A comprehensive genome-wide cross-trait analysis of sexual factors and uterine leiomyoma.

Age at first sexual intercourse (AFS) and lifetime number of sexual partners (NSP) may influence the pathogenesis of uterine leiomyoma (UL) through their associations with hormonal concentrations and uterine infections. Leveraging summary statistics from large-scale genome-wide association studies conducted in European ancestry for each trait (NAFS = 214,547; NNSP = 370,711; NUL = 302,979), we observed a significant negative genomic correlation for UL with AFS (rg = -0.11, P = 7.83×10-4), but not with NSP (rg = 0.01, P = 0.62). Four specific genomic regions were identified as contributing significant local genetic correlations to AFS and UL, including one genomic region further identified for NSP and UL. Partitioning SNP-heritability with cell-type-specific annotations, a close clustering of UL with both AFS and NSP was identified in immune and blood-related components. Cross-trait meta-analysis revealed 15 loci shared between AFS/NSP and UL, including 7 novel SNPs. Univariable two-sample Mendelian randomization (MR) analysis suggested no evidence for a causal association between genetically predicted AFS/NSP and risk of UL, nor vice versa. Multivariable MR adjusting for age at menarche or/and age at natural menopause revealed a significant causal effect of genetically predicted higher AFS on a lower risk of UL. Such effect attenuated to null when age at first birth was further included. Utilizing participant-level data from the UK Biobank, one-sample MR based on genetic risk scores yielded consistent null findings among both pre-menopausal and post-menopausal females. From a genetic perspective, our study demonstrates an intrinsic link underlying sexual factors (AFS and NSP) and UL, highlighting shared biological mechanisms rather than direct causal effects. Future studies are needed to elucidate the specific mechanisms involved in the shared genetic influences and their potential impact on UL development.

Investigating the shared genetic architecture of uterine leiomyoma and breast cancer: A genome-wide cross-trait analysis.

Little is known regarding the shared genetic architecture or causality underlying the phenotypic association observed for uterine leiomyoma (UL) and breast cancer (BC). Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) conducted in each trait, we investigated the genetic overlap and causal associations of UL with BC overall, as well as with its subtypes defined by the status of estrogen receptor (ER). We observed a positive genetic correlation between UL and BC overall (rg = 0.09, p = 6.00 × 10-3), which was consistent in ER+ subtype (rg = 0.06, p = 0.01) but not in ER- subtype (rg = 0.06, p = 0.08). Partitioning the whole genome into 1,703 independent regions, local genetic correlation was identified at 22q13.1 for UL with BC overall and with ER+ subtype. Significant genetic correlation was further discovered in 9 out of 14 functional categories, with the highest estimates observed in coding, H3K9ac, and repressed regions. Cross-trait meta-analysis identified 9 novel loci shared between UL and BC. Mendelian randomization demonstrated a significantly increased risk of BC overall (OR = 1.09, 95% CI = 1.01-1.18) and ER+ subtype (OR = 1.09, 95% CI = 1.01-1.17) for genetic liability to UL. No reverse causality was found. Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis, pleiotropic loci, as well as a putative causal relationship between UL and BC, highlighting an intrinsic link underlying these two complex female diseases.

Phenotypic and genetic effect of carotid intima-media thickness on the risk of stroke.

While carotid intima-media thickness (cIMT) as a noninvasive surrogate measure of atherosclerosis is widely considered a risk factor for stroke, the intrinsic link underlying cIMT and stroke has not been fully understood. We aimed to evaluate the clinical value of cIMT in stroke through the investigation of phenotypic and genetic relationships between cIMT and stroke. We evaluated phenotypic associations using observational data from UK Biobank (N = 21,526). We then investigated genetic relationships leveraging genomic data conducted in predominantly European ancestry for cIMT (N = 45,185) and any stroke (AS, Ncase/Ncontrol=40,585/406,111). Observational analyses suggested an increased hazard of stroke per one standard deviation increase in cIMT (cIMTmax-AS: hazard ratio (HR) = 1.39, 95%CI = 1.09-1.79; cIMTmean-AS: HR = 1.39, 95%CI = 1.09-1.78; cIMTmin-AS: HR = 1.32, 95%CI = 1.04-1.68). A positive global genetic correlation was observed (cIMTmax-AS: [Formula: see text]=0.23, P=9.44 × 10-5; cIMTmean-AS: [Formula: see text]=0.21, P=3.00 × 10-4; cIMTmin-AS: [Formula: see text]=0.16, P=6.30 × 10-3). This was further substantiated by five shared independent loci and 15 shared expression-trait associations. Mendelian randomization analyses suggested no causal effect of cIMT on stroke (cIMTmax-AS: odds ratio (OR)=1.12, 95%CI=0.97-1.28; cIMTmean-AS: OR=1.09, 95%CI=0.93-1.26; cIMTmin-AS: OR=1.03, 95%CI = 0.90-1.17). A putative association was observed for genetically predicted stroke on cIMT (AS-cIMTmax: beta=0.07, 95%CI = 0.01-0.13; AS-cIMTmean: beta=0.08, 95%CI = 0.01-0.15; AS-cIMTmin: beta = 0.08, 95%CI = 0.01-0.16) in the reverse direction MR, which attenuated to non-significant in sensitivity analysis. Our work does not find evidence supporting causal associations between cIMT and stroke. The pronounced cIMT-stroke association is intrinsic, and mostly attributed to shared genetic components. The clinical value of cIMT as a surrogate marker for stroke risk in the general population is likely limited.

