Associations between metabolic obesity phenotypes and prostate cancer in the U.S. population: a cross-sectional study

Introduction

Prostate cancer (PCa) is the second most common cancer in men and the fifth leading cause of cancer-related death worldwide. In 2024, approximately 299,010 new cases are expected in the U.S., accounting for nearly 30% of all new cancer diagnoses in men, with an estimated 35,250 cancer-related deaths expected (1). The incidence rate of PCa has fluctuated over the years, with a recent annual increase of about 2% to 3%, particularly in advanced-stage cases (1). Therefore, identifying risk factors and reliably pinpointing individuals at increased risk is essential for the development of effective prevention and intervention strategies.

Obesity is a leading global public health threat and a major risk factor for PCa recurrence and poor prognosis (2). In recent decades, obesity rates in the U.S. have risen sharply, with an estimated incidence of 41.9% in adults (aged >20 years) and 19.7% in youths (aged 2–19 years) (3). By 2030, it is estimated that over 2 billion adults worldwide are projected to be overweight or have obesity (4). Previous studies have demonstrated that obesity increases the risk of aggressive PCa and its biochemical recurrence after prostatectomy or radiotherapy (5,6). However, recent evidence suggests that individuals with higher body mass index (BMI) may have a lower risk of being diagnosed with PCa (7). The complex relationship between obesity and PCa risk remains poorly understood, highlighting the need for further investigation.

Obesity often coexists with metabolic conditions such as dyslipidemia, insulin resistance, and hypertension, leading to systemic and local changes that promote cancer development. Notably, obesity phenotypes vary, and a significant portion of the obese population does not exhibit metabolic abnormalities (8). Recent evidence suggests that, in addition to BMI, the presence of metabolic abnormalities can better stratify obesity as a risk factor for various cancers, including thyroid (9), lung (10), breast (11), and colorectal cancer (12). Similarly, a metabolically unhealthy phenotype has been associated with an increased prevalence of PCa. A study analyzing data from the National Health Check-ups database in Korea found that PCa risk increased proportionally with the number of metabolic syndrome (MetS) components (13). Another study in China showed that metabolically abnormal obesity increased the risk of advanced PCa among patients undergoing radical prostatectomy (14).

To date, no study has examined the relationship between metabolically defined obesity phenotypes and PCa in the U.S. population. To address this gap, we analyzed data from the National Health and Nutrition Examination Surveys (NHANES), a nationwide database collecting health and nutritional information from a representative sample of the U.S. population (15). NHANES combines interviews and physical examinations to assess health status and risk factors, providing data on physical characteristics and metabolic factors.

This study aimed to assess the relationship between metabolic obesity phenotypes and PCa prevalence. We focused on the population aged 50 years and older, as PCa diagnoses and related mortalities are rare in men under 50 years, with approximately 85% of PCa cases diagnosed after age 65 years (16). Furthermore, we conducted subgroup analyses based on age to investigate age-specific relationships. This manuscript is written in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-538/rc).

Methods

Data source

The NHANES, conducted by the National Center for Health Statistics (NCHS), biennially captures nationally representative data of the U.S. non-institutionalized civilian population using a complex survey design and population-specific sample weights (15,17). The dataset is publicly available and can be accessed at https://www.cdc.gov/nchs/nhanes/. In this cross-sectional study, we analyzed data from 10 cycles (1999–2018) during which PCa questionnaire information was available. The NCHS Institutional Review Board approved NHANES, and all participants provided written informed consent. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Population selection

A total of 101,316 individuals were included from NHANES (1999–2018). Initially, we excluded 4,550 individuals due to incomplete weight data. An additional 85,182 individuals were excluded based on the following criteria: (I) age under 50 years; (II) female; (III) inability to define obesity, hyperglycemia, hypertension, or dyslipidemia; (IV) missing data on the cancer questionnaire; or (V) renal failure. Finally, 11,584 individuals were included for analysis (Figure 1).

Figure 1 Flowchart of study population selection. NHANES, National Health and Nutrition Examination Surveys.

Exposure measures

Exposure was defined based on a combination of BMI and metabolic health. Obesity was defined as BMI ≥30 kg/m². Metabolic health was assessed using three components of MetS. Individuals were considered metabolically unhealthy if they had at least two of the following conditions: hyperglycemia (prediabetes and diabetes: fasting plasma glucose ≥5.6 mmol/L), hyperlipidemia (triglycerides ≥1.7 mmol/L or high-density lipoprotein cholesterol <1.0 mmol/L), and hypertension (systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg). Based on these criteria, participants were categorized into four metabolic obesity phenotypes: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO). MHNO was used as the reference group. These phenotypes, based on BMI and metabolic health status, offer a more nuanced classification than BMI alone, allowing for better capture of obesity-related risk heterogeneity (8,18).

