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Abstract Advances in cancer genomics have significantly expanded our understanding of cancer biology. However, the high cost of drug development limits our ability to translate this knowledge into precise treatments. Approved non-oncology drugs, comprising a large repository of chemical entities, offer a promising avenue for repurposing in cancer therapy. Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. Utilizing protein–protein interaction networks, CHANCE harmonizes multilevel mutation annotations and integrates pharmacological information across different drugs into a single model. We systematically benchmarked the performance of CHANCE and show its predictions are better than previous model and highly interpretable. Applying CHANCE to approximately 5000 cancer samples indicated that >30% might respond to at least one non-oncology drug, with 11% non-oncology drugs predicted to have anticancer activities. Moreover, CHANCE predictions suggested an association between SMAD7 mutations and aspirin treatment response. Experimental validation using tumor cells derived from seven patients with pancreatic or esophageal cancer confirmed the potential anticancer activity of at least one non-oncology drug for five of these patients. To summarize, CHANCE offers a personalized and interpretable approach, serving as a valuable tool for mining non-oncology drugs in the precision oncology era.

Abstract Background Asthma poses a significant global health challenge, characterized by high rates of morbidity and mortality. Despite available treatments, many severe asthma patients remain poorly managed, highlighting the need for novel therapeutic strategies. This study aims to identify potential drug targets for asthma by examining the influence of circulating plasma proteins on asthma risk. Methods This study employs summary-data-based Mendelian randomization (MR) and two-sample MR methods to investigate the association between 2940 plasma proteins from the UK Biobank study and asthma. The analysis includes discovery (FinnGen cohort) and replication (GERA cohort) phases, with Bayesian colocalization used to validate the relationships between proteins and asthma. Furthermore, protein–protein interaction and druggability assessments were conducted on high-evidence strength protein biomarkers, and candidate drug prediction and molecular docking were performed for proteins without targeted drugs. Given the complexity of asthma pathogenesis, the study also explores the relationships between plasma proteins and asthma-related endpoints (e.g., obesity-related asthma, infection-related asthma, childhood asthma) to identify potential therapeutic targets for different subtypes. Results In the discovery cohort, 75 plasma proteins were associated with asthma, including IL1RAP, IL1RL1, IL6, CXCL5, and CXCL8. Additionally, 6 proteins (IL4R, LTB, CASP8, MAX, PCDH12, and SCLY) were validated through co-localization analysis and validation cohort. The assessment of drug targetability revealed potential drug targets for IL4R, CASP8, and SCLY, while candidate drugs were predicted for LTB and MAX proteins. MAX exhibited strong binding affinity with multiple small molecules indicating a highly stable interaction and significant druggability potential. Analysis of the 75 proteins with 9 asthma-related endpoints highlighted promising targets such as DOK2, ITGAM, CA1, BTN2A1, and GZMB. Conclusion These findings elucidate the link between asthma, its related endpoints, and plasma proteins, advancing our understanding of molecular pathogenesis and treatment strategies. The discovery of potential therapeutic targets offers new insights into asthma drug target research.

BackgroundWater, coffee and tea are the primary sources of daily hydration. However, the sex-specific relationship between these beverages and cardiovascular disease (CVD) among remains unclear.MethodsIn total, 210,239 men and 251,383 women from the UK Biobank were included. The consumption of water, coffee and tea were self-reported. CVDs, including coronary heart disease (CHD), stroke and heart failure (HF) were followed till March 1st, 2023. Sex-specific Cox models were utilized to evaluate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations.ResultsDuring a median follow-up of 8.7 years, 11,098 (2.40%) participants developed new-onset HF, 33,426 (7.24%) participants developed new-onset CHD, and 9,706 (2.10%) participants developed new-onset stroke. After adjustments, higher water consumption was generally associated with reduced risk of CVDs among both men and women. In contrast, heavy coffee consumption (particularly ≥6 cups/day) was associated with a greater risk of HF [1.16 (1.03–1.31) in men vs. 1.25 (1.12–1.40) in women], a greater risk of CHD [1.27 (1.18–1.36) in men vs. 1.21 (1.14–1.29) in women] and a greater risk of stroke [1.13 (0.99–1.29) in men vs. 1.20 (1.03–1.31) in women]. Similarly, heavy tea consumption was associated with an increased risk of HF (men: HR 1.19 [1.08–1.31]; women: HR 1.12 [1.02–1.23]) and CHD (men: HR 1.12 [1.05–1.18]; women: HR 1.18 [1.12–1.24]).ConclusionOur study revealed that water consumption was associated with a lower risk of CVDs, with a lower risk of CVDs, while heavy coffee or tea consumption was linked to a higher risk. Notably, coffee and tea consumption partially attenuated the protective association of water intake with CVDs. Furthermore, significant sex differences were observed in the associations between coffee or tea consumption and CHD incidence.

Abstract Physical activity (PA) has been shown to reduce diabetes mortality, but largely based on imprecise self-reported data, which may hinder the development of related recommendations. Here, we perform a prospective cohort study of 4003 individuals with type 2 diabetes (T2D) from the UK Biobank with a median follow-up of 6.9 years. Duration and intensity of PA are measured by wrist-worn accelerometers over a 7-day period. We observe L-shaped associations of longer duration of PA, regardless of PA intensity, with risks of all-cause and cancer mortality, as well as a negatively linear association with cardiovascular disease mortality. 18.8%, 28.0%, and 31.1% of deaths are attributable to the lowest level of light-intensity PA, moderate-intensity PA, and vigorous-intensity PA, respectively. Collectively, our findings provide insights for clinical guidelines that should highlight the potential value of adherence to greater intensity and duration of PA for patients with T2D.

