Association of body roundness index with prostate cancer: a population-based cross-sectional study using NHANES data

Introduction

Prostate cancer (PCa), a disease with multiple clinical and molecular characteristics, represents the second most prevalent cancer globally and one of the leading causes of cancer-related death (1). By the year 2030, it was estimated that the global incidence of PCa will reach approximately 1.7 million new cases, resulting in around 499,000 deaths (2). In the United States, PCa is among the most frequently diagnosed malignancies in men, with an estimated lifetime risk of 11% for diagnosis and 2.5% for mortality (3). In 2023, PCa was documented as the second primary cause of cancer-related mortality in the United States (4). Consequently, it is necessary to identify potential PCa high-risk populations and explore effective PCa prevention measures.

Established risk factors for PCa include non-modifiable factors such as advanced age, family history, genetic mutations, and race/ethnicity (5). In recent years, there has been growing emphasis on modifiable risk factors, particularly obesity. There is a strong link between obesity and cancer, with obesity as measured by body mass index (BMI) being considered a risk factor for 13 types of cancer (6-9). However, a significant limitation of BMI is its inability to distinguish between lean and fat mass, or to account for the distribution of adipose tissue. This is a critical shortcoming, as accumulating evidence indicates that visceral obesity, rather than general obesity, plays a more central role in carcinogenesis by mediating cancer-related immune, metabolic, and endocrine alterations (10,11). In particular, excess visceral adipose tissue (VAT) has been specifically associated with an increased risk of PCa progression and mortality (12). The body roundness index (BRI) is a novel anthropometric indicator that accounts for the distribution of fat across different body regions and is utilized to predict % body fat and % VAT, providing a more precise reflection of obesity status and its potential health risks (13). Correlations have been identified between BRI and various diseases (14-17), but no studies have yet explored the relationship between BRI and PCa.

This study was designed to investigate the potential relationship between BRI and PCa by utilizing data from the National Health and Nutrition Examination Survey (NHANES). We aimed to evaluate whether BRI, as a refined indicator of visceral obesity, is associated with PCa and to compare its performance with conventional anthropometric measures. From a public health perspective, establishing such a link could provide a non-invasive, cost-effective tool for improved risk stratification, potentially enabling earlier identification of high-risk individuals and informing targeted prevention strategies aimed at reducing the burden of PCa. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-372/rc).

Methods

Study population

The NHANES is a national cross-sectional research program supported by the National Center for Health Statistics. This initiative is designed to evaluate the health and nutritional status of the non-institutionalized population of the United States, utilizing a stratified, multistage sampling methodology. The database is publicly available at https://www.cdc.gov/nches/nhanes. All research protocols were approved by the National Research Ethics Committee and participants’ informed consent was obtained (18). This study used NHANES open anonymous data, according to the NHANES data use policy (https://www.cdc.gov/nchs/nhanes/irba98.htm), secondary analysis without additional ethical approval or consent. During or after data collection, the authors were not privy to any information that could potentially identify individual participants. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

The NHANES employs a complex, multistage, probability sampling design to select a representative sample of the non-institutionalized civilian US population. Survey weights were applied in analyses to account for the complex survey design, non-response, and oversampling of specific subgroups, ensuring nationally representative estimates. NHANES data collection follows strict protocols and undergoes rigorous quality control by the National Center for Health Statistics (NCHS), including standardized physical examinations, laboratory procedures, and validated questionnaires administered by trained personnel (19,20).

This study included data from the 1999–2018 NHANES cycle. A total of 26,473 male participants aged ≥20 years were included in the study. After excluding individuals with missing PCa, BRI, and covariate data, 18,732 participants were finally included (Figure 1).

Figure 1 Flowchart of the sample selection from NHANES 1999–2018. BMI, body mass index; BRI, body roundness index; NHANES, National Health and Nutrition Examination Survey; PIR, poverty income ratio.

Definition of PCa

The primary outcome was a self-reported PCa diagnosis. PCa patients were identified by NHANES Questionnaire Project MCQ220 “Ever been told you had cancer or a malignancy of any kind” and MCQ230 “What kind of cancer was it” in the medical conditions section of the questionnaire item. PCa status was defined solely by self-report. This approach has recognized limitations: (I) potential recall bias; (II) lack of clinical verification; (III) inability to distinguish aggressive from indolent disease; and (IV) absence of pathological or staging data.

