Race-Stratified Differences in BMR Among Men with Elevated PSA: Insights from Wearable Device Data

Race-Stratified Differences in BMR Among Men with Elevated PSA: Insights from Wearable Device Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Race-Stratified Differences in BMR Among Men with Elevated PSA: Insights from Wearable Device Data Yash Kadakia, Muhammed Hammad, Elia Abou Chawareb, Sarah Al-Halawani, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9336029/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Purpose Prostate-specific antigen (PSA) elevation is common among older men and often prompts downstream testing despite frequently reflecting benign conditions. Metabolic dysfunction has been linked to cancer biology, yet the relationship between basal metabolic rate (BMR), a known marker of metabolic health, and PSA elevation has been unexplored. The goal is to evaluate differences in wearable-derived BMR between men with elevated PSA and healthy controls across racial groups. Methods In this retrospective cross-sectional study, mean daily BMR (Calories) was derived from Fitbit Daily Activity Summary data. Comparisons were performed within racial groups using Welch’s t tests. Mean BMR values were averaged at the participant level for primary analyses, with complementary record-level analyses conducted to assess overall population patterns and support robustness of findings. Results Men with elevated PSA consistently demonstrated lower mean BMR across racial groups with sufficient representation. Compared with healthy controls, men with elevated PSA had significantly lower mean BMR among Asian, Black/African American, and White participants, with differences of -106.12, -134.03, and − 99.12 Calories, respectively. These findings remained consistent in sensitivity analyses excluding benign and malignant prostate disease. Elevated PSA was associated with lower BMR across multiple racial groups. Conclusions These findings suggest that PSA elevation may reflect broader systemic metabolic alterations beyond prostate-specific pathology, highlighting the potential value of integrating wearable-derived metabolic measures into longitudinal, intervention-based strategies aimed at improving metabolic health and refining prostate risk stratification. Basal Metabolic Rate Metabolic Syndrome Prostate-Specific Antigen Race Factors Wearable Electronic Devices Figures Figure 1 Introduction Basal metabolic rate (BMR), which reflects the body’s energy expenditure at rest, serves as an indicator of metabolic activity and overall physiologic function 1 . Altered BMR has been associated with metabolic dysfunction, systemic inflammation, and increased susceptibility to several cancers, such as breast, colorectal, and bladder cancer 2 . These associations suggest that BMR may serve as an important physiological marker of metabolic health. Prostate-specific antigen (PSA), a glycoprotein produced by both benign and malignant prostate epithelial cells, serves as a biomarker for screening and monitoring prostate cancer (PCa) 3 , 4 . Elevated PSA levels are frequently detected in men aged 50–70, a demographic with an increased risk of PCa. However, elevated PSA levels do not always indicate malignancy, as increases may also result from benign prostatic hyperplasia, prostatitis, or urinary retention 3 . Over the last two decades, most of the efforts have focused on risk stratification of patients with elevated PSA to identify patients who harbor prostate cancer, and at the same time, avoid unnecessary biopsy. However, patients with elevated PSA represent a challenge to the urology community, as downstream procedures and testing accounted for 72% of the overall cost of PSA-based prostate cancer screening 5 . While metabolic dysfunction has been linked to cancer biology, the relationship between BMR and elevated PSA remains largely unexplored 6 . No prior studies have examined BMR patterns in men with elevated PSA across multiple racial groups using large-scale wearable health data. Exploring this may provide valuable insight into the metabolic correlation of PSA elevation and identify physiologic patterns that distinguish benign PSA elevations from those that might be associated with early-stage malignancy. Hence, this study aims to compare BMR between healthy men and men with elevated PSA aged 50–70. Furthermore, it seeks to determine whether BMR can serve as a biomarker to guide targeted lifestyle interventions. Methods Study Design We conducted a retrospective cross-sectional study using data from the National Institutes of Health (NIH) All of Us Research Program Controlled Tier v8 database. The All of Us database includes data from sources such as electronic health records (EHR), surveys, and wearable devices. Data extraction and analyses were performed within the All of Us Researcher Workbench. Participants Male participants aged 50 to 70 years with available Fitbit wearable data were included in the study. Two cohorts were defined using standardized EHR concept identifiers. The elevated PSA cohort included men with at least one PSA value of 4.0 ng/mL or above. The control cohort included those with PSA values < 4.0 ng/mL, no history of prostate cancer or prostate-related issues, non-diabetic, and normal renal function (Fig. 1 ). Race and ethnicity were categorized according to the classification found within the All of Us database (White, Black/African American, Asian, American Indian/Alaska Native, and Middle Eastern/North African). A sensitivity analysis was performed, excluding men with documented prostate cancer or BPH to evaluate whether observed differences in BMR were independent of underlying prostate pathology. The restricted cohort was analyzed at the record level to preserve statistical power, and results were compared descriptively with the primary analysis to assess consistency in the direction and magnitude of associations. This diagram illustrates the stepwise application of inclusion and exclusion criteria used to identify the final control cohort. Starting with all male participants in the All of Us database, patients were sequentially restricted to those aged 50 to 70 years, those with normal kidney function (eGFR ≥ 60), non-diabetic, and no history of prostate cancer, prostate tumors, benign prostatic hyperplasia (BPH), or elevated PSA values. Exposure and Outcome Measure The primary exposure being measured was PSA status (elevated vs. normal). The primary outcome being analyzed