Outcome measures

PCa was identified by self-reported previous diagnosis using the MCQ220 and MCQ230 items in the Medical Conditions section of the Questionnaire Data. The answer code ‘30’ for the question “What kind of cancer” was used to identify PCa.

Covariate data

Patient demographics, including age, race/ethnicity, smoking history, and alcohol drinking history, were obtained. The population was divided into two groups: younger (50 to 64 years) and older (age ≥65 years). Smoking status was coded as 1 for those currently smoking (“Some days” or “Every day”) and 0 for non-smokers (“Not at all”). Alcohol consumption was coded as 1 for those who had at least 12 alcoholic drinks in the past year and 0 for non-drinkers. Blood biochemical variables, including total cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, blood urea nitrogen, uric acid, and creatinine, were also measured.

Statistical analysis

We applied appropriate weighting methodologies according to NHANES guidelines (19). Continuous variables were presented as medians (interquartile range) and analyzed using the weighted Wilcoxon rank-sum test. Categorical variables were expressed as weighted percentages and analyzed using the weighted Chi-squared test. After adjusting for age, race/ethnicity, smoking status, and alcohol consumption, multivariable logistic regression assessed the association between metabolic obesity phenotypes and PCa, with MHNO as the reference group. The results were reported as odds ratios (ORs) with 95% confidence intervals (CIs) and P values. Subgroup analyses were conducted based on age classification. Two-sided P values <0.05 were considered statistically significant. All statistical analyses were performed using R software version 4.3.1.

Results

Baseline characteristics

Table 1 summarizes the baseline characteristics of patients stratified by metabolic obesity phenotypes. The median age of the overall population was 64 years. Among the participants, 5,021 had hyperglycemia (including 2,758 with diabetes), 8,772 had dyslipidemia, and 7,078 had hypertension. Compared to participants with MHNO, those in the MUNO group were older and had a lower smoking prevalence. Statistically significant differences were observed in most blood biochemical variables across the four groups, including glucose, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, blood urea nitrogen, creatinine, and alanine aminotransferase, except for aspartate aminotransferase and alkaline phosphatase. Both systolic and diastolic blood pressure were significantly higher in the metabolically unhealthy population. However, no statistically significant differences were found between the groups in terms of alcohol consumption. Table S1 provides a summary of the weighted baseline characteristics. Tables S2-S5 present the unweighted and weighted baseline characteristics stratified by metabolically unhealthy phenotypes.

Obesity and the PCa prevalence

In the total population (Figure 2A), the weighted prevalence of PCa was 4.51% in patients with obesity and 5.02% in those without obesity, with no significant difference between the two groups (P=0.33). Subgroup analysis showed no significant difference in PCa prevalence between the obese and non-obese groups in both younger (P=0.08) and older individuals (P=0.33). Compared to non-obese individuals (Table 2), those with obesity showed no significant difference in PCa prevalence across younger, older, and total populations (all P>0.05).

Figure 2 The weighted prevalence of prostate cancer grouped by obesity and metabolic abnormalities. (A) Total population. (B) Younger population (50 to 64 years old). (C) Older population (≥65 years old).

Metabolic abnormality and PCa prevalence

In the total population (Figure 2A), patients with hyperglycemia showed a higher PCa prevalence than those without (P=0.004). PCa prevalence was also higher among patients with hypertension (P<0.001). In multivariable logistic regression (Table 2), hyperglycemia remained associated with increased PCa risk (OR =1.259, 95% CI: 1.006–1.574; P=0.044). Obesity (OR =1.157; 95% CI: 0.909–1.472; P=0.23), hypertension (OR =1.178; 95% CI: 0.919–1.511; P=0.19), and dyslipidemia (OR =1.292; 95% CI: 0.980–1.705; P=0.07) were not associated with PCa risk.

In the younger group (Figure 2B), patients with hyperglycemia had a higher PCa prevalence compared to those without (P=0.02). PCa prevalence was also significantly higher in patients with hypertension (P=0.02). Compared to non-hyperglycemia participants (Table 2), those with hyperglycemia were associated with an 86.5% increase in PCa risk (OR =1.865; 95% CI: 1.022–3.405; P=0.042). Obesity (OR =1.605; 95% CI: 0.927–2.777; P=0.09), hypertension (OR =1.726; 95% CI: 0.916–3.253; P=0.09), and dyslipidemia (OR =1.365; 95% CI: 0.470–3.962; P=0.57) were not associated with PCa risk. In the older group (Figure 2C), there were no significant differences in PCa prevalence by obesity status or by any individual metabolic abnormality. No significant associations were found between obesity or any individual metabolic abnormality and PCa risk (Table 2).

