To evaluate the utility of polygenic risk scores (PRSs) in identifying high-risk individuals, different publicly available PRSs for breast (n=85), prostate (n=37), colorectal (n=22), and lung cancers (n=11) were examined in a prospective study of 21,694 Chinese adults.We constructed PRS using weights curated in the online PGS Catalog. PRS performance was evaluated by distribution, discrimination, predictive ability, and calibration. Hazard ratios (HR) and corresponding confidence intervals (CI) of the common cancers after 20 years of follow-up were estimated using Cox proportional hazard models for different levels of PRS.A total of 495 breast, 308 prostate, 332 female-colorectal, 409 male-colorectal, 181 female-lung, and 381 male-lung incident cancers were identified. The area under receiver operating characteristic curve for the best-performing site-specific PRS were 0.61 , 0.70 , 0.65 , 0.60 , 0.56 , and 0.58 , respectively. Compared to the middle quintile, individuals in the highest cancer-specific PRS quintile were 64% more likely to develop cancers of the breast, prostate, and colorectal. For lung cancer, the lowest cancer-specific PRS quintile was associated with 28–34% decreased risk compared to the middle quintile. In contrast, the HR observed for quintiles 4  and 5  were not significantly different from that for the middle quintile.Site-specific PRSs can stratify the risk of developing breast, prostate, and colorectal cancers in this East Asian population. Appropriate correction factors may be required to improve calibration.This work is supported by the National Research Foundation Singapore (NRF-NRFF2017-02), PRECISION Health Research, Singapore (PRECISE) and the Agency for Science, Technology and Research (A*STAR). WP Koh was supported by National Medical Research Council, Singapore (NMRC/CSA/0055/2013). CC Khor was supported by National Research Foundation Singapore (NRF-NRFI2018-01). Rajkumar Dorajoo received a grant from the Agency for Science, Technology and Research Career Development Award (A*STAR CDA - 202D8090), and from Ministry of Health Healthy Longevity Catalyst Award (HLCA20Jan-0022).The Singapore Chinese Health Study was supported by grants from the National Medical Research Council, Singapore (NMRC/CIRG/1456/2016) and the U.S. National Institutes of Health (NIH) (R01 CA144034 and UM1 CA182876). Although humans contain the same genes, the sequence within these DNA sites can vary from person to person. These small variations, also known as genetic variants, can increase the risk of developing certain diseases. While each variant will only have a weak effect, if multiple variations are present the odds of developing the disease becomes significantly higher..To determine which variants are linked to a disease, researchers carry out genome-wide association studies which involve analyzing the genomes of individuals with and without the condition and comparing their genetic codes. This data is then used to calculate how different combinations of variants impact a person’s chance of getting the disease, also known as a polygenic risk scoreCurrently, most genome-wide association studies only incorporate genetic data from people with European ancestry. Consequently, polygenic risk scores performed using this information may not accurately predict the risk of developing the disease for individuals with other ethnicities, such as people with Asian ancestry.Here, Ho et al. evaluated how well previously calculated polygenic risk scores for the four most common cancers  worked on individuals of East Asian descent. The scores were tested on a dataset containing the genetic sequence, medical history, diet and activity levels of over 21,000 people living in Singapore in the 1990s. Ho et al. found that the polygenic risk scores for breast, prostate and colorectal cancer were able to predict disease risk. However, the score for lung cancer did not perform as well. The polygenic risk score for breast cancer was the most accurate, and was able to stratify individuals into distinct risk bands at an earlier age than other scores.These findings shed light on which existing polygenic risk scores will be effective at assessing cancer risk in individuals with East Asian ancestry. Indeed, Ho et al. have already incorporated the polygenic risk score for breast cancer into a pilot study screening individuals in a comparable population in Singapore. However, the polygenic risk scores tested still performed better on individuals with European ancestry, highlighting the need to address the lack of Asian representation in genome-wide association studies. Polygenic risk scores (PRSs) for a range of health traits and conditions have been developed in recent years. These scores, which are based on summary statistics from genome-wide association studies (GWAS), can be used to stratify people depending on their genetic risk of acquiring various diseases, to improve screening and preventative interventions, as well as patient care . PrecisiThe contributions of heritable genetic factors are different for different cancers. Twin studies have highlighted statistically significant effects of heritable genetic risk factors for cancers of the prostate, colorectal, and breast . The amoThe area under receiver operating characteristic curve (AUC) is an important discrimination index for evaluating the performance of PRS. The greater the AUC, the better the discriminatory ability to separate cases from non-cases. A value of 0.5 suggests that the tool is performing no better than chance, while a value of 1 is obtained when cases and non-cases are perfectly separated. The range of reported AUC associated with published PRS ranged from 0.584 to 0.678 for breast cancer , 0.591–0While prediction of individual cancer risks through PRS remains moderate, emerging data supports the use of PRS for population-based cancer risk stratification. In previous work, Ho et al. examined the overlap of women identified to be at high risk of developing breast cancer based on family history for the disease, a non-genetic breast cancer risk prediction model, a breast cancer PRS, and carriership of rare pathogenic variants in established breast cancer predisposition genes . The oveDisparities in the genetic risk of cancer among various ancestry populations are poorly understood. Ideally, selected genetic variants that make up PRS should be relevant to the population being screened. The development of training datasets of PRS are dominated by samples of European ancestry, resulting in ancestry bias and issues with transferability to other populations . The misThe Singapore Chinese Health Study (SCHS) is a population-based prospective cohort study of ethnic Chinese men and women recruited between April 1993 and December 1998 . ParticiAn in-person baseline interview was performed at recruitment to collect data on diet using a validated 165-item food frequency questionnaire, smoking, alcohol, physical activity, medical history, and menstrual and reproductive history from women.In Singapore, between 2015 and 2019, colorectal cancer, the most prevalent cancer in men, accounted for nearly 17% of cancer diagnoses, while breast cancer, the most common cancer in women, accounted for about three out of ten cancer diagnoses . During A unique National Registration Identity Card (NRIC) number for every Singaporean enables the compilation and linkage of data from national register data to the same individual . IdentifDeath date was obtained by record linkage with the database Birth and Death Registry of Singapore . The datBetween 1999 and 2004, a total of 28,346 subjects contributed blood samples. A total of 25,273 SCHS participants were genotyped between the years 2017–2018 with the Illumina Infinium Global Screening Array (GSA) v1.0 and v2.0 .Details on the sample quality control (QC) processes are previously described . Briefly–6), monomorphic SNPs, or those with a minor allele frequency (MAF) < 1.0%, and SNPs with low call rates (<95.0%). We imputed for additional autosomal SNPs using IMPUTE v2  outliers  . Of the kx is the dosage of risk allele (0–2) for SNP kk, β is the corresponding weight.where Two-sided, two-sample t-tests with a type I error of 0.05 were used to examine whether there was a difference in the distribution of standardised PRS  between site-specific cancer cases and non-cancer controls.Discrimination was quantified by the area under the receiver operating characteristic curve (AUC), using logistic regression models, and their corresponding 95% CI. An AUC of 0.9–1.0 is considered excellent, 0.8–0.9 very good, 0.7–0.8 good, 0.6–0.7 sufficient, and 0.5–0.6 insufficient . The sitSubjects were classified into PRS percentile groups. Person-years of follow-up were calculated for each subject from the date of enrolment to the date of cancer diagnosis, death, or 31 December 2015 (the date of linkage with the Singapore Cancer Registry), whichever came first. Follow-up time was censored at 20 years after recruitment. The associations between cancer-specific PRS quintiles  and the incidence of site-specific cancers were investigated using Cox proportional hazards modelling to estimate hazard ratios (HR) and corresponding 95% confidence intervals (CI), using time since recruitment as the time scale, and adjusted for age at recruitment. Tests for trends were conducted using two-sided Wald tests with a type I error of 0.05. Assumptions for proportional hazards were checked using the cox.zph function in the ‘survival’ package in R, where a formal score test is done to test if a time-dependent variable is required.2), cigarette smoking , alcohol consumption , moderate physical activity , vigorous work/strenuous physical activity at least once a week (no or yes), and familial history of cancer (no or yes).HR and corresponding 95% CI were also estimated for every SD increase in PRS. PRS is known to have ‘portability’ issues related to genetic ancestry and demographics . Hence, The 5-year absolute risks of developing breast, prostate, colorectal, and lung cancers were computed for PRS groups of increasing five percentiles over the follow-up period. Incidence (between 2013 and 2017) and mortality (the year 2016) statistics in Singapore  was used to study the overall calibration. A curve close to the diagonal indicates that predicted cancer risks correspond well to observed proportions. A slope above 1 implies that the model underestimates the absolute risk. Conversely, a slope below 1 implies that the model overestimates the absolute risk. In addition, we used the Hosmer-Lemeshow test to check the goodness-of-fit.Among PRS for breast (n=85), prostate (n=37), colorectal (n=22), and lung cancers (n=11) examined, the reported source of variant associations or GWAS used to build PRS were from predominantly European ancestry populations . Only siB) discrimination, (C) absolute risk association, and (D) calibration of the best-performing PRS (based on AUC) (t-test <0.00273). on AUC)  for the  on AUC) , prostat on AUC) , colorec on AUC) , and lun on AUC) . All PRS on AUC) . The meaThe highest AUC obtained from logistic models was observed for prostate cancer , followed by female breast cancer (0.61 [0.58–0.63]), male colorectal cancer , female colorectal cancer (0.56 [0.52–0.60]), male lung cancer (0.58 [0.55–0.61]), and female lung cancer (0.55 [0.50–0.59]) .