Genetic contribution of reproductive traits to risk of uterine leiomyomata: a large-scale, genome-wide, cross-trait analysis.

BACKGROUND: Although phenotypic associations between female reproductive characteristics and uterine leiomyomata have long been observed in epidemiologic investigations, the shared genetic architecture underlying these complex phenotypes remains unclear. OBJECTIVE: We aimed to investigate the shared genetic basis, pleiotropic effects, and potential causal relationships underlying reproductive traits (age at menarche, age at natural menopause, and age at first birth) and uterine leiomyomata. STUDY DESIGN: With the use of large-scale, genome-wide association studies conducted among women of European ancestry for age at menarche (n=329,345), age at natural menopause (n=201,323), age at first birth (n=418,758), and uterine leiomyomata (ncases/ncontrols=35,474/267,505), we performed a comprehensive, genome-wide, cross-trait analysis to examine systematically the common genetic influences between reproductive traits and uterine leiomyomata. RESULTS: Significant global genetic correlations were identified between uterine leiomyomata and age at menarche (rg, -0.17; P=3.65×10-10), age at natural menopause (rg, 0.23; P=3.26×10-07), and age at first birth (rg, -0.16; P=1.96×10-06). Thirteen genomic regions were further revealed as contributing significant local correlations (P<.05/2353) to age at natural menopause and uterine leiomyomata. A cross-trait meta-analysis identified 23 shared loci, 3 of which were novel. A transcriptome-wide association study found 15 shared genes that target tissues of the digestive, exo- or endocrine, nervous, and cardiovascular systems. Mendelian randomization suggested causal relationships between a genetically predicted older age at menarche (odds ratio, 0.88; 95% confidence interval, 0.85-0.92; P=1.50×10-10) or older age at first birth (odds ratio, 0.95; 95% confidence interval, 0.90-0.99; P=.02) and a reduced risk for uterine leiomyomata and between a genetically predicted older age at natural menopause and an increased risk for uterine leiomyomata (odds ratio, 1.08; 95% confidence interval, 1.06-1.09; P=2.30×10-27). No causal association in the reverse direction was found. CONCLUSION: Our work highlights that there are substantial shared genetic influences and putative causal links that underlie reproductive traits and uterine leiomyomata. The findings suggest that early identification of female reproductive risk factors may facilitate the initiation of strategies to modify potential uterine leiomyomata risk.

Migraine, chronic kidney disease and kidney function: observational and genetic analyses.

Epidemiological studies demonstrate an association between migraine and chronic kidney disease (CKD), while the genetic basis underlying the phenotypic association has not been investigated. We aimed to help avoid unnecessary interventions in individuals with migraine through the investigation of phenotypic and genetic relationships underlying migraine, CKD, and kidney function. We first evaluated phenotypic associations using observational data from UK Biobank (N = 255,896). We then investigated genetic relationships leveraging genomic data in European ancestry for migraine (Ncase/Ncontrol = 48,975/540,381), CKD (Ncase/Ncontrol = 41,395/439,303), and two traits of kidney function (estimated glomerular filtration rate [eGFR, N = 567,460] and urinary albumin-to-creatinine ratio [UACR, N = 547,361]). Observational analyses suggested no significant association of migraine with the risk of CKD (HR = 1.13, 95% CI = 0.85-1.50). While we did not find any global genetic correlation in general, we identified four specific genomic regions showing significant for migraine with eGFR. Cross-trait meta-analysis identified one candidate causal variant (rs1047891) underlying migraine, CKD, and kidney function. Transcriptome-wide association study detected 28 shared expression-trait associations between migraine and kidney function. Mendelian randomization analysis suggested no causal effect of migraine on CKD (OR = 1.03, 95% CI = 0.98-1.09; P = 0.28). Despite a putative causal effect of migraine on an increased level of UACR (log-scale-beta = 0.02, 95% CI = 0.01-0.04; P = 1.92 × 10-3), it attenuated to null when accounting for both correlated and uncorrelated pleiotropy. Our work does not find evidence supporting a causal association between migraine and CKD. However, our study highlights significant biological pleiotropy between migraine and kidney function. The value of a migraine prophylactic treatment for reducing future CKD in people with migraine is likely limited.

