Habitually skipping breakfast may promote the initiation and progression of gastrointestinal (GI) cancers, which have never been systematically explored in large-scale prospective studies.We prospectively examined the effects of breakfast frequency on the occurrence of GI cancers among 62,746 participants. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) of GI cancers were calculated by Cox regression. The CAUSALMED procedure was used to perform the mediation analyses.P for mediation effect > 0.05).During a median follow-up of 5.61 (5.18 ~ 6.08) years, 369 incident GI cancer cases were identified. Participants who consumed 1–2 times breakfasts per week exhibited an increased risk of stomach  and liver cancer . Participants who did not eat breakfast had an elevated risk of esophageal , colorectal , liver , gallbladder, and extrahepatic bile duct cancer . In the mediation effect analyses, BMI, CRP, and TyG (fasting triglyceride-glucose) index did not mediate the association between breakfast frequency and the risk of GI cancer incidence (all Habitually skipping breakfast was associated with a greater risk of GI cancers including esophageal, gastric, colorectal, liver, gallbladder, and extrahepatic bile duct cancer.http://www.chictr.org.cn/showprojen.aspx?proj=8050Kailuan study, ChiCTR–TNRC–11001489. Registered 24 August, 2011-Retrospectively registered, The online version contains supplementary material available at 10.1007/s11606-023-08094-7. In China, stomach cancer is the second leading cause of cancer death, followed by liver, esophageal, and colorectal cancer.2 As one of the most replicative tissues in the body, the GI tract’s cells have a short lifespan. Physical, biological, and chemical damage continually affects the digestive system, increasing the chance of oncogenic alterations. Even though risk factors for GI malignancies differ depending on the site, there are still certain common risk factors, such as drinking,3 smoking,4 obesity,5 infectious agents,6 and chronic inflammation.7 Exploring the common risk factors for GI cancers and performing high-risk population screenings can help to prevent GI cancers.Gastrointestinal (GI) cancers are costly and deadly. According to 2020 global cancer statistics,8 A sizable amount of evidence on the benefits of breakfast focuses on health outcomes rather than nutritional results. In recent years, breakfast skipping has been proven to be associated with obesity,9 impaired glucose metabolism,10 cardiovascular disease,11 impaired cognitive function,12 and even cancer.13 Recently, a prospective study conducted in China found that habitually skipping breakfast is associated with chronic inflammation, as assessed by the C-reactive protein (CRP) concentration.14 As described previously, due to the close association between inflammation and GI cancers, the habitual breakfast skipping may result in the initiation and progression of malignant tumors in the digestive system. However, no prospective study has been conducted to explore this association.Breakfast is commonly referred to as the most essential meal of the day and provides nutrients for the body after overnight fasting.The Kailuan study is an ongoing, longitudinal cohort study, and its purpose is to explore the risk factors for chronic diseases, including cancer. The assessment of breakfast frequency and the follow-up of incident cancer events offered us a great opportunity to explore whether breakfast frequency was associated with the risk of GI cancer incidence.15 In short, the Kailuan study is a population-based prospective study based in the Kailuan community, which includes many employees of the Kailuan coal mining industry in Tangshan, Hebei Province in North China. From 2006 to 2007, 101,510 people  were included in the study and were followed up biennially. Each follow-up included face-to-face questionnaire surveys, body measurements, clinical examinations, and laboratory tests. In 2014, all individuals provided information on their habitual dietary intake with a semiquantitative Food Frequency Questionnaire (FFQ). Among the 74,592 participants who completed the FFQ, 625 were excluded due to a history of cancer, and 11,221 were excluded due to missing data on age, sex, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG) level, total cholesterol (TC) level, triglyceride (TG) level, CRP level, uric acid (UA) level, serum creatinine (Scr) level, occupation, family income, educational background, physical exercise, consumption of tobacco and alcoholic beverages, family history of cancer, sedentary lifestyle, tea consumption, high salt intake, high-fat diets, fatty liver, cirrhosis, gallstones, gallbladder polyps, hepatitis B surface antigen (HBsAg) level, hepatitis C virus (HCV) infection, and diet quality score. A total of 62,746 individuals were finally enrolled in this study, including 10,503 women and 52,243 men  checking routine follow-up data for participants every 2 years until December 31, 2020, and (2) checking medical records from provincial vital statistics data, the Tangshan medical insurance system, and the Kailuan Social Security Information System once a year to obtain additional missing information. The Tangshan medical insurance system and the Kailuan social security system covered nearly all the participants’ health information. All cancer cases were reconfirmed based on either specific clinical features or positive histopathological results from hospitals where patients had received malignant tumor treatment. Potential cases were further assessed by two oncologists when pathological results were lacking. Only when two professionals made the same cancer diagnosis were cancer cases confirmed. GI cancers were documented as follows using the International Classification of Diseases, Tenth Revision (ICD-10): liver (C22); gallbladder or extrahepatic bile duct (C23-C24); gastric (C16); pancreatic (C25); small intestine (C17); esophageal (C15); and colorectal (C18-C21) cancers.The data of all covariates was collected in 2014. Information on age, sex, lifestyle , sociodemographic data , and personal and family medical histories  was collected. Current alcohol usage was defined as drinking at least 100 mL of an alcoholic beverage each day for more than 6 months. Smoking was defined as having at least one cigarette per day for at least 6 months. Physical activity was defined as exercising at least 3 times per week, 30 min per session.14 The scale runs from 0 (worst) to 5 (best).In 2014, dietary data were gathered using a validated semiquantitative FFQ. The semiquantitative FFQ comprises thirty-three food items and seven condiments and asks participants how frequently they consumed each food item in the previous year, with options of never, daily, weekly, or monthly. The FFQ was also used to calculate the amount of each meal consumed in liangs (50 g/liang). Adherence to the AHA recommendations (referred to as the “AHA diet score” in the current study) was used to assess diet quality, which was calculated based on the consumption of the following six components: fruits and vegetables, fish, Na, sweets, sugar-sweetened beverages, and whole grains, as previously described.2). After participants were sitting still, their SBP and DBP were recorded twice, and the average of the two readings was used in subsequent analysis. Hypertension was defined as an SBP ≥ 140 mmHg, a DBP ≥ 90 mmHg, a previous diagnosis, or the use of antihypertensive medication. An autoanalyzer  was used to measure FBG, TC, TG, CRP, SUA, Scr, and HBsAg levels. Diabetes was defined as an FBG level ≥ 7.0 mmol/L, a previous diagnosis, or the use of an oral hypoglycemic agent. Abdominal ultrasonography was used to detect liver cirrhosis, fatty liver, gallstone disease, and gallbladder polyps based on previously clinically established criteria17 or medical data from the Tangshan medical insurance system.Weight and height were measured during the study visit, and BMI was calculated as weight (kg)/height  and median (interquartile range), and the comparisons were examined using the one-way analysis of variance (ANOVA) or the nonparametric Kruskal–Wallis test. Categorical variables were reported as absolute values with percentages and compared using the chi-squared test. Person-years were computed from the time of the baseline examination (2014–2015) until the date of the GI cancer diagnosis, death, or December 31, 2020, whichever occurred first. The proportional hazard assumption of breakfast skipping was evaluated by the Schoenfeld residual test. The Cox proportional hazards model was used to estimate the hazard ratios (HRs) and their 95% confidence intervals (CIs) for the association of breakfast skipping with the risk of GI cancer incidence.Multivariate analyses included age (every 10 years), sex, BMI, TC level, TG level, Scr level, UA level, smoking status, drinking status, physical activity level, sedentary lifestyle, tea consumption, high-fat diet, diabetes, occupation type, family history of cancer, and diet quality score. In the pooled GI cancer studies, we only included the first reported cancer type. Site-specific studies were performed for all individuals with multiple relevant GI malignancies. In the site-specific analyses, multivariate analyses were further adjusted for liver cirrhosis, fatty liver, HBV infection, and HCV infection in the model of liver cancer; in addition, gallstone disease and gallbladder polyps were adjusted in the analyses of the gallbladder and biliary cancer. Subgroup analyses were performed for pooled GI cancers stratified by the median age (< 52 vs. ≥ 52 years), sex (men vs. women), and diet quality score (< 3 vs. ≥ 3). The interactions were further tested using multiplicative models.Due to the close association of breakfast skipping with the risk of obesity, impaired glucose metabolism, and inflammation, we further explored whether the associations of breakfast consumption and GI cancer risk were mediated by obesity, inflammation, and insulin resistance, which were assessed by BMI, CRP level, and the TyG (fasting triglyceride-glucose) index (ln [TG (mmol/L) × FBG (mmol/L)]/2), respectively. The CAUSALMED procedure was used to perform the mediation analyses based on the variance–covariance matrix and the maximum likelihood method. This procedure calculated the total effect (the sum of the direct and indirect effects), the direct effect (the effect without the mediator’s influence), and the indirect effect .In the sensitivity analyses, we eliminated participants with cirrhosis and HCV infection at baseline due to the possible association of liver cirrhosis and HCV infection with GI cancer risk and reanalyzed the relationship to assess the robustness of our findings. To rule out the possibility of reverse causality, those who were diagnosed with cancer within the first year of follow-up were also eliminated.The mean ± SD age for the study population was 51.30 ± 13.25 years (range: 18–98 years). Among the 62,746 participants in the current study, 53,796 (86%) reported breakfast consumption every day, and 5037 (8%) reported having no breakfast. Among the 4 prespecified groups, significant differences were found in the following: age; the levels of FBG, TC, TG, CRP, SUA, Scr, SBP, DBP, and BMI; and the percentages of men, educational levels, family income, family history of cancer, physical exercise, smoking status, drinking status, occupation type, sedentary lifestyle, tea drinking, high-fat diets, hypertension, diabetes mellitus, fatty liver, gallstone disease, gallbladder polyps, HBsAg seropositivity, and diet quality score. The prevalence of cirrhosis and HCV infection, on the other hand, did not differ significantly among the breakfast frequency groups (Table n = 39), gastric cancer (n = 67), small-intestine cancer (n = 6), colorectal cancer (n = 136), liver cancer (n = 83), gallbladder or extrahepatic bile duct cancer (n = 15), pancreatic cancer (n = 26)); among them, 3 participants were diagnosed