–25 for prostate cancer to 0.008 for female lung cancer, During the follow-up period of 20 years, the risk of acquiring breast, colorectal, or lung cancer increased significantly with higher PRS after adjusting for age at recruitment. Compared to the first PRS quintile, individuals in the highest quintile were more likely to develop the four cancers studied. The highest HR observed was for prostate cancer (8.99 [95%CI: 5.27–15.35]) and lowest for female lung cancer (1.69 [1.03–2.79]), adjusted for age at recruitment . SignifiCompared to the middle cancer-specific PRS quintile, individuals in the highest PRS quintile were 64% more likely to develop cancers of the breast, prostate, and colorectal . Individ–8), adjusted for age, dialect group, education, BMI, smoking status, physical activity, alcohol consumption, and family history . Age at recruitment is significantly associated with elevated risks of developing all cancers, with the exception of female breast cancer . Highest education level and BMI were positively correlated with breast cancer risk. Smoking was significantly associated with an ~30% reduction in risk of prostate cancer but increased the risk of lung cancer by approximately two- and fivefold for past and current smokers, compared to non-smokers, respectively. Alcohol consumption increased the risk of both female and male colorectal cancer by approximately 60% but was only significant for male colorectal cancer. Family history of cancer was only significantly associated with an increased risk for prostate cancer .Every SD increase in PRS is associated with 39–108% elevated risks of breast, prostate, and colorectal cancers (p<1.06 × 10 history . The inccox-zph for PRS were >0.05).All Cox models presented in In terms of the 5-year absolute risk of developing site-specific cancers, the largest difference between the highest and lowest PRS categories was observed for prostate cancer, followed by breast cancer . A separIn general, predicted risks for the higher PRS categories did not correspond well to the observed proportions for female breast, male prostate, and female lung cancers ; in partPrecision prevention in oncology is based on the idea that an individual’s risk, which is influenced by genetics, environment, and lifestyle factors, is linked to the amount of benefit achieved through cancer screening . Risk stIn an increasingly inclusive world, genetic studies fall short on diversity. According to a 2009 study, an overwhelming 96% of people who took part in genome-wide association studies (GWAS) were of European ancestry . GWAS reGenetic studies in 2016 showed that the proportion of people not of European ancestry included in GWAS has increased to approximately 20% . Most ofIn accordance with published Polygenic Risk Score Reporting Standards, we reported PRS distribution, discrimination, absolute risk association, and calibration for each of the four common cancers studied . Our resPRS may be of limited use in predicting female colorectal and female/male lung cancer. The least predictive value was in lung cancer, which could be related to the higher prevalence of EGFR mutant lung cancer which has an Asian predilection, thus less amenable to PRS developed in Caucasian population . It is rThere is room for improvement in the discriminatory ability of PRS . As noteAs highlighted by Wei et al., the reliability of score values is necessary for application at the individual level . Even whPoorly calibrated PRS can be misleading and have clinical repercussions . UnderesWhile the study population used in this analysis comprises less than a thousand cases of the most common cancers examined, the SCHS, established between April 1993 and December 1998, is one of the largest population-based Asian cohorts in the world with high-quality prospective data on exposure and comprehensive capture of morbidity and mortality. All cancer cases are incident cases diagnosed over three decades of follow-up. Blood samples were collected from a subset of SCHS participants who were alive and contactable between 1999 and 2004 (after the recruitment period 1993–1998). While we attempted to adjust associations between PRSs and incident cancers in the study by including multiple related risk factors as covariates in Cox proportional hazards models, we acknowledge the potential of survival biases in the study. This is one of the best resources to evaluate the utility of PRS in a prospective manner. The findings open a window in our current understanding of which PRS is relevant and ready to be deployed in risk-based cancer screening studies. Nonetheless, it should be noted that among the PRS interrogated from the PGS Catalog, the ‘best’ PRS selected in this study may be only superficially superior in terms of AUC  over the ‘next best’ PRS. We further tested the sensitivity of the PRS selection using a time-to-event metric for AUC (at 5 years), and the differences found were non-informative from the logistic regression . To incrEthnic representation in PRS model development, PRS validation, limited discriminative ability in the general population, ill calibration, insufficient healthcare professional and patient education, and healthcare system integration are all hurdles that must be crossed before PRS can be implemented responsibly as a public health instrument . While n Utilizing one of the largest prospective Asian cohorts with long-term follow-up data, the important study reveals the utility of polygenic risk scores in identifying high-risk individuals from specific cancers. The convincing results reached by the high-quality data highlight the translational significance of this study, which may be important for future exploration in the field of cancer epidemiology. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Caigang Liu as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Polygenic risk scores for the prediction of common cancers in East Asians: A population-based prospective cohort study" for consideration by The reviewers have discussed their reviews with one another, and revisions are necessary for the manuscript. For your guidance, reviewers' comments are appended below. If you decide to revise your manuscript, please revise your work guided by the reviewers' suggestions, and provide a point-by-point response to the following suggestions and concerns.Reviewer #1 (Recommendations for the authors):This is an important paper that has the potential to contribute to our understanding of how to take forward polygenic risk scores developed primarily based on individuals of European descent on the large Asian population.1. However, by focusing on a combined analysis of 4 cancers, the main findings appear to have been lost.a. The authors note in the discussion that the PRSes developed in individuals of European descent has not well calibrated for the Asian population One would not expect PRS with poor AUC to be well calibrated, hence poor calibration for lung cancer-PRS may be expected in this study. For other cancer-PRSs, the confidence interval of the calibration slopes indicating that they are not significantly different from one, hence no strong suggestion of poor calibration overall, except maybe for prostate PRS. Would it be possible that observed prostate cancer incidence is underestimated due to survival bias?b. For each of the 4 cancers, it would be helpful to report features associated with translatability of PRS from Europeans to Asians. The authors report that "no significant association (P>0.05) was found between number of variants included in the various PRS evaluated for each cancer and discriminatory ability" – an exploratory analysis of other features associated with performance would be helpful.c. In Table 2, the authors appear to have grouped individuals into quintiles based on a combined score. It is not clear what is the intent for this analysis – is the aspiration to roll out a risk stratified screening strategy based on "high risk for any of 4 cancers" and then to screen for all 4 cancers? The corresponding text in Line 315-330 needs clarification, it is not clear what is the intent for repeating the analyses with different PRS quintiles as reference group.d. In Table 3, the authors reported the association of PRS together with other demographic/risk factors. It hasn't been made clear the reason for such exhaustive adjustment in the model. If the intent is to quantify the attenuated