Factors affecting the prognosis of prostate cancer in the United States: results from the national health and nutrition examination survey in 1999-2016

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This study aims to explore several common lifestyle factors that may influence the overall survival time among prostate cancer patients of different ethnic groups. Methods Data obtained from the National Health and Nutrition Examination Survey database (NHANES) between 1999 and 2016 was analyzed. Weighted multivariate cox regression and subgroup analysis were used to explore the underlying factors that affect the prognosis of prostate cancer. Kaplan-Meier survival curves were drawn to demonstrate the different survival time and survival rates in prostate cancer patients with different levels of exposure factors. Results A total of 639 prostate cancer patients were enrolled in this study. People with an earlier age of onset tended to have a longer survival time (P < 0.05). Non-Hispanic blacks seemed to be diagnosed with prostate cancer at an earlier stage (P < 0.05). Light to moderate alcohol consumption was a protective factor [HR 0.38, 95%CI:(0.16,0.89)] in Non-Hispanic Blacks with prostate cancer, which could provide longer overall survival time to prostate cancer patients than those who didn’t drink for at least one year. Smoking could be a harmful factor to the whole population [HR 2.37, 95%CI:(1.39,4.04)], while keeping BMI between 25 to 30 had protective effects [HR 0.62, 95%CI:(0.47,0.83)]. Non-Hispanic Whites were more likely to be affected by smoking [HR 3.24, 95%CI:(1.84,5.71)] and BMI [HR 0.57, 95%CI:(0.41,0.78)] compared to Non-Hispanic Blacks. Conclusion Appropriate overweight (BMI between 25 to 30) and mild to moderate alcohol consumption seemed to be protective factors in prostate cancer, while smoking showed a detrimental effect. The heterogeneity between ethnicities can’t be ignored and future more large-scale studies are required to verify our findings. Prostate cancer Alcohol consumption Smoking status BMI Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Prostate cancer is considered as the most widely influential tumor among adult men in more than half of countries and the incidence rate has been increasing in recent years [ 1 , 2 ]. There are more and more advanced treatment options for patients over the past few decades, such as robot-assisted radical prostatectomy, external beam radiotherapy, low-dose-rate brachytherap for localised disease and new hormonal agents, docetaxel for metastatic disease, but prostate cancer remains a major threat to the survival of patients [ 3 , 4 ]. In addition to advanced drugs and precise surgery, some common life factors also perform great influence on the outcomes of patients, such as alcohol consumption, smoking status and BMI [ 5 – 7 ]. Although studies have demonstrated the appropriate lifestyle could provide benefits for the prognosis of individuals with prostate cancer, it’s still difficult to make perfect personal life recommendations for patients due to the heterogeneity of this disease and the inconsistent research results [ 8 , 9 ], so we conducted this study to investigate the effects of alcohol consumption, smoking status and BMI on prostate cancer. Alcohol is an indispensable part of human culture and is closely related to daily life of many people around the world. Only a tiny part of alcohol we absorbed is excreted without metabolism, more than 90% is transformed to others metabolites that usually are harmful to health [ 10 ]. Reports have reported alcohol consumption could increase the risk of alcohol-related cancers, such as upper gastrointestinal cancers, colorectal cancer, liver cancer and breast cancer [ 11 , 12 ]. There were some evidences illustrated that alcohol consumption promoted the formation and development of prostate cancer [ 13 ], but other studies showed the contrary results [ 14 ]. The hazard of cigarette to human health and environment is well recognized in the academic community [ 15 ]. Tobacco and tobacco smoke is a very complex mixture containing about 10,000 compounds, more than 80 of which are identified as carcinogens and tobacco shows great harm to human health due to its prevalence all over the world [ 16 ]. Active or secondhand smoke exposure is a clear risk factor for breast cancer and lung cancer and has a weaker association with total cancer [ 17 , 18 ]. Previous studies have shown that smoking exposure acted as a risk factor contributing to prostate cancer that also shortened the overall survival time of patients, while declining smoking exposure may accordingly decrease the hazard [ 19 , 20 ]. Research focused on the heterogeneity of the effects of smoking exposure based on ethnicity is relatively lack. Body mass index (BMI), an important predictor to measure obesity, is closely related to a person’s overall health status. People with a BMI greater than or equal to 25 to 29.9 is overweight and those who has a BMI greater than or equal to 30 is considered as obesity [ 21 ]. Many studies have indicated that high BMI could damage human health and people with a high BMI tended to suffer a greater risk of lots of diseases, such as diabetes, hypertension and hypercholesterolemia [ 22 ]. Except facing a higher risk of sub-optimal health status, health population with high BMI usually had a shorter healthy and chronic disease-free life expectancy and the global burden of health caused by high BMI was increase continuously in recent years [ 23 , 24 ]. The influence of BMI performed on human health was a complex thing, people whose BMI was too low also faced a worse physical health [ 25 ]. In general, excessive obesity promote the formation of various cancers, such as endometrial cancer, breast cancer, colorectal cancer and gallbladder cancer [ 26 , 27 ]. A considerable number of studies showed that higher BMI also performed worse effects on patients with prostate cancer [ 28 , 29 ]. However, there were also some complicated situations that high BMI may play a dual role in prostate cancer (promoted advanced prostate cancer but not localized prostate cancer) [ 30 , 31 ]. The present research aims to further investigate the potential effects of common life factors (alcohol consumption, smoking status and BMI) perform on the prognosis of prostate cancer. Analysis based on different ethnicities can provide more detailed information for urologists to develop personalized disease management schedules. Methods Study design and population The patients involved in this research were extracted from the NHANES database, which is a program designed to assess the health and nutritional status of adults and children in the United States. This study included 9 continuous cycles of the NHANES (1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014 and 2015–2016). A total of 92060 participants were included in the NHANES 1999–2016, among which 747 participants were diagnosed with prostate cancer. The diagnosis was based on questions from personal interview data of NHANES, “Have you ever been told by a doctor or health professional that you had prostate cancer? How old were you when prostate cancer was first diagnosed?” Of these, patients with age of onset < 40 years old (n = 8) were excluded due to the rarity of this disease in young men. Patients with missing date of alcohol consumption (n = 99), missing date on the assessment of smoking status (n = 1) were also excluded (Fig. 1 ). The procedures and protocols of NHANES received approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board and all participants have provided the written informed consent [ 32 ]. Alcohol consumption, smoking and BMI Participants provided self-reported information about smoking status, and alcohol consumption. Those who had drank less than 12 drinks of any type of alcoholic beverage in entire life were identified as nondrinkers, who had drank more than 12 drinks in lifetime but did not drink last year were classified as former drinkers. Heavy drinking was identified as four drinks or more than four drinks per day, moderate alcohol use was considered to have three drinks per day and no more than two drinks was mild drinking [ 33 ]. Smoking less than 100 cigarettes was considered as never smoking, more than 100 cigarettes but not at all now was former smoking. Those smoked more than 100 cigarettes in life and smoked some days or every day were current smokers. BMI, weight in kilograms divided by the square of height in meters, was extracted from the body measures data collected in the Mobile Examination Center by trained health technicians [ 34 ]. Covariates The covariates of interest were the potential confounding factors that might be associated with the mortality of patients with prostate cancer. Sociodemographic variables were as follows: age of onset, ethnic background (non-Hispanic black, non-Hispanic white and others), marital status (divorced/separated/widowed, never, married/living with partner), educational level (high school or equivalent, less than high school, more than high school), poverty to income ratio (PIR). Disease states including diabetes mellitus (DM) and hypertension. DM is determined if one of the following conditions is met: (1). doctor told you have diabetes, (2). glycohemoglobin HbA1c(%) > = 6.5, (3). fasting glucose (mmol/l) > = 7.0, (4) random blood glucose (mmol/l) > = 11.1, (5). two-hour OGTT blood glucose (mmol/l) > = 11.1, (6). use of diabetes medication or insulin. Hypertension was identified if average systolic pressure was higher than 140 mmHg or average diastolic pressure was higher than 90 mmHg. The average blood pressure was calculated by the following protocol: if only one blood pressure reading was obtained, that reading is the average. If there is more than one blood pressure reading, the first reading is always excluded from the average. Outcomes: All-cause mortality The linkage of NHANES survey participant data with the National Death Index (NDI) death certificate records made it feasible to investigate the mortality status of NHANES participants. The mortality follow-up data have been updated to December 31, 2019 [ 35 ]. Statistical analysis All analyses were conducted according to the instructions for using NHANES data, we took the complex survey sampling design of the NHANES into consideration [ 36 ]. To increase the strength of the results, all calculations were weighted unless stated otherwise. “4 Year MEC Weight” was used for 2 cycles: 1999–2000 and 2001–2002, other 7 cycles were calculated with “2 Year MEC Weight”. Patients involved in this study were divided into 3 subgroups according to the age of onset (A1: =60y, =70y). BMI was transformed into a non-continuous variable BMI3 based on the threshold values of 25 and 30. Categorical variables were described as counts and proportions (after weighting), means (standard errors) were presented for continuous variables. We used one-way ANOVA to compare continuous variables and Chi-square test to compare categorical variables. Four cox regression models were constructed to evaluate the association between explored factors and all-cause mortality. Model 1 was crude model, model 2 was adjusted for the age of onset, model 3 was adjusted for the age of onset, ethnicity, marital status, education and PIR and model 4 was adjusted for the age of onset, ethnicity, marital status, education, PIR, DM and hypertension. We used Kaplan-Meier survival curves and log-rank analyses to compare the survival probabilities. Then subgroup analysis was performed to strength the results. All statistical analyses were conducted using the R language (version 4.3.1) and statistical significance was defined as a 2-sided P value less than 0.05. Results Baseline characteristics of study population A total of 639 NHANES participants, representing about 1902,315 patients with prostate cancer in the United States, were enrolled in this research. The weighted baseline characteristics of the selected respondents were shown in Table 1 . The overall mean (SE) age at onset of prostate cancer was 65.10(0.45) years old and the patients with later onset tended to have shorter survival time (P = 0.038). Non-Hispanic Whites showed the oldest age of onset, while the Non-Hispanic Blacks had the earliest age of onset. There was no statistically significant association between the onset of age and marital status, educational level, smoking, alcohol consumption, hypertension and diabetes. Factors influencing the outcomes of prostate cancer In order to investigate the influence of alcohol consumption, smoking status and BMI on the outcomes of prostate cancer, we constructed weighted multivariate cox regressions (Table 2 ). In the crude model, we found that alcohol consumption in a mild to moderate way could supply different degree of benefits to the outcomes of prostate cancer [HR (95%CI): 0.67(0.50,0.90), P = 0.01], and even in patients with heavy degree [HR (95%CI): 0.32(0.11,0.94), P = 0.04]. Patients with a BMI between 25 to 30 tended to possess a better prognosis than those who had a BMI less than 25 [HR (95%CI): 0.74(0.56,0.98)], but when it came to BMI was over 30, the better prognosis was no longer. However, smoking status didn’t affect the prognosis of prostate cancer without adjusting for covariates. After adjusting for potential covariates, former smoker suffered a worse outcome than those who had never smoked [HR (95%CI): 1.39(1.04,1.87), P = 0.03], and patients who smoked currently were inclined to the worst situation [HR (95%CI): 2.37(1.39,4.04), P = 0.001]. Having a BMI between 25 to 30 remained to act as a protective factor for prostate cancer patients compared to with a BMI less than 25 [HR (95%CI): 0.62(0.47,0.83), P = 0.001], but alcohol consumption showed no influence on the overall survival. We further drew a weighted restricted cubic spline to explore the non-linear relationship between BMI and all-cause mortality in prostate cancer patients (Fig. 2 A). The piecewise cox regression after adjusting for covariates showed that higher BMI could be a protective factor to prostate cancer patients when BMI was less than 26.6 [HR (95%CI): 0.88(0.81,0.95), P = 0.002], but the protective effect was absent if BMI was more than 26.6. Subgroup analysis In order to further confirm the above results and investigate the effect of these factors in different patient subgroups, we performed subgroup analysis based on different ethnicities. We focused on Non-Hispanic Black people and Non-Hispanic White people due to the limit of population involved and a forest plot was drawn to show the results (Fig. 3 ). In Non-Hispanic Blacks, mild to moderate alcohol consumption had a protective effect on prostate cancer [HR (95%CI): 0.31(0.17,0.60), P < 0.001] and the effect still existed after adjusting for covariates [HR(95%CI): 0.46(0.22,0.97), P = 