was basal metabolic rate (BMR in Calories), derived from the Fitbit Daily Activity Summary dataset 7 . Fitbit BMR represents a device generated estimate of resting energy expenditure calculated from user profile data, including age, sex, height, and weight, using validated metabolic equations such as Mifflin St Jeor and is recorded in the Fitbit Daily Activity Summary as base or resting Calories independent of physical activity. All available BMR values were averaged to obtain a mean daily BMR 7 . Statistical Analysis Comparisons of mean BMR between the elevated PSA and control groups were conducted separately within each racial category. Welch’s t-tests were used due to unequal variances between the groups. Two-tailed P values < 0.05 were considered to be statistically significant. Analyses were conducted using the Jupyter Lab Notebook within the All of Us Researcher Workbench using the programming language Python (All of Us, n.d.) 7 . To account for the repeated-measures structure of wearable-derived data, we conducted analyses at both the record and participant levels. At the participant level, mean BMR was calculated by averaging all daily records per individual and used for primary comparisons to ensure independence. Record-level analyses, treating each daily measurement as an observation, were performed to leverage the granularity of the dataset and characterize population-level patterns. While record-level approaches may reflect cumulative physiologic exposure, they can disproportionately weight individuals with greater device adherence. Therefore, participant-level analyses were prioritized for statistical inference, with record-level findings included to provide additional context and support consistency of observed trends. Ethical Considerations The study used deidentified data within the All of Us secure environment, with all participants providing consent through the program. This study was exempt from review by an institutional review board. Results Baseline characteristics A total of 231,210 male participants were identified in the All of Us database, of whom 89,266 were between the ages of 50–70 years (Fig. 1 ). After restricting to individuals with normal kidney function, non-diabetic, and no history of prostate cancer or prostate-related issues, 5,368 participants remained eligible for inclusion in the control cohort. For the elevated PSA cohort, 9,220 male participants aged 50 to 70 met criteria for having at least one PSA value of 4.0 ng/mL or higher and no documented diagnosis of prostate cancer. A sensitivity analysis excluding prostate cancer and BPH resulted in a reduced analytic sample of 1,617 participants. Across both cohorts, participants were predominantly between 56–70 years of age, with the largest proportion in the 61–65 and 66–70 age groups (Table S1 ). Men with elevated PSA were slightly older overall compared with healthy controls, with a higher proportion aged 61–65 years (30.6% vs 26.9%) and 66–70 years (31.8% vs 25.5%). BMI distributions differed between groups, with overweight and obese categories comprising the majority of participants in both cohorts. Men with elevated PSA had a higher prevalence of overweight status (43.2% vs 34.8%). Racial composition differed modestly between cohorts. White participants comprised the majority in both groups and were more prevalent among men with elevated PSA (69.7% vs 59.6%), while Black or African American participants were more common in healthy controls (17.8% vs 14.4%). Asian participants represented a small proportion of both cohorts (2.6% vs 1.6%), with the remaining racial groups classified as other. Sensitivity Analysis In the sensitivity analysis excluding men with documented BPH or prostate cancer, the overall pattern of lower mean BMR among men with elevated PSA persisted, consistent with the primary findings. In an overall record-level comparison, mean BMR remained lower among men with elevated PSA compared with healthy controls (1776.6 vs 1709.6 Calories, p < 0.05), supporting the robustness of these associations. These analyses were conducted at the record level due to reduced sample sizes following exclusion criteria, allowing for preservation of statistical power while maintaining consistency in the direction and magnitude of observed associations. BMR & Race Across racial groups, both participant-level and record-level analyses demonstrated consistently higher BMR values among healthy control participants compared with men with elevated PSA (Table 1 ; Table 2 ). Healthy control participants demonstrated higher BMR values in all three racial groups. At the participant level, significant differences were observed among Asian (1612.69 vs 1506.57 Calories), Black/African American (1855.22 vs 1721.19 Calories), and White participants (1780.98 vs 1681.86 Calories). Record-level analyses showed similar directional trends across all racial groups, with the largest difference observed among Black/African American (131.53 Calories), followed by White (89.4 Calories), and Asian participants (48.1 Calories). Table 1 BMR comparison between healthy males and males with elevated PSA aged 50–70 by race. (Participant Level) Race Healthy Mean BMR Elevated PSA Mean BMR p-value Asian 1612.69 1506.57 p = 0.03–0.04 Black/African American 1855.22 1721.19 p = 0.02 White 1780.98 1681.86 P < 0.001 Table 2 BMR comparison between healthy males and males with elevated PSA aged 50–70 by race. (Record Level) Race Healthy Mean BMR Elevated PSA Mean BMR p-value Asian 1606.75 1558.65 < 0.001 Black/African American 1907.84 1776.31 < 0.001 White 1769.55 1680.18 < 0.001 Discussion In this cohort of men aged 50–70 years, we observed consistently lower BMR among individuals with elevated PSA compared to healthy controls (Table 1 ; Figure S1 ). These findings suggest that PSA elevation may reflect broader physiological alterations beyond prostatic pathology alone and may correspond to underlying differences in metabolic function 8 . To our knowledge, this is the first study that examines race-stratified metabolic patterns among individuals with elevated PSA via wearable-derived physiologic data in a national cohort. Sedentary behavior may represent an important contextual factor linking PSA elevation to the reduced basal metabolic rate observed in this study. A prior population-based analysis using data from the National Health and Nutrition Examination Survey (NHANES) demonstrated that greater sedentary time is independently associated