with multiple tumors. Results from the Schoenfeld residual test showed that the breakfast frequency met proportional hypothesis (P = 0.217). Compared with participants who had breakfast regularly, the 1–2 breakfasts/week group and no breakfast group were associated with a 2.35-fold  and − 2.06-fold  elevated risk of GI cancer incidence in the multivariate analyses (Table During a median follow-up of 5.61 (5.18 ~ 6.08) years, 369 incident GI cancer cases were identified ; amongTable P for interaction > 0.05). Sensitivity analyses did not substantially alter the conclusions but strengthened the HR to a higher level in some cancer sites. Notably, after excluding participants with GI cancers diagnosed within the 1st year of follow-up, those who never ate breakfast also exhibited a higher risk of cancer; however, the risk of gallbladder and extrahepatic bile duct cancer was eliminated  risk30 as well as its related risk factors.11 Data from the Spanish PESA  cohort emphasized the hypothesis that omitting breakfast was related to increased odds of atherosclerosis, regardless of the presence of traditional cardiovascular risk factors.31 In brief, breakfast intake was related to a decrease in cardiometabolic risk factors.A large portion of the literature on the advantages of breakfast is concerned with health outcomes rather than nutritional results. Several large prospective studies have found a link between breakfast intake and a decreased risk of obesity and weight increase.In the current study, participants who ate breakfast 1–2 times a week had the highest risk of stomach cancer and liver cancer, which can be explained by the relatively short follow-up period. We further assumed that the participants maintained regular eating habits for a long time and applied dietary data from the 2014–2015 examination to the population who participated in the 2012–2013 healthy examination. A significant association of breakfast frequency with GI cancer risk was observed only in participants who did not have breakfast (Supplementary Table 14 Long-term low-grade inflammation promotes tumor development and progression via (1) oxidation32; (2) mutation, DNA methylation and posttranslational variations caused by inflammatory response mediators, including cytokines, free radicals, prostaglandins and growth factors32; and (3) the activation of the NF-κB pathway, which is essential for the invasion of cancer.33 Second, breakfast consumption lowers the risk of obesity and metabolic risk factors. The roles of obesity and metabolic disturbance in cancer etiopathogenesis comprise34 the following: (1) hyperinsulinemia/insulin resistance and abnormalities of the insulin-like growth factor-I (IGF-I) system and signaling; (2) alterations in adipocytokine pathophysiology; (3) an altered intestinal microbiome; and (4) microenvironment and cellular perturbations. Third, compared with regular breakfast eaters,35 habitual breakfast skippers are usually associated with unhealthy lifestyle behaviors, such as smoking and physical inactivity, which are independent risk factors for the development of cancer. However, the association of breakfast consumption and GI cancer risk remained after adjusting for these lifestyle factors. Notably, in the current study, breakfast skippers were relatively younger, with a lower prevalence of smoking, drinking, and physical inactivity.Several underlying mechanisms may help explain the association between breakfast skipping and subsequent GI cancer risk. First, habitually skipping breakfast was associated with elevated concentrations of CRP.A major strength of this current study is that it provided a unique perspective on the possible effects of breakfast consumption on the occurrence of incident GI cancer risk based on a population-based cohort study. Furthermore, this study investigated a wide range of potentially confounding characteristics, such as lifestyle behaviors and a history of cancer-related disorders. The prospective research design and large sample size are further strengths of this study.Helicobacter pylori infection has been reported to be closely associated with chronic gastritis, peptic ulcer disease, and gastric cancer, all of which can cause upper gastrointestinal symptoms and affect lifestyle habits, including breakfast consumption. However, the lacked data on Helicobacter pylori infection hinders us from assessing the absolute risk of breakfast consumption more precisely. Fifth, short median follow-up (5.61 years) and relatively young age may not be sufficient to detect a true relationship between breakfast and GI cancer risk. However, due to the small sample size of the no breakfast group, subgroup analyses stratified by age or sex were yielded to null associations in the current study (data not shown). Future studies with a larger sample size and longer follow-up should be conducted to validate the robustness of the main findings in the current study. Sixth, endoscopy is the golden method of screening for gastrointestinal cancers, especially asymptomatic occult cancers. However, due to the cost-effectiveness policy of the Kailuan cohort, no endoscopy was performed on the individuals. Finally, calorie restriction (CR) is beneficial for longevity,36 and skipping breakfast is also a form of CR. In fact, skipping breakfast is currently mainly due to staying up later, then, getting up later. In addition, staying up late is a common major risk factor for non-communicable diseases (NCDs), including cardiovascular disease (CVD), diabetes, and cancer.39 Ultimately, an unhealthy lifestyle alters the biological rhythm or circadian clock, which then affects the human immune system.39 Unfortunately, the Kailuan study did not collect the information on the overall calorie intake and sleep rhythm. Future studies need to be conducted to explore the relationship between breakfast skipping and overall calorie intake, as well as the impact of breakfast skipping on the human immunity.Limitations in the current study should also be noted. First, breakfast consumption frequency was collected via self-report questionnaires, which may have led to the misclassification of the participants. Second, the current study included more men than women due to the industrial character of the Kailuan Company. However, the sample size of women was still high enough to investigate the association. Third, different morning meals and patterns may have varying effects on cancer risk. However, we did not collect information on the exact meals eaten during breakfast. Fourth, In this large-scale cohort, we found that habitually skipping breakfast was associated with a greater risk of GI cancers, including esophageal, gastric, colorectal, liver, gallbladder, and extrahepatic bile duct cancers. This study should be a stepping stone to further research on breakfast consumption and GI cancer risk. Future research should better elucidate the potential mechanisms of breakfast skipping for carcinogenesis. Importantly, specific prevention efforts could be focused on the population with a habit of skipping breakfast.Supplementary file1 (DOCX 21.5 KB)Below is the link to the electronic supplementary material.


The relationship between migraine and breast cancer risk has generated conflicting findings. We attempted to assess the association between migraine and breast cancer risk using Mendelian randomization (MR) analysis.We selected genetic instruments associated with migraine from a recently published genome-wide association studies (GWAS). Inverse variant weighted (IVW) analysis was adopted as the main method, and we also performed the weighted-median method and the MR‒Egger, MR pleiotropy residual sum and outlier (MR-PRESSO), and MR Robust Adjusted Profile Score (MR-RAPS) methods as supplements.P = 8.78 × 10− 5, false discovery rate (FDR) = 7.36 × 10− 4) and estrogen receptor-positive (ER+) breast cancer  but not estrogen receptor-negative (ER-) breast cancer. In its subtype analysis, women with a history of migraine without aura (MO) had an increased risk of ER- breast cancer , and MO was suggestively associated with the risk of overall breast cancer (FDR > 0.05 and IVW P < 0.05). No significant heterogeneity or horizontal pleiotropy was found in the sensitivity analysis.Our MR suggested that any migraine (AM) was a risk factor for overall breast cancer (IVW: odds ratio (OR) = 1.072, 95% confidence intervals (CI) = 1.035–1.110, This study suggested that women with AM have an increased risk of overall breast cancer and ER + breast cancer. MO was suggestively associated with the risk of overall breast cancer and ER- breast cancer.The online version contains supplementary material available at 10.1186/s12885-023-11337-9. Migraine is a clinically common neurological disorder that is most common in women aged 25–55 , 2. NowaTraditional observational studies have inherent shortcomings in exploring casual links between exposure and outcome. Mendelian randomization (MR) studies, a new approach to epidemiological research, allow the use of large samples of pooled genome-wide association studies (GWAS) data to explore the relationship between risk factors and outcomes –15. MR sWe conducted a two-sample MR study to examine the causal relationship between exposure and outcome. Genetic variation as an instrumental variable must follow three assumptions:  Genetic This MR analysis utilized the largest published GWAS data; all of the participants in those cohorts being of European descent Table . EthicalThe data on any migraine (AM) were derived from a recently published article composed of 102,804 cases and 771,257 controls, which included 14,624 cases of migraine with aura (MA) and 15,055 cases of migraine without aura (MO) from five study collections  after removing overlapping participants. Patients without subtype information were not included in the subtype analysis . The metThe GWAS summary statistics of breast cancer were derived from the Breast Cancer Association Consortium (BCAC) with 122,977 cases  and 21,468 estrogen receptor negative (ER−)) and 105,974 controls of European ancestry from OncoArray and ICOGS arrays [P < 5 × 10 − 8, and the P was derived from original exposure-GWAS with fixed-model [(P < 5 × 10 − 8) and the P was derived from original outcome-GWAS with fixed-model. Fourth, all candidate SNPs were checked to avoid any possible confounders (P < 5 × 10 − 8) by PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/phenoscanner); the removed SNPs as possible confounders for breast cancer, such as body mass index (BMI), NSAIDs, age of menarche or age of menopause. The F statistics were used to detect weak instrumental variables, and there were no weak instrumental variables when F > 10.A series of rigorous steps were applied to the screening of single nucleotide polymorphisms (SNPs). First, we selected the SNPs as instrument variants (IVs) of migraine in the threshold of ed-model . Second,The Wald ratio was used to assess the effect of migraine on breast cancer for each SNP. All SNP effects were meta-analysed by the inverse-variance weighted (IVW) method . We perfFDR). Summary of the different methods used for MR analysis was shown in Supplementary Table In sensitivity analyses, MR‒Egger and weighted median (WM) methods were applied to account for horizontal pleiotropic effects. The MR‒Egger method was based on the Instrument Strength Independent of Direct Effect assumption, which often provides imprecise and low statistical power MR results, especially when meeting small sizes of SNPs e.g., < 10) . In our 0 . In ouP < 0.05). Step 2: We reassessed the MR analysis after removing all outliers (P < 0.05). Step 3: If heterogeneity was still present, we excluded all SNPs with a P value less than 1 in the MR-PRESSO test and reevaluated the MR analysis. Finally, if there were potentially influential SNPs examined by the leave-one-out test, we will explain the results with caution.We performed three steps for our study to eliminate biased