ObjectiveTo analyze the change in the disease burden of cardiovascular disease (CVD) and chronic respiratory disease (CRD) among Chinese residents from 1990 to 2019, and to predict the incidence rate, mortality rate, and disability-adjusted life year (DALY) rate of CVD and CRD from 2020 to 2030, so as to provide a reference for formulating the prevention and control strategies of CVD and CRD in China. MethodsFrom the Global Burden of Disease 2019 (GBD 2019) study, we collected relevant data on the burden of CVD and CRD among Chinese residents from 1990 to 2019. Descriptive analysis was performed using the following indicators: incident cases (in million), incidence rate (1/100 000), standardized incidence rate (1/100 000), number of deaths (in million), mortality rate (1/100 000), standardized mortality rate (1/100 000), DALY (person-years), DALY rate (1/100 000), and standardized DALY rate (1/100 000). The Joinpoint regression model was used to analyze the changes in the disease burden of CVD and CRD among Chinese residents during the period, and the gray prediction model was used to predict the incidence rate, mortality rate, and DALY rate of CVD and CRD among Chinese residents from 2020 to 2030. ResultsComparison between 1990 and 2019 showed an increase in the number of CVD incident cases (5.3007 vs. 12.3411), incidence rate (447.81 vs. 867.65), standardized incidence rate (646.20 vs. 652.21), number of deaths (2.4240 vs. 4.5843), mortality rate (204. 78 vs. 322.30), DALYs (6026.16 × 104 vs. 9193.31 × 104), and DALY rate (5 091.03 vs. 6463.47) among Chinese residents, with overall percentage changes of 132.82%, 93.75%, 0.93%, 89.12%, 57.39%, 52.56%, and 26.96%, respectively, but the decreases in CVD standardized mortality rate (381.21 vs. 276.94) and standardized DALY rate (7 412.81 vs. 4 938.38), with overall percentage changes of – 27.35% and – 33.38%, respectively. The number of CRD incident cases and the incidence rate increased from 8.5808 and 724.92 in 1990 to 11.9075 and 837.17 in 2019, with the overall percentage changes of 38.77% and 15.48%; while there were decreases in standardized incidence rate (863.43 vs. 767.50), number of deaths (1.3010 vs. 1.0853), mortality rate (109.93 vs. 76.30), standardized mortality rate (226.43 vs. 67.98), DALYs (2 899.21 × 104 vs. 2 252.05 × 104), DALY rate (2 449.31 vs. 1 583.33), and standardized DALY rate (3 910.13 vs. 1 269.95) of CRD during the period, with the total percentage changes of – 11.11%, – 16.58%, – 30.59%, – 69.98%, – 22.32%, – 35.36%, and – 67.52%, respectively. The Joinpoint regression model showed an increasing trend in the overall standardized incidence rate of CVD among Chinese residents from 1990 to 2019 (average annual percentage change [AAPC] = 0.03%, P < 0.05), while the standardized mortality rate and standardized DALY rate of CVD and the standardized incidence rate, standardized mortality rate, and standardized DALY rate of CRD all showed a decreasing trend (all P < 0.05). The predicted CVD incidence rate of Chinese residents in 2020 – 2030 is 886.94 – 1 151.61, the predicted mortality rate is 329.00 – 392.92, and the predicted DALY rate is 6 525.22 – 7 260.53. The predicted CVD incidence rate, mortality rate, and DALY rate in 2020 – 2030 continue to increase (all P = 1.00); the predicted CRD incidence rate of Chinese residents in 2020 – 2030 will be 824.71 – 971.00, the predicted mortality rate will be 55.68 – 68.42, and the predicted DALY rate will be 1 145.20 – 1 420.31. The predicted CRD incidence rate will continue to increase, the predicted mortality rate and the predicted DALY rate will continue to decrease (P = 0.85, 1.00 and 1.00).ConclusionFrom 1990 to 2019, the standardized CVD incidence rate of Chinese residents showed an overall increasing trend, while the standardized CVD mortality rate, standardized DALY rate, and CRD standardized incidence rate, standardized mortality rate, and standardized DALY rate showed an overall decreasing trend; from 2020 to 2030, the predicted CVD incidence rate, mortality rate, DALY rate, and predicted CRD incidence rate all continued to increase, while the predicted CRD mortality rate and DALY rate both continued to decrease.

Background The associations between sugary beverages and genetic predisposition to depression risk remain unclear.Aims This study aimed to investigate the associations of sugar-sweetened beverages (SSBs), artificially sweetened beverages (ASBs) and natural juices (NJs) with depression and to assess whether these associations were modified by genetic predisposition.Methods We used data from the UK Biobank of 180 599 individuals aged 39–72 years who were depression-free at baseline. Dietary intake of SSBs, ASBs and NJs was accessed by a 24-hour dietary recall between 2009 and 2012. The Polygenic Risk Score for depression was estimated and categorised as low (lowest tertile), intermediate (tertile 2) and high (highest tertile). Cox proportional hazard and substitution models were conducted to evaluate hazard ratios (HRs) and 95% CIs.Results Over the 12-year follow-up, 4915 individuals developed depression. Higher consumption (>2 units/day) of SSBs (HR: 1.26, 95% CI 1.12 to 1.43) and ASBs (HR: 1.40, 95% CI 1.23 to 1.60) were both associated with an increased risk of depression. However, moderate consumption (>0–1 units/day) of NJs was associated with a lower risk of depression (HR: 0.89, 95% CI 0.83 to 0.95). Furthermore, genetic predisposition did not modify these associations (p interaction>0.05). In substitution models, the HRs for depression risk were 0.94 (95% CI 0.89 to 0.99) and 0.89 (95% CI 0.85 to 0.94), respectively, when 1 unit/day of SSBs or ASBs was replaced by an equivalent intake of NJs.Conclusions Higher consumption of SSBs and ASBs was associated with an increased risk of depression; in contrast, moderate consumption of NJs was inversely associated with a lower risk of depression. In theory, substituting SSBs and ASBs with NJs would suppose a reduction of depression risk.