Measurement of BRI

The calculation for BRI was as follows (21):

BRI=364.2−365.5∗1−(WaistCircumference(m)2π∗0.5∗Height(m))2[1]

Covariates

In this study, we considered a total of 14 confounding factors, including: age (<60, ≥60 years), race (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, other race-including multi-racial), education level (< high school, high school or equivalent, ≥ college or above), marital status (married/living with partner, widowed/divorced/separated, never married), poverty income ratio (PIR) (<1.30, 1.30–3.49, ≥3.50), BMI (≤24.9, 25–29, ≥30 kg/m²), alcohol use (yes/no), smoke (never, former, now), hypertension (yes/no), diabetes (yes/no), coronary heart disease (yes/no), stroke (yes/no), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c).

Statistical analysis

The analyses in this study were performed using R (version 4.2.3) and EmpowerStats software (available online at http://www.empowerstats.com). The statistical analyses were carried out by utilizing the suitable NHANES sampling weights, in line with the recommendations and guidelines outlined by NHANES. In the description of participants’ baseline characteristics, n (%) was used to represent categorical variables, and differences were analyzed by Chi-squared test. Continuous variables were expressed as mean (SD), and the Kruskal-Wallis test was performed. The BRI was categorized into quartiles (Q1–Q4), with Q1 as the reference group. Multivariate logistic regression model was used to analyze the association between BRI and PCa in three different models: Model 1 was not subject to any adjustments. Model 2 incorporated adjustments for age, race, education level, marital status, PIR and BMI. Model 3 included comprehensive adjustments for age, race, education level, marital status, PIR, BMI, alcohol use, smoke, hypertension, diabetes, coronary heart disease, stroke, TC, HDL-c. Furthermore, we conducted an RCS analysis to explore the nonlinear relationship between BRI and PCa. Additionally, an interaction analysis was carried out to investigate the influence of relevant clinical confounding factors on the association between BRI and PCa across various subgroups. We further assessed the performance of BRI, BMI, waist circumference, and weight in identifying the risk of PCa by employing ROC analysis and evaluating the corresponding area under the curve (AUC) values. The AUC values of BRI, BMI, waist circumference and weight were compared by Delong test. A two-tailed P value less than 0.05 was considered to indicate statistical significance.

Results

Baseline characteristics of the participants

This study included 18,732 participants, and Table 1 presents the baseline characteristics of participants with and without PCa. The prevalence of PCa was 2.98% and the mean of BRI was 5.11±2.00. Age, race, education level, marital status, PIR, alcohol use, smoke, hypertension, diabetes, marital status, coronary heart disease, stroke, TC, HDL-c, and BRI between the non-PCA and PCa groups had significant differences. However, no significant differences were noted in BMI.

Associations between the BRI and the risk of PCa

We used a multivariate logistic regression model to study the relationship between BRI and PCa, and the results are shown in Table 2. Our results reveal a positive correlation between BRI and PCa. In the model without adjustment, an elevated BRI was linked to an increase in PCa prevalence [odds ratio (OR) =1.18, 95% confidence interval (95% CI): 1.14–1.23, P<0.001]. This positive relationship persisted even after adjusting for all covariates (OR =1.17, 95% CI: 1.06–1.28, P<0.001). Subsequently, BRI was analyzed as a categorical variable. In the fully adjusted model, individuals in the highest BRI quartile demonstrated a markedly elevated risk of PCa relative to those in the lowest quartile (OR =4.00, 95% CI: 2.32–6.90, P<0.001). PCa risk was shown to increase with BRI in all models (P-trend <0.001).

The nonlinear relationship between BRI and PCa

The dose-response relationship between BRI and PCa was further investigated using restricted cubic spline plots. Figure 2 illustrates a nonlinear association between BRI and PCa (P-nonlinear =0.01), after adjusting for all covariates (P-nonlinear =0.01). The adjusted OR for PCa showed an increase with rising BRI levels.

Figure 2 Relationship between BRI and the odds ratio of PCa. The association was adjusted for age, race, education level, marital status, PIR, alcohol use, smoke, hypertension, diabetes, marital status, coronary heart disease, stroke, TC and HDL-c. The red solid line represents the probability of PCa, and the red shaded area represents the 95% CI curve. BRI, body roundness index; CI, confidence interval; HDL-c, high-density lipoprotein cholesterol; PCa, prostate cancer; PIR, poverty income ratio; TC, total cholesterol.