Metabolic obesity phenotype and PCa prevalence

In the total population (Figure 3A), the PCa prevalence was significantly higher in patients with MUNO compared to those with MHNO (P<0.001), and higher in patients with MUO compared to those with MHO (P=0.005). However, no significant differences in PCa prevalence were found between patients with MHNO and MHO (P=0.41), nor between patients with MUNO and MUO (P=0.11). Compared to MHNO participants (Table 2), those with MUNO had a 42.7% increased risk of PCa (OR =1.427; 95% CI: 1.100–1.851; P=0.008), and those with MUO had a 58.7% increased risk (OR =1.587; 95% CI: 1.171–2.152; P=0.003).

Figure 3 The weighted prevalence of prostate cancer grouped by metabolic obesity phenotypes. (A) Total population. (B) Younger population (50 to 64 years old). (C) Older population (≥65 years old). MHO, metabolically healthy obese; MHNO, metabolically healthy non-obese; MUO, metabolically unhealthy obese; MUNO, metabolically unhealthy non-obese.

In the younger group (Figure 3B), the prevalence of PCa was significantly higher in patients with MUNO compared to those with MHNO (P<0.001), and in patients with MUO compared to those with MHO (P<0.001). However, no significant differences were found between patients with MHNO and MHO (P=0.77), nor between patients with MUNO and MUO (P=0.35). Compared to participants in the MHNO group (Table 2), those with MUNO had a 3.179-fold risk of PCa (OR =3.179; 95% CI: 1.449–6.974; P=0.004), and those with MUO had a 4.345-fold risk of PCa (OR =4.345; 95% CI: 2.076–9.093; P<0.001). In the older group (Figure 3C), there were no significant differences in PCa prevalence between patients with different metabolic obesity phenotypes (all P>0.05). Similarly, no significant associations were between metabolic obesity phenotypes and PCa risk (Table 2).

Metabolically unhealthy phenotypes and PCa prevalence

We further examined the associations between different combinations of metabolic abnormalities within metabolically unhealthy phenotypes and PCa. The PCa prevalence across metabolically unhealthy phenotypes is shown in Figure S1. In the overall population (Table 3), MUNO individuals with dyslipidemia and hypertension (OR =1.365; 95% CI: 1.035–1.799; P=0.03), those with hyperglycemia and dyslipidemia (OR =1.809; 95% CI: 1.077–3.037; P=0.03), and those with all three metabolic abnormalities (OR =1.542; 95% CI: 1.105–2.152; P=0.01) had significantly higher PCa risk compared with MHNO individuals. MUO individuals with all three metabolic abnormalities also had significantly higher PCa risk compared with MHNO individuals (OR =1.724; 95% CI: 1.163–2.556; P=0.007).

In the younger group, all metabolically unhealthy phenotypes were associated with significantly higher PCa risk than MHNO individuals. In the older group, no association was found between metabolically unhealthy phenotypes and PCa (Table 3 and Figure S1).

Discussion

This study investigated the association between metabolic obesity phenotypes and PCa in the U.S. population. It found that MUNO and MUO phenotypes were risk factors for PCa compared to the MHNO phenotype in individuals aged 50 to 64 years. Specifically, obesity was not associated with PCa, regardless of age or metabolic status.

Obesity was widely regarded as a risk factor for poor PCa prognosis (2,20), but paradoxically, it was associated with a lower rate of PCa diagnosis (7). In our study, no significant differences were found in PCa prevalence between obese and non-obesity individuals. Notably, metabolically unhealthy individuals had a higher PCa prevalence than metabolically healthy individuals, regardless of obesity status. These findings suggest that metabolic abnormalities may obscure the relationship between obesity and PCa, highlighting the importance of early detection and management of metabolic factors to reduce PCa risk.

Metabolic abnormalities such as dyslipidemia, hyperglycemia, and hypertension have been widely studied for their potential role in increasing the risk of PCa (21). Dyslipidemia, characterized by altered lipid profiles, has been reported to be associated with a higher risk of PCa in several studies (22,23), while others found no significant or even negative association between lipid abnormalities and PCa progression (24,25). In our study, dyslipidemia was not significantly associated with PCa prevalence, possibly due to the lack of data on statin use, limiting our ability to draw definitive conclusions (26). Similarly, hyperglycemia, particularly insulin resistance or diabetes, has been suggested as a potential driver of PCa risk (27). Insulin and insulin-like growth factor-1 promote cell proliferation and inhibit apoptosis, which may increase PCa risk (28). Additionally, hyperglycemic may increase oxidative stress and DNA damage, contributing to malignant transformation of prostate cells (29). Hypertension may increase PCa risk through mechanisms such as atherosclerosis, reduce blood supply to the prostate, and low-grade chronic inflammation, which alters the immune environment in prostate tissue (30). Furthermore, PCa treatments, such as steroids, antiandrogens, immunotherapy, and radiation, can exacerbate hypertension or induce it in patients with pre-existing conditions (31). Given the strong correlation between aging and PCa (32,33) and the high prevalence of both metabolic abnormalities and prostatic diseases in older men, our findings suggest that aging may primarily drive the increased prevalence of PCa in this group may, rather than metabolic abnormalities alone.