Abstract Background Whether a low-inflammatory diet relates to type 2 diabetes risk remains unclear. We examined the association between a low-inflammatory diet and risk of type 2 diabetes among normoglycemic and prediabetic participants. We also explored whether a low-inflammatory diet modifies genetic risk for type 2 diabetes. Methods Among 142,271 diabetes-free UK Biobank participants (aged 39–72 years), 126,203 were normoglycemic and 16,068 were prediabetic at baseline. Participants were followed for up to 15 years to detect incident type 2 diabetes. At baseline, dietary intake was assessed with a 24-h dietary record. An inflammatory diet index (IDI) was generated based on high-sensitivity C-reactive protein levels and was a weighted sum of 34 food groups (16 anti-inflammatory and 18 pro-inflammatory). Participants were grouped into tertiles corresponding to inflammatory level (low, moderate, and high) based on IDI scores. Prediabetes at baseline was defined as HbA1c 5.7–6.4% in diabetes-free participants. Incident type 2 diabetes and age of onset were ascertained according to the earliest recorded date of type 2 diabetes in the Primary Care and Hospital inpatient data. A diabetes-related genetic risk score (GRS) was calculated using 424 single-nucleotide polymorphisms. Data were analyzed using Cox regression and Laplace regression. Results During follow-up (median 8.40 years, interquartile range 6.89 to 11.02 years), 3348 (2.4%) participants in the normoglycemia group and 2496 (15.5%) in the prediabetes group developed type 2 diabetes. Type 2 diabetes risk was lower in normoglycemic (hazard ratio [HR] = 0.71, 95% confidence interval [CI] 0.65, 0.78) and prediabetic (HR = 0.81, 95% CI 0.73, 0.89) participants with low IDI scores compared to those with high IDI scores. A low-inflammatory diet may prolong type 2 diabetes onset by 2.20 (95% CI 1.67, 2.72) years among participants with normoglycemia and 1.11 (95% CI 0.59, 1.63) years among participants with prediabetes. In joint effect analyses, normoglycemic or prediabetes participants with low genetic predisposition to type 2 diabetes and low IDI scores had a significant 74% (HR = 0.26, 95% CI 0.21, 0.32) or 51% (HR = 0.49, 95% CI 0.40, 0.59) reduction in type 2 diabetes risk compared to those with high genetic risk plus high IDI scores. There were significant additive and multiplicative interactions between IDI and GRS in relation to type 2 diabetes risk in the normoglycemia group. Conclusions A low-inflammatory diet is associated with a decreased risk of type 2 diabetes and may delay type 2 diabetes onset among participants with normal blood glucose or prediabetes. A low-inflammatory diet might significantly mitigate the risk of genetic factors on type 2 diabetes development.

Abstract Background We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM). Methods We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison. Results NODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUCtraining:0.845, AUCtest:0.714), solid tumor (AUCtraining:0.852, AUCtest:0.821), peritumoral edema (AUCtraining:0.817, AUCtest:0.762), and ABV (AUCtraining:0.834, AUCtest:0.779). The performance of the five ROI radiomic texture models in routine MRI was inferior to that of the NODDI-radiomic texture model. Conclusion Preoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM.

Abstract Telomere length (TL) shortening is a pivotal indicator of biological aging and is associated with many human diseases. The genetic determinates of human TL have been widely investigated, however, most existing studies were conducted based on adult tissues which are heavily influenced by lifetime exposure. Based on the analyses of terminal restriction fragment (TRF) length of telomere, individual genotypes, and gene expressions on 166 healthy placental tissues, we systematically interrogate TL-modulated genes and their potential functions. We discover that the TL in the placenta is comparatively longer than in other adult tissues, but exhibiting an intra-tissue homogeneity. Trans-ancestral TL genome-wide association studies (GWASs) on 644,553 individuals identify 20 newly discovered genetic associations and provide increased polygenic determination of human TL. Next, we integrate the powerful TL GWAS with placental expression quantitative trait locus (eQTL) mapping to prioritize 23 likely causal genes, among which 4 are functionally validated, including MMUT, RRM1, KIAA1429, and YWHAZ. Finally, modeling transcriptomic signatures and TRF-based TL improve the prediction performance of human TL. This study deepens our understanding of causal genes and transcriptomic determinants of human TL, promoting the mechanistic research on fine-grained TL regulation.