results due to heterogeneity . The MR estimates at different steps of the causal effect of migraine on breast cancer from the series of analysis methods are presented in Supplementary Tables Four SNPs associated with the confounders for the AM were removed; three of them are related to BMI , and another one (rs1019990) is related to age at menarche. rs10828247 was excluded when the exposure was AM because of the directivity relationship with the outcome P = 8.78 × 10− 5; WM: OR = 1.063, 95% CI = 1.006–1.123, P = 0.0283; MR-Egger: OR = 1.062, 95% CI = 0.956–1.179, P = 0.2639; MR-RAPS: OR = 1.069, 95% CI = 1.026–1.113, P = 0.0015) and a 6.6% higher risk of ER + breast cancer . However, no causal effect of AM on ER- breast cancer was found . The effect estimations from various sensitive analysis all pointed to the same direction, which indicated that the results are robust. An additional, adjusted P value of IVW after FDR was corrected suggested that AM was still a risk factor for overall breast cancer (FDR = 7.36 × 10− 4) and ER + breast cancer (FDR = 0.0108). Detailed information on the MR analysis for different steps is shown in Supplementary Table The MR analysis showed that genetically predicted AM (per log-odds ratio increase) was associated with a 7.2% higher risk of overall breast cancer . Moreover, the leave-one-out test showed that there were no potentially influential SNPs in this part , ER + breast cancer  or ER- breast cancer . The MR estimates of the effect of AM on breast cancer for different methods are presented in Supplementary Table In our MR analysis, only three SNPs were selected as IVs at the criteria of P = 0.0267; WM: OR = 1.030, 95% CI = 0.977–1.086, P = 0.2741; MR-Egger: OR = 1.012, 95% CI = 0.777–1.235, P = 0.5324; MR-RAPS: OR = 1.041, 95% CI = 1.003–1.082, P = 0.0332) and an 8.9% higher risk of ER- breast cancer . No causal effect of AM on ER + breast cancer was found . MO might elevate the risk of ER- breast cancer after FDR control (FDR = 0.0354). The effect estimations from various sensitive analysis all pointed to the same direction, which indicated that the results are robust. However, MO was suggestively associated with the risk of overall breast cancer (FDR > 0.05 and IVW P < 0.05). Detailed information on the MR analysis for different steps is shown in Supplementary Table In the two-sample MR analysis, we found that MO (per log-odds ratio increase) was associated with a 4.2% higher risk of overall breast cancer  due to only 3 SNPs were selected, which requires larger GWAS studies to obtain more genetic data in the future study.Previous observational studies have been controversial as to whether women with a history of migraine have an increased risk of breast cancer. We, as the first study to utilize MR analysis, also made significant discoveries. Our study found that migraine may be a risk factor for overall breast cancer and ER + breast cancer but not ER- breast cancer. In subtype analysis, MO may increase the risk of ER- breast cancer and is suggestively associated with the risk of overall breast cancer but not ER + breast cancer. No evidence to support the association of MA with breast cancer and its subtypes. Therefore, women with a history of migraine, especially MO, should strengthen breast cancer screening to be diagnosed early and treated adequately.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6Supplementary Material 7Supplementary Material 8


Genetic and lifestyle factors both contribute to the pathogenesis of bladder cancer, but the extent to which the increased genetic risk can be mitigated by adhering to a healthy lifestyle remains unclear. We aimed to investigate the association of combined lifestyle factors with bladder cancer risk within genetic risk groups.We conducted a prospective study of 375 998 unrelated participants of European ancestry with genotype and lifestyle data and free of cancer from the UK biobank. We generated a polygenic risk score (PRS) using 16 single nucleotide polymorphisms and a healthy lifestyle score based on body weight, smoking status, physical activity, and diet. Cox models were fitted to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of genetic and lifestyle factors on bladder cancer.During a median follow-up of 11.8 years, 880 participants developed bladder cancer. Compared with those with low PRS, participants with intermediate and high PRS had a higher risk of bladder cancer . An optimal lifestyle was associated with an approximately 50% lower risk of bladder cancer than a poor lifestyle across all genetic strata. Participants with a high genetic risk and a poor lifestyle had 3.6-fold elevated risk of bladder cancer compared with those with a low genetic risk and an optimal lifestyle .Adhering to a healthy lifestyle could substantially reduce the bladder cancer risk across all genetic strata, even for high-genetic risk individuals. For all populations, adopting an intermediate lifestyle is more beneficial than a poor one, and adhering to an optimal lifestyle is the ideal effective strategy for bladder cancer prevention.The online version contains supplementary material available at 10.1186/s12885-023-11455-4. Bladder cancer is one of the most common incident cancers worldwide, with approximately 600 000 new cases annually reported, leading to over 200 000 deaths each year . EnvironModifiable lifestyle risk factors are the leading contributors to global cancer incidence and mortality . PotentiThere is substantial evidence suggesting that adhering to a healthy lifestyle could attenuate the impact of genetic factors on the risk of several cancers, such as cancers of the stomach, prostate and colorectum , 21, 23.Based on the UK Biobank cohort, we aimed to evaluate the extent to which adhering to a healthy lifestyle might mitigate the risk of bladder cancer among individuals with a different genetic risk, particularly among individuals at a high genetic risk defined by the PRS.The UK Biobank is a large ongoing population-based prospective cohort of over 500 000 individuals aged 37–73 years. Between 2006 and 2010, eligible participants were invited to attend one of 22 assessment centers throughout England, Scotland, and Wales. During the baseline assessment, participants were asked to complete a touchscreen questionnaire and a brief interview to collect sociodemographic, lifestyle factors and health-related information. Participants also underwent a range of physical measurements and provided biological samples. More details of UK Biobank could be found elsewhere . Each pan = 69 790), those with a prior cancer diagnosis (n = 31 237), those with incomplete data on lifestyle factors (n = 10 223), and those who subsequently withdrew from the study (n = 921). Figure SIn the present study, we included 488 169 participants with available genetic data and excluded participants of non-European ancestry or related individuals  and the National Health Service Central Register (in Scotland). These registrations recorded the diagnosis of cancer and cancer deaths using the 10th revision of the international classification of diseases (ICD-10) codes. The primary outcome of the study was incident bladder cancer (ICD 10 C67), or bladder cancer listed as the underlying cause of death on the death certificate.At baseline, information on sociodemographic characteristics , lifestyle habits  and health and medical history, were obtained using a self-administered touchscreen questionnaire and nurse-led interviews. Physical activity was assessed using the validated Short International Physical Activity Questionnaire (IPAQ). Height, weight and waist circumference (WC), were measured by trained research staff using standardized procedures. More details of these measures can be found elsewhere .2 as well as WC < 94 cm in men and WC < 80 cm in women, poor weight as BMI ≥ 30 kg/m2, or WC ≥ 102 cm in men and WC ≥ 88 cm in women, or intermediate . Smoking status was defined as optimal if participants never smoked or quit 10 years ago, intermediate if they were previous smokers, or as poor if they were current smokers. Physical activity was defined as optimal if individuals had ≥ 150 min/week per week moderate or ≥ 75 min per week vigorous or an equivalent combination, intermediate as 1– 149 min/week moderate or 1–74 min/week vigorous or 1–149 min/week mixed activity, and poor as not performing any moderate or vigorous activity. A diet score was built based on the sum of intake of fruits and vegetables, whole grains, red and processed meat, and was then categorized into 3 groups based on tertiles . Given that the greater consumption of tea could reduce the risk of bladder cancer based on WCRF/AICR report [We created a healthy lifestyle score (HLS) based on the new World Cancer Research Fund/American Institute of Cancer Research (WCRF/AICR) lifestyle score . SpecifiR report , we addeWe also constructed a weighted healthy lifestyle score based on the β coefficients of each lifestyle factor in the Cox model for bladder cancer risk as a sensitivity analysis. The weighted healthy lifestyle score was derived by summing the product of each lifestyle factor with its corresponding β coefficient, then dividing the sum by the total of the β coefficients, and subsequently categorizing it into three groups based on tertiles .Details of the array design, genotyping process and quality control in the UK Biobank can be found elsewhere . In brieP < 5 × 10−8 were used to construct the PRS in our study  were identified using the LDlink (https://ldlink.nci.nih.gov/) [2 ≥ 0.2), and palindromic SNPs with MAF ≥ 0.45 [In previous published studies, a PRS for bladder cancer was built based on the 15 SNPs identified in previous GWAS among individuals of European ancestry , 18, 28.ih.gov/) . We exclF ≥ 0.45 .PRS for each study participant was constructed by summing the product of the number of risk alleles  for each SNP and its respective weight . The PRSWe calculated person-years from the recruitment date to the date of the first diagnosis of any cancer, death, or the last date of follow-up (31 December 2020), whichever came first. Multivariable Cox proportional hazards models were fitted to evaluate the effects of genetic factors, lifestyle factors, and the combination of genetic and lifestyle factors on bladder cancer risk, by calculating HRs and 95% confidence intervals (CI). To evaluate the extent to which adhering to a healthy lifestyle might mitigate the underlying bladder cancer risk owing to genetic susceptibility, we created joint categories of the genetic and lifestyle factors categories, with the high-risk group  as the reference group. Cox models were adjusted for age, sex, family history of cancer, socioeconomic status, and the first 10 principal components of ancestry. The interaction between the lifestyle and genetic risk score was tested by including an interaction term in the regression model. We further calculated absolute risk difference over a 10-year period, and the number needed to adopt an optimal lifestyle to prevent one bladder cancer case in 10 years, based on the method described by Altman D.G and Andersen P.K [Given that there is considerable gender difference in the incidence of bladder cancer, we repeated all analyses separately by sex. We conducted several sensitivity analyses to test the robustness of the primary results. Firstly, to minimize the possibility of the reverse causality bias resulting from lifestyle changes caused by undiagnosed cancer, we excluded events that occurred within the first 2 years of follow-up. Secondly, we limited our analyses to the participants of white British descent. Thirdly, we used the subdistribution hazard model approach proposed by Fine and Gray to account for the competing risk of death, and diagnosis of any other cancer (except for non-melanoma skin cancer). Fourthly, we created a weighted healthy lifestyle score to control for varied effect estimates of associations between different lifestyle factors and bladder