Subgroup analysis

The subgroup analysis results, stratified by age, race, education level, marital status, PIR, BMI, alcohol use, smoke, hypertension, diabetes, coronary heart disease, stroke, TC, HDL-c, are presented in Figure 3. The results showed that the association between BRI and PCa was positive in all subgroups. However, interaction tests for age were statistically significant (P for interaction =0.002), but BRI were positively associated with PCa risk across all age subgroups. Young and middle-aged people (OR =1.33; 95% CI: 1.18–1.51, P<0.001) with a higher BRI are more likely to develop PCa than elderly people (OR =1.08; 95% CI: 0.99–1.17, P=0.09).

Figure 3 Subgroup analysis of the association between BRI and PCa. Each subgroup analysis was adjusted for age, race, education level, marital status, PIR, BMI, alcohol use, smoke, hypertension, diabetes, coronary heart disease, stroke, TC, HDL-c, except for the covariate defining the subgroup. BMI, body mass index; BRI, body roundness index; CI, confidence interval; HDL-c, high-density lipoprotein cholesterol; OR, odds ratio; PCa, prostate cancer; PIR, poverty income ratio; TC, total cholesterol.

Comparison of BRI, BMI, waist and weight in predicting PCa

BMI and body weight are the most widely used indicators of obesity, while waist circumference is a common indicator of central obesity. ROC curves were utilized as a comparative tool to assess the predictive efficacy of BRI, BMI, weight, and waist circumference in participants diagnosed with PCa (Figure 4). In our study, BRI outperformed all three anthropometric measures (AUC =0.608, 95% CI: 0.587–0.630, P<0.001), with an optimal cut-off value of 4.439 for BRI.

Figure 4 ROC curves of different anthropometric indices for the prediction of PCa risk. AUC, area under the curve; BMI, body mass index; BRI, body roundness index; CI, confidence interval; PCa, prostate cancer; ROC, receiver operating characteristic.

Discussion

The present study, utilizing 10 cycles [1999–2018] of data from the NHANES database, represents the first investigation into the association between the novel anthropometric indicators, BRI, and PCa. Our findings indicate that BRI is positively correlated with an elevated risk of PCa. This association remained statistically significant after adjustment for all covariates. Specifically, for each one-unit increase in BRI, the prevalence of PCa was observed to increase by 17%. RCS analysis revealed a nonlinear relationship between BRI and PCa risk, and PCa risk increasing as BRI values rose. The results of subgroup analysis further indicated that BRI was positively correlated with PCa in various subgroups. There was a significant interaction in the age subgroup. In the population under 60 years old, for every additional unit of BRI, the risk of PCa increased by 33% (OR =1.33; 95% CI: 1.18–1.51, P<0.001), which was much higher than the risk in the population 60 years old and above (OR =1.08; 95% CI: 0.99–1.17, P=0.09). This age-specific effect may be mediated through hormonal pathways. BRI, as a measure of visceral adiposity, is negatively associated with serum testosterone levels (22). Research indicates that experiencing significant testosterone decline (e.g., >20%) at a younger age substantially increases PCa risk (23). Compared to older men with more stable hormonal profiles, middle-aged and younger men may have more dynamic endocrine systems that are particularly susceptible to the metabolic impacts of visceral fat (24). This heightened susceptibility to obesity-induced hormonal fluctuations likely explains the stronger BRI-PCa association we observed in men <60 years. Additionally, our study observed a statistically significant yet modest improvement in PCa risk discrimination using BRI compared to traditional anthropometrics (BMI, waist circumference, and weight). Hence, the marginally improved predictive performance of BRI suggests its potential role in refining risk stratification frameworks for PCa. Our results highlight the significance of taking into account body fat distribution when assessing PCa risk.

Obesity is a critical factor in the development trajectory of various types of cancer (25,26). However, the relationship between obesity and PCa incidence remains controversial. Multiple studies investigating the association between BMI and the risk of PCa have reported inconsistent findings. Some large cohort studies have identified a weak positive correlation between elevated BMI and increased PCa risk (27,28), while other prospective cohort studies have found no significant association or even an inverse association (29,30). In light of these conflicting results, a meta-analysis of prospective cohort studies concluded that there is a statistically significant but modest positive correlation between overall PCa risk and BMI (31). The discrepancies in these findings may stem from the use of BMI as an indicator of weight classification. While BMI is a widely used measure of general obesity, it fails to accurately reflect body composition and fat distribution (32-34). Research by Boehm et al. indicates that abdominal fat distribution serves as a more reliable predictor of PCa risk compared to BMI alone (35). Consequently, the link between VAT and PCa risk has garnered increasing attention.

