an increased risk of frailty, compared to patients who consumed iodized salt. Lower UIC was associated with a higher risk of frailty (1.10, 1.01-1.19). In patients with high TSH and low FT4, the RRs of frailty were 1.20 (1.08-1.34) and 1.15 (1.02-1.29) for non-iodized salt, and 1.14 (1.02-1.28) and 1.12 (0.99-1.27) for low UIC. CONCLUSIONS:Non-iodized salt consumed and low UIC were associated with an increased risk of frailty in diabetes, particularly in those with high TSH and low FT4. Maintaining adequate iodine intake is critically important for preventing frailty in diabetes, especially for individuals with potential thyroid dysfunction.

Background: Much remains unknown about the associations between adverse childhood experiences (ACEs), adverse adulthood experiences (AAEs) and the risk of neurodegenerative diseases, including dementia and Parkinson's disease (PD).Purpose: To examine the associations of ACEs and AAEs with incident dementia and PD, and to evaluate their interactions with genetic risk.Methods: We included 147,942 participants (mean [SD]: 55.9 [7.7] years) without dementia and PD at baseline from UK Biobank. ACEs and AAEs were assessed through an online mental health questionnaire, including emotional neglect, physical abuse, emotional abuse, sexual abuse, and physical neglect. Polygenic risk scores (PRS) were constructed for dementia and PD. Replication analysis was conducted in the China Health and Retirement Longitudinal Study (CHARLS) cohort.Results: During a median follow-up of 15.1 years, 851 incident dementia and 729 PD cases occurred. A greater number of ACEs was associated with increased risks of dementia (HR, 1.14, 95% CI: 1.08-1.21, per additional ACE) and PD (1.11, 1.04-1.18). Similarly, a higher number of AAEs was linked to elevated risks of dementia (1.16, 1.09-1.24) and PD (1.02, 0.95-1.10), though the latter was not statistically significant. Moreover, significant additive interactions between ACEs, AAEs, and genetic risk were observed for dementia, which accounted for an additional 13% to 19% of dementia cases. Results from the CHARLS confirmed the associations of ACEs and AAEs with dementia and PD.Conclusions: Exposure to ACEs or AAEs was associated with increased risks of dementia and PD. The dementia risk associated with ACEs was amplified in individuals with AAEs or high genetic susceptibility. These findings highlight the importance of life-course prevention targeting both ACEs and AAEs in mitigating dementia and PD risks, particularly among individuals with high genetic susceptibility. These findings should be interpreted with caution due to potential recall bias, self-reported assessments, and selection bias.

BACKGROUND:Cardiometabolic risk factors have been associated with the risk of late-onset dementia. However, evidence regarding early-onset dementia was inconsistent, and the impact of clustered cardiometabolic risk factors was unclear. We aimed to investigate the associations of cardiometabolic profiles with incident early-onset and late-onset dementia. METHODS:Among 289 494 UK Biobank participants, cluster analysis was built on 12 common cardiometabolic markers. Analyses were performed on those aged <65 years at baseline (n = 249 870) for early-onset dementia and those ≥65 at the end of follow-up (n = 191 213) for late-onset dementia. RESULTS:During a median follow-up of 14.1 years, 279 early-onset dementia cases and 3167 late-onset dementia cases were documented. Among the five clusters of cardiometabolic profiles identified (cluster 1 [obesity-dyslipidemia pattern], cluster 2 [high blood pressure pattern], cluster 3 [high liver enzymes pattern], cluster 4 [inflammation pattern] and cluster 5 [relatively healthy pattern]), cluster 3 was significantly associated with higher risks of both early-onset and late-onset dementia; however, the risk estimate for early-onset dementia (hazard ratio 2.58, 95% CI 1.61-4.14) was larger than that for late-onset dementia (1.36, 1.09-1.71). Cluster 4 was associated with a higher risk of late-onset dementia (hazard ratio 1.39, 95% CI 1.13-1.72). No significant interactions were observed between cardiometabolic clusters and apolipoprotein E ε4 genotype. CONCLUSIONS:Cardiometabolic patterns characterised by relatively high liver enzyme levels or systemic inflammation were associated with increased risks of early-onset and late-onset dementia. Identification of high-risk subgroups according to distinct cardiometabolic patterns might help develop more precise strategies for dementia prevention regardless of apolipoprotein E (APOE) ε4 status.