Nuclear magnetic resonance-determined lipoprotein profile and risk of breast cancer: a Mendelian randomization study.

PURPOSE: While crudely quantified lipoproteins have been reported to affect the risk of breast cancer, the effects of subclass lipoproteins characterized by particle size, particle number, and lipidomes remain unknown. METHODS: Utilizing nuclear magnetic resonance-based GWAS of 85 lipoprotein traits, we performed two-sample univariable Mendelian randomization (MR) to evaluate the causal relationship between each trait with breast cancer (Ncase/control = 133,384/113,789) and with its estrogen receptor (ER) subtypes. Then, we applied multivariable MR to investigate the independent effects considering both general and central obesity. RESULTS: In univariable MR, a heterogeneous effect of subclass high-density lipoproteins (HDL) was observed, in which small HDL traits (ORs ranged from 0.89 to 0.94) were associated with a decreased risk of breast cancer while non-small HDLs traits (OR ranged from 1.04 to 1.08) were associated with an increased risk of breast cancer. Very-low-density lipoproteins (VLDL) traits and serum total triglycerides (TG) were associated with a decreased risk of breast cancer (ORs ranged from 0.88 to 0.94). Similar association patterns were found for ER + subtype. In multivariable MR, only the protective effects of small HDL, VLDL and TG on ER + subtype remained significant. CONCLUSION: We identified a heterogeneous effect of subclass HDLs and a consistent protective effect of VLDL on breast cancer. Only the effects of small HDL and VLDL on ER + subtype remained robust after controlling for obesity. These findings provide new insight into the causal pathway underlying lipoproteins and breast cancer.

Increases in BMI contribute to worsening inflammatory biomarkers related to breast cancer risk in women: a longitudinal study.

BACKGROUND: Inflammatory adipokines and cytokines play a pivotal role in linking obesity and breast cancer (BC) risk in women. We investigated the longitudinal associations between BMI change and trajectories of inflammatory biomarkers related to BC risk. METHODS: A longitudinal study was conducted among 442 Chinese women with 3-year repeated measures from 2019 to 2021. Plasma circulating inflammatory biomarkers related to BC risk, including adiponectin (ADP), resistin (RETN), soluble leptin receptor (sOB-R), insulin-like growth factor-binding protein-3 (IGFBP-3), and C-reactive protein (CRP), were examined annually. Linear mixed-effect models (LMM) were applied to investigate associations of time-varying BMI with trajectories of biomarkers. We additionally examined the modification effect of baseline BMI groups, menopausal status, and metabolic syndrome. RESULTS: BMI was associated with increased levels of RETN, CRP, sOB-R, and decreased levels of ADP at baseline. An increasing BMI rate was significantly associated with an average 3-year increase in RETN (ß = 0.019, 95% CI 0.004 to 0.034) and sOB-R (ß = 0.022, 95% CI 0.009 to 0.035), as well as a decrease in ADP (ß = - 0.006, 95% CI - 0.012 to 0.001). These associations persisted across different baseline BMI groups. An increasing BMI rate was significantly associated with an average 3-year increase in CRP levels among normal weight (ß = 0.045, 95% CI 0.001 to 0.088) and overweight (ß = 0.060, 95% CI 0.014 to 0.107) women. As BMI increased over time, a more remarkable decrease in ADP was observed among women with metabolic syndrome (ß = - 0.016, 95% CI - 0.029 to - 0.004) than those without metabolic syndrome at baseline. CONCLUSIONS: A higher increase rate of BMI was associated with poorer trajectories of inflammatory biomarkers related to BC risk. Recommendations for BMI reduction may benefit BC prevention in women, particularly for those with metabolic syndrome.

Investigating the relationship between depression and breast cancer: observational and genetic analyses.