Interestingly, previous studies have shown conflicting results regarding the relationship between MetS and PCa risk. A retrospective cohort study in Korea found that MetS significantly impacted PCa prevalence only in older individuals (34). Another retrospective cohort study analyzing 5,370,614 participants in Korea found that MetS components were age-specifically associated with increased PCa incidence in both middle-aged (40–64 years) and older (≥65 years) groups. A prospective Finnish study identified a significant increased risk of PCa in middle-aged men with MetS (35). In contrast, a U.S. community-based study of 6,429 men aged 45–64 years, all initially cancer-free, found that MetS was associated with a reduced PCa prevalence (36).

MetS was defined by the presence of at least three of the following components: abdominal obesity (defined by waist circumference), elevated triglycerides or reduced high-density lipoprotein cholesterol, elevated blood pressure, and impaired glucose regulation. This definition complicates distinguishing the effects of metabolic factors from those of obesity itself, potentially obscuring the true relationship between metabolic abnormalities and PCa. While MetS is a valuable predictor of disease, it may not capture the full spectrum of obesity-related risks as effectively as metabolic obesity phenotypes. This is particularly relevant given that MHO has been shown to have a lower PCa risk than MUO, even though both fall under the obesity category according to BMI.

This study has several strengths. First, the use of a nationally representative sample and survey weights ensures the generalizability of the findings. Second, subgroup analyses found that the association between metabolic obesity phenotypes and PCa prevalence varies by age. Third, this study investigated the associations of both individual metabolic abnormalities and metabolic obesity phenotypes with PCa. Finally, to our knowledge, these findings are the first to emphasize that metabolic abnormalities, rather than obesity alone, are more strongly associated with higher PCa prevalence in the younger men.

However, several limitations must be considered. First, the self-reported data may introduce recall bias, potentially influencing the results. Second, the cross-sectional design of the NHANES database prevents the establishment of causal relationships. Variables such as BMI and metabolic indicators were measured after a PCa diagnosis, making it impossible to determine whether these factors preceded cancer onset or developed subsequently. Third, this study did not consider factors such as healthcare systems, cultural practices, and racial differences (37). Healthcare access and preventive care may influence the management of metabolic abnormalities and the associated PCa risks in ways that vary across populations. Cultural differences in diet, physical activity, and lifestyle behaviors could also contribute to variability in the associations between metabolic health and PCa risk. Ethnic and genetic factors may further complicate the interpretation of global studies. Fourth, the lack of data on PCa grade, stage, and treatment limits our ability to differentiate between indolent and aggressive disease. It is unclear whether the observed associations between metabolic abnormalities and PCa prevalence are driven by clinically significant aggressive disease or by indolent forms with different biological behaviors. Fifth, this study did not account for medication history, including antihypertensive drugs, lipid-lowering drugs, glucose-lowering medications, and hormonal therapies, which could affect the results. For example, statin use has been associated with reduced PCa incidence and progression in prior studies (26), suggesting that failing to account for statin use may have underestimated the association between dyslipidemia and PCa. Similarly, anti-hypertensive or glucose-lowering therapies may have attenuated observed associations for hypertension or hyperglycemia. Conversely, if individuals with more severe metabolic abnormalities were more likely to receive treatment, improved metabolic control might have overestimated these relationships. Overall, underestimation is more likely, but the net direction remains uncertain. Finally, as PCa incidence trends younger, neglecting the population younger than 50 years may missed important insights (36).

Conclusions

This study showed that metabolically unhealthy phenotypes, particularly MUNO and MUO, were significantly associated with increased PCa prevalence in men aged 50 to 64 years. While obesity did not play a role in this cohort; instead, metabolic abnormalities, not obesity, were strongly associated with PCa. These findings suggest that addressing metabolic abnormalities may be more effective for PCa prevention than focusing solely on body weight management, particularly in men aged 50 to 64 years.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-538/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-538/prf

Funding: This work was supported by a grant from the Scientific Plan of Medical and Health Planning of Zhejiang Province (No. 2024KY1605).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-538/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. The 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/.

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