BACKGROUND:Adverse health behaviors have been found to play a role in linking socioeconomic deprivation and mortality, but relevant evidence in patients with type 2 diabetes is lacking. We aimed to quantify the mediation effect of overall lifestyles on the association between socioeconomic deprivation and premature mortality as well as the interaction of deprivation and lifestyle in diabetes. METHODS:This cohort study included 20,463 UK Biobank participants with type 2 diabetes at recruitment between 2006 and 2010. Socioeconomic deprivation level was determined using the Townsend deprivation index. An overall lifestyle score was constructed based on 6 health behaviors including smoking, alcohol consumption, physical activity, diet, sleep duration, and television viewing time. Cox proportional hazards models were employed to investigate the associations of socioeconomic deprivation and lifestyle with premature mortality. RESULTS:Over a mean follow-up of 7.4-12.7 years, 3381, 2382, 1281, and 577 patients with diabetes died before ages 80, 75, 70, and 65 years, respectively. High socioeconomic deprivation showed an association with a higher risk of premature mortality that was partially mediated by overall lifestyles. A significant interaction was found between lifestyle and deprivation on premature mortality, which became more apparent as age at death decreased. The adjusted hazard ratio (HR) for death before age 80 years when comparing the unfavorable versus favorable lifestyle was 1.49 (95% CI 1.21-1.82) in the least deprived group and 1.92 (1.56-2.36) in the most deprived group. Equivalent HRs for death before age 65 years were 1.33 (0.76-2.33) and 3.78 (2.04-7.02), respectively. CONCLUSIONS:In patients with type 2 diabetes, unhealthy lifestyles mediated the association between socioeconomic deprivation and premature mortality and conferred disproportionate risk of premature mortality in more deprived groups.

AIMS:Severe liver disease (SLD) in nonalcoholic fatty liver disease (NAFLD) is often diagnosed late due to the long asymptomatic period of progressive fibrosis. We aimed to identify metabolomic profiles associated with SLD and develop a predictive model to improve risk stratification. MATERIALS AND METHODS:We enrolled 59 579 UK Biobank participants with a positive fatty liver index (≥60) and plasma metabolomic profiles, evaluating the incidence of cirrhosis, decompensated liver disease, hepatocellular carcinoma and/or liver transplantation. Cox regression models were applied to evaluate the associations between individual metabolites and SLD risk. Using an interpretable machine-learning framework, a metabolomics-integrated nomogram prediction model was developed and compared with conventional scoring systems. RESULTS:After Bonferroni correction, 110 of 249 metabolites were significantly associated with the risk of incident SLD in the Cox regression model. Among them, 11 metabolites were ultimately prioritised as predictors to construct the metabolomic score based on the optimal machine learning algorithm. The nomogram integrating metabolomic score, gamma glutamyltransferase, platelet count, waist/hip ratio, diabetes and sex showed better predictive capacity of 10-year SLD risk (area under the receiver operating characteristic 0.841 [95% CI: 0.800-0.881]) than the fibrosis-4 index (0.712, 0.662-0.763), NAFLD fibrosis score (0.659, 0.609-0.709) and aspartate aminotransferase-to-platelet ratio index (0.705, 0.652-0.759) in the validation cohort. Categorisation of participants according to selected cutoffs revealed a distinct cumulative risk of SLD, with a hazard ratio of 25.71 (95% CI: 17.10-38.66) for the high-risk group compared with the low-risk group. CONCLUSIONS:Integrating plasma metabolomics with routine indicators enhanced the predictive capacity for severe liver outcomes of NAFLD, which shows the potential benefits in disease risk stratification and precise interventions.