BACKGROUND: Both depression and breast cancer (BC) contribute to a substantial global burden of morbidity and mortality among women, and previous studies have observed a potential depression-BC link. We aimed to comprehensively characterize the phenotypic and genetic relationships between depression and BC. METHODS: We first evaluated phenotypic association using longitudinal follow-up data from the UK Biobank (N = 250,294). We then investigated genetic relationships leveraging summary statistics from the hitherto largest genome-wide association study of European individuals conducted for depression (N = 500,199), BC (N = 247,173), and its subtypes based on the status of estrogen receptor (ER + : N = 175,475; ER - : N = 127,442). RESULTS: Observational analysis suggested an increased hazard of BC in depression patients (HR = 1.10, 95%CIs = 0.95-1.26). A positive genetic correlation between depression and overall BC was observed ([Formula: see text] = 0.08, P = 3.00 × 10-4), consistent across ER + ([Formula: see text] = 0.06, P = 6.30 × 10-3) and ER - subtypes ([Formula: see text] = 0.08, P = 7.20 × 10-3). Several specific genomic regions showed evidence of local genetic correlation, including one locus at 9q31.2, and four loci at, or close, to 6p22.1. Cross-trait meta-analysis identified 17 pleiotropic loci shared between depression and BC. TWAS analysis revealed five shared genes. Bi-directional Mendelian randomization suggested risk of depression was causally associated with risk of overall BC (OR = 1.12, 95%Cis = 1.04-1.19), but risk of BC was not causally associated with risk of depression. CONCLUSIONS: Our work demonstrates a shared genetic basis, pleiotropic loci, and a putative causal relationship between depression and BC, highlighting a biological link underlying the observed phenotypic relationship; these findings may provide important implications for future studies aimed reducing BC risk.

Impact of gallstone disease on the risk of stroke and coronary artery disease: evidence from prospective observational studies and genetic analyses.

BACKGROUND: Despite epidemiological evidence associating gallstone disease (GSD) with cardiovascular disease (CVD), a dilemma remains on the role of cholecystectomy in modifying the risk of CVD. We aimed to characterize the phenotypic and genetic relationships between GSD and two CVD events - stroke and coronary artery disease (CAD). METHODS: We first performed a meta-analysis of cohort studies to quantify an overall phenotypic association between GSD and CVD. We then investigated the genetic relationship leveraging the largest genome-wide genetic summary statistics. We finally examined the phenotypic association using the comprehensive data from UK Biobank (UKB). RESULTS: An overall significant effect of GSD on CVD was found in meta-analysis (relative risk [RR] = 1.26, 95% confidence interval [CI] = 1.19-1.34). Genetically, a positive shared genetic basis was observed for GSD with stroke ([Formula: see text]=0.16, P = 6.00 × 10-4) and CAD ([Formula: see text]=0.27, P = 2.27 × 10-15), corroborated by local signals. The shared genetic architecture was largely explained by the multiple pleiotropic loci identified in cross-phenotype association study and the shared gene-tissue pairs detected by transcriptome-wide association study, but not a causal relationship (GSD to CVD) examined through Mendelian randomization (MR) (GSD-stroke: odds ratio [OR] = 1.00, 95%CI = 0.97-1.03; GSD-CAD: OR = 1.01, 95%CI = 0.98-1.04). After a careful adjustment of confounders or considering lag time using UKB data, no significant phenotypic effect of GSD on CVD was detected (GSD-stroke: hazard ratio [HR] = 0.95, 95%CI = 0.83-1.09; GSD-CAD: HR = 0.98, 95%CI = 0.91-1.06), further supporting MR findings. CONCLUSIONS: Our work demonstrates a phenotypic and genetic relationship between GSD and CVD, highlighting a shared biological mechanism rather than a direct causal effect. These findings may provide insight into clinical and public health applications.

Understanding the relationship between circulating lipids and risk of chronic kidney disease: a prospective cohort study and large-scale genetic analyses.

BACKGROUND: This study aims to comprehensively investigate the phenotypic and genetic relationships between four common lipids (high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; total cholesterol, TC; and triglycerides, TG), chronic kidney disease (CKD), and estimated glomerular filtration rate (eGFR). METHODS: We first investigated the observational association of lipids (exposures) with CKD (primary outcome) and eGFR (secondary outcome) using data from UK Biobank. We then explored the genetic relationship using summary statistics from the largest genome-wide association study of four lipids (N = 1,320,016), CKD (Ncase = 41,395, Ncontrol = 439,303), and eGFR(N = 567,460). RESULTS: There were significant phenotypic associations (HDL-C: hazard ratio (HR) = 0.76, 95%CI = 0.60-0.95; TG: HR = 1.08, 95%CI = 1.02-1.13) and global genetic correlations (HDL-C: [Formula: see text] = - 0.132, P = 1.00 × 10-4; TG: [Formula: see text] = 0.176; P = 2.66 × 10-5) between HDL-C, TG, and CKD risk. Partitioning the whole genome into 2353 LD-independent regions, twelve significant regions were observed for four lipids and CKD. The shared genetic basis was largely explained by 29 pleiotropic loci and 36 shared gene-tissue pairs. Mendelian randomization revealed an independent causal relationship of genetically predicted HDL-C (odds ratio = 0.91, 95%CI = 0.85-0.98), but not for LDL-C, TC, or TG, with the risk of CKD. Regarding eGFR, a similar pattern of correlation and pleiotropy was observed. CONCLUSIONS: Our work demonstrates a putative causal role of HDL-C in CKD and a significant biological pleiotropy underlying lipids and CKD in populations of European ancestry. Management of low HDL-C levels could potentially benefit in reducing the long-term risk of CKD.

