Objective: To investigate the role of the periodontal pathogen Porphyromonas gingivalis in the etiology of rheumatoid arthritis (RA) by analyzing the antibody response to the P gingivalis virulence factor arginine gingipain type B (RgpB) in relation to anti–citrullinated protein antibodies (ACPAs), smoking, and HLA–DRB1 shared epitope (SE) alleles in patients with periodontitis, patients with RA, and controls.Methods: Anti‐RgpB IgG was measured by enzyme‐linked immunosorbent assay in 65 periodontitis patients and 59 controls without periodontitis, and in 1,974 RA patients and 377 controls without RA from the Swedish population‐based case–control Epidemiological Investigation of Rheumatoid Arthritis (EIRA) study. Autoantibody status, smoking habits, and genetic data were retrieved from the EIRA database. Differences in antibody levels were examined using the Mann‐Whitney U test. Unconditional logistic regression was used to calculate odds ratios (ORs) with 95% confidence intervals (95% CIs) for the association of anti‐RgpB IgG with different subsets of RA patients.Results: Anti‐RgpB antibody levels were significantly elevated in periodontitis patients compared to controls without periodontitis, in RA patients compared to controls without RA, and in ACPA‐positive RA patients compared to ACPA‐negative RA patients. There was a significant association between anti‐RgpB IgG and RA (OR 2.96 [95% CI 2.00, 4.37]), which was even stronger than the association between smoking and RA (OR 1.37 [95% CI 1.07, 1.74]), and in ACPA‐positive RA there were interactions between anti‐RgpB antibodies and both smoking and the HLA–DRB1 SE.Conclusion: Our study suggests that the previously reported link between periodontitis and RA could be accounted for by P gingivalis infection, and we conclude that P gingivalis is a credible candidate for triggering and/or driving autoimmunity and autoimmune disease in a subset of RA patients.

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r_g = 0.57, p = 4.6 × 10⁻⁸), breast and ovarian cancer (r_g = 0.24, p = 7 × 10⁻⁵), breast and lung cancer (r_g = 0.18, p =1.5 × 10⁻⁶) and breast and colorectal cancer (r_g = 0.15, p = 1.1 × 10⁻⁴). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.

Endocrine therapies with SERMs (selective estrogen receptor modulators) or SERDs (selective estrogen receptor downregulators) are standard therapies for patients with estrogen receptor (ER)-positive breast cancer. Multiple small molecule inhibitors targeting the PI3K-AKT-mTOR pathway or CDK4/6 have been developed to be used in combination with anti-estrogen drugs to overcome endocrine resistance. In addition to their direct antitumor effects, accumulating evidence has revealed the tumor immune microenvironment (TIM)-modulating effects of these therapeutic strategies, which have not been properly acknowledged previously. The immune microenvironment of breast tumors plays a crucial role in tumor development, metastasis and treatment response to endocrine therapy and immunotherapy. Therefore, in our current work, we comprehensively review the immunomodulatory effect of endocrine therapy and discuss its potential applications in combination with immune checkpoint inhibitors in breast cancer treatment.

Background: The incidence of prostate cancer is increasing in older males globally. Age, ethnicity, and family history are identified as the well-known risk factors for prostate cancer, but few modifiable factors have been firmly established. The objective of this study was to identify and evaluate various factors modifying the risk of prostate cancer reported in meta-analyses of prospective observational studies and mendelian randomization (MR) analyses. Methods and findings: We searched PubMed, Embase, and Web of Science from the inception to January 10, 2022, updated on September 9, 2023, to identify meta-analyses and MR studies on prostate cancer. Eligibility criteria for meta-analyses were (1) meta-analyses including prospective observational studies or studies that declared outcome-free at baseline; (2) evaluating the factors of any category associated with prostate cancer incidence; and (3) providing effect estimates for further data synthesis. Similar criteria were applied to MR studies. Meta-analysis was repeated using the random-effects inverse-variance model with DerSimonian—Laird method. Quality assessment was then conducted for included meta-analyses using AMSTAR-2 tool and for MR studies using STROBE-MR and assumption evaluation. Subsequent evidence grading criteria for significant associations in meta-analyses contained sample size, P values and 95% confidence intervals, 95% prediction intervals, heterogeneity, and publication bias, assigning 4 evidence grades (convincing, highly suggestive, suggestive, or weak). Significant associations in MR studies were graded as robust, probable, suggestive, or insufficient considering P values and concordance of effect directions. Finally, 92 selected from 411 meta-analyses and 64 selected from 118 MR studies were included after excluding the overlapping and outdated studies which were published earlier and contained fewer participants or fewer instrument variables for the same exposure. In total, 123 observational associations (45 significant and 78 null) and 145 causal associations (55 significant and 90 null) were categorized into lifestyle; diet and nutrition; anthropometric indices; biomarkers; clinical variables, diseases, and treatments; and environmental factors. Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. Twenty-six overlapping factors between meta-analyses and MR studies were identified, with consistent significant effects found for physical activity (PA) (occupational PA in meta: OR = 0.87, 95% CI: 0.80, 0.94; accelerator-measured PA in MR: OR = 0.49, 95% CI: 0.33, 0.72), height (meta: OR = 1.09, 95% CI: 1.06, 1.12; MR: OR = 1.07, 95% CI: 1.01, 1.15, for aggressive prostate cancer), and smoking (current smoking in meta: OR = 0.74, 95% CI: 0.68, 0.80; smoking initiation in MR: OR = 0.91, 95% CI: 0.86, 0.97). Methodological limitation is that the evidence grading criteria could be expanded by considering more indices. Conclusions: In this large-scale study, we summarized the associations of various factors with prostate cancer risk and provided comparisons between observational associations by meta-analysis and genetically estimated causality by MR analyses. In the absence of convincing overlapping evidence based on the existing literature, no robust associations were identified, but some effects were observed for height, physical activity, and smoking.