VAT demonstrates greater pro-carcinogenic potential compared to systemic fat. VAT accumulation drives cancer progression through multidimensional pathways: activation of pro-inflammatory factors (e.g., TNF-α, IL-6) and dysregulated secretion of adipokines (e.g., leptin) jointly foster a pro-tumor inflammatory microenvironment (36-38). Concurrently, disrupted adipokine signaling, abnormal sex hormone levels, and insulin/IGF-1 axis dysfunction trigger metabolic reprogramming, exacerbating immune-metabolic imbalance (39,40). All of these are significantly related to the occurrence and development of PCa (41-43). However, previous studies failed to determine the statistically significant association between VAT and the risk of PCa (44). For this reason, the von Hafe’s team employed Ct-based quantitative analysis of VAT accumulation to investigate this relationship. This study involved 63 patients and 63 matched controls, revealing that participants with higher VAT areas and mean ratios between visceral and subcutaneous adipose tissue areas had a significantly higher risk of PCa (45). Our conclusion further emphasizes the importance of VAT in PCa risk assessment, as BRI has higher accuracy in estimating % body fat and % VAT compared with traditional indicators such as BMI, waist circumference or weight (13).

In this study, we conducted a preliminary investigation into the correlation between the emerging anthropometric index, BRI, and PCa for the first time. By leveraging the extensive sample size of the NHANES database and adjusting for multiple covariates, the reliability and generalizability of our findings were enhanced. Additionally, sensitivity analyses were performed to minimize the likelihood of false-positive results. Nevertheless, several limitations should be acknowledged. First, PCa cases were solely based on self-reported physician diagnoses, which may introduce recall bias. Crucially, epidemiological theory indicates that non-differential misclassification of a binary outcome typically biases effect estimates toward the null (OR =1). If misclassification errors (false positives/negatives) occur equally across BRI strata—a plausible assumption here—our observed associations are likely attenuated. This implies the true relationship between BRI and clinically confirmed PCa may be stronger than reported. Second, as a cross-sectional study, it is challenging to establish definitive causal relationships. We observed a significant association between BRI and self-reported history of PCa, but were unable to infer the chronological order or causal relationship. That is, it is impossible to determine whether the higher BRI is the cause of PCa development or whether PCa or its treatment leads to changes in body fat distribution and an increase in BRI. Large-scale prospective cohort studies will be needed in the future to clarify the causal relationship. Thirdly, despite adjusting for multiple covariates, residual confounding may still exist. Finally, due to the inherent constraints of the NHANES database, detailed information regarding the classification, staging, and treatment of PCa patients was unavailable. We were unable to explore the association between BRI and the invasiveness of PCa (such as high-grade tumors and metastatic diseases). BRI is an indicator mainly reflecting metabolic health. Exploring its relationship with PCa of different risk stratifications will have important clinical significance. We suggest that in the future, cohorts with detailed clinical information (such as hospital-based cohorts) be utilized to conduct in-depth research on this issue. Therefore, our findings should be interpreted as preliminary epidemiological evidence linking BRI to self-reported PCa history.

Conclusions

Our research results show that there is a significant positive correlation between BRI and PCa, suggesting that timely intervention for those with elevated BRI to prevent PCa is of great significance. However, the cross-sectional study design restricts the establishment of causality, underscoring the need for large-scale prospective studies for further validation of our findings. Meanwhile, our results suggest that BRI may offer incremental value beyond traditional indicators, although its clinical efficacy still needs further verification.

Acknowledgments

The authors truly appreciate the NHANES team and all participants involved in the NHANES for their invaluable contributions.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-372/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-372/prf