amount in PRS association after accounting for potential confounders, then unadjusted PRS would need to be presented and the point needs to be made clear in the text.e. In Table 4, the authors intend to quantify the proportion of cases captured by the at-risk groups as defined by PRSs. However, what's the rational of the HR cutoffs? Why not use the absolute risks in Figure 1(c) instead? What is the intent to include other risk factors? Higher proportion of cases captured would be expected with additional risk factors, however, it may be an overestimation here given that the RRs of other risk factors were estimated from the same cohort?2. Have the authors sufficiently addressed survival bias? The cohort of 68k individuals was established in 1993-1998, but blood samples from 28k individuals were only collected in 1999-2004, and of these, 21,694 were analysable. Was the Singapore Cancer Registry data systematically collected from 1993?Reviewer #2 (Recommendations for the authors):1. My primary concern is lack of clarity in why not all cancer PRS were carried forward from Supplemental Table 1 to evaluation in Supplemental Table 2. The PRS from the largest prostate cancer GWAS to date (Conti 2021), including 12% Asian individuals, is represented in Supplemental Table 1 (PGS ID: PGS000662) but is not carried forward for evaluation in Supplemental Table 2. Similarly, a lung cancer PRS developed specifically for a Chinese population (Dai 2020) is also represented in Supplemental Table 1 (PGS ID: PGS000070) but not carried forward for evaluation. I have not performed similar inspection for the breast and colorectal cancer PRS, but this raises the concern about incomplete evaluation of available (and even best) PRS.2. It is not clear that AUC should be used to identify the "best" PRS for each cancer in this population, particularly since from what I can tell the author did not use a time-dependent AUC method and excluded all baseline cases from analyses. A time-to-event metric would seen to be a more appropriate way to identify the "best" PRS in these analyses, either using an AUC method for incident event data or using risk association.3. Although the term "predictive ability" is used in the landmark Wand 2021 paper (ref 31), I recommend using more specific terms in this manuscript, starting with the abstract (line 66). I think the correct terms would be "relative risk association" and "absolute risk association," as appropriate. Still, Ref 31 (Wand 2021) is helpful and should be introduced in the Introduction to orient to reader to the reporting framework the authors use in presenting this work.4. Can the authors justify why they performed only sex-stratified analyses for lung and colorectal cancer? The GWAS for these conditions included both sexes. Given the relatively small number of cases, it would be worth performing all analyses in the cohort overall. Sex-stratified analyses could be presented in the supplemental materials, if desired.5. Lines 182-184: How is it known that only <1% of participants migrated out of Singapore? Are other data available to confirm their ongoing residence in Singapore and absence of cancer diagnosis? E.g., other medical diagnoses or other data?6. It is reassuring to see cigarette smoking as a strong lung cancer risk factor, but the apparent protective effect of smoking on prostate cancer contradicts current evidence. The authors should discuss this in the Discussion.7. Lines 274-292 could be shortened, simply referring the reader to most of these results in Table 1.8. Lines 368-387: This section of the results seems out of order compared with the Methods. I recommend reorganizing the Results as follows: "Characteristics of the study population," "Lack of Asian representation in PRS development," "PRS discriminatory ability," "PRS distribution," "Associations between PRS and relative hazard of developing cancers," "Number of cancers that developed within PRS at-risk groups," "Association of PRS with absolute risk," "PRS calibration."9. Calibration should be formally tested  in addition to the visual inspection presented.10. Lines 470-476 of the Discussion stray a little from the focused thesis of this work.eLife. Your revised article has been evaluated by Caigang Liu (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Polygenic risk scores for the prediction of common cancers in East Asians: A population-based prospective cohort study" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:1. Your response to the initial critique #1 is valid. The response itself contains errors (e.g. incorrect PGS numbers given for specific disease PRS), but the supplemental tables appear correct. The caption for Figure 1 is missing, which should explain which column corresponds to which disease and in which sex.2. Please add the methods of your new time-to-event AUC sensitivity analysis to the main Methods of the manuscript, including a reference to the R package used. Also please state explicitly in the Methods your approach to choosing the best PRS for each disease (or disease-sex combination): logistic regression AUC.3. Please explicitly state this rationale for sex-stratified analyses for these cancers in the Methods. Reviewer #1 (Recommendations for the authors):This is an important paper that has the potential to contribute to our understanding of how to take forward polygenic risk scores developed primarily based on individuals of European descent on the large Asian population.1. However, by focusing on a combined analysis of 4 cancers, the main findings appear to have been lost.a. The authors note in the discussion that the PRSes developed in individuals of European descent has not well calibrated for the Asian population One would not expect PRS with poor AUC to be well calibrated, hence poor calibration for lung cancer-PRS may be expected in this study. For other cancer-PRSs, the confidence interval of the calibration slopes indicating that they are not significantly different from one, hence no strong suggestion of poor calibration overall, except maybe for prostate PRS. Would it be possible that observed prostate cancer incidence is underestimated due to survival bias?We have added the point that the reviewer highlighted about confidence intervals associated with the calibration slopes in the Results:In Results : “In general, predicted risks for the higher PRS categories did not correspond well to the observed proportions for female breast, male prostate, and female lung cancers ; in particular, predicted risks were overestimated for the higher risk categories. Overestimation of risk was observed for all PRS categories for male lung cancer. In contrast, predicted risks were underestimated for both female and male colorectal cancers. Nonetheless, the confidence intervals associated with the calibration slopes for all cancers included 1, with the exception of female (0.32 [-0.31 to 0.95]) and male lung cancers .”The prostate cancer PRS has poor evidence of a good fit.In the Discussion paragraph 5, we have added (in bold):“Our results show that cancer risk estimates based on PRS developed using populations of European ancestry are not optimally calibrated for our Asian study population. However, the confidence intervals associated with the calibration slopes for all cancers except for female and male lung included the value of 1, suggesting that the overall calibration is not poor. For female and male lung cancers associated with low AUC values, poor calibration is not unexpected. All PRSs except PGS000662 (prostate cancer) passed the formal Hosmer-Lemeshow goodness-of-fit test. Males in the first 5 deciles of PGS000662 did not develop prostate cancer, suggesting that a linear fit may not be appropriate. A hard threshold beginning from the 6th decile may perform better at identifying males at elevated risk of developing prostate cancer.”b. For each of the 4 cancers, it would be helpful to report features associated with translatability of PRS from Europeans to Asians. The authors report that "no significant association (P>0.05) was found between number of variants included in the various PRS evaluated for each cancer and discriminatory ability" – an exploratory analysis of other features associated with performance would be helpful.We did not find evidence of association between the number of variants and the calibration  of the PRS in our study population. We summarise the results from the additional analysis here in c. In Table 2, the authors appear to have grouped individuals into quintiles based on a combined score. It is not clear what is the intent for this analysis – is the aspiration to roll out a risk stratified screening strategy based on "high risk for any of 4 cancers" and then to screen for all 4 cancers? The corresponding text in Line 315-330 needs clarification, it is not clear what is the intent for repeating the analyses with different PRS quintiles as reference group.Cancer-specific quintiles were used for each cancer-specific analysis. The premise of this analysis is to identify the best-performing PRS for each cancer for the purpose of informing screening strategies for each respective cancer. To clarify, we have made changes to the text as follows:In Methods (section “Associations between PRS and risk of developing cancers”): “The associations between cancer-specific PRS quintiles  and the incidence of site-specific cancers were investigated using Cox proportional hazards modeling to estimate hazard ratios (HR) and corresponding 95% confidence intervals (CI), using time since recruitment as the time scale, and adjusted for age at recruitment.”In Results (section “Associations between PRS and the relative hazard of developing cancers”): “Compared to the middle cancer-specific PRS quintile, individuals in the highest PRS quintile were 64% more likely to develop cancers of the breast, prostate, and colorectal (Table 