Abstract Background Diet is increasingly recognized as an important risk factor for mental health. However, evidence regarding the association between diet pattern and depressive and anxiety symptoms is limited. We aimed to investigate the associations of dietary patterns characterized by a set of nutrients of interest with depressive and anxiety symptoms. Methods The analyses included a total of 126,819 participants in the UK Biobank who had completed at least two dietary questionnaires. Dietary data were obtained through 24-h dietary assessment at baseline between 2006 and 2010 and four rounds of online follow-ups between 2011 and 2012. Reduced rank regression was applied to derive dietary patterns (DPs) explaining variability in energy density, free sugars, saturated fat, and fiber intakes. Depressive and anxiety symptoms were measured by the Patient Health Questionnaire-9 and General Anxiety Disorder-7 between 2016 and 2017, respectively. Logistic regression models were performed to investigate the associations between dietary patterns and depressive and anxiety symptoms. Results During a mean follow-up of 7.6 years, 2746 cases of depressive symptoms and 2202 cases of anxiety symptoms were recorded. Three major DPs were derived, explaining 74% of the variation in nutrients hypothesized to be related to depressive and anxiety symptoms. DP1 was characterized by high intakes of chocolate, confectionery, butter, and low vegetable/fruit intakes. Compared to the lowest quintile of DP1, the odds ratio (95% confidence interval) of depressive symptoms for Q2–Q5 was 0.82 (0.72–0.93), 0.86 (0.76–0.98), 1.02 (0.90–1.15), and 1.17 (1.03–1.32), respectively. Compared to the lowest quintile of DP1, the odds ratio (95% CI) of anxiety symptoms for Q2–Q5 was 0.84 (0.73–0.97), 0.91 (0.79–1.05), 1.01 (0.88–1.15), and 1.18 (1.03–1.35), respectively. DP2 featured high intakes of sugar-sweetened beverages, added sugars, and low intakes of butter/cheese but showed no significant links to depressive or anxiety symptoms. DP3 was characterized by high butter and milk desserts and low alcohol/bread intakes. Compared to the lowest quintile of DP3, the odds ratio (95% CI) of depressive symptoms for Q2–Q5 was 0.90 (0.79–1.01), 1.00 (0.88–1.13), 1.06 (0.94–1.20), and 1.17 (1.03–1.32), respectively. Compared to the lowest quintile of DP3, the odds ratio (95% CI) of anxiety symptoms for Q2–Q5 was 0.90 (0.78–1.04), 1.05 (0.91–1.20), 1.02 (0.89–1.17), and 1.21 (1.05–1.38), respectively. Conclusions A DP characterized by high intakes of chocolate and confectionery, butter, high-fat cheese, added sugars, along with low intakes of fresh fruit and vegetables, is associated with a higher risk of depressive and anxiety symptoms.

Abstract Background The effects of insulin-like growth factor-1 (IGF-1) deficiency on cognitive decline have been consistently reported in animal studies, but the relationship between IGF-1 and human brain health remains controversial. Our study aimed to investigate the associations of serum IGF-1 concentrations with some brain-related disorders and neuroimaging features. Methods This prospective study included 369,711 participants (55.8 ± 8.1 years) from the UK biobank who had serum IGF-1 measured and were free from brain-related disorders of interest — dementia, stroke, and Parkinson’s disease (PD) — at enrollment (2006–2010). Restricted cubic splines and Cox proportional hazards models were used to detect the associations between IGF-1 concentrations and brain-related diseases. In addition, general linear regressions were applied to explore the relationship between IGF-1 concentrations and neuroimaging features (volumes of white matter, grey matter, and hippocampus and white matter hyperintensity) among a sub-sample of 36,458 participants with magnetic resonance imaging data collected since 2014. Results During a median follow-up of 12.6 years, a total of 4,857 dementia, 6,240 stroke, and 2,116 PD cases were documented. The dose–response analyses yielded U-shaped relationships between IGF-1 concentrations and risks of dementia and stroke (P

Abstract Background A dietary pattern (DP) may impact on cancer incidence more strongly than individual foods, but this association remains uncertain. Here, we aimed to broadly explore the associations of an obesity-related DP with overall and 19 site-specific cancers. Methods This study included 114,289 cancer-free participants with at least two dietary assessments. A total of 210 food items were classified into 47 food groups, and the mean amount of each food group was used in reduced-rank regression to derive the obesity-related DP. Cox regressions were conducted to explore the associations of the obesity-related DP with overall and 19 site-specific cancers. The parallel mediation model was constructed to quantify the mediating roles of potential mediators. Results During a median follow-up period of 9.4 years, 10,145 (8.9%) incident cancer cases were documented. The derived-DP was characterized by a higher intake of beer and cider, processed meat, high sugar beverages, red meat, and artificial sweetener, and a lower intake of fresh vegetables, olive oil, tea, and high fiber breakfast cereals. Observational analysis showed that a higher obesity-related DP Z-score was linearly associated with an increased risk of overall cancer (adjusted hazard ratio (HR) = 1.02, 95% CI: 1.01, 1.04 per 1-SD increase, corrected P

Abstract Background Constipation was associated with incidence of dementia and cognitive decline. Laxatives are the mainstay of constipation management and are commonly used among older populations for both treatment and prevention of constipation. However, the association between use of laxatives and incident dementia, and whether laxatives use may modify the effect of genetic predisposition on dementia remains unclear. Methods We applied 1:3 propensity score matching to balance the baseline characteristics of the laxative users versus non-users and to reduce potential confounders using multi-variates adjusted Cox hazards regression models. We categorized genetic risk into three groups (low, middle, and high) through a genetic risk score of common genetic variants. Information on laxatives use was assessed at baseline and categories into four varieties, including bulk forming laxatives, softeners and emollients, osmotic laxatives, and stimulant laxatives. Results Of 486,994 participants, there were 14,422 laxatives users in UK Biobank. After propensity score matching, participants with use of laxatives (n = 14,422) and matched non-laxative (n = 43,266) exposed individuals were enrolled. Over follow-up to 15 years, there were 1377 participants developed dementia (539 for Alzheimer’s disease, and 343 for vascular dementia). The use of laxatives had greater risk of dementia (HR, 1.72; 95% CI:1.54–1.92), Alzheimer’s disease (HR, 1.36; 95% CI: 1.13–1.63), and vascular dementia (HR, 1.53; 95% CI: 1.23–1.92). Compared to non-laxative exposed participants, those with use of softeners and emollients drugs, stimulant laxatives, and osmotic laxatives were associated with 96% (HR, 1.96; 95 CI: 1.23–3.12; P = 0.005), 80% (HR, 1.80; 95% CI: 1.37–2.37; P