Using human genetics to understand the epidemiological association between obesity, serum urate, and gout.

OBJECTIVES: We aimed to clarify the genetic overlaps underlying obesity-related traits, serum urate, and gout. METHODS: We conducted a comprehensive genome-wide cross-trait analysis to identify genetic correlation, pleiotropic loci, and causal relationships between obesity (the exposure variable), gout (the primary outcome) and serum urate (the secondary outcome). Summary statistics were collected from the hitherto largest genome-wide association studies conducted for BMI (N = 806 834), waist-to-hip ratio (WHR; N = 697 734), WHR adjusted for BMI (WHRadjBMI; N = 694 649), serum urate (N = 288 649), and gout (Ncases = 13 179 and Ncontrols = 750 634). RESULTS: Positive overall genetic correlations were observed for BMI (rg = 0.27, P = 6.62 × 10-7), WHR (rg = 0.22, P = 6.26 × 10-7) and WHRadjBMI (rg = 0.07, P = 6.08 × 10-3) with gout. Partitioning the whole genome into 1703 LD (linkage disequilibrium)-independent regions, a significant local signal at 4q22 was identified for BMI and gout. The global and local shared genetic basis was further strengthened by the multiple pleiotropic loci identified in the cross-phenotype association study, multiple shared gene-tissue pairs observed by Transcriptome-wide association studies, as well as causal relationships demonstrated by Mendelian randomization [BMI-gout: OR (odds ratio) = 1.66, 95% CI = 1.45, 1.88; WHR-gout: OR = 1.57, 95% CI = 1.37, 1.81]. Replacing the binary disease status of gout with its latent pathological measure, serum urate, a similar pattern of correlation, pleiotropy and causality was observed with even more pronounced magnitude and significance. CONCLUSION: Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis and pleiotropic loci, as well as a causal relationship between obesity, serum urate, and gout, highlighting an intrinsic link underlying these complex traits.

Using human genetics to understand the phenotypic association between chronotype and breast cancer.

Little is known regarding the shared genetic influences underlying the observed phenotypic association between chronotype and breast cancer in women. Leveraging summary statistics from the hitherto largest genome-wide association study conducted in each trait, we investigated the genetic correlation, pleiotropic loci, and causal relationship of chronotype with overall breast cancer, and with its subtypes defined by the status of oestrogen receptor. We identified a negative genomic correlation between chronotype and overall breast cancer ( r g $$ {r}_g $$ = -0.06, p = 3.00 × 10-4 ), consistent across oestrogen receptor-positive ( r g $$ {r}_g $$ = -0.05, p = 3.30 × 10-3 ) and oestrogen receptor-negative subtypes ( r g $$ {r}_g $$ = -0.05, p = 1.11 × 10-2 ). Five specific genomic regions were further identified as contributing a significant local genetic correlation. Cross-trait meta-analysis identified 78 loci shared between chronotype and breast cancer, of which 23 were novel. Transcriptome-wide association study revealed 13 shared genes, targeting tissues of the nervous, cardiovascular, digestive, and exocrine/endocrine systems. Mendelian randomisation demonstrated a significantly reduced risk of overall breast cancer (odds ratio 0.89, 95% confidence interval 0.83-0.94; p = 1.30 × 10-4 ) for genetically predicted morning chronotype. No reverse causality was found. Our work demonstrates an intrinsic link underlying chronotype and breast cancer, which may provide clues to inform management of sleep habits to improve female health.

Associations between changes of smartphone pedometer-assessed step counts and levels of obesity-related breast cancer biomarkers in non-cancer women: A population-based observational study.