2).”Table 2 caption: “Table 2. Hazard ratios (HR) and corresponding 95% confidence intervals (CI) associated with polygenic risk score quintiles (Q) compared to the population median, using the Cox proportional hazards model and censored at 20 years after recruitment. Individuals were categorized into cancer-specific quintiles based on their cancer-specific PRS. All models were adjusted for age at recruitment.”d. In Table 3, the authors reported the association of PRS together with other demographic/risk factors. It hasn't been made clear the reason for such exhaustive adjustment in the model. If the intent is to quantify the attenuated amount in PRS association after accounting for potential confounders, then unadjusted PRS would need to be presented and the point needs to be made clear in the text.We have clarified in Methods (section “Associations between PRS and risk of developing cancers”): “PRS is known to have “portability” issues related to genetic ancestry and demographics . Hence, we adjusted for variables in the models, including age at recruitment, dialect group (Hokkien or Cantonese), highest level of education , body mass index , cigarette smoking , alcohol consumption , moderate physical activity , vigorous work/strenuous physical activity at least once a week (no or yes), and familial history of cancer (no or yes).”Results for the unadjusted PRS analyses have been added to Supplementary file 1d, columns N to Q.e. In Table 4, the authors intend to quantify the proportion of cases captured by the at-risk groups as defined by PRSs. However, what's the rational of the HR cutoffs? Why not use the absolute risks in Figure 1(c) instead? What is the intent to include other risk factors? Higher proportion of cases captured would be expected with additional risk factors, however, it may be an overestimation here given that the RRs of other risk factors were estimated from the same cohort?To minimize confusion, we have removed the results from Table 4. Using the absolute risk as the cut-off will not increase information known about the group identified as high-risk. The proportion of individuals identified as at high risk will be greater than the percentile identified by the absolute risk cut-off (x%). In addition, if the PRS is well calibrated, we expect >x% of these high-risk individuals to develop the disease.2. Have the authors sufficiently addressed survival bias? The cohort of 68k individuals was established in 1993-1998, but blood samples from 28k individuals were only collected in 1999-2004, and of these, 21,694 were analysable. Was the Singapore Cancer Registry data systematically collected from 1993?We thank the reviewer for highlighting this point. First, we agree that survival bias could exist since, we could only include participants who survived to participate in the follow-up interview and who also agreed to give blood samples for research. For example, in the breast cancer evaluation, those who gave blood samples were younger (mean age of 55.1 years versus 57.1 years at recruitment) and were also more likely to have received education  compared to women who did not give blood. As such, Cox proportional hazards models evaluating associations between PRS and incident cancers in the study were additionally adjusted for these related risk factors collected at recruitment, including age at recruitment, highest level of education, body mass index, cigarette smoking status, alcohol consumption, physical activity and familial history of cancer. In the revised version of the manuscript, we have further included survival bias as a potential study limitation:In Discussion (second last paragraph): “Blood samples were collected from a subset of SCHS participants who were alive and contactable between 1999 and 2004 (after the recruitment period 1993 – 1998). While we attempted to adjust associations between PRSs and incident cancers in the study by including multiple related risk factors as covariates in Cox proportional hazards models, we acknowledge the potential of survival biases in the study.”We have added more information on the Singapore Cancer Registry:In Methods (section “Selection of common cancers”): “Identification of incident cases of cancer was accomplished by record linkage of all surviving cohort participants with the database of the nationwide Singapore Cancer Registry [20]. The Singapore Cancer Register was founded in 1968. Prior to 2009, reporting of neoplasms by all medical practitioners and pathology laboratories to the registry is voluntary (10.1016/j.canep.2016.06.006). The registry's staff compares cancer patient hospital discharges and death certificates to registered cases for verification. Completeness of reporting in the 1970s is 96% and in the 1990s, it was close to 100% (10.1186/1471-2407-6-261).”Reviewer #2 (Recommendations for the authors):1. As summarized in my public comments, my primary concern is lack of clarity in why not all cancer PRS were carried forward from Supplemental Table 1 to evaluation in Supplemental Table 2. The PRS from the largest prostate cancer GWAS to date (Conti 2021), including 12% Asian individuals, is represented in Supplemental Table 1 (PGS ID: PGS000662) but is not carried forward for evaluation in Supplemental Table 2. Similarly, a lung cancer PRS developed specifically for a Chinese population (Dai 2020) is also represented in Supplemental Table 1 (PGS ID: PGS000070) but not carried forward for evaluation. I have not performed similar inspection for the breast and colorectal cancer PRS, but this raises the concern about incomplete evaluation of available (and even best) PRS.We have included the analysis of the PRSs that did not have training/ testing datasets mentioned and added the results to Supplementary file 1c-f. Thus, we studied a total of 165 PRSs . Figure 1 is updated to the PRSs from this list – PGS000873 (Breast), PGS000662 (Prostate), PGS000055 , PGS000734 , PGS000721 , and PGS000070 .2. It is not clear that AUC should be used to identify the "best" PRS for each cancer in this population, particularly since from what I can tell the author did not use a time-dependent AUC method and excluded all baseline cases from analyses. A time-to-event metric would seen to be a more appropriate way to identify the "best" PRS in these analyses, either using an AUC method for incident event data or using risk association.To obtain a time-to-event metric for AUC at 5-year, we used AUC.cd from the survAUC package in R (10.1186/s12874-017-0332-6). The best PRSs chosen were the same except for female colorectal cancer (PGS000149). However, the AUC at 5-year was 0.66677 for PGS000055 (the chosen PRS by AUC from the logistic model), 0.00005 lower than for PGS000149 (surv.AUC = 0.66682). We find the difference non-informative and have presented the AUCs from the Cox proportional hazards model in Supplementary file 1f.In Discussion (second last paragraph): “We further tested the sensitivity of the PRS selection using a time-to-event metric for AUC (at 5-year), and the differences found were non-informative from the logistic regression (Supplementary file 1g).”3. Although the term "predictive ability" is used in the landmark Wand 2021 paper (ref 31), I recommend using more specific terms in this manuscript, starting with the abstract (line 66). I think the correct terms would be "relative risk association" and "absolute risk association," as appropriate. Still, Ref 31 (Wand 2021) is helpful and should be introduced in the Introduction to orient to reader to the reporting framework the authors use in presenting this work.We have indicated the reporting framework used in the last sentence of the Introduction:“In this study, we evaluated the utility of common PRS, curated in the Polygenic Score (PGS) Catalog, in predicting the risk of the commonly diagnosed cancers with high genetic predisposition  in a prospective cohort comprising 21,694 participants of East Asian descent in Singapore. The reporting framework recommended for the interpretation and evaluation of PRS detailed in Wand et al. is used (10.1038/s41586-021-03243-6).”In Results: “PRS predictive ability” replaced with “PRS absolute risk association”In Results (section “PRS distribution”): “Figure 1 depicts the (A) distribution, (B) discrimination, (C) absolute risk association, and (D) calibration of the best-performing PRS (based on AUC)  for the four cancers studied: breast (PGS000873), prostate (PGS000662), colorectal , and lung .”In Discussion (fourth paragraph): “In accordance with published Polygenic Risk Score Reporting Standards, we reported PRS distribution, discrimination, absolute risk association, and calibration for each of the four common cancers studied [31].”Caption: “Figure 1. Site-specific polygenic risk scores (PRS) performance assessment.