0.04]. The situation is different in Non-Hispanic Whites, alcohol consumption status didn’t influence the outcome of prostate cancer patients, while smoking status and BMI played important roles. After adjusting the covariates, former smokers who suffered prostate cancer likely to face worse outcomes [HR (95%CI): 1.51(1.09,2.10), P = 0.013] than those who had never smoked. Furthermore, patients who smoked now could face the worst outcome [HR (95%CI): 3.24(1.84,5.71), P < 0.001]. What’s more, Patients who were appropriately overweight but not obese could have better survival outcomes [HR (95%CI): 0.57(0.41,0.78), P < 0.001]. Weighted restricted cubic splines were performed to investigate the potential non-linear relationship between BMI and all-cause mortality of certain ethnicity (Fig. 2 B, C). No non-linear relationship was observed among Non-Hispanic Blacks, while a noteworthy non-linear relationship was found among Non-Hispanic Whites and the inflection point was 28.6 (P for non-liner = 0.005). Weighted Kaplan-Meier survival curves showed that mild to moderate alcohol consumption could act as a protective role for prostate cancers among all adults across the United States (P = 0.006) (Fig. 4 ). Different BMI and smoking status didn’t represent significant different outcomes in the whole population, although those who had a BMI between 25 to 30 or never smoked tended to have a longer overall survival time. When it came to a certain race, mild to moderate alcohol consumption could benefit the prognosis in Non-Hispanic Blacks (P < 0.001), Non-Hispanic Whites with a BMI between 25 to 30 likely to have a longer survival time than those with a BMI less than 25 (P = 0.037). Discussion Data obtained from NHANES indicated that the influence of lifestyle factors performed on the prognosis of prostate cancer various by ethinicities. We discovered a protective role of mild to moderate alcohol consumption among the whole population and Non-Hispanic Blacks with prostate cancer. Similarly, moderate drinking was closely related to a lower risk of all-cause mortality in obese women with breast cancer [ 37 ]. A phase III randomized adjuvant trial also showed that stage III colon cancer patients with mild to moderate red wine consumption were likely to have longer overall survival, disease free survival [ 38 ]. The detailed mechanism underlying the benefits of appropriate alcohol consumption was still unclear, it may be explained by the benefit against aortic disease or other cardiovascular disease [ 39 , 40 ]. In the whole population, mild to moderate alcohol consumption could reduce risks for cardiovascular disease and type-2 diabetes and then reduce mortality risk [ 41 ]. Another study also showed that low-volume drinkers had lower mortality than non-drinkers, but the difference was not significant anymore after adjustment for cardiovascular risk factors [ 42 ]. The research on the effect of alcohol consumption on cancer based on different ethinicities was few. But the race heterogeneity was truly existed, Non-Hispanic Blacks had a lower proportion of cancers caused by alcohol consumption than Non-Hispanic Whites [ 43 ]. The specific mechanism was difficult to elucidate and it may be explained partly by the genetic background [ 44 ]. Smoking was a harmful factor in this research and quitting smoking could attenuate the deleterious effect according to the covariate-adjusted cox regression model. We didn’t find a significant difference in weighted Kaplan-Meier survival curves but those who smoked showed a tendency to shorter overall survival time than those who never smoked. What’s more, Non-Hispanic Whites was more allergic to tobacco exposure compared to Non-Hispanic Blacks. A cohort of older men also showed that current smokers had higher mortality risks than non-smokers [HR 3.18, 95%CI: (3.04, 3.31)] [ 45 ]. Many other studies also proved that smoking was an independent detrimental factor for patients with prostate cancer [ 46 , 47 ]. In other cancers, smokers also faced significantly worse overall survival and smoking cessation after cancer diagnosis was beneficial to prolong survival time in various degrees [ 48 , 49 ]. Apart from cancers, the combination of nicotine addiction and toxicants exposure extended the adverse effects of tobacco to multiple organs of people, for example, smokers tended to have worse health conditions in respiratory system and cardiovascular system [ 50 – 52 ]. BMI played a relatively complex role in affecting the prognosis of patients with prostate cancer. We found that those whose BMI were too extreme, either too low or too high, seemed to have shorter overall survival time. The U-shaped curve pattern of the relationship between BMI and high all-cause mortality in the whole population, especially in Non-Hispanic Whites, remind us to keep appropriate overweight (BMI between 25 to 30). Those who were excessive obesity in this research had worse outcomes may be partly explained by the higher risk of cerebro-cardiovascular diseases, such as heart failure, cardiomyopathy and hypertension [ 53 , 54 ]. Patients with too low BMI had a shorter overall survival time may due to malnutrition [ 55 ]. This study focused on the effects of several common lifestyle factors to patients with prostate cancer based on various ethnicities which has been rare previous. The heterogeneity of ethnicities we observed can provide urologists with more precise lifestyle management options, so that patients could get rational suggestions and prolong life span by lifestyle modification. What’s more, using a complex weighted calculation method and the nationally representative U.S. cohort obtained by reasonable sampling methods made the results more reliable. However, there were some limitations. Firstly, we didn’t consider whether patients had received other treatments due to nature of the database. Second, there may be other unmeasured or residual confounders. Thirdly, cancer diagnosis was based on questionnaire data in self-reported interviews. Nevertheless, professional investigators has minimized errors through professional training. The detailed mechanisms of heterogeneity between ethnicities still unclear. In brief, future large-scale studies are required to validate our results, as well as basic research to explain the specific mechanisms. Conclusion In men with prostate cancer, smoking increased the all-cause mortality and smoking cessation could reduce the deleterious effect. Those who kept BMI between 25 to 30 tended to have longer overall survival time. Subgroup analysis showed that Non-Hispanic Blacks could benefit from mild to moderate alcohol consumption rather than Non-Hispanic Whites, while Non-Hispanic Whites could benefit from keeping appropriate overweight and be more sensitive to tobacco exposure. However, due to the limited sample size in this study, future research with large sample size including more ethnic groups is needed to verify our results. Declarations Acknowledgements We appreciate everyone who participated in NHANES. Thanks to Zhang Jing (Second Department of Infectious Disease, Shanghai Fifth People's Hospital, Fudan University) for his work on the NHANES database. Funding This research was supported by the National Natural Science Foundation of China (grant numbers 82203280), Sichuan Province Science and Technology Support Program (grant numbers 2022YFS0305) Author contributions YS and ZL conceptualized the study. XW and YS collected the data. YS, XP and ZW conducted data analysis. YS wrote the original draft. ZL and JL reviewed and edited the manuscript. All authors read and approved the final manuscript. Data availability Data analyzed in this research are available on the NHANES website: https://www.cdc.gov/nchs/nhanes. Conflict of interest The authors declared that they have no competing interests. Ethical approval The study was conducted in accordance with the Declaration of Helsinki. 