cancer. Fifth, we developed a new lifestyle score without smoking status to identify whether smoking status might drive the associations between lifestyle with bladder cancer.A total of 375 998 participants were included in the final analysis  and reverse bias cannot be fully excluded. However, the findings remained robust after excluding bladder cancer cases occurred within the first 2 years, suggesting that the reverse bias would be likely to be minimal.In summary, our study suggested that adhering to an intermediate or optimal lifestyle can reduce the risk of bladder cancer across all genetic risk strata of the population, even in those at a high genetic risk of bladder cancer. For all populations, adopting an intermediate lifestyle is more beneficial than a poor one, and adhering to an optimal lifestyle is the ideal effective strategy for bladder cancer prevention, especially among individuals with a high genetic risk.Additional file 1. The file contains additional analysis. Table S1. Definitions of lifestyle factors. Table S2. Singlenucleotide polymorphisms used to build the genetic risk score for bladder cancer. Table S3. Associations between the polygenic risk score and lifestyle factors. Table S4. Risk of bladder cancer according to different risk levels of polygenic risk score. Table S5. Hazard ratio (95% CI) of bladder cancer according to genetic risk category in sensitivity analyses. Table S6. RERI, AP and SI for additive interaction between genetic and lifestyle categories. Table S7. Population-attributable fraction per behavioral lifestyle group. Table S8. Association of genetic and lifestyle factors with bladder cancer risk stratified by gender. Table S9. Associations of lifestyle categories and risk of bladder cancer stratified by PRS category and gender. Table S10. Risk of incident bladder cancer according to genetic and lifestyle category stratified by gender. Table 11. Risk of incident bladder cancer according to genetic and lifestyle risk stratified by smoking status. Table S12. Risk of incident bladder cancer according to genetic and lifestyle risk stratified by smoking status. Table S13. Hazard ratio (95% CI) of bladder cancer according to lifestyle category in sensitivity analyses. Table S14. Associations of lifestyle score and risk of bladder cancer stratified by PRS category in sensitivity analyses. Table S15. Risk of incident bladder cancer according to genetic and lifestyle risk in sensitivity analyses. Table S16. The joint effect of genetic and lifestyle factors on early-and late-onset of bladder cancer risk. Figure S1. Flowchart for the selection of the study population from UK Biobank study. Figure S2. Risk of incident bladder cancer according to genetic risk and lifestyle factors. Figure S3. Risk of incident bladder cancer according to genetic risk stratified by sex. Figure S4. Risk of incident bladder cancer according to lifestyle profile stratified by sex. Figure S5. Risk of incident bladder cancer according to genetic and lifestyle risk (without smoking status).


Despite mammography-based screening for breast cancer has been conducted in many countries, there are still little data on participation and diagnostic yield in population-based breast cancer screening in China.We enrolled 151,973 eligible women from four cities in Hebei Province within the period 2013–2021 and followed up until December 31, 2021. Participants aged 40–74 who assessed as high risk were invited to undergo breast ultrasound and mammography examination. Overall and group-specific participation rates were calculated. Multivariable analyses were used to estimate the factors associated with participation rates. The diagnostic yield of both screening and no screening groups was calculated. We further analyzed the stage distribution and molecular subtype of breast cancer cases by different modes of cancer detection.P = 0.003). We observed a higher detection rate of breast cancer in the screening group, with ORs of 2.42 (95% CI 1.72–3.41) for early stage (stages 0–I) and 2.12 (95% CI 1.26–3.54) for luminal A subtype. SBCs had higher proportion of early stage (71.93%) and luminal A subtype (47.22%) than other groups.A total of 42,547 participants were evaluated to be high risk of breast cancer. Among them, 23,009 subjects undertook screening services, with participation rate of 54.08%. Multivariable logistic regression model showed that aged 45–64, high education level, postmenopausal, current smoking, alcohol consumption, family history of breast cancer, and benign breast disease were associated with increased participation of screening. After median follow-up of 3.79 years, there were 456 breast cancer diagnoses of which 65 were screen-detected breast cancers (SBCs), 27 were interval breast cancers (IBCs), 68 were no screening cancers, and 296 were cancers detected outside the screening program. Among them, 92 participants in the screening group (0.40%) and 364 in the non-screening group (0.28%) had breast cancer detected, which resulted in an odds ratio of 1.42 (95% CI 1.13–1.78; The significant differences in breast cancer diagnosis between the screening and non-screening group imply an urgent need for increased breast cancer awareness and early detection in China. Breast cancer is the most common malignancy and primary cause of cancer mortality among women globally, with an estimated 2.26 million new cases and 685,000 new deaths in 2020  were small in size, node-negative, of early stages and positive for estrogen receptor (ER) and progesterone receptor (PR)  . The reporting standard in both examinations was the Breast Imaging Reporting and Data System (BI-RADS) classification . Any differences from the original diagnosis were discussed until an agreement was reached.Women who screened with positive results were followed up by telephone or retrieval of medical record information to obtain the final diagnosis and outcome. The entire cohort population was passively followed up using the population-based Hebei Cancer Registry Database from October 1, 2013 until December 31, 2021. Tumor characteristics  were collected from pathological reports by trained investigators. All breast cancer cases were reported according to the International Statistical Classification of Disease, Tenth Revision (codes D05.0-D05.9 and C50.0-C50.9).Screen-detected breast cancer (SBC) referred to cancer that is identified within 0–6 months after a positive screening result. Interval breast cancer (IBC) was defined as cancer that was detected between 0 and 24 months after a negative screening result  or PR (PR+)  was defined as positive nuclear staining intensity in "2+" and "3+" of tumor cells  7th edition  and 95% confidence intervals (CIs) were estimated by logistic regression. Diagnostic yield among different groups, including detection rates of stage at diagnosis, histological type, and molecular subtype of breast cancer, was calculated. We further analyzed the stage distribution and molecular subtype of breast cancer cases by different cancer detection modes. We used R software (version 4.1.2) for all analyses, and considered N = 109,362), those with history of cancer (N = 54), and ineffective risk assessment results (N = 10), 42,547 participants were identified as high risk for breast cancer . We found that participation rate was associated with age, education level, current smoking, alcohol consumption, menopause status, benign breast disease, and family history of breast cancer. For instance, women with benign breast disease had 30% greater likelihood of undertaking screening than those without . The odds of participants with family history undergoing screening were 29% higher odds than participants without . While women aged 70–74 were less likely to undergo screening compared to those aged 40–45 . We additionally adjusted the study sites and recruitment year in the model II, and the odds ratios did not change significantly.In univariate analyses, women aged 45–69 years; married; postmenopausal; current smoking; alcohol consumption; high level of education; with benign disease and family history were more likely to participate in the study. To explore the potential factors associated with participation rate, we also conducted multivariable logistic regression models Table . We founAfter a median time of 3.79 year follow-up, there are overall 456 breast cancer diagnoses of which 65 were screen-detected breast cancers (SBCs), 27 were interval breast cancers (IBCs), 68 were noncompliant breast cancers (NBCs), and 296 were cancers detected outside the screening program, yielding the detection rates for SBCs, IBCs, NBCs, and cancers detected outside the screening program at 0.28%, 0.12%, 0.35%, and 0.27%, respectively (Table P = 0.003). For stage at diagnosis, we observed a higher detection rate of breast cancer in the screening group, with ORs of 2.42 (95% CI 1.72–3.41) for stage 0–I and 1.83 (95% CI 1.19–2.80) for stage II. For histological type and molecular subtype, we observed a higher detection rate of breast cancer in the screening group, with ORs of 1.71 (95% CI 1.28–2.28) for ductal type and 2.12 (95% CI 1.26–3.54) for luminal A subtype.The tumor characteristics of breast cancer stage, histologic type, and molecular subtype by screening status are summarized in Table This study reported the results of 151,973 participates underwent breast cancer screening from 2013 to 2021 in China. Our results suggest that the detection rate and early diagnosis rates were higher in the screening group than in the non-screening group. In addition, interval cancers (IBCs) were more likely than screen-detected cancers (SBCs) to be of HER2-enriched and triple-negative subtype. To our knowledge, this is the first to report detection rate by molecular subtype of breast cancer screening program in a large multi-center population-based dataset in China. The results suggest that we need to further improve the diagnostic yield especially in interval cancers. Our study underscores the urgency to increase breast cancer awareness and early detection in China.In our study, the overall participation rate among the high-risk women was different (58.19%) from other studies 47.27%-48.2% , the Netherlands (0.6%), and Japan (0.5%) (Barlow et al. The essence of cancer screening is early diagnosis and early treatment of cancer to reduce mortality. We observed a higher detection rate of early stage cases in screening group than in non-screening group, in accordance with the previous reports (Zhang et al. Our research supports prior studies which indicate that there was a noticeably higher proportion of luminal A subtype in SBCs (Sihto et al. Our study has some limitations. First, the participants were recruited from four urban areas, where healthcare was fairly accessible. Therefore, this study population may not be represent the entire population of Hebei Province. Second, while detailed epidemiological information was collected in a standardized manner by trained study staff, smoking and drinking status were self-reported, which may have led to misclassification. Third, outcome information for breast cancer patients is still being obtained through ongoing follow-up work. Further studies are needed to evaluate the screening effect on breast cancer mortality.In summary, in this large-scale screening program, breast cancer screening participation rates were affected by age, education level, postmenopausal status, smoking, drinking, benign breast disease, and family history of breast cancer. We illustrated higher detection rate for both early stage cases and luminal A subtype in screening group than non-screening group. Women who participated in population screening and had interval cancers had a worse subtype and stage distribution. Our results indicate that we need to improve the diagnostic yield, especially in interval cancer, in the future. These findings will provide data support for optimizing population-based breast cancer screening practices in China.