Funding: This work was supported by the National Natural Science Foundation of China (No. 82302263) and Fujian Provincial Health Technology Project (No. 2023GGA020).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-372/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022. CA Cancer J Clin 2022;72:7-33. [Crossref] [PubMed]
2. Deng J, Hua J, Zeng T, et al. Associations between inflammatory burden index, prostate cancer, and mortality among middle-aged and elderly individuals. World J Urol 2024;42:538. [Crossref] [PubMed]
3. Taitt HE. Global Trends and Prostate Cancer: A Review of Incidence, Detection, and Mortality as Influenced by Race, Ethnicity, and Geographic Location. Am J Mens Health 2018;12:1807-23. [Crossref] [PubMed]
4. Hamed MA, Wasinger V, Wang Q, et al. Prostate cancer-derived extracellular vesicles metabolic biomarkers: Emerging roles for diagnosis and prognosis. J Control Release 2024;371:126-45. [Crossref] [PubMed]
5. Jochems SHJ, Fritz J, Häggström C, et al. Smoking and Risk of Prostate Cancer and Prostate Cancer Death: A Pooled Study. Eur Urol 2023;83:422-31. [Crossref] [PubMed]
6. Sung H, Siegel RL, Torre LA, et al. Global patterns in excess body weight and the associated cancer burden. CA Cancer J Clin 2019;69:88-112. [Crossref] [PubMed]
7. Deng Y, Huang J, Wong MCS. Association between serum uric acid and prostate cancer risk in East Asian populations: a Mendelian randomization study. Eur J Nutr 2023;62:1323-9. [Crossref] [PubMed]
8. Hursting SD, Digiovanni J, Dannenberg AJ, et al. Obesity, energy balance, and cancer: new opportunities for prevention. Cancer Prev Res (Phila) 2012;5:1260-72. [Crossref] [PubMed]
9. Bhaskaran K, Douglas I, Forbes H, et al. Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5·24 million UK adults. Lancet 2014;384:755-65. [Crossref] [PubMed]
10. Bonneau GA, Pedrozo WR, Berg G. Adiponectin and waist circumference as predictors of insulin-resistance in women. Diabetes Metab Syndr 2014;8:3-7. [Crossref] [PubMed]
11. Doyle SL, Donohoe CL, Lysaght J, et al. Visceral obesity, metabolic syndrome, insulin resistance and cancer. Proc Nutr Soc 2012;71:181-9. [Crossref] [PubMed]
12. Perez-Cornago A, Dunneram Y, Watts EL, et al. Adiposity and risk of prostate cancer death: a prospective analysis in UK Biobank and meta-analysis of published studies. BMC Med 2022;20:143. [Crossref] [PubMed]
13. Thomas DM, Bredlau C, Bosy-Westphal A, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring) 2013;21:2264-71. [Crossref] [PubMed]
14. Li ZY, Tan YT, Wang J, et al. Dose-response relationship between fat distribution and liver cancer incidence: A prospective cohort study in Chinese men. Cancer Epidemiol 2022;76:102091. [Crossref] [PubMed]
15. Gao W, Jin L, Li D, et al. The association between the body roundness index and the risk of colorectal cancer: a cross-sectional study. Lipids Health Dis 2023;22:53. [Crossref] [PubMed]
16. Rico-Martín S, Calderón-García JF, Sánchez-Rey P, et al. Effectiveness of body roundness index in predicting metabolic syndrome: A systematic review and meta-analysis. Obes Rev 2020;21:e13023. [Crossref] [PubMed]
17. Li Y, He Y, Yang L, et al. Body Roundness Index and Waist-Hip Ratio Result in Better Cardiovascular Disease Risk Stratification: Results From a Large Chinese Cross-Sectional Study. Front Nutr 2022;9:801582. [Crossref] [PubMed]
18. CfDCa P. NCHS research ethics review board approval national center for health statistics. In: 2022. Available online: https://www.cdc.gov/nchs/nhanes/irba98.htm
19. Yang X, Chen H, Zhang J, et al. Association of cigarette use with risk of prostate cancer among US males: a cross-sectional study from NHANES 1999-2020. BMC Public Health 2025;25:608. [Crossref] [PubMed]
20. Jin X, Tong W, Sun L, et al. Association of composite dietary antioxidant index with high risk of prostate cancer in middle-aged and elderly men: insights from NHANES. Front Immunol 2025;16:1530174. [Crossref] [PubMed]
21. Liang H, Si W, Li L, et al. Association between body roundness index and osteoarthritis: a cross-sectional analysis of NHANES 2011-2018. Front Nutr 2024;11:1501722. [Crossref] [PubMed]