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Cancer 129 (24):3938-3951. https://doi:10.1002/cncr.34972 Phipps AI, Shi Q, Limburg PJ, Nelson GD, Sargent DJ, Sinicrope FA, Chan E, Gill S, Goldberg RM, Kahlenberg M, Nair S, Shields AF, Newcomb PA, Alberts SR (2016) Alcohol consumption and colon cancer prognosis among participants in north central cancer treatment group phase III trial N0147. International Journal of Cancer 139 (5):986-995. https://doi:10.1002/ijc.30135 Delker E, Brown Q, Hasin DS. Alcohol Consumption in Demographic Subpopulations: An Epidemiologic Overview. Alcohol Res. 2016;38(1):7-15. PMID: 27159807; PMCID: PMC4872616. de Gaetano G, Costanzo S, Di Castelnuovo A, Badimon L, Bejko D, Alkerwi A, Chiva-Blanch G, Estruch R, La Vecchia C, Panico S, Pounis G, Sofi F, Stranges S, Trevisan M, Ursini F, Cerletti C, Donati MB, Iacoviello L (2016) Effects of moderate beer consumption on health and disease: A consensus document. Nutrition, Metabolism and Cardiovascular Diseases 26 (6):443-467. https://doi:10.1016/j.numecd.2016.03.007 Hendriks HFJ (2020) Alcohol and Human Health: What Is the Evidence? Annual Review of Food Science and Technology 11 (1):1-21. https://doi:10.1146/annurev-food-032519-051827 Moissl AP, Delgado GE, Krämer BK, Dawczynski C, Stojakovic T, März W, Kleber ME, Lorkowski S (2021) Alcohol consumption and mortality: The Ludwigshafen Risk and Cardiovascular Health (LURIC) study. Atherosclerosis 335:119-125. https://doi:10.1016/j.atherosclerosis.2021.08.014 Liu F, Liu Y, Sun X, Yin Z, Li H, Deng K, Zhao Y, Wang B, Ren Y, Liu X, Zhang D, Chen X, Cheng C, Liu L, Liu D, Chen G, Hong S, Wang C, Zhang M, Hu D (2020) Race- and sex-specific association between alcohol consumption and hypertension in 22 cohort studies: A systematic review and meta-analysis. Nutrition, Metabolism and Cardiovascular Diseases 30 (8):1249-1259. https://doi:10.1016/j.numecd.2020.03.018 Wall TL, Luczak SE, Hiller-Sturmhöfel S. Biology, Genetics, and Environment: Underlying Factors Influencing Alcohol Metabolism. Alcohol Res. 2016;38(1):59-68. PMID: 27163368; PMCID: PMC4872614. Nash SH, Liao LM, Harris TB, Freedman ND (2017) Cigarette Smoking and Mortality in Adults Aged 70 Years and Older: Results From the NIH-AARP Cohort. American Journal of Preventive Medicine 52 (3):276-283. https://doi:10.1016/j.amepre.2016.09.036 Murta-Nascimento C, Romero AI, Sala M, Lorente JA, Bellmunt J, Rodero NJ, Lloreta J, Hospital À, Burón A, Castells X, Macià F (2015) The effect of smoking on prostate cancer survival. European Journal of Cancer Prevention 24 (4):335-339. https://doi:10.1097/cej.0000000000000075 Riviere P, Kumar A, Luterstein E, Vitzthum LK, Nalawade V, Sarkar RR, Bryant AK, Einck JP, Mundt AJ, Murphy JD, Rose BS (2019) Tobacco smoking and death from prostate cancer in US veterans. Prostate Cancer and Prostatic Diseases 23 (2):252-259. https://doi:10.1038/s41391-019-0178-6 Darmon S, Park A, Lovejoy LA, Shriver CD, Zhu K, Ellsworth RE (2022) Relationship between Cigarette Smoking and Cancer Characteristics and Survival among Breast Cancer Patients. International Journal of Environmental Research and Public Health 19 (7). https://doi:10.3390/ijerph19074084 Barnett TE, Lu Y, Gehr AW, Ghabach B, Ojha RP (2020) Smoking cessation and survival among people diagnosed with non-metastatic cancer. BMC Cancer 20 (1). https://doi:10.1186/s12885-020-07213-5 Saracen A. Cigarette Smoking and Respiratory System Diseases in Adolescents. Adv Exp Med Biol. 2017;944:81-85. doi: 10.1007/5584_2016_60. PMID: 27826883. Hecht SS, Hatsukami DK (2022) Smokeless tobacco and cigarette smoking: chemical mechanisms and cancer prevention. Nature Reviews Cancer 22 (3):143-155. https://doi:10.1038/s41568-021-00423-4 Mainali P, Pant S, Rodriguez AP, Deshmukh A, Mehta JL (2014) Tobacco and Cardiovascular Health. Cardiovascular Toxicology 15 (2):107-116. https://doi:10.1007/s12012-014-9280-0 Bendor CD, Bardugo A, Pinhas-Hamiel O, Afek A, Twig G (2020) Cardiovascular morbidity, diabetes and cancer risk among children and adolescents with severe obesity. Cardiovascular Diabetology 19 (1). https://doi:10.1186/s12933-020-01052-1 Oktay AA, Lavie CJ, Ventura HO (2018) Obesity and the Obesity Paradox in Heart Failure. In: Encyclopedia of Cardiovascular Research and Medicine. pp 546-564. https://doi:10.1016/b978-0-12-809657-4.10899-3 Bullock AF, Greenley SL, McKenzie GAG, Paton LW, Johnson MJ (2020) Relationship between markers of malnutrition and clinical outcomes in older adults with cancer: systematic review, narrative synthesis and meta-analysis. European Journal of Clinical Nutrition 74 (11):1519-1535. https://doi:10.1038/s41430-020-0629-0 Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1and2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3922947","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271284369,"identity":"bee1ab40-0d1a-4df5-983c-0f8c97e915c9","order_by":0,"name":"Yu Shen","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Shen","suffix":""},{"id":271284370,"identity":"033fa65d-a2cd-40c4-9c75-2371fb5e183c","order_by":1,"name":"Xiuyi Pan","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiuyi","middleName":"","lastName":"Pan","suffix":""},{"id":271284371,"identity":"720b325e-6377-45bf-a8ea-7c25fb28654b","order_by":2,"name":"Xinyuan Wei","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Wei","suffix":""},{"id":271284372,"identity":"38c524f6-cadf-4b95-afec-6c1e4c423fe1","order_by":3,"name":"Zilin Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zilin","middleName":"","lastName":"Wang","suffix":""},{"id":271284373,"identity":"b597491c-6f86-4a3f-86ec-16ca9566d4af","order_by":4,"name":"Jiayu Liang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jiayu","middleName":"","lastName":"Liang","suffix":""},{"id":271284374,"identity":"7597f517-a494-4859-922e-eec8bf730fe5","order_by":5,"name":"Zhenhua Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACCeaGAwwVUDaRWhiBWs6QqoWBsY0ULfKzGxsP886rkzc4wHzwNg+DXR5BLYxzDjYc5t122HDDAbZkax6G5GKCWpglEkFaDiQYHOAxk+ZhOJDYQEgLG1jLnDqgFv5vxGnhAWtpYAbZwkacFgmgloNzjh02nHmYzdhyjkEyYS3yM5IPf3hTUyfPd7z54Y03FXaEtSAAM4gwIF79KBgFo2AUjAI8AAAk3jmALL+xRQAAAABJRU5ErkJggg==","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-02-03 06:44:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3922947/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3922947/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50877492,"identity":"d959dc8f-6be1-469f-81db-18ca40a9ada0","added_by":"auto","created_at":"2024-02-08 19:21:47","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68003,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study design\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3922947/v1/b00da73f3c6da1288eca02d3.jpeg"},{"id":50876240,"identity":"4e579f8c-8c76-42fc-b78e-5fb9dd527596","added_by":"auto","created_at":"2024-02-08 19:13:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":223945,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline plot for the relationship between BMI and all-cause mortality in different ethnicities. A: the whole cohort, B: Non-Hispanic Whites, C: Non-Hispanic Blacks.