To the Editor, We read with great interest the article entitled “Increased Prevalence of Breast and All-cause Cancer in Female Orthopedic Surgeons” by Chou et al.1 (2022). The study, which used a survey-based sample of 672 female orthopaedic surgeons, found a 1.89 and 3.97-fold increased prevalence of all-cause cancer and breast cancer, respectively, among female orthopaedic surgeons compared with matched US women from the general population.The results raise notable concern as to the reasons for the increased prevalence of breast and other cancers. In their discussion, the authors propose reasons including higher socioeconomic status, delayed age of childbearing, and occupational factors. The authors also note that poor-fitting lead protection garments among female trainees could lead to increased radiation exposure to breast tissues and subsequently advocate for change in this area.lower among surgeons in subspecialties likely to have greater radiation exposure. In addition, the authors did not find an increased breast cancer risk by radiation exposure group or specialty type.While we agree with recommendations to have proper-fitting lead protection garments for all surgeons and believe that additional efforts toward equity are of paramount importance in medicine, the claim that radiation exposure may be contributing to the higher risk of breast and other cancers among female orthopaedic surgeons was generally not supported by the results of the study. The authors found no notable difference in the prevalence of cancer by individual subspecialty, use of fluoroscopy, and use of polymethyl methacrylate (PMMA), nor use of protective shielding. In fact, the authors found that cancer prevalence was markedly 1The authors also noted that, compared with general population–based female samples, female orthopaedic surgeons in their sample were more likely to have breast cancer protective factors such as being of a younger age, having a normal body mass index, having never smoked, being White, and having had less exposure to hormone replacement therapy. The authors stated, “Despite these protective factors, female orthopedic surgeons still reported a higher prevalence of cancer compared with matched US women. Additional investigation is needed to further delineate these variables and their correlation with cancer risk.”2 Alcohol consumption as low as three drinks per week has been associated with a 15% increased risk of breast cancer.3Despite many protective factors being present in their sample, the authors neglected to discuss the markedly increased prevalence of high-risk alcohol consumption among their sample. Alcohol is a class 1 carcinogen, and there is notable evidence of alcohol's contribution to breast and other cancer risk.4) at approximately 3.7 times the rate of women in the general population (34.7% vs 9.5%). In the surgeon group, 25.3% of women were consuming 3 to 5 drinks per week compared with 8.6% of the general population and 9.4% of surgeons were drinking six or more drinks per week compared with 0.9% of the general population.Despite not being reported in the body of the study, Chou et al. (2022) collected data on alcohol consumption among their sample of female orthopaedic surgeons that were reported in Appendix Table A2. Their data showed that female orthopaedic surgeons were consuming alcohol at a level considered to markedly increase risk of breast cancer . Such drinking behavior markedly increases the risk of breast cancer.Despite the notable concern for high-risk alcohol consumption among their sample, Chou et al. (2022) did not analyze the rate of cancer diagnosis in relation to levels of alcohol intake. We think such an association is very likely, and the lack of analysis represents a missed opportunity for identification of another potential cause of the increased cancer rate in this population.6 This raises concerns about surgeon well-being beyond cancer risk. As per the report by Chou et al. (2022), the percentage of female orthopaedic surgeons is increasing. It is, therefore, imperative that efforts focus not only on hazards encountered in the workplace but also on social and emotional aspects of wellness.Beyond increased cancer risk, the markedly increased levels of high-risk alcohol consumption raise concerns about the psychosocial well-being of many female surgeons who may be consuming alcohol to cope with stress. This group is drinking more with less free time than the general population. A national survey found that 25.6% of female surgeons engaged in patterns of alcohol use concerning for “abuse” or dependence and that this group was more likely to report burnout, depression, and a major medical error in the previous 3 months.In conclusion, the increased rates of cancer and breast cancer among female orthopaedic surgeons reported by Chou et al. (2022) raise notable concerns. Although the authors focused mainly on radiation exposure as a contributory cause, their evidence did not generally support that association. While we agree that radiation protection is an actionable change that should be pursued, we think that the evidence presented by the authors suggests a possible role of alcohol consumption for the increased cancer rate among female surgeons. Additional research is needed to determine contributory causes of increased cancer prevalence so that evidence-based interventions can be developed that will lead to reduced cancer risk and ultimately cancer prevention. Finally, the increased prevalence of high-risk alcohol consumption among this population warrants additional investigation and the development of clinical and policy-based interventions to address this key risk factor for negative health consequences.


The SNP-SNP interaction analysis revealed noteworthy interaction terms with TPH2- and MAPK-related SNPs . In line with the light-at-night hypothesis that links shift work with elevated breast-cancer risks our results point to SNPs in TPH2 and MAPK-genes that may impact the intricate network of circadian regulation.Light-at-night triggers the decline of pineal gland melatonin biosynthesis and secretion and is an IARC-classified probable breast-cancer risk factor. We applied a large-scale molecular epidemiology approach to shed light on the putative role of melatonin in breast cancer. We investigated associations between breast-cancer risk and polymorphisms at genes of melatonin biosynthesis/signaling using a study population of 44,405 women from the Breast Cancer Association Consortium . Genotype data of 97 candidate single nucleotide polymorphisms (SNPs) at 18 defined gene regions were investigated for breast-cancer risk effects. We calculated adjusted odds ratios (ORs) and 95% confidence intervals (CI) by logistic regression for the main-effect analysis as well as stratified analyses by estrogen- and progesterone-receptor  status. SNP-SNP interactions were analyzed via a two-step procedure based on logic regression. The Bayesian false-discovery probability (BFDP) was used for all analyses to account for multiple testing. Noteworthy associations (BFDP < 0.8) included 10 linked SNPs in The online version contains supplementary material available at 10.1007/s10654-023-01048-7. Breast cancer is the most common cancer and the leading cause of cancer death for women worldwide with a higher incidence among women in developed countries . BesidesThe circadian system is orchestrated by a multisynaptic pathway that is governed by a master clock, the suprachiasmatic nucleus (SCN), located in the hypothalamus. Following photic input, the pathway is set into operation via the retino-hypothalamic tract by intrinsically photosensitive retinal ganglion cells. The light signal is directly projected into the SCN to finally synapse with the pineal gland via complex networks including the sympathetic nervous system, superior cervical ganglions as well as other participating hypothalamic areas  , 8. In tL-tryptophan catalyzed by tryptophan hydroxylase (TPH). Decarboxylation of 5-hydroxy-L-tryptophan by L-aromatic amino acid decarboxylase (AADC) results in the neurotransmitter serotonin, the acetylation of which by aralkylamine N-acetyltransferase (AANAT) and methylation by N-acetylserotonin O-methyltransferase  finally yields melatonin [Melatonin (N-acetyl-5-methoxytryptamine) is the key-player for this synchronization of bodily circadian rhythms. Its biosynthesis follows a multistep process starting with the hydroxylation of the precursor molecule elatonin . During elatonin .Melatonin is mainly secreted from the pineal gland upon photic neural input, but also produced by other ocular tissues such as photoreceptors and ciliary body epithelium, albeit to a lesser extent, as well as other bodily tissues , 14. WitThe light-at-night-associated breast-cancer risk has been attributed to a reduced nocturnal biosynthesis and lower secretion of melatonin , 17. In We screened 106,621 breast-cancer cases and control subjects with available pheno- and genotype data deposited in the database of the Breast Cancer Association Consortium (BCAC) , 20 at tTPH1 and TPH2), melatonin receptors (MTNR1A and MTNR1B) as well as various MAP kinases . All 97 SNPs are listed in Supplementary table S2 together with their characteristics in the study population. Corresponding genotypes were retrieved from the BCAC database Cambridge. They were previously generated within the framework of the Collaborative Oncological Gene-environment Study (COGS) using a custom Illumina iSelect array with 211,155 SNPs as described elsewhere [We focused on 97 single nucleotide polymorphisms (SNPs) at 18 genes including melatonin biosynthesis . To consider multiple testing, we used 0.05 divided by the number of analyzed SNPs as threshold for p values.We checked for Hardy–Weinberg-Equilibrium (HWE) by ΧFor the main-effect analysis of each SNP, we estimated odds ratios (ORs) and 95% confidence intervals (CIs) for each SNP assuming different genetic models . All models were adjusted for reference age, study, and a set of eight principal components to consider a possible population stratification effect. Furthermore, we adjusted these models for parity (nulliparous/1+ full term pregnancies/unknown), breast-feeding status (never/ever/unknown), smoking status (never/ever/unknown), and current use of estrogen-progesterone combined menopausal hormone therapy (MHT) (no/yes/unknown). Regarding menopausal hormone therapy, current use was defined as ‘use at reference date or within six months prior to the reference date’. Missing values in categorical covariates were coded as ‘unknown’. In a sensitivity analysis, we additionally adjusted the