While a higher level of physical activity (PA) is inversely associated with a higher breast cancer (BC) risk, the health benefits of daily steps on obesity-related BC biomarkers remain unclear. We aimed to understand the associations of changes in step counts with levels of five obesity-related BC biomarkers during a two-year follow-up. In total, 144 non-cancer women (47.96 ± 5.72) were observed on both 2019 and 2021. A structured questionnaire, daily steps and fasting blood samples were collected before (t0, 2019) and after (t1, 2021). Levels of biomarkers (IGF-binding proteins 3, adiponectin, soluble leptin receptor, C-reactive protein, and resistin) were assayed by ELISA. Participants were divided into persistent low steps, decreasing steps, increasing steps, and persistent high steps. Associations of categories on proposed biomarkers were estimated using linear regression models, with persistent low steps as reference. Associations between time-varying step counts with biomarkers were quantified using mixed linear models. Compared with persistent low steps, increasing steps is associated with a reduction in C-reactive protein level (ß=-0.74, 95%CI=-1.23--0.26, P-value = 2.98 × 10-3). An inverse association between time-varying step counts with C-reactive protein level was identified, consistent across different obesity types and baseline step level categories. No association with daily step counts was observed for other proteins.

[Effect of Interactions Among Obesity-Related Proteins on Breast Cancer Risk: A Preliminary Study].

Objective: To explore the potential interactions among obesity-related proteins in the pathogenic process of breast cancer (BC) in women. Methods: We conducted a case-control study, enrolling 279 primary breast cancer cases and 260 age-frequency-matched healthy women between April 2014 and May 2015. Based on the evidence of previous published literature on obesity-related proteins and BC risks, we selected proteins that received more attention and measured the plasma levels of these proteins by enzyme-linked immunosorbent assay (ELISA). After stratification of the subjects according to their menopausal status, an analytic strategy combining multivariate logistic regression and generalized multifactor dimensionality reduction (GMDR) was used to explore the effect of the possible interactions of these proteins on BC risk. Results: There were marginal high-order interactions among insulin-like growth factor 1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), C-reactive protein (CRP), resistin (RETN), soluble leptin receptor (sOB-R), and adiponectin (ADP) in premenopausal women (with the balanced accuracy for the testing set being 59.01%, cross-validation consistency being 10/10, and permutation test P=0.05). There were high-order interactions among leptin (LEP), sOB-R, ADP, CRP, IGFBP3 and visfatin (VF) in postmenopausal women (with the balanced accuracy for the testing set being 67.31%, cross-validation consistency being 10/10, and permutation test P=0.01). Along with an increase in the number of obesity-related proteins to which the subjects were exposed, the risk of developing breast cancer gradually increased in both pre- and postmenopausal women ( OR pre =2.18, 95% CI: 1.69-2.82; OR post =2.41, 95% CI: 1.75-3.32). Conclusions: This preliminary study suggested high-order interactions among obesity-related proteins on BC risk in both pre- and postmenopausal women. In future studies, close attention should be given to these potential interactions when these proteins are used jointly as predictors, as well as in developing a comprehensive risk scoring system for BC.

A network meta-analysis of secondary attack rates of COVID-19 in different contact environments.

As the corona virus disease 2019 (COVID-19) pandemic continues around the world, understanding the transmission characteristics of COVID-19 is vital for prevention and control. We conducted the first study aiming to estimate and compare the relative risk of secondary attack rates (SARs) of COVID-19 in different contact environments. Until 26 July 2021, epidemiological studies and cluster epidemic reports of COVID-19 were retrieved from SCI, Embase, PubMed, CNKI, Wanfang and CBM in English and Chinese, respectively. Relative risks (RRs) were estimated in pairwise comparisons of SARs between different contact environments using the frequentist NMA framework, and the ranking of risks in these environments was calculated using the surface under the cumulative ranking curve (SUCRA). Subgroup analysis was performed by regions. Thirty-two studies with 68 260 participants were identified. Compared with meal or gathering, transportation (RR 10.55, 95% confidence interval (CI) 1.43-77.85), medical care (RR 11.68, 95% CI 1.58-86.61) and work or study places (RR 10.15, 95% CI 1.40-73.38) had lower risk ratios for SARs. Overall, the SUCRA rankings from the highest to the lowest were household (95.3%), meal or gathering (81.4%), public places (58.9%), daily conversation (50.1%), transportation (30.8%), medical care (18.2%) and work or study places (15.3%). Household SARs were significantly higher than other environments in the subgroup of mainland China and sensitive analysis without small sample studies (<100). In light of the risks, stratified personal protection and public health measures need to be in place accordingly, so as close contacts categorising and management.

Epidemiological and Genetic Analyses of Schizophrenia and Breast Cancer.