22. Seidell JC, Björntorp P, Sjöström L, et al. Visceral fat accumulation in men is positively associated with insulin, glucose, and C-peptide levels, but negatively with testosterone levels. Metabolism 1990;39:897-901. [Crossref] [PubMed]
23. Xu X, Zhang X, Zhong Y, et al. Dynamic Patterns of Testosterone Levels in Individuals and Risk of Prostate Cancer among Hypogonadal Men: A Longitudinal Study. J Urol 2018;199:465-73. [Crossref] [PubMed]
24. Gao Q, Lin Z, Zhang Y, et al. The association between waist-to-height index and prostate cancer risk: evidence from the NHANES study. Front Nutr 2025;12:1610288. [Crossref] [PubMed]
25. Demark-Wahnefried W, Rogers LQ, Alfano CM, et al. Practical clinical interventions for diet, physical activity, and weight control in cancer survivors. CA Cancer J Clin 2015;65:167-89. [Crossref] [PubMed]
26. Wilson RL, Taaffe DR, Newton RU, et al. Obesity and prostate cancer: A narrative review. Crit Rev Oncol Hematol 2022;169:103543. [Crossref] [PubMed]
27. Putnam SD, Cerhan JR, Parker AS, et al. Lifestyle and anthropometric risk factors for prostate cancer in a cohort of Iowa men. Ann Epidemiol 2000;10:361-9. [Crossref] [PubMed]
28. Andersson SO, Wolk A, Bergström R, et al. Body size and prostate cancer: a 20-year follow-up study among 135006 Swedish construction workers. J Natl Cancer Inst 1997;89:385-9. [Crossref] [PubMed]
29. Giovannucci E, Rimm EB, Liu Y, et al. Body mass index and risk of prostate cancer in U.S. health professionals. J Natl Cancer Inst 2003;95:1240-4. [Crossref] [PubMed]
30. Schuurman AG, Goldbohm RA, Dorant E, et al. Anthropometry in relation to prostate cancer risk in the Netherlands Cohort Study. Am J Epidemiol 2000;151:541-9. [Crossref] [PubMed]
31. MacInnis RJ, English DR. Body size and composition and prostate cancer risk: systematic review and meta-regression analysis. Cancer Causes Control 2006;17:989-1003. [Crossref] [PubMed]
32. Salji M, Hendry J, Patel A, et al. Peri-prostatic Fat Volume Measurement as a Predictive Tool for Castration Resistance in Advanced Prostate Cancer. Eur Urol Focus 2018;4:858-66. [Crossref] [PubMed]
33. Qiu X, Kuang J, Huang Y, et al. The association between Weight-adjusted-Waist Index (WWI) and cognitive function in older adults: a cross-sectional NHANES 2011-2014 study. BMC Public Health 2024;24:2152. [Crossref] [PubMed]
34. Hainer V, Aldhoon-Hainerová I. Obesity paradox does exist. Diabetes Care 2013;36:S276-81. [Crossref] [PubMed]
35. Boehm K, Sun M, Larcher A, et al. Waist circumference, waist-hip ratio, body mass index, and prostate cancer risk: results from the North-American case-control study Prostate Cancer & Environment Study. Urol Oncol 2015;33:494.e1-7. [Crossref] [PubMed]
36. Stattin P, Lukanova A, Biessy C, et al. Obesity and colon cancer: does leptin provide a link? Int J Cancer 2004;109:149-52. [Crossref] [PubMed]
37. Bulcão C, Ferreira SR, Giuffrida FM, et al. The new adipose tissue and adipocytokines. Curr Diabetes Rev 2006;2:19-28. [Crossref] [PubMed]
38. Allott EH, Masko EM, Freedland SJ. Obesity and prostate cancer: weighing the evidence. Eur Urol 2013;63:800-9. [Crossref] [PubMed]
39. Perry RJ, Shulman GI. Mechanistic Links between Obesity, Insulin, and Cancer. Trends Cancer 2020;6:75-8. [Crossref] [PubMed]
40. Gallagher EJ, LeRoith D. Hyperinsulinaemia in cancer. Nat Rev Cancer 2020;20:629-44. [Crossref] [PubMed]
41. Moore SC, Leitzmann MF, Albanes D, et al. Adipokine genes and prostate cancer risk. Int J Cancer 2009;124:869-76. [Crossref] [PubMed]
42. López Fontana C, Maselli Artola ME, Vanrell Rodríguez MC, et al. Advances on the influence of adipose tissue on prostate cancer. Actas Urol Esp 2009;33:242-8. [PubMed]
43. Zhong W, Wang X, Wang Y, et al. Obesity and endocrine-related cancer: The important role of IGF-1. Front Endocrinol (Lausanne) 2023;14:1093257. [Crossref] [PubMed]
44. Greco F, Piccolo CL, D’Andrea V, et al. Fat Matters: Exploring Cancer Risk through the Lens of Computed Tomography and Visceral Adiposity. J Clin Med 2024;13:453. [Crossref] [PubMed]
45. von Hafe P, Pina F, Pérez A, et al. Visceral fat accumulation as a risk factor for prostate cancer. Obes Res 2004;12:1930-5. [Crossref] [PubMed]