models for menopausal status . Post-menopausal was defined as ‘last menstruation more than 12 months before the reference date’. We also calculated ORs separately for estrogen-receptor (ER) positive/negative (ER±) and progesterone-receptor (PR) positive/negative (PR±) cases.To analyze interactions between SNPs, we used a two-staged procedure based on logic regression models . In shorrequired .In the first stage of our procedure, we selected interactions for the logic tree by using the logic Feature Selection (logicFS) algorithm to avoid overfitting . Here, wp value < 0.05 in the adjusted models, assuming a four-fold cost of a false non-discovery compared to a false discovery as suggested by Wakefield [To account for multiple testing and an increased type I error rate, we calculated the Bayesian false-discovery probability (BFDP) for SNPs/interaction terms with a akefield . EffectsThe statistical software R, version 3.4.2, was used for all calculations . The R-pThe study population of 44,405 women was contributed by 14 case–control studies (Suppl. table S1) of which the smallest study comprised 243 women (NBHS) and the largest 16,746 women (SEARCH). Among the eligible 22,992 cases and 21,413 controls, the mean reference age was 56 years for cases and 57 years for controls with a standard deviation of 10 and 9 years, respectively. Most women had at least one full term pregnancy (76%) and nearly 50% had ever breastfed (Table RORB). The minor allele frequency (MAF) in controls ranged from 2–49%. Detailed information for all SNPs is provided in Supplementary table S2. HWE was not met for rs10765576 (MTNR1B) and rs14303 (MAP2K1), and therefore, these polymorphisms were not followed up further.Most of the 97 analyzed SNPs had very few missing values, with a maximum of 4% for rs10217741 (TPH2 intronic polymorphisms. ORs (95% CIs) of individual SNPs with a p value < 0.05 and respective BFDPs with different priors  for an effect in the adjusted dominant or recessive model are given in Table MAPK8, OR = 1.11, 95% CI 1.04–1.18, BFDP < 0.8 at prior 0.01). Increasing the prior to 0.05 also revealed eight linked noteworthy dominant TPH2 SNPs  with similar adjusted ORs around 1.07 (95% CIs 1.02–1.12) in the dominant model. At a prior of 0.1, protective effects were revealed for two TPH2 SNPs in the recessive model , as well as recessive rs13515 . Recessive rs7075976  was also noteworthy. The additive model showed similar results (Suppl. table S3). A sensitivity analysis with additional adjustment for menopausal status did not change the results. When we compared our results with those obtained in the meta GWAS analysis, none of the noteworthy SNP associations reported here were identified at the genome wide association level (5E-08) [Noteworthy associations (BFDP < 0.8) between individual SNPs and breast-cancer risk have been identified particularly for  (5E-08) , 26, 27.MAPK8 SNP rs10857561 in the ER+ subgroup  and two tightly linked TPH2 SNPs rs7300641 and rs1386492 in the PR+ group . In the ER− subgroup the two linked TPH2 SNPs rs1473473 and rs1487276  and the RORA SNP rs17237290  showed noteworthy associations, while none of the associations in the PR− subgroup were noteworthy at prior 0.05. Besides rs17237290, the variants mentioned above were also noteworthy in the main analysis at prior 0.05 under the identical genetic models. At a prior of 0.1, in total 25 noteworthy associations of 19 SNPs were observed for all four breast cancer subtypes.Tumor hormone-receptor status was ER-positive (ER+) for 14,724 patients and ER-negative (ER−) for 3516 patients, as well as PR-positive (PR+) for 10,016 patients and PR-negative (PR−) for 4,768 patients . The adjusted logistic regression models for these terms yielded ten interaction terms with a p-value < 0.05 (Suppl. table S6), hence suitable for BFDP calculation.R ∧ !rs1347069D , rs10857561R, , and rs1386483R ∧ rs1473473D ∧ rs3729931D . With increased priors of 0.05 and 0.1, in total six and eight interaction terms reached noteworthiness, respectively. All noteworthy interactions  included at least one SNP that showed noteworthy associations, respectively, in the main effect analysis .With a prior probability for an effect of 0.01, we found three noteworthy interaction terms in the adjusted models . The triple interaction of TPH2 rs1386483 and rs1473473 as well as RAF1 rs3729931 increased breast-cancer risk by 20%, a dual interaction of MAPK8 rs10857561 and MAP2K1 rs1347069 by 15%, and MAPK8 rs10857561 alone by 11% . In most instances, risk effects were evident at the individual SNP level both, in main and stratified risk analyses by hormone-receptor status. To the best of our knowledge and based on the combined iCOGs/Oncoarray meta-analysis of the BCAC cohort [TPH2 polymorphisms have been reported in the literature within the context of psychiatric disorder related endpoints such as antidepressant response and GABA concentration, conditions in which effects of serotonin are underlying biological mechanisms [The strongest observed risk effects were driven by various interactions of polymorphisms at C cohort , these bchanisms .TPH2 as a putative breast cancer susceptibility locus is plausible, as the enzyme is exclusively expressed in neuronal cells of the central nervous system where it catalyzes the rate limiting step in serotonin (5-HT) biosynthesis, the chemical precursor of melatonin [TPH2 mRNA expression levels compared to male counterparts [TPH2 gene expression that was shown to be under the circadian control of melatonin, estrogen, and corticoids in rodents and primates [TPH2 is also rhythmically expressed in ocular tissues with rhythmical release of melatonin, the levels of which are highest in darkness and lowest in the light [TPH2 variants and increased breast-cancer risk are well in line with the notion that such variants impact on serotonin formation, thereby disrupting the SCN pacemaker circuitry feedback. In particular, the observed increased breast-cancer risk in TPH2 variant carriers may result from modified brain melatonin levels due to a dysfunctional SCN (together with reduced melatonin levels in the retina) that upon light-at-night periods may reduce pineal melatonin secretion.The involvement of elatonin . Its perterparts , therebyterparts , 34. Theprimates . This unprimates , 36, 37.he light , 38. Inthe light . Hence, TPH2 variant-associated increased breast-cancer risk may in addition relate to local serotonergic effects accountable for increased prolactin production in the anterior pituitary gland. Of note, circulating prolactin is an established breast-cancer risk factor that has been confirmed in a series of analyses from the prospective Nurses’ Health Study, particularly with respect to ER-positive breast cancer [If the disruption of pineal gland melatonin biosynthesis during extended light-at-night periods affects nocturnal serotonin levels and transmission to the hypothalamic nuclei SCN and PVN , 40, we t cancer , and a lt cancer  with empt cancer . As prolt cancer .MAPK8  in our study has been associated with breast-cancer risk at rs10857561, both in the individual main and hormone-receptor positive (ER + and PR +) stratified analyses, as well as in the SNP-SNP interaction analyses. While all three canonical MAPK pathways  serve as input to the circadian clock in distinct ways [nct ways , JNK in nct ways .MAPK8 showed noteworthy risk estimates for breast cancer in general as well as ER-positive and PR-positive subgroups in our analyses, this SNP has been previously shown to be associated with rectal cancer once more highlighting the role in carcinogenesis [Aside from their role as master clock regulators via SCN or peripheral tissue, MAPK genes play a role in cellular processes like proliferation and cell death . It is togenesis .A major strength of this study is the large number of study participants  retrieved from population-based case–control studies with defined reference age  and availability of comprehensive epidemiological and tumor immunohistochemistry as well as genotype data. This allowed us to calculate precise SNP-specific OR main effect and interaction estimates. Our hypothesis-driven approach of a putative role of polymorphic regulators or signaling mediators of melatonin in breast-cancer risk limited the number of potentially detectable false-positive associations. Moreover, we used the BFDP to measure the noteworthiness of our effect estimates and to account for false-positive results via multiple testing. Furthermore, our interaction analysis based on logic regression models enabled us to model the effects of complex interaction scenarios considering the multivariate structure of SNP interplay.In spite of the large study size, limitations of the study include a high number of missing values in included confounders. We used the category ‘unknown’ for categorical variables in these participants to maintain the remaining information. Moreover, the sample size in subgroup analyses was reduced, for example, ER status was missing for 21% of samples and PR status for 36% of samples. However, missingness is likely to be random with respect to genotypes. There was also minor heterogeneity in definition of stage, grade, and cut-off levels for ER and PR across studies. The tumor subtypes were strictly defined by immunohistochemical markers as other data, such as intrinsic subtypes from expression profiles nowadays used for subtype definition, are not available in large-scale epidemiological studies. Finally, our interpretations strictly rely on functional and physiological data reported in the literature and include in vitro, animal in vivo as well as post mortem findings.TPH2 and MAPK8 highlight their cooperation as putative breast-cancer risk modulators and call upon the comprehensive scrutiny of the circadian clock system and its input and output effectors in large breast-cancer cohorts. This research holds the potential to reveal new insights into the breast-cancer risk of women exposed to light-at-night which is particularly relevant for female night shift workers.Our newly identified breast-cancer risk associations justify continued research into the relationship between breast-cancer risk and putative modulators of the intricate network of rhythmic circadian regulators such as melatonin and serotonin upon photic stimulation at night. The observed interactions between genetic variants of Supplementary file1 (XLSX 20 KB)Supplementary file2 (XLSX 30 KB)Supplementary file3 (XLSX 43 KB)Supplementary file4 (XLSX 130 KB)Supplementary file5 (XLSX 15 KB)Supplementary file6 (XLSX 21 KB)Below is the link to the electronic supplementary material.