BACKGROUND AND HYPOTHESIS: While the phenotypic association between schizophrenia and breast cancer has been observed, the underlying intrinsic link is not adequately understood. We aim to conduct a comprehensive interrogation on both phenotypic and genetic relationships between schizophrenia and breast cancer. STUDY DESIGN: We first used data from UK Biobank to evaluate a phenotypic association and performed an updated meta-analysis incorporating existing cohort studies. We then leveraged genomic data to explore the shared genetic architecture through a genome-wide cross-trait design. STUDY RESULTS: Incorporating results of our observational analysis, meta-analysis of cohort studies suggested a significantly increased incidence of breast cancer among women with schizophrenia (RR = 1.30, 95% CIs = 1.14-1.48). A positive genomic correlation between schizophrenia and overall breast cancer was observed (rg = 0.12, P = 1.80 × 10-10), consistent across ER+ (rg  = 0.10, P = 5.74 × 10-7) and ER- subtypes (rg = 0.09, P = .003). This was further corroborated by four local signals. Cross-trait meta-analysis identified 23 pleiotropic loci between schizophrenia and breast cancer, including five novel loci. Gene-based analysis revealed 27 shared genes. Mendelian randomization demonstrated a significantly increased risk of overall breast cancer (OR = 1.07, P = 4.81 × 10-10) for genetically predisposed schizophrenia, which remained robust in subgroup analysis (ER+: OR = 1.10, P = 7.26 × 10-12; ER-: OR = 1.08, P = 3.50 × 10-6). No mediation effect and reverse causality was found. CONCLUSIONS: Our study demonstrates an intrinsic link underlying schizophrenia and breast cancer, which may inform tailored screening and management of breast cancer in schizophrenia.

Bidirectional relationship between type 2 diabetes mellitus and coronary artery disease: Prospective cohort study and genetic analyses.

BACKGROUND: While type 2 diabetes mellitus (T2DM) is considered a putative causal risk factor for coronary artery disease (CAD), the intrinsic link underlying T2DM and CAD is not fully understood. We aimed to highlight the importance of integrated care targeting both diseases by investigating the phenotypic and genetic relationships between T2DM and CAD. METHODS: We evaluated phenotypic associations using data from the United Kingdom Biobank ( N = 472,050). We investigated genetic relationships by leveraging genomic data conducted in European ancestry for T2DM, with and without adjustment for body mass index (BMI) (T2DM: Ncase / Ncontrol = 74,124/824,006; T2DM adjusted for BMI [T2DM adj BMI]: Ncase / Ncontrol = 50,409/523,897) and for CAD ( Ncase / Ncontrol = 181,522/984,168). We performed additional analyses using genomic data conducted in multiancestry individuals for T2DM ( Ncase / Ncontrol = 180,834/1,159,055). RESULTS: Observational analysis suggested a bidirectional relationship between T2DM and CAD (T2DM→CAD: hazard ratio [HR] = 2.12, 95% confidence interval [CI]: 2.01-2.24; CAD→T2DM: HR = 1.72, 95% CI: 1.63-1.81). A positive overall genetic correlation between T2DM and CAD was observed ( rg = 0.39, P = 1.43 × 10 -75 ), which was largely independent of BMI (T2DM adj BMI-CAD: rg = 0.31, P = 1.20 × 10 -36 ). This was corroborated by six local signals, among which 9p21.3 showed the strongest genetic correlation. Cross-trait meta-analysis replicated 101 previously reported loci and discovered six novel pleiotropic loci. Mendelian randomization analysis supported a bidirectional causal relationship (T2DM→CAD: odds ratio [OR] = 1.13, 95% CI: 1.11-1.16; CAD→T2DM: OR = 1.12, 95% CI: 1.07-1.18), which was confirmed in multiancestry individuals (T2DM→CAD: OR = 1.13, 95% CI: 1.10-1.16; CAD→T2DM: OR = 1.08, 95% CI: 1.04-1.13). This bidirectional relationship was significantly mediated by systolic blood pressure and intake of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, with mediation proportions of 54.1% (95% CI: 24.9-83.4%) and 90.4% (95% CI: 29.3-151.5%), respectively. CONCLUSION: Our observational and genetic analyses demonstrated an intrinsic bidirectional relationship between T2DM and CAD and clarified the biological mechanisms underlying this relationship.

Shared genetic basis connects smoking behaviors and bone health: Insights from a genome-wide cross-trait analysis.