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3922947/v1/7b5b661b842db590f203a0de.jpg"},{"id":50876245,"identity":"81e7c950-edf3-4191-91ae-70e7b612340b","added_by":"auto","created_at":"2024-02-08 19:13:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":244775,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted HR for all-cause mortality with different factors based on various ethnicities. HR: hazard ratio, CI: confdence interval, Black: Non-Hispanic Blacks,\u003c/p\u003e\n\u003cp\u003eWhite: Non-Hispanic Whites.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3922947/v1/c6973809dcc72425ff5a3d4d.png"},{"id":50876244,"identity":"7d0dca5c-a4c5-4257-88d8-c84b466fbfa3","added_by":"auto","created_at":"2024-02-08 19:13:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":875617,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted Kaplan-Meier survival curves for the relationship between the factors and all-cause mortality in different ethnicities. (A, D, G): the whole cohort, (B, E, H): Non-Hispanic Whites, (C, F, I): Non-Hispanic Blacks.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3922947/v1/3fd28db460026a614cd571a6.png"},{"id":50956814,"identity":"de9f4dd4-76f4-4966-8fcb-214a20014554","added_by":"auto","created_at":"2024-02-10 19:00:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1327273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3922947/v1/ecd95d3a-4961-420f-b595-800850935cee.pdf"},{"id":50876241,"identity":"e296c6ef-18bd-417b-b728-d31e2c18a250","added_by":"auto","created_at":"2024-02-08 19:13:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1495692,"visible":true,"origin":"","legend":"","description":"","filename":"Table1and2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3922947/v1/7298d4d395ef966f57c926e2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors affecting the prognosis of prostate cancer in the United States: results from the national health and nutrition examination survey in 1999-2016","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer is considered as the most widely influential tumor among adult men in more than half of countries and the incidence rate has been increasing in recent years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. There are more and more advanced treatment options for patients over the past few decades, such as robot-assisted radical prostatectomy, external beam radiotherapy, low-dose-rate brachytherap for localised disease and new hormonal agents, docetaxel for metastatic disease, but prostate cancer remains a major threat to the survival of patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition to advanced drugs and precise surgery, some common life factors also perform great influence on the outcomes of patients, such as alcohol consumption, smoking status and BMI [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although studies have demonstrated the appropriate lifestyle could provide benefits for the prognosis of individuals with prostate cancer, it\u0026rsquo;s still difficult to make perfect personal life recommendations for patients due to the heterogeneity of this disease and the inconsistent research results [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], so we conducted this study to investigate the effects of alcohol consumption, smoking status and BMI on prostate cancer.\u003c/p\u003e \u003cp\u003eAlcohol is an indispensable part of human culture and is closely related to daily life of many people around the world. Only a tiny part of alcohol we absorbed is excreted without metabolism, more than 90% is transformed to others metabolites that usually are harmful to health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Reports have reported alcohol consumption could increase the risk of alcohol-related cancers, such as upper gastrointestinal cancers, colorectal cancer, liver cancer and breast cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. There were some evidences illustrated that alcohol consumption promoted the formation and development of prostate cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but other studies showed the contrary results [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The hazard of cigarette to human health and environment is well recognized in the academic community [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Tobacco and tobacco smoke is a very complex mixture containing about 10,000 compounds, more than 80 of which are identified as carcinogens and tobacco shows great harm to human health due to its prevalence all over the world [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Active or secondhand smoke exposure is a clear risk factor for breast cancer and lung cancer and has a weaker association with total cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Previous studies have shown that smoking exposure acted as a risk factor contributing to prostate cancer that also shortened the overall survival time of patients, while declining smoking exposure may accordingly decrease the hazard [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Research focused on the heterogeneity of the effects of smoking exposure based on ethnicity is relatively lack.\u003c/p\u003e \u003cp\u003eBody mass index (BMI), an important predictor to measure obesity, is closely related to a person\u0026rsquo;s overall health status. People with a BMI greater than or equal to 25 to 29.9 is overweight and those who has a BMI greater than or equal to 30 is considered as obesity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Many studies have indicated that high BMI could damage human health and people with a high BMI tended to suffer a greater risk of lots of diseases, such as diabetes, hypertension and hypercholesterolemia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Except facing a higher risk of sub-optimal health status, health population with high BMI usually had a shorter healthy and chronic disease-free life expectancy and the global burden of health caused by high BMI was increase continuously in recent years [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The influence of BMI performed on human health was a complex thing, people whose BMI was too low also faced a worse physical health [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In general, excessive obesity promote the formation of various cancers, such as endometrial cancer, breast cancer, colorectal cancer and gallbladder cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A considerable number of studies showed that higher BMI also performed worse effects on patients with prostate cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, there were also some complicated situations that high BMI may play a dual role in prostate cancer (promoted advanced prostate cancer but not localized prostate cancer) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present research aims to further investigate the potential effects of common life factors (alcohol consumption, smoking status and BMI) perform on the prognosis of prostate cancer. Analysis based on different ethnicities can provide more detailed information for urologists to develop personalized disease management schedules.