(A) Distribution, (B) discrimination, (C) absolute risk association and (D) calibration for each of the four common cancers studied. Two-sided, two-sample t-tests with a type I error of 0.05 were used to examine whether there was a difference in the distribution of standardised PRS  between site-specific cancer cases and non-cancer controls (A). The PRS showcased are the best-performing scores based on Area Under the Receiver Operator Characteristic Curve (AUC) values in the female and male populations, (i) unadjusted [solid line], and (ii) adjusted for age at recruitment [dashed line] (B). Each colored line in the plots for absolute risk association denotes a five percentile increase in the standardised PRS score in (C). Calibration calculated based on five-year absolute risk by PRS deciles in (D). A prediction tool is considered more accurate when the AUC is larger. An AUC of 0.9–1.0 is considered excellent, 0.8–0.9 very good, 0.7–0.8 good, 0.6–0.7 sufficient, 0.5–0.6 bad, and less than 0.5 considered not useful (PMID: 27683318).”4. Can the authors justify why they performed only sex-stratified analyses for lung and colorectal cancer? The GWAS for these conditions included both sexes. Given the relatively small number of cases, it would be worth performing all analyses in the cohort overall. Sex-stratified analyses could be presented in the supplemental materials, if desired.Gender differences in colorectal and lung cancer incidence have been reported in Singapore.From Kok et al. (DOI: 10.1007/s00384-007-0421-9): “Male colorectal cancer rates between 1968 and 2002 from 20 to 40 per 100,000 person years. The increase was sharpest among older men, for whom there was a significant AC effect. Female colorectal cancer rates increased until 1992  and stabilized afterward.”From Lim et al. (DOI: 10.1016/j.lungcan.2014.01.007): “Lung cancer incidence rates were more than two times higher in males compared to females.”The AUCs associated with selected PRS for combined colorectal  and combined lung  were not appreciably different from the PRS selected for the sex-stratified analyses . We have presented the combined-sex analysis for colorectal and lung cancer in Supplementary file 1g.In Discussion (second last paragraph): “To increase the number of events, we combined the males and females for lung and colorectal cancers. The resulting AUCs (from the logistic regression) were not appreciably different from the sex-specific analysis (Supplementary file 1g).”5. Lines 182-184: How is it known that only <1% of participants migrated out of Singapore? Are other data available to confirm their ongoing residence in Singapore and absence of cancer diagnosis? E.g., other medical diagnoses or other data?The data on migration in our cohort was collected during our subsequent follow-up interviews, and we were informed about the migration by the family members of cohort members who had migrated. To our best knowledge, we only knew 47 participants who had migrated to other countries in our cohort for the duration of this study.In Methods (section “Follow-up”): “The data on migration in our cohort was collected during our subsequent follow-up interviews, and we were informed about the migration by the family members of cohort members who had migrated.”We have systematically done linkage analysis between our cohort data and the nationwide Singapore Cancer Registry to identify incident cancer cases in our cohort since recruitment in the 1990s. The nationwide cancer registry has been in place since 1968 and has been shown to be comprehensive in its recording of incident cancer cases . Furthermore, compulsory reporting of cases to Singapore Cancer Registry has been mandated by law since 2010. In addition, we were able to ascertain survival status of all cohort participants via record linkage with the population-based Singapore Registry of Births and Deaths. Hence, we are confident that the capture of cancer cases via linkage with national registry can be considered to be virtually complete.In Methods (section “Selection of common cancers”): “The Singapore Cancer Register was founded in 1968. Prior to 2009, reporting of neoplasms by all medical practitioners and pathology laboratories to the registry is voluntary (10.1016/j.canep.2016.06.006). The registry's staff compares cancer patient hospital discharges and death certificates to registered cases for verification. Completeness of reporting in the 1970s is 96% and in the 1990s, it was close to 100% (10.1186/1471-2407-6-261).”6. It is reassuring to see cigarette smoking as a strong lung cancer risk factor, but the apparent protective effect of smoking on prostate cancer contradicts current evidence. The authors should discuss this in the Discussion.We have added in the Discussion:In Discussion (fifth paragraph): “It is reassuring to see tobacco smoking is a strong risk factor for lung cancer in our dataset. However, smoking appeared to be associated with a protective effect for prostate cancer. While smoking is a well-known risk factor for many cancers (10.18632/oncotarget.24724), in particular lung cancer, observational studies frequently show that smokers are associated with a lower incidence of prostate cancer 1097-0215(19960917)67:6<764::AID-IJC3>3.0.CO;2-P, 10.1054/bjoc.1999.1105, 10.1007/BF00051881, 10.2105/AJPH.2008.15050, 10.1016/j.eururo.2014.08.059, 10.1186/s12916-016-0607-5, 10.1002/ijc.22788). However, a Mendelian randomisation study did not support the association .”7. Lines 274-292 could be shortened, simply referring the reader to most of these results in Table 1.We have removed the second paragraph under Characteristics of the study population, keeping only details relevant to numbers of incident cancers and age of diagnosis:In Results (section “Characteristics of the study population”): “Table 1 shows the characteristics of the 21,694 participants who were cancer-free at recruitment. The median follow-up time for the cohort was 20 years (IQR: 18 to 22). As of December 2015, 495 women developed breast cancer, 308 men developed prostate, 774 (332 women and 409 men) colorectal cancer, and 562 (181 women and 381) lung cancer. The median age at recruitment was 54 years (interquartile range [IQR]: 49 to 61). The median age at diagnosis was 65 years (IQR: 59-70) for female breast cancers, 72 years (IQR: 67 to 77) for prostate cancers, 71 years (IQR: 65 to 76) for male colorectal cancers, 71 years (IQR: 64 to 78) for female colorectal cancers, 74 years (IQR: 68 to 78) for male lung cancers and 74 years (IQR: 66 to 79) for female lung cancers.“8. Lines 368-387: This section of the results seems out of order compared with the Methods. I recommend reorganizing the Results as follows: "Characteristics of the study population," "Lack of Asian representation in PRS development," "PRS discriminatory ability," "PRS distribution," "Associations between PRS and relative hazard of developing cancers," "Number of cancers that developed within PRS at-risk groups," "Association of PRS with absolute risk," "PRS calibration."We have reordered the sections as suggested without the section on “Number of cancers that developed within PRS at-risk groups” (based on the comments by Reviewer 1).9. Calibration should be formally tested  in addition to the visual inspection presented.We did not observe any lack of calibration based on the Hosmer-Lemeshow test (using 10 groups) for all PRSs except PGS000662 (prostate cancer). However, males in the first 5 deciles of PGS000662 did not develop prostate cancer. This may indicate that a linear fit is not appropriate, but a hard threshold (here the start of the 6th decile) is more appropriate to indicate males at elevated risk of developing prostate cancer.In Methods : “In addition, we used the Hosmer-Lemeshow test to check the goodness-of-fit.”In Discussion (paragraph 7): “All PRSs except PGS000662 (prostate cancer) passed the formal Hosmer-Lemeshow goodness-of-fit test. Males in the first 5 deciles of PGS000662 did not develop prostate cancer, suggesting that a linear fit may not be appropriate. A hard threshold beginning from the 6th decile may perform better at identifying males at elevated risk of developing prostate cancer.”10. Lines 470-476 of the Discussion stray a little from the focused thesis of this work.We have removed these lines from the Discussion.The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:1. Your response to the initial critique #1 is valid. The response itself contains errors (e.g. incorrect PGS numbers given for specific disease PRS), but the supplemental tables appear correct. The caption for Figure 1 is missing, which should explain which column corresponds to which disease and in which sex.We corrected the errors in the response for the PGS numbers, table numbers, and supplementary files numbers.“Thus, we studied a total of 165 PRSs . Figure 1 is updated to the PRSs from this list – PGS000873 (Breast), PGS000662 (Prostate), PGS000055 , PGS000734 , PGS000721 , and PGS000070 .”Caption for figure 1:“Figure 1. Site-specific polygenic risk scores (PRS) performance assessment.(A) Distribution, (B) discrimination, (C) absolute risk association and D) calibration for each of the four common cancers studied  between site-specific cancer cases and non-cancer controls (A). The PRS showcased are the best-performing scores based on Area Under the Receiver Operator Characteristic Curve (AUC) values in the female and male populations, (i) unadjusted [solid line], and (ii) adjusted for age at recruitment [dashed line] (B). Each colored line in the plots for absolute risk association denotes a five percentile increase in the standardised PRS score in (C). Calibration calculated based on five-year absolute risk by PRS deciles in (D). A prediction tool is considered more accurate when the AUC is larger. An AUC of 0.9–1.0 is considered excellent, 0.8–0.9 very good, 0.7–0.8 good, 0.6–0.7 sufficient, 0.5–0.6 bad, and less than 0.5 considered not useful (PMID: 27683318).”2. Please add the methods of your new time-to-event AUC sensitivity analysis to the main Methods of the manuscript, including a reference to the R package used. Also please state explicitly in the Methods your approach to choosing the best PRS for each disease (or disease-sex combination): logistic regression AUC.We added the information in Methods as recommended.In Methods (PRS discrimination):“The site-specific PRS with the highest AUC (logistic regression models) was selected. To test the sensitivity of the PRS selection, we obtained a time-to-event metric for AUC at 5-year, we used AUC.cd from the survAUC package in R (10.1186/s12874-017-0332-6).”3. Please explicitly state this rationale for sex-stratified analyses for these cancers in the Methods.We have added the information as recommended.In Methods (Selection of common cancers)“We further stratified the analysis by sex as differences in colorectal and lung cancer incidence by sex have been reported in Singapore [10.1007/s00384-007-0421-9].”