Abstract Depression is the consequence of both environment and genes working together. Genetic factors increase depression risk, but it is unclear whether this association can be offset by time spent in outdoor light. The study was undertaken to investigate the optimal time spent in outdoor light for lowering the risk of depression and the joint association of time spent in outdoor light and depression genetic risk. In UK Biobank, 380,976 depression-free individuals were included in this study. Polygenic risk score (PRS) was categorized into three groups in terms of tertiles. Time spent in outdoor light on a typical day in summer or winter originated from the questionnaire survey. Depression was defined as hospital admission. The potential dose-response relationship between time spent in outdoor light and depression risk was shown by a restricted cubic spline. Data were analyzed using Cox regressions and Laplace regression. After the median follow-up of 12.6 years, 13,636 individuals suffered from depression in the end. A nonlinear (J-shaped relationship) trend was observed between time spent in outdoor light and depression risk. On average, 1.5 h/day of outdoor light was related to the minimum risk of depression. Individuals below and above this optimal time both had elevated depression risk (below, HR = 1.09, 95% CI: 1.02–1.16; above, HR = 1.13, 95% CI: 1.07–1.20), and the time to incident depression were both shortened by 0.46 years (50th percentile differences [PD] = −0.46, 95% CI: −0.78, −0.14) and 0.63 years (50th PD = −0.63, 95% CI: −0.90, −0.35) years, respectively. In a comparison of individuals with the lowest tertile of PRS and average 1.5 h/day outdoor light, the HRs and 95% CIs of depression were 1.36 (1.21–1.53) and 1.43 (1.29–1.58) in those with the highest tertile of PRS and below/above this reference value, respectively. Significant multiplicative interactions were observed between intermediate genetic risks and longer time spent in outdoor light. We found that an average of 1.5 h/day spent in outdoor light was associated with a lower depression risk whatever the degree of depression genetic predisposition. Moderate time spent in outdoor light may contribute to a decreased depression risk even among people with a higher genetic risk of depression.

Abstract Background Growing evidence has showed an association between habitual glucosamine use and type 2 diabetes (T2D). However, the effect of habitual glucosamine use on risk of dementia remains poorly understood. Our study aimed to examine the association between glucosamine use and risk of dementia and further to identify the mediating role of T2D in the association. Methods A total of 495,942 participants from UK Biobank who completed a questionnaire on habitual glucosamine use were included at baseline (2006–2010) and then followed up for incidence of dementia until 2020. Cox proportional hazard regressions were performed to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident dementia. Markov multi-state models were used to explore the role of incidence of T2D during the follow-up in the association. Results Overall, 18.80% of the participants reported habitual use of glucosamine at baseline. A total of 6831 dementia events were recorded during a median follow-up of 11 years. In fully adjusted models, habitual glucosamine use was associated with a significantly lower risk of dementia (HR = 0.87, 95% CI: 0.82–0.93). Multi-state models showed that the association between glucosamine use and dementia was mediated by the incidence of T2D during the follow-up (HR of dementia without T2D: 0.92, 95% CI: 0.86–0.99; HR of post-T2D dementia: 0.79, 95% CI: 0.67–0.93). Conclusions Our findings reveal that habitual use of glucosamine supplement is associated with a lower risk of dementia, which might be explained by incidence of T2D.

Abstract Background Previous studies suggested that moderate coffee and tea consumption are associated with lower risk of mortality. However, the association between the combination of coffee and tea consumption with the risk of mortality remains unclear. This study aimed to evaluate the separate and combined associations of coffee and tea consumption with all-cause and cause-specific mortality. Methods This prospective cohort study included 498,158 participants (37–73 years) from the UK Biobank between 2006 and 2010. Coffee and tea consumption were assessed at baseline using a self-reported questionnaire. All-cause and cause-specific mortalities, including cardiovascular disease (CVD), respiratory disease, and digestive disease mortality, were obtained from the national death registries. Cox regression analyses were conducted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results After a median follow-up of 12.1 years, 34,699 deaths were identified. The associations of coffee and tea consumption with all-cause and cause-specific mortality attributable to CVD, respiratory disease, and digestive disease were nonlinear (all P nonlinear < 0.001). The association between separate coffee consumption and the risk of all-cause mortality was J-shaped, whereas that of separate tea consumption was reverse J-shaped. Drinking one cup of coffee or three cups of tea per day seemed to link with the lowest risk of mortality. In joint analyses, compared to neither coffee nor tea consumption, the combination of < 1–2 cups/day of coffee and 2–4 cups/day of tea had lower mortality risks for all-cause (HR, 0.78; 95% CI: 0.73–0.85), CVD (HR, 0.76; 95% CI: 0.64–0.91), and respiratory disease (HR, 0.69; 95% CI: 0.57–0.83) mortality. Nevertheless, the lowest HR (95% CI) of drinking both < 1–2 cup/day of coffee and ≥ 5 cups/day of tea for digestive disease mortality was 0.42 (0.34–0.53). Conclusions In this large prospective study, separate and combined coffee and tea consumption were inversely associated with all-cause and cause-specific mortality.