with higher PSA concentrations 9 . These findings suggest that PSA elevation may, in part, reflect systemic physiologic states associated with reduced energy expenditure rather than prostate-specific pathology alone. In the context of this study, the consistently lower BMR observed among men with elevated PSA across racial groups in our cohort aligns with prior evidence that sedentary behavior and diminished metabolic activity may be linked to altered PSA biology. Although physical activity was not directly included, wearable-derived BMR may reflect underlying metabolic and activity-related physiology 9 . Together, these findings suggest an association between PSA elevation and broader metabolic dysregulation, underscoring the value of interpreting PSA within a whole-body physiologic framework. Biological mechanisms may explain the observed association between elevated PSA and reduced BMR. Chronic and subclinical prostatic inflammation is a well-established cause of PSA elevation in the absence of any malignancies 10 . Chronic low-grade inflammation (mediated by cytokines such as IL-6 and TNF-α) is associated with mitochondrial dysfunction, which contributes to the progressive decline in resting energy expenditure and metabolic rate observed during aging 11 , 12 . Thus, the reduced BMR in men with elevated PSA may reflect metabolic consequences of underlying inflammatory processes rather than prostate-specific disease mechanisms alone. Metabolic syndrome and obesity can further complicate the relationship between PSA kinetics and systemic physiology 13 . Multiple studies demonstrate that metabolic syndrome can alter PSA concentrations and screening performance 14 , 15 . Obesity can reduce PSA values through increased plasma volume (hemodilution) and through metabolic alterations associated with insulin resistance 14 , 16 . Our findings extend this literature by highlighting that even when PSA is elevated, individuals demonstrate altered metabolic signatures in the form of lower BMR across most demographic groups (Figure S1 ). This pattern suggests shared physiological pathways between metabolic health and PSA biology. Exploratory age- and BMI-stratified analyses demonstrated consistent directional trends, with lower BMR observed among men with elevated PSA across most subgroups, suggesting that these differences were not solely attributable to age or body composition; however, these results were not reported due to small subgroup sizes that did not meet the All of Us Research Program reporting threshold. The consistency of these findings suggests that differences in body composition alone may not fully explain the observed metabolic alterations and supports the presence of shared physiologic pathways linking PSA elevation with systemic metabolic function. The race-stratified results in this study align with known population-level differences in both metabolism and prostate health. Prior studies have documented that resting BMR can vary by race, even after adjusting for age and body composition 17 . Racial disparities also exist in prostate cancer risk, PSA kinetics, and inflammation 18 , 19 . The persistence of lower BMR across racial groups suggests that this association is robust across diverse physiologic backgrounds (Table 1 ; Figure S1 ). These findings underscore the importance of examining biomarker-physiology relationships in racially diverse populations, particularly given the known variation in PSA performance across groups. A key methodological consideration is the distinction between record-level and participant-level analyses of wearable data. Record-level analyses capture high-frequency measurements and characterize overall patterns, whereas participant-level aggregation ensures equal weighting and is more appropriate for inference. The consistency of findings across both approaches supports the robustness of the results, while attenuation of statistical significance at the participant level highlights the importance of accounting for non-independence in wearable datasets. This study has several limitations that should be considered when interpreting the findings. First, the American Indian/Alaska Native and Middle Eastern/North African racial groups had limited sample sizes, restricting the ability to examine metabolic trends for these groups. Small subgroup sizes informed the inclusion of complementary record-level analyses; future studies with larger, more balanced samples are needed for robust participant-level comparisons. As with all database studies, misclassification and coding bias are possible, and exclusions relied on diagnostic codes rather than clinical verification. Although BMR estimates derived from wearable devices provide useful, accessible approximations, they remain indirect measures and may not capture energy expenditure with the precision of clinical calorimetry. PSA elevations were based on single-time-point measurements, limiting the distinction between transient and clinically significant elevations. Additionally, lack of data on family history and prior PSA evaluation, along with limited clinical guidance on meaningful BMR differences, restricts interpretation of the findings to a certain extent. This study identifies a consistent association between elevated PSA and reduced BMR across multiple racial groups. Integrating wearable-derived metabolic measures with clinical biomarkers offers a novel approach to characterizing whole-body physiology in diverse populations. Future longitudinal research should incorporate hormonal panels, inflammatory markers, and direct metabolic measurements to further determine whether reduced BMR is associated with elevated PSA. Future Directions The findings from this study suggest that altered BMR may represent a modifiable physiologic correlate of PSA elevation, raising the possibility that lifestyle interventions aimed at improving metabolic health could mitigate downstream risks associated with PSA-driven diagnostic testing and procedures. A recent clinical trial demonstrated that exercise therapy is a feasible and safe method associated with favorable changes in the tumor proliferation marker Ki67 and PSA levels among men with prostate cancer 20 . Together with our findings, these data suggest that structured physical activity may influence prostate biology through systemic metabolic pathways, potentially including improvements in basal energy expenditure. Future longitudinal studies integrating wearable-derived