Breast cancer is presently the most commonly diagnosed cancer in women, and it stands as the leading cause of cancer-related deaths worldwide. Notably, breast cancer rates have seen a significant increase in sub-Saharan African countries, including Ethiopia. Several risk factors contribute to breast cancer, some of which can be modified, while others are inherent. Promoting a healthier diet is strongly encouraged as a preventive measure against breast cancer. However, it’s noteworthy that no previous research has investigated the connection between dietary patterns and the risk of breast cancer among Ethiopian women. Therefore, the primary objective of the current study is to examine the relationship between dietary patterns, socioeconomic and behavior factors associated with breast cancer in Ethiopian women.A case-control study was conducted at an institution in Bahir Dar, Northwest Ethiopia, involving 260 women, comprising 86 cases and 174 controls. We administered a standardized and validated questionnaire to assess a range of sociodemographic, reproductive, clinical, lifestyle, and dietary characteristics through face-to-face interviews. To analyze the differences between the cases and controls, we employed the Chi-square test. Furthermore, we assessed the relationships between these variables using binary multivariate logistic regression. To measure the association between variables, we utilized odds ratios with 95% confidence intervals.The results of the multivariate analysis indicated that participants in the younger age group had significantly lower odds of developing breast cancer  compared to those in the older age group. Additionally, women who breastfed their children for shorter durations were 3.66 times more likely to develop breast cancer  than those who breastfed for longer periods. Furthermore, women with sedentary lifestyles faced a significantly higher risk of breast cancer, with odds 10.53 times greater  than their counterparts who engaged in moderate or highly active lifestyles. Lastly, participants who had previously undergone chest therapy were 6.43 times more likely to develop breast cancer  compared to those who had not.Breast cancer prevention interventions, including breastfeeding counseling and increased physical activity should be recognized as a central strategy for lowering breast cancer risk. Furthermore, healthcare providers should aim to minimize exposure to chest radiation therapy.The online version contains supplementary material available at 10.1186/s12885-023-11501-1. Breast cancer is currently the most commonly diagnosed cancer among women and is the leading cause of cancer-related deaths worldwide . ShockinWhile several well-established risk factors for breast cancer, such as family history, early menarche, late menopause, adult-attained height, and other reproductive histories, are challenging to modify , diet stWhile specific foods have been implicated in breast cancer risk in some instances, it’s important to recognize that food is a complex interplay of various nutrients. As a result, the overall body of evidence remains inconclusive . Many epDue to limited treatment options with poor prognoses, a significant number of Ethiopian women are diagnosed with advanced breast cancer . ConsequWe conducted an institution-based case-control study from May 16 to July 16, 2021, at Felege Hiwot Comprehensive Specialized Hospital (FHCSH) in Bahir Dar, Northwest Ethiopia. In the first phase, we selected all 88 cases of women aged 18 and above with pathologically confirmed breast cancer who were referred to the chemotherapy clinics of FHCSH, which serves as a cancer diagnosis referral center. In the second phase, we chose 175 control participants from among women aged 18 and above admitted to other sections of the same hospital for various non-neoplastic diseases unrelated to long-term modification of diet.The sample size was determined based on several factors: a 1 to 2 ratio of cases to controls, a confidence level of 95%, a power of 80%, and an odds ratio of 2.7. The odds ratio was derived from a prior case-control study conducted in Addis Ababa, Ethiopia. To arrive at the total sample size, we started with 239 individuals. To account for potential non-responses, we added a 10% contingency, resulting in an additional 24 individuals. Therefore, the final sample size was 263 participants, comprising 88 cases and 175 controls.The study’s data collection was conducted by trained nurse professionals who conducted private face-to-face interviews and reviewed medical records. These interviewers received training from nutritionist professionals. Dietary patterns were categorized into ‘healthy’ and ‘unhealthy’. A healthy dietary pattern includes the daily consumption of fruits, vegetables, and whole grains, as well as never to little (never to 1–3 per month) consumption of processed food, red meat, and sweetened beverages ,.An unhealthy dietary pattern includes 1–2 times per week to daily consumption of fatty and greasy foods, sweetened beverages and foods, highly flavored food, overly pungent food, canned and processed foods, and excessive alcohol consumption includes (1–2 times per week to daily).Data were gathered using a validated semi-quantitative food frequency questionnaire (FFQ)  19], wh, wh19], Pathologically confirmed breast cancer was the outcome of this study. Pathologically confirmed breast cancer refered to a diagnosis of breast cancer that has been definitively confirmed through the examination of tissue samples (biopsy) obtained from the breast.2), Normal weight (18.5–24.9 kg/m2) and overweight/obese ( > = 25.0 kg/m2). Physical activity level was assessed as sedentary , light activity , moderate activity , very active and, extremely active (including running and carrying heavy load). Cigarette Smoking (yes/no) and alcohol drinking  behaviors were also collected furthermore. We also collected data on breast cancer cases including stage of breast cancer (I-IV) and affected breast (right/left) Previous chest radiation therapy (yes/no) was collected for both case and control patients as well. See the supplementary information (questionnaire) on annex We gathered self-reported demographic and socioeconomic information including age ; marital status (Married/unmarried); residence ; religion (orthodox/others); education ; occupation ; and income/monthly  was collected. Detailed information was collected regarding history of health, family history of breast cancer, reproductive risk factors, age at menarche (≥ 15/>15 years old) and menopause status (pre-menopause/post-menopause), history of child birth(yes/no), age at first birth , breast-feeding history at for each live birth (≤ 1 year/>1 year), and use of modern contraceptives (yes/no). Anthropometric measurements  were also collected. BMI was calculated using measured height and weight (kg/m2)  22]. BM. BM22]. We conducted data analysis using SPSS version 20, a statistical software package. To evaluate the differences between cases and controls, we employed the Chi-square test, presenting the results as frequencies and percentages. For assessing the relationships between the explanatory variables and breast cancer, we utilized both bivariate and multivariate logistic regression, calculating odds ratios and 95% confidence intervals (95% CI). Variables with a p-value of less than 0.25 in the bivariate analysis were included in the multivariable model. We assessed the goodness-of-fit of each model using the Hosmer and Lemeshow Test, where a higher p-value indicates a better model fit. Importantly, all models demonstrated p-values greater than 0.05.Since the Bahir Dar Institute of Technology (BiT) lacks an institutional review board, we obtained ethical clearance through our partner institute, the Amhara Public Health Institute (APHI). Furthermore, we ensured the informed consent of all study participants by explaining the study’s objectives. For participants who were unable to read the consent form, healthcare providers read it aloud to them. Those who willingly volunteered to participate provided their consent by either signing the form or applying a thumb impression. We assured respondents that their names would not be disclosed, and all information provided would remain confidential. Additionally, participants were given the opportunity to ask questions about the study and had the option to refuse or discontinue their participation at any time. The APHI research review committee approved that all methods adhered to the applicable ethical standards and regulations outlined in the Declaration of Helsinki.Table Table The results of the multivariate analysis are shown in Table Our study makes a significant contribution to our understanding of the factors associated with breast cancer risk among Ethiopian women, especially in a region where the prevalence and incidence of this cancer are notably high. The current study found that women’s age, duration of breastfeeding, physical exercise status, and chest radiation therapy were statistically significant associated with breast cancer. Despite the fact that this study has significant limitations due to its reliance on self-reported data and recall bias, it is the first to investigate the connection of dietary patterns and other factors with breast cancer risk in Ethiopia.Contrary to our initial hypothesis and in contrast to the findings of previous studies  24] [25 [2524] [This study uncovered a notable finding: younger participants demonstrated a lower likelihood of developing breast cancer compared to their older counterparts. This observation aligns with extensive evidence indicating an increased breast cancer risk with advancing age  27] [28 [2827]  [38 [3837] , our resThis study uncovered a statistically significant relationship between physical activity levels and the risk of breast cancer. Notably, women who engaged in sedentary behavior faced a higher risk of developing breast cancer compared to those who maintained moderate or vigorous physical activity. This finding is consistent with previous research, which has consistently shown that regular exercise can significantly lower the risk of breast cancer  42] [43 [4342] , which, Consistent with findings from other studies  47], ou, ou47], It is important to acknowledge potential limitations when interpreting these results. One notable limitation is the possibility of misclassification due to our reliance on self-reported dietary patterns. Additionally, the lengthy recall periods could lead to inaccurate reporting of past exposures. In light of these limitations, future studies should aim to address these issues for a more comprehensive understanding of the topic.In our study, we identified several statistically significant associations with breast cancer risk, including women’s advanced age, duration of breastfeeding, physical exercise status, and chest radiation therapy. These findings underscore the importance of focusing on primary prevention strategies. Interventions should prioritize counseling on optimal breastfeeding duration, encouraging moderate to vigorous physical activity, and emphasizing the need for healthcare providers to limit chest radiation therapy exposure. These measures collectively contribute to reducing the risk of breast cancer.Below is the link to the electronic supplementary material.Supplementary Material 1