BACKGROUND:The associations of cardiometabolic diseases (CMDs) on the incidence of neurological and psychiatric disorders (NPDs) and progression to neuropsychiatric multimorbidity (NPM) and subsequent death are unclear. We aimed to evaluate the associations between CMDs and dynamic transitions of NPDs. MATERIALS AND METHODS:This prospective cohort study included 402 950 participants from the UK Biobank. NPM was defined as the coexistence of at least two NPDs (dementia, Parkinson disease, anxiety, depression and sleep disorders). A multi-state model was used to explore the association between CMDs (type 2 diabetes, hypertension, ischaemic heart disease and stroke) and the progression trajectory of NPDs. RESULTS:During a median follow-up of 14.1 years, 43 359 participants developed at least one NPD, 9087 developed NPM and 31 307 died. CMDs were significantly associated with different stages of NPD progression. The hazard ratios (95% confidence intervals) per additional CMD were 1.28 (1.26, 1.29) and 1.07 (1.04, 1.10) for transitions from healthy to first neuropsychiatric disease (FNPD), and from FNPD to NPM, and 1.38 (1.36, 1.40), 1.19 (1.16, 1.23) and 1.19 (1.13, 1.25) for death from healthy, FNPD and NPM respectively. When dividing FNPD into individual NPDs, the associations of single and combined CMDs with NPD transitions varied depending on disease types and specific combinations of CMDs, even within the same transition stage. Results from the China Health and Retirement Longitudinal Study cohort confirmed the associations between CMDs and NPDs. CONCLUSION:CMDs could play important roles in basically all transitions of NPD progression, highlighting the significance of CMD management for the prevention and control of NPDs.

Existing proteomic aging clocks have been derived from the overall population, with little consideration of extended models tailored to individuals with different glycemic status. We aimed to quantify glycemic status-dependent proteomic signatures of aging and developed proteomic aging scores (ProAS) for health risk prediction. A total of 2923 plasma proteins were measured using Olink in 46,047 UK Biobank participants, including 37,353 with normoglycemia, 5977 with prediabetes, and 2717 with diabetes. Using a three-step screening approach, we identified 11, 23, and 21 representative protein biomarkers associated with all-cause mortality among individuals with normoglycemia, prediabetes, and diabetes, respectively. Three proteins (GDF15, EDA2R, and WFDC2) were shared across all groups, with GDF15 emerging as the top-ranked important protein in normoglycemia and prediabetes and WFDC2 in diabetes. The protein-based ProAS according to glycemic status showed significant associations with diverse health outcomes. Adding the ProAS in the models improved the predictive accuracy of mortality and incident diseases beyond conventional risk factors, but the performance progressively diminished as glycemic status deteriorated. In addition, 72, 51, and 36 out of 102 modifiable factors spanning seven categories were identified as determinants for ProRS in normoglycemia, prediabetes, and diabetes, respectively. Our findings extend the current proteomic clocks by revealing glycemic status-specific aging patterns and their ability to predict age-related outcomes, potentially refining risk stratification and targeted interventions for healthy aging.

Social determinants of health (SDHs) are the primary drivers of health inequalities, but whether biological aging plays a role in linking SDHs to health outcomes remains unclear. Here we utilize detailed information on social determinants across five domains, clinical parameters and electronic health records from the UK Biobank and US NHANES to examine the associations between combined SDHs, accelerated biological aging, and health outcomes. Compared with participants in the favourable SDH group, participants in the unfavourable SDH group had increased KDM-BA and phenotypic age acceleration. Moreover, unfavourable SDHs were associated with elevated risks of mortality and incident diseases. Accelerated biological aging significantly mediated the association between SDHs and all-cause and cause-specific mortality (UK Biobank: mediation proportion 13.46%-25.21%; US NHANES: 7.62%-22.16%). Also, accelerated biological aging served as a mediator between SDHs and incident diseases in the UK Biobank, with the mediation proportions ranging from 6.20% to 30.48%. The estimates were likely specific to the UK Biobank cohort considering its healthy volunteer bias and limited socioeconomic diversity. Overall, our study reveals that the biological aging discrepancy partially explains the associations of combined SDHs with mortality and chronic diseases. Assessing and delaying aging acceleration may be an effective way to narrow the health disparities caused by SDHs.

Background:Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.Methods:This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.Results:During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.Conclusions:Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.Funding:Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).

This study investigates the spheroidization behavior of the O phase in Ti2AlNb alloy during high temperature deformation through a designed high-throughput experimental approach. The results of the high-throughput deformation experiments indicate that temperature, strain, and strain rate influence the spheroidization behavior of the O phase. Specifically, an increase in temperature and strain promotes the spheroidization of the O phase, while the strain rate exhibits the opposite effect. Moreover, the spheroidization mechanisms of this alloy during high-temperature deformation can be identified and primarily involve grain boundary separation mechanism, terminal dissolution mechanism, continuous dynamic recrystallization mechanism, edge spheroidization mechanism, and shear spheroidization mechanism. Furthermore, the analysis of experimental results reveals that the different morphologies of the spheroidized O phase have varying effects on the microscale mechanical response. In the region of large-sized high-density spheroidized O phase, the influence of back stress may extend to the entire B2 phase, thereby enhancing the B2 phase and subjecting the O phase and B2 phase to similar strains. Therefore, a small quantity of O phase is affected by the forward stress. Conversely, in the region of small-sized low-density spheroidized O phase, a small quantity of B2 phase is affected by the back stress, and the majority of the O phase is affected by forward stress. Eventually, the interaction mechanism between O phase and B2 phase during high-temperature deformation is explored for the first time through theoretical analysis.