metabolic measures with exercise interventions are needed to assess whether improving BMR through targeted lifestyle modifications can reduce PSA elevation and improve prostate risk stratification. Declarations Ethics Approval and Consent to Participate This study did not involve human participants, identifiable personal data, or clinical interventions, and therefore did not require ethical approval. All data analyzed in this study were accessed through the NIH All of Us Research Program Researcher Workbench, a secure, cloud-based platform housing controlled-access participant data. Consent for publication Not applicable. Author contributions MDS and YK conceptualized the study. YK retrieved, and analyzed the data, and wrote/edited the manuscript. MDS, MAHM, EAC, MM, SA, AD, FAY, TA, DIY wrote/edited the manuscript. MDS, MAHM, YK contributed to project conception/design and manuscript writing/editing. All authors read and approved the final manuscript. Acknowledgments We thank the NIH All of Us Research Program and its participants for making this research possible. Funding None. Conflict of Interest Statement YAF: Coloplast: Advisory board, speaker; Endo: Advisory board; Haleon: Advisory board; Halozyme: Advisory board, speaker; Masimo: Intellectual property; Softwave: Advisory board; Sprout: Consultant; Vertica: Research investigator; Xialla: Advisory board. The remaining Authors have no conflicts of interest to disclose. Data Availability Statement The data generated and analyzed in this study were derived from the All of Us Research Program and include wearable-derived metabolic estimates and corresponding clinical biomarker measurements. Statistical analysis was performed using the Jupyter Notebook embedded within the All of Us Researcher Workbench via the use of the Python. All processed data tables and relevant materials are included within the published article. References Johnstone AM, Murison SD, Duncan JS, Rance KA, Speakman JR (2005) Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. Am J Clin Nutr 82(5):941–948. 10.1093/ajcn/82.5.941 Karra P, Winn M, Pauleck S et al (2022) Metabolic dysfunction and obesity-related cancer: Beyond obesity and metabolic syndrome. Obesity 30(7):1323–1334. 10.1002/oby.23444 Hsieh KL, Chang CH, Lin YC, Huang TJ, Chen MY (2024) Lifestyle and risk factors associated with elevated prostate-specific antigen levels in rural men: implications for health counseling. Front Oncol 14:1451941. 10.3389/fonc.2024.1451941 Shill DK, Roobol MJ, Ehdaie B, Vickers AJ, Carlsson SV (2021) Active surveillance for prostate cancer. Transl Androl Urol 10(6):2809–2819. 10.21037/tau-20-1370 Kim DD, Daly AT, Koethe BC et al (2022) Low-Value Prostate-Specific Antigen Test for Prostate Cancer Screening and Subsequent Health Care Utilization and Spending. JAMA Netw Open 5(11):e2243449. 10.1001/jamanetworkopen.2022.43449 De Nunzio C, Aronson W, Freedland SJ, Giovannucci E, Parsons JK (2012) The correlation between metabolic syndrome and prostatic diseases. Eur Urol 61(3):560–570. 10.1016/j.eururo.2011.11.013 All of Us Research Hub. Accessed December 24 (2025) https://www.researchallofus.org/ Burton AJ, Tilling KM, Holly JM et al (2010) Metabolic imbalance and prostate cancer progression. Int J Mol Epidemiol Genet 1(4):248–271 Loprinzi PD, Kohli M (2014) Health Characteristics and Sedentary Behavior Impact on Prostate-Specific Antigen Levels in a National U.S. Sample. J Phys Act Health 11(8):1587–1592. 10.1123/jpah.2013-0073 Nadler RB, Humphrey PA, Smith DS, Catalona WJ, Ratliff TL (1995) Effect of inflammation and benign prostatic hyperplasia on elevated serum prostate specific antigen levels. J Urol 154(2 Pt 1):407–413. 10.1097/00005392-199508000-00023 Petersen AMW, Pedersen BK (2005) The anti-inflammatory effect of exercise. J Appl Physiol 98(4):1154–1162. 10.1152/japplphysiol.00164.2004 Hotamisligil GS (2006) Inflammation and metabolic disorders. Nature 444(7121):860–867. 10.1038/nature05485 Falkner B, Cossrow NDFH (2014) Prevalence of Metabolic Syndrome and Obesity-Associated Hypertension in the Racial Ethnic Minorities of the United States. Curr Hypertens Rep 16(7):449. 10.1007/s11906-014-0449-5 Hsing AW, Sakoda LC, Chua SC (2007) Obesity, metabolic syndrome, and prostate cancer. Am J Clin Nutr 86(3):843S–857S. 10.1093/ajcn/86.3.843S Kim Y, Cho Y, Oh J, Jeon Y, Lee S, Kim W (2008) The association between metabolic syndrome and prostate-specific antigen levels. Int J Urol 15(10):905–909. 10.1111/j.1442-2042.2008.02137.x Bañez LL, Hamilton RJ, Partin AW et al (2007) Obesity-Related Plasma Hemodilution and PSA Concentration Among Men With Prostate Cancer. JAMA 298(19):2275. 10.1001/jama.298.19.2275 de Boer JO, van Es AJ, Voorrips LE, Blokstra F, Vogt JE (1988) Energy metabolism and requirements in different ethnic groups. Eur J Clin Nutr 42(12):983–997 Lowder D, Rizwan K, McColl C et al (2022) Racial disparities in prostate cancer: A complex interplay between socioeconomic inequities and genomics. Cancer Lett 531:71–82. 10.1016/j.canlet.2022.01.028 Vidal AC, Chen Z, Howard LE et al (2017) Racial differences in prostate inflammation: results from the REDUCE study. Oncotarget 8(42):71393–71399. 10.18632/oncotarget.10690 Jones LW, Moskowitz CS, Lee CP et al (2024) Neoadjuvant Exercise Therapy in Prostate Cancer: A Phase 1, Decentralized Nonrandomized ControlledTrial. JAMA Oncol 10(9):1187. 10.1001/jamaoncol.2024.2156 Additional Declarations No competing interests reported. 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08:21:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of Participant Selection for the Control Cohort\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9336029/v1/2b0ee54ca9d0387b0b750e61.png"},{"id":108804362,"identity":"f0e94466-e267-4970-bdaf-f46f0ce399e6","added_by":"auto","created_at":"2026-05-08 15:19:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":244159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9336029/v1/5ebcb03b-79f8-435f-a116-e6b46a49f672.pdf"},{"id":108397366,"identity":"fc376d80-87ac-45ee-9098-b15b0e7cc4ac","added_by":"auto","created_at":"2026-05-04 08:21:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":99990,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsWJU.docx","url":"https://assets-eu.researchsquare.com/files/rs-9336029/v1/8c4f2db84906110903fcc83c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Race-Stratified Differences in BMR Among Men with Elevated PSA: Insights from Wearable Device Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBasal metabolic rate (BMR), which reflects the body\u0026rsquo;s energy expenditure at rest, serves as an indicator of metabolic activity and overall physiologic function\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Altered BMR has been associated with metabolic dysfunction, systemic inflammation, and increased susceptibility to several cancers, such as breast, colorectal, and bladder cancer\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These associations suggest that BMR may serve as an important physiological marker of metabolic health.