Abstract Background We aimed to assess the impact of healthy cardiovascular health (CVH) on diabetic complications, mortality, and life expectancy among people with type 2 diabetes and to explore whether inflammation marker mediate these associations. Methods This prospective cohort study included 33,236 participants (aged 40–72) with type 2 diabetes from the UK Biobank with annual follow-up from 2006 to 2010 to 2020. Type 2 diabetes was ascertained from self-report, glycated hemoglobin ≥ 6.5%, hospital inpatient registry, or glucose-lowering medication use. Information on mortality was derived from the national death registry. Favorable CVH metrics consisted of non-smoker, regular physical activity, a healthy diet, non-overweight, untreated resting blood pressure 3 mg/L). Data were analyzed using Cox regression models, flexible parametric survival models, and mediation models. Results During the follow-up (median: 11.7 years), 3133 (9.4%) cases of diabetes complications and 4701 (14.1%) deaths occurred. Compared to unfavorable CVH, favorable CVH was associated with a reduced risk of diabetes complications (HR, 0.35; 95% CI, 0.26–0.47) and all-cause mortality (HR, 0.53; 95% CI, 0.43–0.65). In participants with unfavorable CVH, life expectancy at age 45 had a significantly reduction of 7.20 (95% CI, 5.48–8.92) years compared to those with a favorable CVH. Among people with type 2 diabetes, the proportions of diabetes complications and all-cause mortality that would be reduced by promoting the favorable CVH was 61.5% and 39.1%, respectively. CRP level mediated 14.3% and 29.7% of the associations between CVH and diabetic complication and all-cause mortality, respectively. Conclusion A favorable CVH was associated with lower risk of diabetes complications and mortality risk, and was associated with a longer life expectancy among people with type 2 diabetes. This association may be in part accounted for by inflammatory processes. Our findings highlight the importance of favorable CVH for the prevention of diabetic complications and all-cause mortality among people with type 2 diabetes, and underscores the need to monitor inflammation among people with unfavorable CVH.

Abstract Background Risk genes linked to the development of gout have been identified, and lifestyle factors are related to gout risk. It remains unclear whether healthy lifestyle factors can mitigate the genetic risk of gout. Therefore, we aimed to explore whether and to what extent a healthy lifestyle can mitigate the risk of gout related to genetic factors. Methods Within the UK Biobank, 416,481 gout-free participants (aged 37–74) were identified at baseline. Polygenic risk for gout was assessed and categorized as low (lowest tertile), middle (tertile 2), and high (highest tertile). Healthy lifestyle factors included no/moderate alcohol consumption, no smoking, physical activity, and a healthy diet. Participants were categorized into three groups according to their number of healthy lifestyle factors: unfavorable (0 or 1), intermediate (any 2), and favorable (3 or 4). Data were analyzed using Cox proportional hazard models. Results Over the follow-up (median: 12.1 years), 6206 participants developed gout. Compared to low genetic risk, the hazard ratios (HRs) and 95% confidence intervals (CIs) of gout was 1.44 (1.35–1.54) for middle and 1.77 (1.66–1.89) for high genetic risk. The HRs (95% CIs) of gout were 0.63 (0.59–0.67) for a favorable lifestyle and 0.79 (0.75–0.85) for an intermediate lifestyle, compared to an unfavorable lifestyle. In joint effect analysis, compared to participants with low genetic predisposition and a favorable lifestyle, the HRs (95% CIs) of gout were 2.39 (2.12–2.70)/3.12 (2.79–3.52) in those with middle and high genetic predisposition plus unfavorable lifestyle profiles, and 1.53 (1.35–1.74)/1.98 (1.75–2.24) for those with middle and high genetic predisposition plus favorable lifestyle profiles, respectively. Moreover, compared to an unfavorable lifestyle, the HRs of gout related to a favorable lifestyle was 0.64 (95% CI, 0.56–0.73) for low genetic risk, 0.65 (95% CI, 0.58–0.72) for middle genetic risk, and 0.62 (95% CI, 0.57–0.69) for high genetic risk. There was a significant additive interaction between unfavorable lifestyle and high genetic risk on gout. Conclusions Healthy lifestyle was associated with a lower risk of gout and may attenuate the risk of gout related to genetic factors by almost a third.

BackgroundSeveral studies have explored the predictive performance of machine learning–based breast cancer risk prediction models and have shown controversial conclusions. Thus, the performance of the current machine learning–based breast cancer risk prediction models and their benefits and weakness need to be evaluated for the future development of feasible and efficient risk prediction models. ObjectiveThe aim of this review was to assess the performance and the clinical feasibility of the currently available machine learning–based breast cancer risk prediction models. MethodsWe searched for papers published until June 9, 2021, on machine learning–based breast cancer risk prediction models in PubMed, Embase, and Web of Science. Studies describing the development or validation models for predicting future breast cancer risk were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and the clinical applicability of the included studies. The pooled area under the curve (AUC) was calculated using the DerSimonian and Laird random-effects model. ResultsA total of 8 studies with 10 data sets were included. Neural network was the most common machine learning method for the development of breast cancer risk prediction models. The pooled AUC of the machine learning–based optimal risk prediction model reported in each study was 0.73 (95% CI 0.66-0.80; approximate 95% prediction interval 0.56-0.96), with a high level of heterogeneity between studies (Q=576.07, I2=98.44%; P