Background Little is known about the associations between choline metabolites (total choline, phosphatidylcholine, and glycine) and the incidence of heart failure (HF). Objectives The purpose of this study was to assess the associations of choline metabolites with incident HF and examine the effect modification by genetic susceptibility. Methods This prospective cohort study followed 245,072 participants from the UK Biobank from baseline (2006-2010) until March 30, 2023. Participants were free of cardiovascular diseases at baseline. Circulating choline metabolites were quantitated using nuclear magnetic resonance spectrometer. Cox proportional hazards models were fitted to assess the association of choline metabolites and genetics with incident HF. Two-sample Mendelian randomization analyses were implemented to confirm the findings in observational analysis. Results During a median follow-up of 14.1 years, 5,468 incident HF cases were documented. Total choline and phosphatidylcholine were positively associated with HF risk (HR: 1.08 [95% CI: 1.04-1.12] and HR: 1.08 [95% CI: 1.05-1.12], per one SD increase, respectively). Compared with the lowest quartile group, the HR for the highest quartile group was 1.23 (95% CI: 1.12-1.35) for total choline and 1.23 (95% CI: 1.12-1.34) for phosphatidylcholine. Glycine was inversely associated with HF risk (HR: 0.97 [95% CI: 0.94-0.99], per one SD increase). Participants with high polygenic risk score and high total choline or phosphatidylcholine had the highest risk of HF, whereas participants with low polygenic risk score and high glycine had the lowest risk. No statistically significant interactions were observed between choline metabolites and genetic susceptibility to HF. The Mendelian randomization analysis supported the potential causal associations of total choline (OR: 1.71 [95% CI: 1.01-1.35]) and glycine (OR: 0.93 [95% CI: 0.88-0.99]) with HF. Conclusions Circulating choline metabolites were associated with the risk of incident HF, independent of genetic susceptibility. Whether targeting the metabolic pathway of choline might be a potential strategy for improving heart health warrants further validation.

Abstract Background Type 2 diabetes (T2D) is associated with an increased risk of premature death. Whether multifactorial risk factor modification could attenuate T2D-related excess risk of death is unclear. We aimed to examine the association of risk factor target achievement with mortality and life expectancy among patients with T2D, compared with individuals without diabetes. Methods In this longitudinal cohort study, we included 316 995 participants (14 162 with T2D and 302 833 without T2D) free from cardiovascular disease (CVD) or cancer at baseline between 2006 and 2010 from the UK Biobank. Participants with T2D were categorised according to the number of risk factors within target range (non-smoking, being physically active, healthy diet, guideline-recommended levels of glycated haemoglobin, body mass index, blood pressure, and total cholesterol). Survival models were applied to calculate hazard ratios (HRs) for mortality and predict life expectancy differences. Results Over a median follow-up of 13.8 (IQR 13.1–14.4) years, deaths occurred among 2105 (14.9%) participants with T2D and 18 505 (6.1%) participants without T2D. Compared with participants without T2D (death rate per 1000 person-years 4.51 [95% CI 4.44 to 4.57]), the risk of all-cause mortality among those with T2D decreased stepwise with an increasing number of risk factors within target range (0–1 risk factor target achieved: absolute rate difference per 1000 person-years 7.34 [4.91 to 9.78], HR 2.70 [2.25 to 3.25]; 6–7 risk factors target achieved: absolute rate difference per 1000 person-years 0.68 [-0.62 to 1.99], HR 1.16 [0.93 to 1.43]). A similar pattern was observed for CVD and cancer mortality. The association between risk factors target achievement and all-cause mortality was more prominent among participants younger than 60 years than those 60 years or older (P for interaction = 0.012). At age 50 years, participants with T2D who had 0–1 and 6–7 risk factors within target range had an average 7.67 (95% CI 6.15 to 9.19) and 0.99 (-0.59 to 2.56) reduced years of life expectancy, respectively, compared with those without T2D. Conclusions Individuals with T2D who achieved multiple risk factor targets had no significant excess mortality risk or reduction in life expectancy than those without diabetes. Early interventions aiming to promote risk factor modification could translate into improved long-term survival for patients with T2D.