It was reported that educational attainment and household income are associated with oropharyngeal cancer. However, whether such an association is causal is still unknown.The Mendelian randomization (MR) design was performed to disentangle their causal relationship. Initially, genetic variants proxied for educational attainment and household income were extracted from the largest genome-wide association studies (GWAS), and two oropharyngeal GWAS datasets were used in the discovery and validation stages separately. A reverse MR analysis was carried out to judge whether oropharyngeal cancer affects educational attainment and household income. The results from the two stages were combined using meta-analysis. The heterogeneity and horizontal pleiotropy were appraised using several methods.All selected genetic variants were valid. In the discovery stage, genetically elevated years of education might decrease the risk of oropharyngeal cancer , while such a result became insignificant in the validation stage . Household income cannot change the risk of oropharyngeal cancer at both stages. The reverse MR suggested that oropharyngeal cancer should slightly alter household income  in the discovery set, but the result cannot be replicated in the validation stage. The meta-analysis did not find any significant results either. The results were also assessed by sensitivity analyses, and there was no heterogeneity or horizontal pleiotropy in the analyses. The statistical powers were all above 80% at the discovery stage.There should be no causal association between educational attainment, household income, and oropharyngeal cancer. Head and neck cancer (HNC) was the seventh most common cancer worldwide in 2018 and squamous-cell carcinomas of it can be divided into four categories based on the anatomical sites: the oral cavity, sinonasal cavity, pharynx, and larynx . The GloMendelian randomization (MR) is an emerging epidemiological method for causal inference, using genetic variants as instrumental variables (IV), and has made a substantial contribution to identifying or ruling out risk factors for specified diseases . Thanks In this study, we aim to use MR to explore whether genetically-determined educational attainment and household income can causally affect the risk of all forms of oropharyngeal cancer, hoping to explain their causal relationships.The GWAS summary statistics of educational attainment were defined as years of education, and this GWAS was performed on 126,559 individuals of European ancestry, adjusting for sex and the interaction term between sex and birth year . We extrPreliminarily, educational attainment and household income were treated as exposures, and oropharyngeal cancer was the outcome. Considering that “GCST90011806” has a larger sample size, we treated it as the discovery set and “ieu-b-96” as the validation set. A reverse MR was performed as well, where oropharyngeal cancer was the exposure and educational attainment and household income were the outcomes, hoping to clarify reverse causation. A meta-analysis was employed to combine the results of both the discovery and validation stages, hoping to obtain a more precise estimation with an enlarged sample size. A brief demonstration of the study design can be found in −8) and we further clumped them to obtain independent IVs based on linkage disequilibrium (LD r2 = 0.01). An SNP with a low minor allele frequency (MAF <0.01) was removed from further analysis. Mainly, the inverse-variance weighted (IVW) method was utilized to estimate the effect of exposure on the outcome. Besides, MR-Egger regression and weighted-median methods were used as supplementary methods. As heterogeneity and horizontal pleiotropy can usually bias MR estimation, several methods have been applied to control them. Cochran’s Q value was calculated to assess heterogeneity (A single nucleotide polymorphism (SNP) was selected if it reached the genome-wide significance . The number of IVs for educational attainment and household income was 4 and 28, respectively, and the number of IVs for oropharyngeal cancer was 27 and 18, respectively. In this MR study, we did not find a robust causal relationship between household income, years of education, and oropharyngeal cancer. The F statistic of each IV was greater than the empirical threshold of 10, suggesting the results are less likely to be biased by weak instruments. The MR-Steiger test further corroborated the tested causal direction from exposure to the outcome, where the selected genetic variants explained more variance of exposure than that of the outcome.Generally, most study participants were aged 40–69, and the proportion of females was 54.30% , and it was confirmed in the validation set with 25 instruments . No significant result was found in the meta-analysis  Figure 2The reverse MR suggested that genetic predisposition to oropharyngeal cancer should marginally alter household income  in the discovery set; however, it was not corroborated in the validation stage  Figure 3When treating oropharyngeal cancer, its genetic liability cannot alter education year level , and it was corroborated in the replication set . The meta-analysis did not obtain a significant result . All the other supplementary methods support such a null association Table 2.Generally, our MR analyses ruled out the causal effect of household income on oropharyngeal cancer. However, higher educational attainment seemed to lower its risk, but the results were not consistent in the two-outcome datasets “ieu-b-96” and “GCST90011806.” The reverse MR analysis indicated that oropharyngeal cancer could not affect income and educational attainment.via behavioral lifestyle factors and/or psychosocial, physical, and life-course pathways  the sample size is relatively large for each GWAS, and the statistical power is sufficient; (2) MR is a robust method of causal inference and can avoid reversing causation. However, some limitations should be pointed out: (1) Horizontal pleiotropy is a natural flaw of MR, and it should affect the results undetectably, though we used various methods to reduce it; (2) we revealed no difference in outcomes between control subject sources, which mitigates the potential for selection bias; 3) another limitation of our analysis was the absence of data from Asia, notably from South East Asia, which has a high rate of head and neck cancer. The most recent study pinpointed that risk factors for oropharyngeal cancer should include tobacco and alcohol consumption, low- and middle-income countries, sex behavior, and HPV infection  another . This stOur study indicates that either genetically determined educational attainment or household income can affect the risk of oropharyngeal cancer. Furthermore, oropharyngeal cancer does not affect a patient’s degree of education or income. This study highlights that previously reported associations should not be causal.https://www.ebi.ac.uk/gwas/.Publicly available datasets were analyzed in this study. This data can be found here: Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.WL initiated the idea and designed the whole study. LQ performed the main analyses and wrote the original manuscript. WB checked the whole analytic process and substantially revised the manuscript. SW and XD revised the manuscript and advised on statistical methods. WL was responsible for the accuracy and integrity of this study. All authors contributed to the article and approved the submitted version.


Binary logistic regression analysis revealed a significant association between educational level and nutritional risk, indicating that lower educational level was associated with higher odds of nutritional risk . In addition, regarding BMI, it was observed that a BMI above 20.5 kg/m2 was associated with a higher likelihood of nutritional risk . Conclusions: It is crucial to consider the nutritional status of breast cancer patients, alongside clinical factors, to offer comprehensive and personalized care. Gaining insight into the sociodemographic variables linked to nutritional risk can significantly contribute to our understanding of breast cancer. This knowledge, in turn, can aid in identifying effective strategies for public policy, health promotion, and prevention efforts aimed at tackling this condition.Background: Breast cancer poses a significant public health concern owing to its high prevalence and the risk of mortality associated with delayed diagnosis and treatment. The aim of this study was to assess the nutritional status of women with non-metastatic breast cancer and to identify factors associated with it. Methods: A cross-sectional observational study was conducted at a High Complexity Oncology Assistance Center in the southeast region of Brazil, with the aim of assessing the nutritional status in women undergoing treatment for stage I, II, or III breast cancer. Patients in palliative care or undergoing reconstructive surgery were excluded. Data collection took place between June 2022 and March 2023 and included questionnaires, physical examinations, laboratory tests, and anthropometric assessments. Nutritional status was assessed using measures such as BMI and skinfold thickness, while nutritional risk was assessed using the Nutritional Risk Screening (NRS-2002) tool. Results: Significant associations were found between nutritional risk and educational level ( The World Health Organization predicts that by 2030, cancer will reach approximately 27 million new cases worldwide, with 17 million deaths caused by the disease and around 75 million survivors. The impact will be greatest in low-income countries [In Brazil, according to the latest National Cancer Institute’s (INCA) estimate, 704,000 new cancer cases are expected each year during the 2023–2025 triennium. Female breast cancer is the most common cancer in all Brazilian regions (10.5%), excluding non-melanoma skin tumors. Specifically, in women, an estimated 74,000 new cases of breast cancer are projected per year during the 2023–2025 triennium [Breast cancer is a multifactorial disease. Its causes include factors such as age, ethnicity, geographic location, family history of cancer before the age of forty, lack of pregnancy, first pregnancy and childbirth at an advanced age, early menarche, late menopause, exposure to radiation, use of oral contraceptives, and hormonal changes such as excessive estrogen exposure. Lifestyle factors such as alcohol consumption, poor diet, tobacco use, and lack of physical activity also play a role. In addition, a higher body mass index (BMI) is associated with an increased risk of developing breast cancer and a worse prognosis ,6,7,8.Alterations in nutritional status in cancer patients are associated with the location, stage, and symptomatology of the disease, as well as the type of antitumor treatment used ,10. FurtAccording to the guidelines established by the European Society for Clinical Nutrition (ESPEN) with the publication of clinical nutrition guidelines in cancer, a comprehensive assessment of nutritional status should be performed shortly after diagnosis . ContinuThe nutritional status of breast cancer patients might be negatively affected by the therapeutic process due to side effects such as mucositis, nausea, and vomiting, which occur together as a cluster of gastrointestinal symptoms, along with anorexia, all of which are determinants of patients’ dietary habits and consequently impact on their nutritional status ,15,16. TSpecialized nutrition therapy is of paramount importance to help restore and/or maintain the patient’s nutritional status, quality of life, and functionality. However, studies that include factors associated with the nutritional status of stage I, II, and III breast cancer patients, especially those who are hospitalized, remain scarce in the literature. The aim of this study was to assess the nutritional status of women with non-metastatic breast cancer and to identify factors associated with it.An observational cross-sectional study was conducted at a cancer treatment referral hospital  in the southeast region of Brazil.Ethical approval was obtained from the Research Ethics Committee of the Health Sciences Center (CEP-CCS-UFES) of the Federal University of Espírito Santo, Brazil, with a Certificate of Ethical Appreciation (CAAE) number: 57491022.1.0000.5060 and approval number: 5.400.652. After identifying eligible patients with stage I, II or III breast cancer at the hospital, informed consent was obtained from each patient who voluntarily agreed to participate in this research, and they signed the informed consent form.The eligibility criteria for this study were as follows: females, over 18 years of age, with an anatomopathological diagnosis of stage I, II, or III breast cancer according to the International Classification of Diseases (ICD-10): C50 , and