Summary: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

Abstract Genetic factors increase the risk of depression, but the extent to which this can be offset by modifiable lifestyle factors is unknown. We investigated whether a combination of healthy lifestyles is associated with lower risk of depression regardless of genetic risk. Data were obtained from the UK Biobank and consisted of 339,767 participants (37–73 years old) without depression between 2006 and 2010. Genetic risk was categorized as low, intermediate, or high according to polygenic risk score for depression. A combination of healthy lifestyles factors—including no current smoking, regular physical activity, a healthy diet, moderate alcohol intake and a body mass index

Background:The EAT-Lancet Commission has promulgated a sustainable dietary guideline and recommended that it was designed to improve the human health and support environmental sustainability.Objective:This research was designed to explore the association between this healthy diet pattern (EAT-Lancet diet pattern, EAT-LDP) and risk of type 2 diabetes (T2D).Methods:Between 2006 and 2010, a total of 59,849 participants from the UK Biobank without diabetes, cardiovascular disease, or cancers were included at baseline. The EAT-LDP score was constructed on the sum of 14 food components and then categorized into three tertiles. Multivariable Cox proportional hazards regression models were conducted to explore the association between EAT-LDP score and the risk of incident T2D. A mediation analysis was also implemented to disentangle the role of body mass index (BMI) and waist circumference in the relationship between EAT-LDP score and T2D.Results:During a median follow-up of 10 years, 2,461 incident T2D cases were recorded. In analyses that compared tertile 3 of the EAT-LDP score (highest) with tertile 1 (lowest), the hazard ratio (HR) for T2D was 0.81 (95% CI: 0.72–0.90) after adjusting for sociodemographic status and health-related factors. Participants who reported a one-point increase in the diet score were associated with a 6% decrease in risk of T2D (HR: 0.94, 95% CI: 0.91–0.97). A significant indirect association was observed between the EAT-LDP score and T2D (β: 0.66, 95% CI: 0.65–0.67), indicating that 44% of the association of EAT-LDP score with T2D was mediated by BMI. Additionally, 40% of the association of EAT-LDP score with T2D was mediated by waist circumference was also observed.Conclusions:Our findings indicate that a higher adherence to EAT-LDP contributes to lower risk of T2D. Further independent validation is needed to be conducted before applying the EAT-LDP to inform dietary guidelines.

BackgroundMobile phone use has brought convenience, but the long or improper use of mobile phones can cause harm to the human body. ObjectiveWe aimed to assess the impact of improper mobile phone use on the risks of accidents and chronic disorders. MethodsWe systematically searched in PubMed, EMBASE, Cochrane, and Web of Science databases for studies published prior to April 5, 2019; relevant reviews were also searched to identify additional studies. A random-effects model was used to calculate the overall pooled estimates. ResultsMobile phone users had a higher risk of accidents (relative risk [RR] 1.37, 95% CI 1.22 to 1.55). Long-term use of mobile phones increased accident risk relative to nonuse or short-term use (RR 2.10, 95% CI 1.63 to 2.70). Compared with nonuse, mobile phone use resulted in a higher risk for neoplasms (RR 1.07, 95% CI 1.01 to 1.14), eye diseases (RR 2.03, 95% CI 1.27 to 3.23), mental health disorders (RR 1.16, 95% CI 1.02 to 1.32), and headaches (RR 1.25, 95% CI 1.18 to 1.32); the pooled risk of other chronic disorders was 1.20 (95% CI 0.90 to 1.59). Subgroup analyses also confirmed the increased risk of accidents and chronic disorders. ConclusionsImproper use of mobile phones can harm the human body. While enjoying the convenience brought by mobile phones, people have to use mobile phones properly and reasonably.

BackgroundPrevious studies have revealed the involvement of coffee and tea in the development of stroke and dementia. However, little is known about the association between the combination of coffee and tea and the risk of stroke, dementia, and poststroke dementia. Therefore, we aimed to investigate the associations of coffee and tea separately and in combination with the risk of developing stroke and dementia.Methods and findingsThis prospective cohort study included 365,682 participants (50 to 74 years old) from the UK Biobank. Participants joined the study from 2006 to 2010 and were followed up until 2020. We used Cox proportional hazards models to estimate the associations between coffee/tea consumption and incident stroke and dementia, adjusting for sex, age, ethnicity, qualification, income, body mass index (BMI), physical activity, alcohol status, smoking status, diet pattern, consumption of sugar-sweetened beverages, high-density lipoprotein (HDL), low-density lipoprotein (LDL), history of cancer, history of diabetes, history of cardiovascular arterial disease (CAD), and hypertension. Coffee and tea consumption was assessed at baseline. During a median follow-up of 11.4 years for new onset disease, 5,079 participants developed dementia, and 10,053 participants developed stroke. The associations of coffee and tea with stroke and dementia were nonlinear (P for nonlinear ConclusionsWe found that drinking coffee and tea separately or in combination were associated with lower risk of stroke and dementia. Intake of coffee alone or in combination with tea was associated with lower risk of poststroke dementia.