Conflicting evidence exists on the relationship between body mass index (BMI) and serum uric acid (SUA), and importantly, the causal role of BMI in SUA remains unclear. This study investigated the BMI-SUA relationship and its causality among Chinese adults.

The Ti2AlNb alloy is a refractory material with the potential to replace Ni-based alloys in the manufacturing process of aerospace engines. However, the development of this alloy is still in the research stage, requiring further investigation to promote its industrial application. Therefore, this paper provides an overview of the alloy, starting from its elemental composition and encompassing its microstructural morphology, fabrication processes, and mechanical properties. First, this paper presents the nine common alloying elements (Al, Nb, Mo, Zr, Fe, V, W, Ta, and Si), which play various roles in determining the alloy's microstructure and mechanical performance. Then, the paper presents three typical microstructures and the corresponding microstructure regulation processes, providing references for microstructure regulation. In the regulation process, although there are seven manufacturing processes (Casting, Forming under pressure, Machining, Welding and joining, Powder metallurgy, Additive manufacturing, and Surface treatment) currently applied to the industrialization of this alloy, certain shortcomings still exist, indicating significant research opportunities. Finally, the paper summarizes the relationships between the alloy's typical microstructures and its mechanical properties. In conclusion, the work presented in this paper offers a clear reference for advancing the industrial application of the alloy and encourages future researchers to contribute to the further development of this field based on the foundation established by this review.

CONTEXT:Vitamin D status has been associated with risk of type 2 diabetes (T2D), but evidence is scarce regarding whether such relation differs by glycemic status. OBJECTIVE:To prospectively investigate the association between serum 25-hydroxyvitamin D (25(OH)D) and risk of incident T2D across the glycemic spectrum and the modification effect of genetic variants in the vitamin D receptor (VDR). METHODS:This prospective study included 379 699 participants without T2D at baseline from the UK Biobank. Analyses were performed according to glycemic status and HbA1c levels. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% CIs. RESULTS:During a median of 14.1 years of follow-up, 6315 participants with normoglycemia and 9085 patients with prediabetes developed T2D. Compared with individuals with 25(OH)D < 25 nmol/L, the multivariable-adjusted HRs (95% CIs) of incident T2D for those with 25(OH)D ≥ 75 nmol/L was 0.62 (0.56, 0.70) among the normoglycemia group and 0.64 (0.58, 0.70) among the prediabetes group. A significant interaction was observed between 25(OH)D and VDR polymorphisms among participants with prediabetes (P interaction = .017), whereby the reduced HR of T2D associated with higher 25(OH)D was more prominent in those carrying the T allele of rs1544410. Triglyceride levels mediated 26% and 34% of the association between serum 25(OH)D and incident T2D among participants with normoglycemia and prediabetes, respectively. CONCLUSION:Higher serum 25(OH)D concentrations were associated with lower T2D risk across the glycemic spectrum below the threshold for diabetes, and the relations in prediabetes were modified by VDR polymorphisms. Improving the lipid profile, mainly triglycerides, accounted for part of the favorable associations.

Evidence for reciprocal comorbidity of schizophrenia (SCZ) and body mass index (BMI) has grown in recent years. However, little is known regarding the shared genetic architecture or causality underlying the phenotypic association between SCZ and BMI. Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) on each trait, we investigated the genetic overlap and causal associations of SCZ with BMI. Our study demonstrated a genetic correlation between SCZ and BMI, and the correlation was more evident in local genomic regions. The cross-trait meta-analysis identified 27 significant SNPs shared between SCZ and BMI, most of which had the same direction of influence on both diseases. Mendelian randomization analysis showed the causal association of SCZ with BMI, but not vice versa. Combining the gene expression information, we found that the genetic correlation between SCZ and BMI is enriched in six regions of brain, led by the brain frontal cortex. Additionally, 34 functional genes and 18 specific cell types were found to have an impact on both SCZ and BMI within these regions. Taken together, our comprehensive genome-wide cross-trait analysis suggests a shared genetic basis including pleiotropic loci, tissue enrichment, and shared function genes between SCZ and BMI. This work provides novel insights into the intrinsic genetic overlap of SCZ and BMI, and highlights new opportunities and avenues for future investigation.