\u003c/p\u003e \u003cp\u003eProstate-specific antigen (PSA), a glycoprotein produced by both benign and malignant prostate epithelial cells, serves as a biomarker for screening and monitoring prostate cancer (PCa)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Elevated PSA levels are frequently detected in men aged 50\u0026ndash;70, a demographic with an increased risk of PCa. However, elevated PSA levels do not always indicate malignancy, as increases may also result from benign prostatic hyperplasia, prostatitis, or urinary retention\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Over the last two decades, most of the efforts have focused on risk stratification of patients with elevated PSA to identify patients who harbor prostate cancer, and at the same time, avoid unnecessary biopsy. However, patients with elevated PSA represent a challenge to the urology community, as downstream procedures and testing accounted for 72% of the overall cost of PSA-based prostate cancer screening\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile metabolic dysfunction has been linked to cancer biology, the relationship between BMR and elevated PSA remains largely unexplored\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. No prior studies have examined BMR patterns in men with elevated PSA across multiple racial groups using large-scale wearable health data. Exploring this may provide valuable insight into the metabolic correlation of PSA elevation and identify physiologic patterns that distinguish benign PSA elevations from those that might be associated with early-stage malignancy. Hence, this study aims to compare BMR between healthy men and men with elevated PSA aged 50\u0026ndash;70. Furthermore, it seeks to determine whether BMR can serve as a biomarker to guide targeted lifestyle interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cross-sectional study using data from the National Institutes of Health (NIH) All of Us Research Program Controlled Tier v8 database. The All of Us database includes data from sources such as electronic health records (EHR), surveys, and wearable devices. Data extraction and analyses were performed within the All of Us Researcher Workbench.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eMale participants aged 50 to 70 years with available Fitbit wearable data were included in the study. Two cohorts were defined using standardized EHR concept identifiers. The elevated PSA cohort included men with at least one PSA value of 4.0 ng/mL or above. The control cohort included those with PSA values\u0026thinsp;\u0026lt;\u0026thinsp;4.0 ng/mL, no history of prostate cancer or prostate-related issues, non-diabetic, and normal renal function (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Race and ethnicity were categorized according to the classification found within the All of Us database (White, Black/African American, Asian, American Indian/Alaska Native, and Middle Eastern/North African).\u003c/p\u003e \u003cp\u003eA sensitivity analysis was performed, excluding men with documented prostate cancer or BPH to evaluate whether observed differences in BMR were independent of underlying prostate pathology. The restricted cohort was analyzed at the record level to preserve statistical power, and results were compared descriptively with the primary analysis to assess consistency in the direction and magnitude of associations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis diagram illustrates the stepwise application of inclusion and exclusion criteria used to identify the final control cohort. Starting with all male participants in the All of Us database, patients were sequentially restricted to those aged 50 to 70 years, those with normal kidney function (eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60), non-diabetic, and no history of prostate cancer, prostate tumors, benign prostatic hyperplasia (BPH), or elevated PSA values.\u003c/p\u003e\n\u003ch3\u003eExposure and Outcome Measure\u003c/h3\u003e\n\u003cp\u003eThe primary exposure being measured was PSA status (elevated vs. normal). The primary outcome being analyzed was basal metabolic rate (BMR in Calories), derived from the Fitbit Daily Activity Summary dataset\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Fitbit BMR represents a device generated estimate of resting energy expenditure calculated from user profile data, including age, sex, height, and weight, using validated metabolic equations such as Mifflin St Jeor and is recorded in the Fitbit Daily Activity Summary as base or resting Calories independent of physical activity. All available BMR values were averaged to obtain a mean daily BMR\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eComparisons of mean BMR between the elevated PSA and control groups were conducted separately within each racial category. Welch\u0026rsquo;s t-tests were used due to unequal variances between the groups. Two-tailed P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be statistically significant. Analyses were conducted using the Jupyter Lab Notebook within the All of Us Researcher Workbench using the programming language Python (All of Us, n.d.)\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo account for the repeated-measures structure of wearable-derived data, we conducted analyses at both the record and participant levels. At the participant level, mean BMR was calculated by averaging all daily records per individual and used for primary comparisons to ensure independence. Record-level analyses, treating each daily measurement as an observation, were performed to leverage the granularity of the dataset and characterize population-level patterns. While record-level approaches may reflect cumulative physiologic exposure, they can disproportionately weight individuals with greater device adherence. Therefore, participant-level analyses were prioritized for statistical inference, with record-level findings included to provide additional context and support consistency of observed trends.