ObjectiveThis study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE.MethodsThis retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical–radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong’s test was used to compare the performance of the models. A two-sided t-test and Fisher’s exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p

Abstract Background Serum uric acid (SUA) had been associated with incident hypertension, but it is uncertain whether and to what extent the effect of SUA is mediated by other metabolic factors. Methods Data from the China Health and Retirement Longitudinal Study (CHARLS) during 2011 to 2015 was employed for this study. A total of 7639 participants aged between 35 and 96 years without hypertension was included. Cox proportional hazards model was used to investigate the association between elevated SUA and hypertension. A mediation model was used to separately explore mediating effects (MEs) of metabolic factors on the association between SUA and incident hypertension. Results During a median 4.0 years of follow-up, 2348 individuals were diagnosed with hypertension. After adjustment for metabolic confounders, participants with the highest SUA quartile had a hazard ratio of 1.16 (1.02–1.33) compared with the lowest category for incident hypertension. The association between SUA and incident hypertension were partially mediated by waist circumference (WC; ME = 0.034), body mass index (BMI; ME = 0.016), triglycerides (TG; ME = 0.024), total cholesterol (TC; ME = 0.009), high density lipoprotein cholesterol (HDL-C; ME = 0.009), fasting plasma glucose (FPG; ME = 0.005), and glycated hemoglobin (HbA1c; ME = − 0.002). Additionally, proportional mediation was 32.7% by WC and 15.4% by BMI for obesity indicators; 23.1% by TG, 8.7% by TC, and 8.7% by HDL-C for blood lipid; and 4.8% by FPG and − 1.9% by HbA1c for blood glucose. Conclusions The positive association between elevated SUA concentration and hypertension was reconfirmed in a Chinese population. Obesity indicators, blood lipids, and blood glucose may play important mediating roles in the pathways.

Background: Although many cardiovascular risk factors for stroke have been reported before, it has not been investigated comprehensively for all major stroke types in large datasets. This study aimed to evaluate the associations of healthy behaviors, biological phenotypes and cardiovascular health (CVH) with long-term risks of strokes events, overall and stroke subtypes (ischemic stroke [IS], intracerebral hemorrhage [ICH], subarachnoid hemorrhage [SAH], and unspecified stroke). Methods: Between 2006 and 2010, a total of 354,976 participants (age 40–70 years) in the UK Biobank free of stroke and coronary heart disease were examined and thereafter followed up to 2020. According to American Heart Association guideline, the global CVH included four behavioral (smoking, diet, physical activity, body mass index) and three biological (blood glucose, blood cholesterol, blood pressure) metrics. The behavioral, biological and global CVH score was the sum of four, three, and seven metrics, respectively, and then was categorized into poor, intermediate and ideal group. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of stroke events. Findings: A total of 5804 incident stroke cases, including 3664 IS, 714 ICH and 453 SAH, were documented over a median follow-up of 11 years. The risk of stroke decreased significantly and linearly with both increasing behavioral CVH score and biological CVH score. Ideal behavioral CVH group was significantly associated with lower risks of all stroke subtypes, biological CVH was related to stroke events except for SAH. Additionally, the 1-point increment in global CVH score was associated with 11%,13%, 8% and 13% lower risks of stroke, IS, ICH and unspecified stroke, however, there was no significant dose-dependent association between global CVH and SAH. Interpretation: Our findings suggest inverse linear associations of behavioral, biological and global CVH with long-term risks of stroke and stroke subtypes, except for SAH, highlighting the benefits of maintaining better CVH status as a primordial prevention strategy of stroke. Funding: The National Natural Science Foundation of China (71,910,107,004, 91,746,205).

BACKGROUND: Accumulating evidence suggests that domestic water hardness is linked to health outcomes, but its association to all-cause and causespecific cancers warrants investigation. OBJECTIVE: The aim of this study was to investigate the association of domestic hard water with all-cause and cause-specific cancers METHODS: In the prospective cohort study, a total of 447,996 participants from UK Biobank who were free of cancer at baseline were included and followed up for 16 y. All-cause and 22 common cause-specific cancer diagnoses were ascertained using hospital inpatient records and self-reported data until 30 November 2022. Domestic water hardness, measured by CaCO3 concentrations, was obtained from the local water supply companies across England, Scotland, and Wales in 2005. Data were analyzed using Cox proportional hazard models, with adjustments for known measured confounders, including demographic, socioeconomic, clinical, biochemical, lifestyle, and environmental factors RESULTS: Over a median follow-up of 13.6 y (range: 12.7–14.4 y), 58,028 all-cause cancer events were documented. A U-shaped relationship between domestic water hardness and all-cause cancers was observed (푝 for nonlinearity <0.001). In comparison with individuals exposed to soft water (0–60 mg/L), the hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause cancer were 1.00 (95% CI: 0.98, 1.02) for those exposed to moderate hard water (>60–120 mg/L), 0.88 (95% CI: 0.84, 0.91) for those exposed to hard water (>120–180 mg/L) and 1.06 (95% CI: 1.04, 1.08) for those exposed to very hard water (>180 mg/L). Additionally, domestic water hardness was associated with 11 of 22 cause-specific cancers, including cancers of the esophagus, stomach, colorectal tract, lung, breast, prostate, and bladder, as well as non-Hodgkin lymphoma, multiple myeloma, malignant melanoma, and hematological malignancies. Moreover, we observed a positive linear relationship between water hardness and bladder cancer DISCUSSION: Our findings suggest that domestic water hardness was associated with all-cause and multiple cause-specific cancers. Findings from the UK Biobank support a potentially beneficial association between hard water and the incidence of all-cause cancer. However, very hard water may increase the risk of all-cause cancer. [ABSTRACT FROM AUTHOR]

This article summarizes a study published in the journal Thorax that explores the relationship between lung function and cardiovascular risk. The study analyzed data from the UK Biobank and found that lung function impairments, particularly restrictive and obstructive impairments, were associated with cardiovascular outcomes. The study suggests that spirometry indicators could be considered in cardiovascular risk assessment, but further research is needed. The article also provides a list of references and citations related to the study, which can be useful for patrons conducting research on respiratory epidemiology, COPD, and cardiovascular health. [Extracted from the article]