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe patients involved in this research were extracted from the NHANES database, which is a program designed to assess the health and nutritional status of adults and children in the United States. This study included 9 continuous cycles of the NHANES (1999\u0026ndash;2000, 2001\u0026ndash;2002, 2003\u0026ndash;2004, 2005\u0026ndash;2006, 2007\u0026ndash;2008, 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014 and 2015\u0026ndash;2016). A total of 92060 participants were included in the NHANES 1999\u0026ndash;2016, among which 747 participants were diagnosed with prostate cancer. The diagnosis was based on questions from personal interview data of NHANES, \u0026ldquo;Have you ever been told by a doctor or health professional that you had prostate cancer? How old were you when prostate cancer was first diagnosed?\u0026rdquo; Of these, patients with age of onset\u0026thinsp;\u0026lt;\u0026thinsp;40 years old (n\u0026thinsp;=\u0026thinsp;8) were excluded due to the rarity of this disease in young men. Patients with missing date of alcohol consumption (n\u0026thinsp;=\u0026thinsp;99), missing date on the assessment of smoking status (n\u0026thinsp;=\u0026thinsp;1) were also excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe procedures and protocols of NHANES received approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board and all participants have provided the written informed consent [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAlcohol consumption, smoking and BMI\u003c/h2\u003e \u003cp\u003eParticipants provided self-reported information about smoking status, and alcohol consumption. Those who had drank less than 12 drinks of any type of alcoholic beverage in entire life were identified as nondrinkers, who had drank more than 12 drinks in lifetime but did not drink last year were classified as former drinkers. Heavy drinking was identified as four drinks or more than four drinks per day, moderate alcohol use was considered to have three drinks per day and no more than two drinks was mild drinking [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Smoking less than 100 cigarettes was considered as never smoking, more than 100 cigarettes but not at all now was former smoking. Those smoked more than 100 cigarettes in life and smoked some days or every day were current smokers. BMI, weight in kilograms divided by the square of height in meters, was extracted from the body measures data collected in the Mobile Examination Center by trained health technicians [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eThe covariates of interest were the potential confounding factors that might be associated with the mortality of patients with prostate cancer. Sociodemographic variables were as follows: age of onset, ethnic background (non-Hispanic black, non-Hispanic white and others), marital status (divorced/separated/widowed, never, married/living with partner), educational level (high school or equivalent, less than high school, more than high school), poverty to income ratio (PIR). Disease states including diabetes mellitus (DM) and hypertension. DM is determined if one of the following conditions is met: (1). doctor told you have diabetes, (2). glycohemoglobin HbA1c(%)\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;6.5, (3). fasting glucose (mmol/l)\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;7.0, (4) random blood glucose (mmol/l)\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;11.1, (5). two-hour OGTT blood glucose (mmol/l)\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;11.1, (6). use of diabetes medication or insulin. Hypertension was identified if average systolic pressure was higher than 140 mmHg or average diastolic pressure was higher than 90 mmHg. The average blood pressure was calculated by the following protocol: if only one blood pressure reading was obtained, that reading is the average. If there is more than one blood pressure reading, the first reading is always excluded from the average.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes: All-cause mortality\u003c/h2\u003e \u003cp\u003eThe linkage of NHANES survey participant data with the National Death Index (NDI) death certificate records made it feasible to investigate the mortality status of NHANES participants. The mortality follow-up data have been updated to December 31, 2019 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted according to the instructions for using NHANES data, we took the complex survey sampling design of the NHANES into consideration [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To increase the strength of the results, all calculations were weighted unless stated otherwise. \u0026ldquo;4 Year MEC Weight\u0026rdquo; was used for 2 cycles: 1999\u0026ndash;2000 and 2001\u0026ndash;2002, other 7 cycles were calculated with \u0026ldquo;2 Year MEC Weight\u0026rdquo;. Patients involved in this study were divided into 3 subgroups according to the age of onset (A1: \u0026lt;60y; A2: \u0026gt;=60y, \u0026lt;\u0026thinsp;70y; A3: \u0026gt;=70y). BMI was transformed into a non-continuous variable BMI3 based on the threshold values of 25 and 30.\u003c/p\u003e \u003cp\u003eCategorical variables were described as counts and proportions (after weighting), means (standard errors) were presented for continuous variables. We used one-way ANOVA to compare continuous variables and Chi-square test to compare categorical variables. Four cox regression models were constructed to evaluate the association between explored factors and all-cause mortality. Model 1 was crude model, model 2 was adjusted for the age of onset, model 3 was adjusted for the age of onset, ethnicity, marital status, education and PIR and model 4 was adjusted for the age of onset, ethnicity, marital status, education, PIR, DM and hypertension. We used Kaplan-Meier survival curves and log-rank analyses to compare the survival probabilities. Then subgroup analysis was performed to strength the results.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using the R language (version 4.3.1) and statistical significance was defined as a 2-sided P value less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eBaseline characteristics of study population\u003c/h2\u003e\n\u003cp\u003eA total of 639 NHANES participants, representing about 1902,315 patients with prostate cancer in the United States, were enrolled in this research. The weighted baseline characteristics of the selected respondents were shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The overall mean (SE) age at onset of prostate cancer was 65.10(0.45) years old and the patients with later onset tended to have shorter survival time (P\u0026thinsp;=\u0026thinsp;0.038). Non-Hispanic Whites showed the oldest age of onset, while the Non-Hispanic Blacks had the earliest age of onset. There was no statistically significant association between the onset of age and marital status, educational level, smoking, alcohol consumption, hypertension and diabetes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eFactors influencing the outcomes of prostate cancer\u003c/h2\u003e\n\u003cp\u003eIn order to investigate the influence of alcohol consumption, smoking status and BMI on the outcomes of prostate cancer, we constructed weighted multivariate cox regressions (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In the crude model, we found that alcohol consumption in a mild to moderate way could supply different degree of benefits to the outcomes of prostate cancer [HR (95%CI): 0.67(0.50,0.90), P\u0026thinsp;=\u0026thinsp;0.01], and even in patients with heavy degree [HR (95%CI): 0.32(0.11,0.94), P\u0026thinsp;=\u0026thinsp;0.04]. Patients with a BMI between 25 to 30 tended to possess a better prognosis than those who had a BMI less than 25 [HR (95%CI): 0.74(0.56,0.98)], but when it came to BMI was over 30, the better prognosis was no longer. However, smoking status didn\u0026rsquo;t affect the prognosis of prostate cancer without adjusting for covariates.