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The study used deidentified data within the All of Us secure environment, with all participants providing consent through the program. This study was exempt from review by an institutional review board.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 231,210 male participants were identified in the All of Us database, of whom 89,266 were between the ages of 50\u0026ndash;70 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After restricting to individuals with normal kidney function, non-diabetic, and no history of prostate cancer or prostate-related issues, 5,368 participants remained eligible for inclusion in the control cohort. For the elevated PSA cohort, 9,220 male participants aged 50 to 70 met criteria for having at least one PSA value of 4.0 ng/mL or higher and no documented diagnosis of prostate cancer. A sensitivity analysis excluding prostate cancer and BPH resulted in a reduced analytic sample of 1,617 participants.\u003c/p\u003e \u003cp\u003eAcross both cohorts, participants were predominantly between 56\u0026ndash;70 years of age, with the largest proportion in the 61\u0026ndash;65 and 66\u0026ndash;70 age groups (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Men with elevated PSA were slightly older overall compared with healthy controls, with a higher proportion aged 61\u0026ndash;65 years (30.6% vs 26.9%) and 66\u0026ndash;70 years (31.8% vs 25.5%). BMI distributions differed between groups, with overweight and obese categories comprising the majority of participants in both cohorts. Men with elevated PSA had a higher prevalence of overweight status (43.2% vs 34.8%). Racial composition differed modestly between cohorts. White participants comprised the majority in both groups and were more prevalent among men with elevated PSA (69.7% vs 59.6%), while Black or African American participants were more common in healthy controls (17.8% vs 14.4%). Asian participants represented a small proportion of both cohorts (2.6% vs 1.6%), with the remaining racial groups classified as other.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eIn the sensitivity analysis excluding men with documented BPH or prostate cancer, the overall pattern of lower mean BMR among men with elevated PSA persisted, consistent with the primary findings. In an overall record-level comparison, mean BMR remained lower among men with elevated PSA compared with healthy controls (1776.6 vs 1709.6 Calories, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), supporting the robustness of these associations. These analyses were conducted at the record level due to reduced sample sizes following exclusion criteria, allowing for preservation of statistical power while maintaining consistency in the direction and magnitude of observed associations.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBMR \u0026amp; Race\u003c/h2\u003e \u003cp\u003eAcross racial groups, both participant-level and record-level analyses demonstrated consistently higher BMR values among healthy control participants compared with men with elevated PSA (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Healthy control participants demonstrated higher BMR values in all three racial groups. At the participant level, significant differences were observed among Asian (1612.69 vs 1506.57 Calories), Black/African American (1855.22 vs 1721.19 Calories), and White participants (1780.98 vs 1681.86 Calories). Record-level analyses showed similar directional trends across all racial groups, with the largest difference observed among Black/African American (131.53 Calories), followed by White (89.4 Calories), and Asian participants (48.1 Calories).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBMR comparison between healthy males and males with elevated PSA aged 50\u0026ndash;70 by race. (Participant Level)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy Mean BMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevated PSA Mean BMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1612.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1506.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.03\u0026ndash;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1855.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1721.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1780.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1681.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBMR comparison between healthy males and males with elevated PSA aged 50\u0026ndash;70 by race. (Record Level)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy Mean BMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevated PSA Mean BMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1606.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1558.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1907.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1776.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1769.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1680.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cohort of men aged 50\u0026ndash;70 years, we observed consistently lower BMR among individuals with elevated PSA compared to healthy controls (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These findings suggest that PSA elevation may reflect broader physiological alterations beyond prostatic pathology alone and may correspond to underlying differences in metabolic function\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. To our knowledge, this is the first study that examines race-stratified metabolic patterns among individuals with elevated PSA via wearable-derived physiologic data in a national cohort.