Epidemiological studies have generated conflicting findings on the relationship between glucose-lowering medication use and cancer risk. Naturally occurring variation in genes encoding glucose-lowering drug targets can be used to investigate the effect of their pharmacological perturbation on cancer risk.cis-acting genome-wide significant (p<5×10−8) SNPs permitted to be in weak linkage disequilibrium (r2<0.20). Summary genetic association estimates for these SNPs were obtained from genome-wide association study (GWAS) consortia for the following cancers: breast ; colorectal ; prostate ; and overall (i.e. site-combined) cancer . Inverse-variance weighted random-effects models adjusting for linkage disequilibrium were employed to estimate causal associations between genetically proxied drug target perturbation and cancer risk. Co-localisation analysis was employed to examine robustness of findings to violations of Mendelian randomisation (MR) assumptions. A Bonferroni correction was employed as a heuristic to define associations from MR analyses as ‘strong’ and ‘weak’ evidence.We developed genetic instruments for three glucose-lowering drug targets ]; glucagon-like peptide 1 receptor [GLP1R]) using summary genetic association data from a genome-wide association study of type 2 diabetes in 148,726 cases and 965,732 controls in the Million Veteran Program. Genetic instruments were constructed using 1c: OR 1.75 , p=0.02). In histological subtype-stratified analyses, genetically proxied PPARG perturbation was weakly associated with lower risk of oestrogen receptor-positive breast cancer . In co-localisation analysis, however, there was little evidence of shared causal variants for type 2 diabetes liability and cancer endpoints in the PPARG locus, although these analyses were likely underpowered. There was little evidence to support associations between genetically proxied PPARG perturbation and colorectal or overall cancer risk or between genetically proxied ABCC8 or GLP1R perturbation with risk across cancer endpoints.In MR analysis, genetically proxied PPARG perturbation was weakly associated with higher risk of prostate cancer ; and overall prostate cancer . Summary genetic association data for colorectal cancer can be accessed by contacting GECCO (kafdem at fredhutch.org). Summary genetic association data on advanced prostate cancer can be accessed by contacting PRACTICAL . Summary genetic association data on type 2 diabetes from Vujkovic et al  can be accessed through dbGAP under accession number phs001672.v3.p1 (pha004945.1 refers to the European-specific summary statistics). UK Biobank data can be accessed by registering with UK Biobank and completing the registration form in the Access Management System (AMS) (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).Summary genetic association data for select cancer endpoints were obtained from the public domain: breast cancer (The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-023-05925-4. Globally, an estimated 460 million individuals have type 2 diabetes, most of whom require long-term use of glucose-lowering medications to maintain glycaemic control . SeveralPreclinical studies have variably reported both carcinogenic and antineoplastic effects of glucose-lowering medications. For example, in vitro studies have suggested that metformin, an insulin sensitiser and first-line therapy for type 2 diabetes, can reduce cell proliferation, induce apoptosis and cause cell cycle arrest . ThiazolEpidemiological studies of glucose-lowering medication use have provided some support for findings from laboratory studies. For example, some observational studies have reported that metformin users have lower risk of several cancers while sulfonylurea use has been associated with an increased risk of site-specific  and overall cancer –17. In aThe causal nature of associations reported between glucose-lowering medication use and cancer risk in conventional epidemiological studies is often unclear. This is because of the susceptibility of such studies to residual confounding (e.g. due to indication) and various forms of bias , which can undermine robust causal inference . While cDrug target Mendelian randomisation (MR) uses germline variants in genes encoding drug targets as instruments (‘proxies’) for these targets to estimate the effect of their pharmacological perturbation on disease endpoints . Since gcis-acting instruments  with risk of breast, colorectal and prostate cancer, common cancers with epidemiological evidence suggesting a link between glucose-lowering medication use and their onset, and overall (i.e. site-combined) cancer , PPARG, SGLT2, DPP4 and GLP1R). We also aimed to evaluate associations of genetically proxied perturbation of three of these targets with reliable ) cancer , 26–28.Summary genetic association data were obtained from three cancer-specific genome-wide association study (GWAS) consortia. Summary genetic association estimates for overall and oestrogen receptor (ER)-stratified breast cancer risk in up to 122,977 cases and 105,974 controls were obtained from the Breast Cancer Association Consortium (BCAC) . Summaryhttp://www.icd9data.com/2007/Volume1/default.htm) and ICD-10 (http://apps.who.int/classifications/icd10/browse/2016/en) with data completed to April 2019 and controls were defined as individuals who did not have any cancer code (ICD9 or ICD10) and did not self-report a cancer diagnosis. GWAS were performed using a linear mixed model as implemented in BOLT-LMM (v2.3) (to account for relatedness and population stratification) and adjusted for age, sex and genotyping array , p=0.01) and AST . Co-localisation analysis suggested that type 2 diabetes associations in the PPARG locus had a 92% and 84% probability of sharing a causal variant with ALT and AST, respectively . Co-localisation analysis suggested that type 2 diabetes and BMI associations had a 94.0% posterior probability of sharing a causal variant in ABCC8 . Co-localisation analysis applied to both marginal and conditionally independent associations for type 2 diabetes and BMI in the GLP1R locus did not support shared causal variants across these traits   Figs .Fig. 6Rep=0.02) but little evidence of association with other cancer endpoints  . This finding was consistent in iterative leave-one-out analysis  of both traits sharing one or more causal variants within this region   and H4  across these analyses. We found little evidence of association of genetically proxied GLP1R or ABCC8 perturbation with cancer risk.In this MR analysis of up to 287,829 cases and 606,790 controls, we found weak evidence for an association of genetically proxied PPARG perturbation with a higher risk of prostate cancer and lower risk of ERDespite in vivo studies suggesting an important role for PPARG in prostate tumour growth and conventional epidemiological studies suggesting a link between pioglitazone use and elevated prostate cancer risk, our combined MR and co-localisation analyses did not find consistent evidence for an association of genetically proxied PPARG perturbation with prostate cancer risk , 18. Likcis-acting variants, in close proximity to the genes that code for the drug targets of interest, horizontal pleiotropy should be minimised. In addition, we used strict positive control analysis (i.e. testing drug targets against established secondary effects of medications) and co-localisation analyses  to validate the selected instruments. Our use of a summary-data MR approach permitted us to leverage large-scale genetic data from several GWAS consortia, enhancing statistical power and precision of causal estimates.Among the strengths of our analysis is the strict instrument selection and validation process employed. By using 1c (~6.75 mmol/mol [~0.61%]) and therefore cannot rule out more modest effects of the drug targets examined on cancer risk. In clinical trials, monotherapy with sulfonylureas, thiazolidinediones  and the GLP1R agonist liraglutide has been shown to reduce HbA1c by around 8–17 mmol/mol (0.7–1.5%), as compared with placebo [GLP1R, the presence of which would not necessarily influence the validity of this as an instrument for GLP1R signalling perturbation’s effect on glycaemic control [+ breast cancer risk attenuated towards the null in iterative leave-one-out analysis removing rs4135247 from the PPARG instrument, 95% CIs overlapped across models with and without this variant. Though this attenuation in association is consistent with sampling error, we cannot rule out the possibility that this attenuation was driven, in part, through horizontally pleiotropic mechanisms linking this variant to cancer risk. Eighth, though we found strong and suggestive evidence for associations of genetically proxied PPARG perturbation with ER+ breast cancer and prostate cancer risk, respectively, after applying a Bonferroni correction to account for multiple testing, we cannot rule out the possibility that these findings represent false-positive results. Ninth, the MR estimates reported represent long-term effects of target modulation in non-diabetic populations, whereas the clinical effects of these medications may be more pronounced among individuals with type 2 diabetes and could depend on length of medication use. Tenth, we cannot rule out the possibility that controls in cancer GWAS included individuals with latent, undiagnosed cancer, the presence of which would bias associations towards or away from the null, depending on the site of undiagnosed cancer and the relationship between drug targets examined and this cancer. We also cannot rule out the possibility of survival bias influencing genetic association estimates from cancer GWAS consortia that employed case–control study designs. If, for example, genetic variants used to instrument glucose-lowering drug target perturbation increased cancer risk and subsequent mortality prior to enrolment in a case–control study, this could induce an artificial ‘protective’ association between perturbation of this drug target and cancer risk. Finally, samples were restricted to individuals of European ancestry and therefore the generalisability of these findings to non-European populations is unclear.There were several limitations to this analysis. First, we had sufficient statistical power to detect large effect sizes only per SD decrease in HbA placebo –50. Seco control . Fourth,+ breast cancer. There was little evidence of co-localisation for these findings, a necessary precondition to infer causality between PPARG perturbation and these cancer endpoints, possibly reflecting either the absence of shared causal variants across type 2 diabetes liability and these cancer endpoints in PPARG or the low statistical power of these analyses. Further assessment of these drug targets using alternative molecular epidemiological approaches (e.g. using protein or expression quantitative trait loci or using direct circulating measures of these proteins) and/or studies using medical registry data  may help to further corroborate findings presented in this analysis. Finally, we found little evidence for an association of genetically proxied ABCC8 and GLP1R perturbation with risk of breast, colorectal, prostate or overall cancer risk.In conclusion, we developed novel instruments for PPARG, ABCC8 and GLP1R using strict validation protocols and evaluated the association of genetically proxied perturbation of these targets with risk of cancer. In MR analysis we found weak evidence that genetically proxied PPARG perturbation was associated with a higher risk of prostate cancer and a lower risk of ERSupplementary file1 (PDF 226 KB)Below is the link to the electronic peer-reviewed but unedited supplementary material.