To understand a putative causal link for depression and pain, we retrieved summary statistics from genome-wide association studies conducted for pain at 7 different body sites (N = 151,922-226,683) and major depression disorder (MDD, N_case/control = 246,363/561,190). We conducted a bidirectional Mendelian randomization analysis using distinct genome-wide association studies-identified single nucleotide polymorphisms for each trait as instrumental variables and performed several sensitivity analyses to verify Mendelian randomization assumptions. We also conducted functional annotation analysis using 396 tissue-specific annotations from the roadmap project. Across 7 different body sites, genetic predisposition to depression was associated with pain at the neck/shoulder (odds ratio [OR] = 1.08 per one log-unit increase in depression risk, 95% confidence interval [CI]: 1.06-1.10), back (OR = 1.05, 95% CI: 1.04-1.07), abdominal/stomach (OR = 1.03, 95% CI: 1.02-1.04), as well as headache (OR = 1.10, 95% CI: 1.07-1.12), but not with pain on the face, hip, and knee. In the reverse direction, genetically instrumented multisite chronic pain (OR = 1.78 per one increment in the number of pain site, 95% CI: 1.51-2.11) and headache (OR = 1.55 per one log-unit increase in headache risk, 95% CI = 1.13-2.10) were associated with MDD. Functional annotation analysis showed differential clustering patterns where depression clustered closely with headache and neck/shoulder pain, exhibiting substantial brain tissue enrichment. Our study indicates that depression is a causal risk factor for headache and pain localized at neck/shoulder, back, and abdominal/stomach, rather than pain at face, hip, and knee, and suggests common neurological pathologies underlying the development of depression, headache, and neck/shoulder pain.

Background: The comorbidity between polycystic ovary syndrome (PCOS) and obesity has long been observed in clinical settings, but their shared genetic basis remains unclear. Methods: Leveraging summary statistics of large-scale GWAS(s) conducted in European-ancestry populations on body mass index (adult BMI, N_female=434,794; childhood BMI, N=39,620), waist-to-hip ratio (WHR, N_female=381,152), WHR adjusted for BMI (WHR_adjBMI, N_female=379,501), and PCOS (N_case=10,074, N_control=103,164), we performed a large-scale genome-wide cross-trait analysis to quantify overall and local genetic correlation, to identify shared loci, and to infer causal relationship. Results: We found positive genetic correlations between PCOS and adult BMI (r_g=0.47, P=2.19×10⁻¹⁶), childhood BMI (r_g=0.31, P=6.72×10⁻⁵), and WHR (r_g=0.32, P=1.34×10⁻¹⁰), all withstanding Bonferroni correction. A suggestive significant genetic correlation was found between PCOS and WHR_adjBMI (r_g=0.09, P=0.04). Partitioning the whole genome into 1703 nearly independent regions, we observed a significant local genetic correlation for adult BMI and PCOS at chromosome 18: 57630483–59020751. We identified 16 shared loci underlying PCOS and obesity-related traits via cross-trait meta-analysis including 9 loci shared between BMI and PCOS (adult BMI and PCOS: 5 loci; childhood BMI and PCOS: 4 loci), 6 loci shared between WHR and PCOS, and 5 loci shared between WHR_adjBMI and PCOS. Mendelian randomization (MR) supported the causal roles of both adult BMI (OR=2.92, 95% CI=2.33–3.67) and childhood BMI (OR=2.76, 95% CI=2.09–3.66) in PCOS, but not WHR (OR=1.19, 95% CI=0.93–1.52) or WHR_adjBMI (OR=1.03, 95% CI=0.87–1.22). Genetic predisposition to PCOS did not seem to influence the risk of obesity-related traits. Conclusions: Our cross-trait analysis suggests a shared genetic basis underlying obesity and PCOS and provides novel insights into the biological mechanisms underlying these complex traits. Our work informs public health intervention by confirming the important role of weight management in PCOS prevention.