\u003c/p\u003e \u003cp\u003eSedentary behavior may represent an important contextual factor linking PSA elevation to the reduced basal metabolic rate observed in this study. A prior population-based analysis using data from the National Health and Nutrition Examination Survey (NHANES) demonstrated that greater sedentary time is independently associated with higher PSA concentrations\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These findings suggest that PSA elevation may, in part, reflect systemic physiologic states associated with reduced energy expenditure rather than prostate-specific pathology alone. In the context of this study, the consistently lower BMR observed among men with elevated PSA across racial groups in our cohort aligns with prior evidence that sedentary behavior and diminished metabolic activity may be linked to altered PSA biology. Although physical activity was not directly included, wearable-derived BMR may reflect underlying metabolic and activity-related physiology\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Together, these findings suggest an association between PSA elevation and broader metabolic dysregulation, underscoring the value of interpreting PSA within a whole-body physiologic framework.\u003c/p\u003e \u003cp\u003eBiological mechanisms may explain the observed association between elevated PSA and reduced BMR. Chronic and subclinical prostatic inflammation is a well-established cause of PSA elevation in the absence of any malignancies\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Chronic low-grade inflammation (mediated by cytokines such as IL-6 and TNF-α) is associated with mitochondrial dysfunction, which contributes to the progressive decline in resting energy expenditure and metabolic rate observed during aging\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Thus, the reduced BMR in men with elevated PSA may reflect metabolic consequences of underlying inflammatory processes rather than prostate-specific disease mechanisms alone.\u003c/p\u003e \u003cp\u003eMetabolic syndrome and obesity can further complicate the relationship between PSA kinetics and systemic physiology\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Multiple studies demonstrate that metabolic syndrome can alter PSA concentrations and screening performance\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Obesity can reduce PSA values through increased plasma volume (hemodilution) and through metabolic alterations associated with insulin resistance\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Our findings extend this literature by highlighting that even when PSA is elevated, individuals demonstrate altered metabolic signatures in the form of lower BMR across most demographic groups (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This pattern suggests shared physiological pathways between metabolic health and PSA biology. Exploratory age- and BMI-stratified analyses demonstrated consistent directional trends, with lower BMR observed among men with elevated PSA across most subgroups, suggesting that these differences were not solely attributable to age or body composition; however, these results were not reported due to small subgroup sizes that did not meet the All of Us Research Program reporting threshold. The consistency of these findings suggests that differences in body composition alone may not fully explain the observed metabolic alterations and supports the presence of shared physiologic pathways linking PSA elevation with systemic metabolic function.\u003c/p\u003e \u003cp\u003eThe race-stratified results in this study align with known population-level differences in both metabolism and prostate health. Prior studies have documented that resting BMR can vary by race, even after adjusting for age and body composition \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Racial disparities also exist in prostate cancer risk, PSA kinetics, and inflammation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The persistence of lower BMR across racial groups suggests that this association is robust across diverse physiologic backgrounds (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These findings underscore the importance of examining biomarker-physiology relationships in racially diverse populations, particularly given the known variation in PSA performance across groups.\u003c/p\u003e \u003cp\u003eA key methodological consideration is the distinction between record-level and participant-level analyses of wearable data. Record-level analyses capture high-frequency measurements and characterize overall patterns, whereas participant-level aggregation ensures equal weighting and is more appropriate for inference. The consistency of findings across both approaches supports the robustness of the results, while attenuation of statistical significance at the participant level highlights the importance of accounting for non-independence in wearable datasets.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. First, the American Indian/Alaska Native and Middle Eastern/North African racial groups had limited sample sizes, restricting the ability to examine metabolic trends for these groups. Small subgroup sizes informed the inclusion of complementary record-level analyses; future studies with larger, more balanced samples are needed for robust participant-level comparisons. As with all database studies, misclassification and coding bias are possible, and exclusions relied on diagnostic codes rather than clinical verification. Although BMR estimates derived from wearable devices provide useful, accessible approximations, they remain indirect measures and may not capture energy expenditure with the precision of clinical calorimetry. PSA elevations were based on single-time-point measurements, limiting the distinction between transient and clinically significant elevations. Additionally, lack of data on family history and prior PSA evaluation, along with limited clinical guidance on meaningful BMR differences, restricts interpretation of the findings to a certain extent.\u003c/p\u003e \u003cp\u003eThis study identifies a consistent association between elevated PSA and reduced BMR across multiple racial groups. Integrating wearable-derived metabolic measures with clinical biomarkers offers a novel approach to characterizing whole-body physiology in diverse populations. Future longitudinal research should incorporate hormonal panels, inflammatory markers, and direct metabolic measurements to further determine whether reduced BMR is associated with elevated PSA.