\u003c/p\u003e\n\u003cp\u003eAfter adjusting for potential covariates, former smoker suffered a worse outcome than those who had never smoked [HR (95%CI): 1.39(1.04,1.87), P\u0026thinsp;=\u0026thinsp;0.03], and patients who smoked currently were inclined to the worst situation [HR (95%CI): 2.37(1.39,4.04), P\u0026thinsp;=\u0026thinsp;0.001]. Having a BMI between 25 to 30 remained to act as a protective factor for prostate cancer patients compared to with a BMI less than 25 [HR (95%CI): 0.62(0.47,0.83), P\u0026thinsp;=\u0026thinsp;0.001], but alcohol consumption showed no influence on the overall survival. We further drew a weighted restricted cubic spline to explore the non-linear relationship between BMI and all-cause mortality in prostate cancer patients (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The piecewise cox regression after adjusting for covariates showed that higher BMI could be a protective factor to prostate cancer patients when BMI was less than 26.6 [HR (95%CI): 0.88(0.81,0.95), P\u0026thinsp;=\u0026thinsp;0.002], but the protective effect was absent if BMI was more than 26.6.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\n\u003cp\u003eIn order to further confirm the above results and investigate the effect of these factors in different patient subgroups, we performed subgroup analysis based on different ethnicities. We focused on Non-Hispanic Black people and Non-Hispanic White people due to the limit of population involved and a forest plot was drawn to show the results (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn Non-Hispanic Blacks, mild to moderate alcohol consumption had a protective effect on prostate cancer [HR (95%CI): 0.31(0.17,0.60), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and the effect still existed after adjusting for covariates [HR(95%CI): 0.46(0.22,0.97), P\u0026thinsp;=\u0026thinsp;0.04]. The situation is different in Non-Hispanic Whites, alcohol consumption status didn\u0026rsquo;t influence the outcome of prostate cancer patients, while smoking status and BMI played important roles. After adjusting the covariates, former smokers who suffered prostate cancer likely to face worse outcomes [HR (95%CI): 1.51(1.09,2.10), P\u0026thinsp;=\u0026thinsp;0.013] than those who had never smoked. Furthermore, patients who smoked now could face the worst outcome [HR (95%CI): 3.24(1.84,5.71), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. What\u0026rsquo;s more, Patients who were appropriately overweight but not obese could have better survival outcomes [HR (95%CI): 0.57(0.41,0.78), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\u003c/p\u003e\n\u003cp\u003eWeighted restricted cubic splines were performed to investigate the potential non-linear relationship between BMI and all-cause mortality of certain ethnicity (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). No non-linear relationship was observed among Non-Hispanic Blacks, while a noteworthy non-linear relationship was found among Non-Hispanic Whites and the inflection point was 28.6 (P for non-liner\u0026thinsp;=\u0026thinsp;0.005). Weighted Kaplan-Meier survival curves showed that mild to moderate alcohol consumption could act as a protective role for prostate cancers among all adults across the United States (P\u0026thinsp;=\u0026thinsp;0.006) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Different BMI and smoking status didn\u0026rsquo;t represent significant different outcomes in the whole population, although those who had a BMI between 25 to 30 or never smoked tended to have a longer overall survival time. When it came to a certain race, mild to moderate alcohol consumption could benefit the prognosis in Non-Hispanic Blacks (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Non-Hispanic Whites with a BMI between 25 to 30 likely to have a longer survival time than those with a BMI less than 25 (P\u0026thinsp;=\u0026thinsp;0.037).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eData obtained from NHANES indicated that the influence of lifestyle factors performed on the prognosis of prostate cancer various by ethinicities. We discovered a protective role of mild to moderate alcohol consumption among the whole population and Non-Hispanic Blacks with prostate cancer. Similarly, moderate drinking was closely related to a lower risk of all-cause mortality in obese women with breast cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A phase III randomized adjuvant trial also showed that stage III colon cancer patients with mild to moderate red wine consumption were likely to have longer overall survival, disease free survival [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The detailed mechanism underlying the benefits of appropriate alcohol consumption was still unclear, it may be explained by the benefit against aortic disease or other cardiovascular disease [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In the whole population, mild to moderate alcohol consumption could reduce risks for cardiovascular disease and type-2 diabetes and then reduce mortality risk [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Another study also showed that low-volume drinkers had lower mortality than non-drinkers, but the difference was not significant anymore after adjustment for cardiovascular risk factors [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The research on the effect of alcohol consumption on cancer based on different ethinicities was few. But the race heterogeneity was truly existed, Non-Hispanic Blacks had a lower proportion of cancers caused by alcohol consumption than Non-Hispanic Whites [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The specific mechanism was difficult to elucidate and it may be explained partly by the genetic background [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSmoking was a harmful factor in this research and quitting smoking could attenuate the deleterious effect according to the covariate-adjusted cox regression model. We didn\u0026rsquo;t find a significant difference in weighted Kaplan-Meier survival curves but those who smoked showed a tendency to shorter overall survival time than those who never smoked. What\u0026rsquo;s more, Non-Hispanic Whites was more allergic to tobacco exposure compared to Non-Hispanic Blacks. A cohort of older men also showed that current smokers had higher mortality risks than non-smokers [HR 3.18, 95%CI: (3.04, 3.31)] [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Many other studies also proved that smoking was an independent detrimental factor for patients with prostate cancer [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In other cancers, smokers also faced significantly worse overall survival and smoking cessation after cancer diagnosis was beneficial to