This review synthesized and appraised the evidence for an effect of inflammation on breast cancer risk. Systematic searches identified prospective cohort and Mendelian randomization studies relevant to this review. Meta-analysis of 13 biomarkers of inflammation were conducted to appraise the evidence for an effect breast cancer risk; we examined the dose–response of these associations. Risk of bias was evaluated using the ROBINS-E tool and the quality of evidence was appraised with Grading of Recommendations Assessment, Development, and Evaluation. Thirty-four observational studies and three Mendelian randomization studies were included. Meta-analysis suggested that women with the highest levels of C-reactive protein (CRP) had a higher risk of developing breast cancer  compared with women with the lowest levels. Women with highest levels of adipokines, particularly adiponectin  had a reduced breast cancer risk, although this finding was not supported by Mendelian randomization analysis. There was little evidence of an effect of cytokines, including TNFα and IL6, on breast cancer risk. The quality of evidence for each biomarker ranged from very low to moderate. Beyond CRP, the published data do not clearly support the role of inflammation in the development of breast cancer. Observational evidence suggests that physical activity has a protective effect on breast cancer risk . The undin vivo and ex vivo studies that implicate inflammatory biomarkers in all stages of tumor development  independently screened the titles and abstracts; where there was consensus that studies were not relevant, they were excluded. Two authors (M.W.C. Lou and A.E. Drummond) then reviewed the full text of remaining papers; studies were included on the basis of consensus. Data were extracted into pre-piloted tables and summarized descriptively. For biomarkers with results reported from at least three separate studies (excluding Mendelian randomization studies), random-effects meta-analysis was conducted to estimate the effect size (risk ratio) for the highest category of biomarker compared with the lowest category. Multiple estimates from the same study were included in the same meta-analysis where it was clear that samples did not overlap. Where heterogeneity was identified, a sub-group analysis on the effect size by menopausal status at baseline was conducted. Sensitivity analyses were also completed to assess study estimate differences by exogenous hormone use . Where it was not indicated that hormone use was controlled for either in statistical analysis or by sample restriction, the study was left out of the sensitivity analysis. A one-stage, dose–response meta-analysis of summarized data using restricted cubic splines was performed . The RisThe search results are presented in n = 26 independent studies; 34 publications), with 16 of these also including premenopausal women  metabolites . Follow-up time in eligible studies ranged from less than one year to 25 years. All observational studies included postmenopausal women at baseline blood collection . All observational studies had at least moderate ROB, due to the potential for uncontrolled or residual confounding. Assessment results found one serious  and 31 mMeta-analysis results for each inflammatory biomarker are presented as forest plots and can be found in n = 16; risk ratio (RR) = 1.13; 95% confidence interval (CI), 1.01–1.26; I2 = 34.87%; n = 6; RR = 1.02; 95% CI, 0.84–1.21; I2 = 0%; Supplementary Methods and Materials; n = 14; RR = 1.16; 95% CI, 0.98–1.34; I2 = 44.12%), though the estimate was imprecisely estimated ; no difference was reported between ER+ and ER- breast cancers  and postmenopausal women  to conduct a meta-analysis was IL6. We found little evidence of an association with breast cancer risk . However, there was moderate heterogeneity and a wide CI, limiting the certainty of this result. In a sensitivity analysis excluding studies with unknown status for exogenous hormone use, the positive association in postmenopausal women was strengthened, with a large reduction in heterogeneity , compared with those with the lowest levels . Sub-group analyses revealed that adiponectin is protective for postmenopausal women , and showed evidence of a dose–response effect . There was little evidence of an association for premenopausal women, with high heterogeneity . A Mendelian randomization study did not support a causal effect of adiponectin on breast cancer risk , with wide and overlapping CIs from the meta-analysis  in one study , low , or moderate (adiponectin).The robust methodology used to search, synthesize, and appraise the current evidence on inflammatory biomarkers and breast cancer risk is a key strength of our review. The prespecified list of biomarkers in our search strategy improved identification of relevant articles compared with a previous review . We haveThis systematic review revealed a sparsity of evidence from prospective cohorts and Mendelian randomization studies. Of the 13 biomarkers searched, publications included in the review examined 10, and only five biomarkers met criteria to facilitate a meta-analysis. Studies that measured biomarkers in premenopausal women were limited (17 of the 34 publications). Given that risk factors and mechanisms for breast cancer development likely differ between pre- and postmenopausal women , and thaThere was moderate to high heterogeneity noted for all meta-analyses. Subgroup meta-analyses were conducted to assess any potential differences by menopausal status. While menopause explained some of the heterogeneity for leptin, the sources of heterogeneity could not be further clarified for other markers due to the limited reporting of stratified results and inconsistent control for confounders between studies.Risk factors contributing to breast cancer may differ by disease subtypes . HoweverExogenous hormone use was mostly well described in studies and was adjusted for in statistical analyses, as is typical in studies on breast cancer. We included women who were taking exogenous hormones in our main analyses, although sensitivity analyses removed these and findings remained consistent. In contrast, the use of NSAIDs or aspirin was not routinely reported or adjusted for. This may be because NSAID and aspirin use is more difficult to measure, and use is typically occasional. Anti-inflammatory medication may pose a potential source of confounding in the prospective cohort studies. A recent meta-analysis of randomized controlled trials suggested that use of low-dose aspirin may offer a small degree of protection against breast cancer .Almost all studies measured biomarkers only once at baseline. Correspondingly, exposure measurements were potentially limited by non-differential misclassification, leading to a potential underestimation of effects. Sensitivity analyses excluding cases diagnosed within 12/24 months of blood draw were not consistently reported amongst studies. However, no significant differences in risk estimates were observed in studies that conducted sensitivity analyses investigate risk with a time-lag until diagnosis . Funnel Prior narrative reviews suggest that inflammation increases cancer risk . Chronicn = 4), and it may reflect the complex dual roles of TNFα in both its antitumor functions and inflammatory tumor-promoting functions (The results for TNFα were not anticipated, as increased levels of the pro-inflammatory biomarker appeared to decrease risk of breast cancer. These findings should be interpreted with caution due to the small number of studies (unctions . Resultsunctions . Leptin unctions . While aThe overarching aim of this two-part series of reviews was to determine whether the inflammation mediates the inverse relationship between physical activity and breast cancer risk. Although Part 1 identified decreases in CRP, TNFα, IL6, and leptin following exercise interventions , in PartFigure S1ASupplementary Figure 1A presents forest plots for CRP and breast cancer risk, by menopausal statusClick here for additional data file.Figure S1BSupplementary Figure 1B presents forest plots for CRP and breast cancer risk, excluding studies where exogenous hormone use status was unknownClick here for additional data file.Figure S1CSupplementary Figure 1C present forest plots for CRP and breast cancer risk, excluding studies that had a moderate risk of biasClick here for additional data file.Figure S1DSupplementary Figure 1D presents dose-response curves for CRP and breast cancer risk, by menopausal statusClick here for additional data file.Figure S1ESupplementary Figure 1E presents the funnel plot from the meta-analysis of CRP and breast cancer riskClick here for additional data file.Figure S2ASupplementary Figure 2A presents forest plots for TNF-alpha and breast cancer risk, by menopausal statusClick here for additional data file.Figure S2BSupplementary Figure 2B presents forest plots for TNF-alpha and breast cancer risk, excluding studies where exogenous hormone use status was unknownClick here for additional data file.Figure S2CSupplementary Figure 2C presents the funnel plot from the meta-analysis of TNF-alpha and breast cancer riskClick here for additional data file.Figure S3ASupplementary Figure 3A presents forest plots for IL-6 and breast cancer risk, by menopausal statusClick here for additional data file.Figure S3BSupplementary Figure 3B presents the funnel plot from the meta-analysis of IL-6 and breast cancer riskClick here for additional data file.Figure S4ASupplementary Figure 4A presents forest plots for leptin and breast cancer risk, by menopausal statusClick here for additional data file.Figure S4BSupplementary Figure 4B presents forest plots for leptin and breast cancer risk, excluding studies where exogenous hormone use status was unknownClick here for additional data file.Figure S4CSupplementary Figure 4C presents dose-response curves for leptin and breast cancer risk, by menopausal statusClick here for additional data file.Figure S4DSupplementary Figure 4D presents the funnel plots from the meta-analysis of leptin and breast cancer riskClick here for additional data file.Figure S5ASupplementary Figure 5A presents the forest plots for adiponectin and breast cancer risk, by menopausal statusClick here for additional data file.Figure S5BSupplementary Figure 5B presents forest plots for adiponectin and breast cancer risk, excluding studies where exogenous hormone use status was unknownClick here for additional data file.Figure S5CSupplementary Figure 5C presents dose-response curves for adiponectin and breast cancer risk, by menopausal statusClick here for additional data file.Figure S5DSupplementary Figure 5D presents the funnel plots from the meta-analysis of adiponectin and breast cancer riskClick here for additional data file.Table S1Supplementary Table 1 presents the search terminology for the systematic reviewClick here for additional data file.Table S2ASupplementary Table 2A presents the study characteristics of the Mendelian randomization studiesClick here for additional data file.Table S2BSupplementary Table 2B presents the study characteristics of the prospective cohort studiesClick here for additional data file.Table S3Supplementary Table 3 presents the risk of bias assessment, using the ROBINS-EClick here for additional data file.