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003eThe findings from this study suggest that altered BMR may represent a modifiable physiologic correlate of PSA elevation, raising the possibility that lifestyle interventions aimed at improving metabolic health could mitigate downstream risks associated with PSA-driven diagnostic testing and procedures. A recent clinical trial demonstrated that exercise therapy is a feasible and safe method associated with favorable changes in the tumor proliferation marker Ki67 and PSA levels among men with prostate cancer\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Together with our findings, these data suggest that structured physical activity may influence prostate biology through systemic metabolic pathways, potentially including improvements in basal energy expenditure. Future longitudinal studies integrating wearable-derived metabolic measures with exercise interventions are needed to assess whether improving BMR through targeted lifestyle modifications can reduce PSA elevation and improve prostate risk stratification.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003cbr\u003e\u003c/strong\u003eThis study did not involve human participants, identifiable personal data, or clinical interventions, and therefore did not require ethical approval. All data analyzed in this study were accessed through the NIH All of Us Research Program Researcher Workbench, a secure, cloud-based platform housing controlled-access participant data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cbr\u003e MDS and YK conceptualized the study. YK retrieved, and analyzed the\u003cbr\u003e data, and wrote/edited the manuscript. MDS, MAHM, EAC, MM, SA, AD, FAY, TA, DIY wrote/edited the manuscript. MDS, MAHM, YK contributed to project conception/design and manuscript writing/editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the NIH All of Us Research Program and its participants for making this research possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement \u003cbr\u003e\u003c/strong\u003eYAF: Coloplast: Advisory board, speaker; Endo: Advisory board; Haleon: Advisory board; Halozyme: Advisory board, speaker; Masimo: Intellectual property; Softwave: Advisory board; Sprout: Consultant; Vertica: Research investigator; Xialla: Advisory board. The remaining Authors have no conflicts of interest to disclose.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated and analyzed in this study were derived from the All of Us Research Program and include wearable-derived metabolic estimates and corresponding clinical biomarker measurements. Statistical analysis was performed using the Jupyter Notebook embedded within the All of Us Researcher Workbench via the use of the Python. All processed data tables and relevant materials are included within the published article. \u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohnstone AM, Murison SD, Duncan JS, Rance KA, Speakman JR (2005) Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. 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JAMA Oncol 10(9):1187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaoncol.2024.2156\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2024.2156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"world-journal-of-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjur","sideBox":"Learn more about [World Journal of Urology](https://link.springer.com/journal/345)","snPcode":"345","submissionUrl":"https://submission.nature.com/new-submission/345/3","title":"World Journal of Urology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Basal Metabolic Rate, Metabolic Syndrome, Prostate-Specific Antigen, Race Factors, Wearable Electronic Devices","lastPublishedDoi":"10.21203/rs.3.rs-9336029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9336029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eProstate-specific antigen (PSA) elevation is common among older men and often prompts downstream testing despite frequently reflecting benign conditions. Metabolic dysfunction has been linked to cancer biology, yet the relationship between basal metabolic rate (BMR), a known marker of metabolic health, and PSA elevation has been unexplored. The goal is to evaluate differences in wearable-derived BMR between men with elevated PSA and healthy controls across racial groups.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective cross-sectional study, mean daily BMR (Calories) was derived from Fitbit Daily Activity Summary data. Comparisons were performed within racial groups using Welch\u0026rsquo;s t tests. Mean BMR values were averaged at the participant level for primary analyses, with complementary record-level analyses conducted to assess overall population patterns and support robustness of findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMen with elevated PSA consistently demonstrated lower mean BMR across racial groups with sufficient representation. Compared with healthy controls, men with elevated PSA had significantly lower mean BMR among Asian, Black/African American, and White participants, with differences of -106.12, -134.03, and \u0026minus;\u0026thinsp;99.12 Calories, respectively. These findings remained consistent in sensitivity analyses excluding benign and malignant prostate disease. Elevated PSA was associated with lower BMR across multiple racial groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings suggest that PSA elevation may reflect broader systemic metabolic alterations beyond prostate-specific pathology, highlighting the potential value of integrating wearable-derived metabolic measures into longitudinal, intervention-based strategies aimed at improving metabolic health and refining prostate risk stratification.\u003c/p\u003e","manuscriptTitle":"Race-Stratified Differences in BMR Among Men with Elevated PSA: Insights from Wearable Device Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 08:21:15","doi":"10.21203/rs.3.rs-9336029/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-21T17:57:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T18:29:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T11:49:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Urology","date":"2026-04-06T16:41:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjur","sideBox":"Learn more about [World Journal of Urology](https://link.springer.com/journal/345)","snPcode":"345","submissionUrl":"https://submission.nature.com/new-submission/345/3","title":"World Journal of Urology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8933b2b4-7540-487e-90b7-6b8874b85a4e","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T08:21:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 08:21:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9336029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9336029","identity":"rs-9336029","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}