Although the negative association of tobacco smoking with osteoporosis is well-documented, little is known regarding the shared genetic basis underlying these conditions. In this study, we aim to investigate a shared genetic architecture between smoking and heel estimated bone mineral density (eBMD), a reliable proxy for osteoporosis. We conducted a comprehensive genome-wide cross-trait analysis to identify genetic correlation, pleiotropic loci and causal relationship of smoking with eBMD, leveraging summary statistics of the hitherto largest genome-wide association studies conducted in European ancestry for smoking initiation (Nsmoker = 1 175 108, Nnonsmoker = 1 493 921), heaviness (cigarettes per day, N = 618 489), cessation (Ncurrent smoker = 304 244, Nformer smoker = 843 028), and eBMD (N = 426 824). A significant negative global genetic correlation was found for smoking cessation and eBMD (${r}_g$ = -0.051, P = 0.01), while we failed to identify a significant global genetic correlation of smoking initiation or heaviness with eBMD. Partitioning the whole genome into independent blocks, we observed six significant shared local signals for smoking and eBMD, with 22q13.1 showing the strongest regional genetic correlation. Such a genetic overlap was further supported by 71 pleiotropic loci identified in the cross-trait meta-analysis. Mendelian randomization identified no causal effect of smoking initiation (beta = -0.003 g/cm2, 95%CI = -0.033-0.027) or heaviness (beta = -0.017 g/cm2, 95%CI = -0.072-0.038) on eBMD, but a putative causal effect of genetic predisposition to being a current smoker was associated with a lower eBMD compared to former smokers (beta = -0.100 g/cm2, 95%CI = -0.181- - 0.018). Our study demonstrates a pronounced biological pleiotropy as well as a putative causal link between current smoking status and eBMD, providing novel insights into the primary prevention and modifiable intervention of osteoporosis by advocating individuals to avoid, reduce or quit smoking as early as possible.

We conducted a comprehensive genome-wide cross-trait analysis to investigate the shared genetic basis and causal relationship underlying smoking and osteoporosis. Our findings revealed that smoking and eBMD are inherently linked through biological pleiotropy. Importantly, our study discovered that quitting smoking significantly reduced the risk of lower eBMD. We recommend individuals to avoid, reduce, or quit smoking as early as possible to protect bone health.

Observational and genetic analyses of the bidirectional relationship between depression and hypertension.

BACKGROUND: While the association between depression and hypertension has been extensively investigated, the pattern and nature of such association remain inconclusive. We sought to investigate the bidirectional relationship between depression and hypertension and its causal. METHODS: We first performed observational analyses using longitudinal data from the UK Biobank. We then performed genetic analyses leveraging summary statistics from large-scale genome-wide association studies (GWASs) conducted in European ancestry for depression and hypertension. RESULTS: Observational analysis suggested a significant bidirectional phenotypic association between depression and hypertension (Depression â†’ Hypertension: HR = 1.27, 95 % CI: 1.19, 1.36; Hypertension â†’ Depression: HR = 1.65, 95 % CI: 1.58, 1.72). Linkage disequilibrium score regression demonstrated a positive genetic correlation between the two conditions (rg=0.15, P = 5.75 × 10-10). Bidirectional two-sample Mendelian randomization (MR) suggested that genetic liability to depression was significantly associated with an increased risk of hypertension (OR = 1.27, 95 % CI: 1.12, 1.43), while the genetic liability to hypertension was not associated with the risk of depression (OR = 1.01, 95 % CI: 0.99, 1.03). Multivariate MR, after adjusting for smoking, drinking, and body mass index, further supported an independent causal effect of genetic liability to depression on hypertension risk (OR = 1.10, 95 % CI: 1.02, 1.18). LIMITATIONS: (1) interference of confounders, (2) absence of adequate statistical power, and (3) limitation to European populations. CONCLUSION: Our study indicates depression is a causal risk factor for hypertension, whereas the reverse maybe not. Findings support that prevention of depression might help in decreasing hypertension incidence.

A large-scale genome-wide cross-trait analysis for the effect of COVID-19 on female-specific cancers.

Little is known regarding the long-term adverse effects of COVID-19 on female-specific cancers, nor the shared genetic influences underlying these conditions. We performed a comprehensive genome-wide cross-trait analysis to investigate the shared genetic architecture between COVID-19 (infection, hospitalization, and critical illness) with three female-specific cancers (breast cancer (BC), epithelial ovarian cancer (EOC), and endometrial cancer (EC)). We identified significant genome-wide genetic correlations with EC for both hospitalization (rg = 0.19, p = 0.01) and critical illness (rg = 0.29, p = 3.00 × 10-4). Mendelian randomization demonstrated no valid association of COVID-19 with any cancer of interest, except for suggestive associations of genetically predicted hospitalization (ORIVW = 1.09, p = 0.04) and critical illness (ORIVW = 1.06, p = 0.04) with EC risk, none withstanding multiple correction. Cross-trait meta-analysis identified 20 SNPs shared between COVID-19 with BC, 15 with EOC, and 5 with EC; and transcriptome-wide association studies revealed multiple shared genes. Findings support intrinsic links underlying these complex traits, highlighting shared mechanisms rather than causal associations.