prolong survival time in various degrees [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Apart from cancers, the combination of nicotine addiction and toxicants exposure extended the adverse effects of tobacco to multiple organs of people, for example, smokers tended to have worse health conditions in respiratory system and cardiovascular system [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBMI played a relatively complex role in affecting the prognosis of patients with prostate cancer. We found that those whose BMI were too extreme, either too low or too high, seemed to have shorter overall survival time. The U-shaped curve pattern of the relationship between BMI and high all-cause mortality in the whole population, especially in Non-Hispanic Whites, remind us to keep appropriate overweight (BMI between 25 to 30). Those who were excessive obesity in this research had worse outcomes may be partly explained by the higher risk of cerebro-cardiovascular diseases, such as heart failure, cardiomyopathy and hypertension [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Patients with too low BMI had a shorter overall survival time may due to malnutrition [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study focused on the effects of several common lifestyle factors to patients with prostate cancer based on various ethnicities which has been rare previous. The heterogeneity of ethnicities we observed can provide urologists with more precise lifestyle management options, so that patients could get rational suggestions and prolong life span by lifestyle modification. What\u0026rsquo;s more, using a complex weighted calculation method and the nationally representative U.S. cohort obtained by reasonable sampling methods made the results more reliable. However, there were some limitations. Firstly, we didn\u0026rsquo;t consider whether patients had received other treatments due to nature of the database. Second, there may be other unmeasured or residual confounders. Thirdly, cancer diagnosis was based on questionnaire data in self-reported interviews. Nevertheless, professional investigators has minimized errors through professional training. The detailed mechanisms of heterogeneity between ethnicities still unclear. In brief, future large-scale studies are required to validate our results, as well as basic research to explain the specific mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn men with prostate cancer, smoking increased the all-cause mortality and smoking cessation could reduce the deleterious effect. Those who kept BMI between 25 to 30 tended to have longer overall survival time. Subgroup analysis showed that Non-Hispanic Blacks could benefit from mild to moderate alcohol consumption rather than Non-Hispanic Whites, while Non-Hispanic Whites could benefit from keeping appropriate overweight and be more sensitive to tobacco exposure. However, due to the limited sample size in this study, future research with large sample size including more ethnic groups is needed to verify our results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe appreciate everyone who participated in NHANES. Thanks to Zhang Jing (Second Department of Infectious Disease, Shanghai Fifth People\u0026apos;s Hospital, Fudan University) for his work on the NHANES database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research was supported by the National Natural Science Foundation of China (grant numbers 82203280), Sichuan Province Science and Technology Support Program (grant numbers 2022YFS0305)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eYS and ZL conceptualized the study. XW and YS collected the data. YS, XP and ZW conducted data analysis. YS wrote the original draft. ZL and JL reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eData analyzed in this research are available on the NHANES website: https://www.cdc.gov/nchs/nhanes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declared that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003eThe study was conducted in accordance with the Declaration of Helsinki. Studies involving human participants are reviewed and approved by the NCHS Ethics Review Committee. 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European Journal of Clinical Nutrition 74 (11):1519-1535. https://doi:10.1038/s41430-020-0629-0\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prostate cancer, Alcohol consumption, Smoking status, BMI","lastPublishedDoi":"10.21203/rs.3.rs-3922947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3922947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eProstate cancer is one of the most harmful and widespread cancers in elderly men. This study aims to explore several common lifestyle factors that may influence the overall survival time among prostate cancer patients of different ethnic groups.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData obtained from the National Health and Nutrition Examination Survey database (NHANES) between 1999 and 2016 was analyzed. Weighted multivariate cox regression and subgroup analysis were used to explore the underlying factors that affect the prognosis of prostate cancer. Kaplan-Meier survival curves were drawn to demonstrate the different survival time and survival rates in prostate cancer patients with different levels of exposure factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 639 prostate cancer patients were enrolled in this study. People with an earlier age of onset tended to have a longer survival time (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Non-Hispanic blacks seemed to be diagnosed with prostate cancer at an earlier stage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Light to moderate alcohol consumption was a protective factor [HR 0.38, 95%CI:(0.16,0.89)] in Non-Hispanic Blacks with prostate cancer, which could provide longer overall survival time to prostate cancer patients than those who didn\u0026rsquo;t drink for at least one year. Smoking could be a harmful factor to the whole population [HR 2.37, 95%CI:(1.39,4.04)], while keeping BMI between 25 to 30 had protective effects [HR 0.62, 95%CI:(0.47,0.83)]. Non-Hispanic Whites were more likely to be affected by smoking [HR 3.24, 95%CI:(1.84,5.71)] and BMI [HR 0.57, 95%CI:(0.41,0.78)] compared to Non-Hispanic Blacks.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAppropriate overweight (BMI between 25 to 30) and mild to moderate alcohol consumption seemed to be protective factors in prostate cancer, while smoking showed a detrimental effect. The heterogeneity between ethnicities can\u0026rsquo;t be ignored and future more large-scale studies are required to verify our findings.\u003c/p\u003e","manuscriptTitle":"Factors affecting the prognosis of prostate cancer in the United States: results from the national health and nutrition examination survey in 1999-2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-08 19:13:42","doi":"10.21203/rs.3.rs-3922947/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d00ccaac-f1e5-4e6a-97ee-dad7d8b3164a","owner":[],"postedDate":"February 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-10T19:00:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-08 19:13:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3922947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3922947","identity":"rs-3922947","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}