in any phase of antineoplastic treatment. Patients in palliative care, those admitted for reconstructive surgery or non-cancer clinical problems, and/or those with any cognitive impairment that would interfere with their ability to respond to the data collection instruments were excluded from this study.2 · p · (1 − p)/Z2 · p · (1 − p) + e2 · N − 1 where n is the calculated sample size, N is the population, Z is the standard normal variable, p is the true probability of the event, and e is the sampling error—and considering the population of breast cancer patients diagnosed at Santa Rita Hospital in 2019 (n = 225), based on the ICD-10: C50 , setting α at 5% (sampling error), with a confidence level of 95%, β at 0.2 (giving 80% power of the test), and with a minimum percentage of 12%—the sample size for this research was determined to be 95 patients.The sample size calculation was based on the latest estimate of the INCA for the triennium 2023–2025, which projected 74,000 cases of female breast cancer in Brazil, with 84.46/100,000 cases expected for the southeast region —n = N · Data collection took place over 10 months, between June 2022 and March 2023. A sociodemographic and clinical questionnaire was developed by the principal investigators of this study, divided into three components: (I) Medical history . (II) General and specific physical examination. (III) Laboratory tests that were part of the hospital routine and accessed from the patients’ medical records (complete blood count and capillary glycemia).Nutritional status was assessed using anthropometric evaluation, including weight (kg), height (cm), triceps skinfold thickness (mm), arm circumference (cm), and calf circumference (cm), with each measurement performed three times and the arithmetic mean obtained. Subsequently, the BMI was classified using the World Health Organization guidelines 1995) . The ade95 . The 2, weight loss in the last three months, reduced food intake in the last week, and presence of severe illness. In the second step, each criterion was quantified based on nutritional status and severity of illness, and one point was added to the score for patients aged ≥ 70 years. A total score < 3 was classified as “no nutritional risk” and a total score ≥ 3 was classified as “nutritional risk” [In addition, the NRS-2002 instrument was used to assess nutritional risk in the patients. The NRS-2002 is a tool developed by Kondrup et al. (2003)  and certal risk” .Categorical variables were presented as absolute and relative frequencies, and numerical variables were described using measures of central tendency and dispersion. Associations between categorical variables were examined using Pearson’s chi-squared test or Fisher’s exact test when appropriate . Binary Regarding the breast cancer characteristics, three histologic types were observed, with ductal in situ (38%) and invasive carcinoma (33%) being the most common. In terms of the TNM stage, the most common categories were T2N2M0 (29%) and T3N1M0 (21%). In terms of staging, most patients were classified as stage I (51%), followed by stage III (36%). Regarding comorbidities, most patients did not have systemic arterial hypertension (71%) or diabetes mellitus (87%), or dyslipidemia (96%). Regarding surgical procedures, most patients had undergone previous surgery (64%). Finally, regarding glycemic levels, 40% of patients had normal levels, 27% were classified as pre-diabetic, and 33% had diabetes.2 and 64% not having experienced weight loss in the last 3 months. Regarding reduced food intake, no patient reported this condition. Regarding severity of illness, no patient was classified as severely ill.Regarding nutritional status according to the NRS tool, 62% of the patients were classified as normal or eutrophic, and there were no cases of severe malnutrition. In terms of severity of illness, 59% of the patients were classified as mildly ill. The total NRS score indicated that only 25% of women were at nutritional risk.2 was associated with a higher likelihood of nutritional risk.In this study, we examined a diverse cohort of participants characterized with a wide age range, predominantly low-income individuals, primarily housewives who were married, and individuals who did not use alcohol and tobacco. Furthermore, the participants in this study reported no comorbidities, but a high prevalence of overweight individuals was identified. Upon analyzing the association between nutritional risk and various factors using the NRS nutritional score, we discovered a significant association with both schooling levels and BMI.The analysis of sociodemographic variables provides crucial insights for understanding and guiding the diagnosis and care of these participants. We observed an average age of 59.1 years, aligning with the incidence of cancer in women over 50 years. This finding underscores the significance of the age range considered in analyzing and planning targeted interventions for this specific group of women . The parOur analysis revealed that a significant portion of the participants (corresponding to 77%) were overweight. These results are consistent with the existing literature, which recognizes being overweight as an established risk factor for the development of cancer . The assThe relationship between cancer and obesity remains a complex subject, prompting extensive research. To gain a deeper understanding of this association , one hypApart from being a well-known risk factor for cancer development, obesity poses a significant public health concern. Data from Vigitel (2021)  indicateThe municipality where this study was conducted mirrors the national landscape concerning overweight and obese individuals. An ecological study, utilizing data from the Food and Nutrition Surveillance System of Brazil (SISVAN), assessed the prevalence of these health issues from 2009 to 2018. The results demonstrated a consistent upward trend of overweight and obese individuals across all age groups, with a particular emphasis on children and adult women in the south and central region of the state of Espírito Santo. These findings are in line with our study’s results , furtherThe nutritional assessment using the NRS scale revealed that most of the sample (approximately 75%) presented low nutritional risk. These findings are consistent with other studies that have also reported favorable nutritional conditions in most evaluated cancer patients ,41. Howe2, this result aligns with the existing literature, which has found associations between obesity and more developed stages of breast cancer [Regarding the association indicating a higher risk of malnutrition in patients with a BMI above 20.5 kg/mt cancer . Moreovet cancer ,44.A statistical correlation was identified between the NRS nutritional score and the level of education, suggesting that lower levels of education may be associated with a higher nutritional risk. This association is discussed in other studies that also point to a connection between education levels, cancer diagnosis, and the initiation of treatment. Factors such as lack of information or limited access to health plans and medical consultations have been cited as potential causes of this association ,46.It is important to emphasize that the educational level often serves as an indirect indicator of income, which, in turn, is associated with access to healthcare services. Moreover, delays in commencing treatment or diagnosis have been linked to a deterioration in the patient’s health status and survival rate . NumerouIn a cross-sectional study that examined the trend of breast cancer incidence in São Paulo, ethnicity was found to play a significant role. Black women exhibited an increasing trend in the occurrence of breast cancer, while white women showed a decreasing trend during the period from 2000 to 2017. These results suggest potential factors such as delays in diagnosis, challenges in accessing high-complexity services, or biological differences between racial groups . It is iWhen analyzing the relationship between schooling and breast cancer, various studies consistently reveal that higher education levels are associated with lower mortality rates. This connection is attributed to education often being used as an indirect indicator of income ,52,53, aMoreover, an examination of breast cancer mortality in Brazil indicates an increase in mortality rates among women aged between 50 and 54, with even higher rates observed in older age groups ,55. SeveIn contrast, other research pointed out that the chemotherapy treatment used can cause weight gain in some patients, because the antineoplastic agents used (when associated with the use of glucocorticoids) often influence water retention and increase body fat . These fIt is worth noting that the results observed in the participants of this study do not indicate malnutrition; on the contrary, the majority of them are overweight. This can be attributed to the specific characteristics of the participants, particularly the fact that they are not in advanced stages of cancer, which influences their nutritional status.The clinical and research implications of this investigation are profound, as they firmly establish breast cancer as a multifactorial disease. Often, the focus is solely on disease treatment and diagnosis, while neglecting other associated risk factors that play a pivotal role in its development and progression. This study offers a comprehensive perspective by identifying and envisioning such risk factors. Understanding the complexity and interconnectedness of these factors is essential to improve the care and prevention of this disease.Moreover, by considering sociodemographic and nutritional variables, this study emphasizes the significance of approaching breast cancer in a holistic manner. This information enables us to direct efforts towards developing more effective public policies aimed at promoting women’s health and breast cancer prevention. By addressing these multifaceted aspects, we can enhance our understanding and approach to combating breast cancer, leading to improved outcomes and a greater impact on women’s well-being. Since the treatment of non-metastatic breast cancer includes various combinations of surgery, radiation therapy, as well as systemic chemotherapy, and hormone therapy, it is recommended that future studies consider treatment-induced nutritional changes in the analyses, for instance, stratifying by each type of antineoplastic treatment.This study has some limitations that should be acknowledged. Firstly, its single-centered nature restricts the generalizability of the findings to a broader population. Secondly, the cross-sectional design included patients undergoing various antineoplastic therapy schemes and at different stages, potentially influencing their nutritional status. Additionally, the small sample size, particularly in the group of women with stage III disease, may limit the statistical power and precision of the results. Also, it was not possible to stratify the analyses by the types of antineoplastic treatments received by the participants which may affect nutritional status. Despite these limitations, this study also boasts several strengths. Notably, the inclusion of a sample with the same non-metastatic tumor type ensures a more homogeneous group for analysis. Moreover, this study benefits from the reliability of anthropometric measurements and the use of the NRS scale to assess nutritional status, both of which are methods recommended by esteemed organizations such as ESMO, ESPEN , and theEpidemiologic and dietary changes influence nutritional status, including that of oncology patients. In this study, attention is drawn to the fact that most of the population was classified as overweight, and that factors such as educational level and BMI were associated with this outcome. Therefore, understanding the specific characteristics of this population, as well as the risk factors for this disease, is essential for a better understanding of this pathology and for the formulation of more effective and equitable public policies.