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Abstract

The aim of this study is to systematically evaluate the association between metabolic syndrome (MetS) and key clinical parameters of benign prostatic hyperplasia (BPH), including International Prostate Symptom Score (IPSS), prostate volume (PV), and the prevalence of MetS among men with BPH. A systematic search of PubMed, Embase, Web of Science, Scopus, and the Cochrane Library was conducted from inception to March 31, 2025, with the search implementation completed on April 5, 2025 for all databases. Observational studies comparing BPH patients with and without MetS were included. Random-effects models were used to pool standardized mean differences (SMDs), mean differences (MDs), and prevalence estimates. Heterogeneity, publication bias, subgroup analyses, sensitivity analyses, and meta-regression were performed according to prespecified criteria. Ten studies involving 3,947 BPH patients met the inclusion criteria. Pooled analyses demonstrated that MetS was associated with larger prostate volume (PV) and a tendency toward higher IPSS, although the latter showed substantial variability across studies. The prevalence of MetS among BPH patients was high, with considerable geographic and temporal variation. Subgroup analyses indicated that sample size influenced effect estimates, and meta-regression identified age, sample size, and publication year as contributors to heterogeneity. MetS is closely associated with prostate enlargement and symptom burden in men with BPH. These findings highlight the metabolic–urological interplay and support the integration of metabolic assessment into BPH management strategies. Large-scale prospective studies are needed to clarify causality and evaluate whether metabolic interventions can alter BPH progression.

Keywords

metabolic syndrome benign prostatic hyperplasia prostatic hyperplasia systematic review

Introduction

Benign prostatic hyperplasia (BPH) is one of the most common urological conditions affecting aging men, frequently leading to lower urinary tract symptoms (LUTS) and reduced quality of life (Cai et al., 2025; Feng et al., 2025). Its prevalence increases sharply after the age of 50, reflecting complex interactions among hormonal, inflammatory, and metabolic factors (Chiarot et al., 2025; Ren et al., 2024). In parallel, metabolic syndrome (MetS)—a cluster of cardiometabolic abnormalities characterized by central obesity, insulin resistance, dyslipidemia, and hypertension—has emerged as a global health burden (He et al., 2025; O’Mahony et al., 2025; Yang et al., 2025). The growing coexistence of BPH and MetS in aging populations has prompted increasing interest in their potential pathophysiological links.

Accumulating evidence suggests that MetS may contribute to prostatic overgrowth and LUTS through multiple mechanisms, including hyperinsulinemia-driven stromal proliferation, chronic low-grade inflammation, endothelial dysfunction, and alterations in sex hormone metabolism (He et al., 2025; Jan et al., 2024; Ren et al., 2024). Several epidemiological studies have reported that men with MetS have larger PVs, higher International Prostate Symptom Score (IPSS), and a greater likelihood of BPH progression (Bertocchi et al., 2020; Bratchikov et al., 2018; Urgesa et al., 2025). However, findings across studies remain inconsistent, partly due to variations in study design, diagnostic criteria, population characteristics, and sample size.

Given these uncertainties, a comprehensive synthesis of available evidence is needed to clarify the relationship between MetS and the clinical manifestations of BPH. Understanding this association is clinically relevant, as it may inform risk stratification, early identification of high-risk individuals, and development of metabolic-targeted therapeutic strategies for BPH. Therefore, we conducted a systematic review and meta-analysis to evaluate the impact of MetS on PV, LUTS severity, and the prevalence of MetS among patients with BPH, and to explore potential sources of heterogeneity across studies.

Method

Literature Search Strategy and Registration

This systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 guidelines (Parums, 2021). A comprehensive and database-specific search strategy was developed to identify observational studies examining the association between BPH and MetS. Five electronic databases—PubMed, Embase, Web of Science Core Collection, Scopus, and the Cochrane Library—were systematically searched from their inception to March 31, 2025, with the search implementation completed on April 5, 2025 for all databases.

PubMed: PubMed searches combined Medical Subject Headings (MeSH) with free-text terms using Boolean operators: (“Prostatic Hyperplasia”[Mesh] OR “benign prostatic hyperplasia” OR BPH OR “lower urinary tract symptoms” OR LUTS OR IPSS) AND (“Metabolic Syndrome”[Mesh] OR “metabolic syndrome” OR “insulin resistance” OR obesity OR dyslipidemia) AND (“prostate volume” OR “prostatic enlargement” OR inflammation).

Embase: In Embase, Emtree terms were combined with title/abstract keywords:(“benign prostatic hyperplasia”/exp OR “benign prostatic hyperplasia” OR BPH OR LUTS OR IPSS) AND (“metabolic syndrome”/exp OR “metabolic syndrome” OR “insulin resistance” OR obesity OR dyslipidemia) AND (“prostate volume” OR “prostatic growth” OR inflammation).

Web of Science Core Collection: Topic searches (title, abstract, keywords) were applied: TS = ((“benign prostatic hyperplasia” OR BPH OR LUTS OR IPSS) AND (“metabolic syndrome” OR insulin resistance OR obesity OR dyslipidemia) AND (“prostate volume” OR “prostatic enlargement” OR inflammation)).

Scopus: Searches were conducted in titles, abstracts, and keywords: TITLE-ABS-KEY (“benign prostatic hyperplasia” OR BPH OR LUTS OR IPSS) AND TITLE-ABS-KEY (“metabolic syndrome” OR insulin resistance OR obesity OR dyslipidemia) AND TITLE-ABS-KEY (“prostate volume” OR “prostatic growth” OR inflammation).

Cochrane Library: Given the simpler indexing structure, a broad search strategy was applied: (“benign prostatic hyperplasia” OR BPH) AND (“metabolic syndrome”) AND (“prostate volume” OR LUTS OR IPSS).

No restrictions were applied regarding language, region, or publication year. To ensure completeness, the reference lists of all eligible articles and related reviews were manually screened, and forward citation tracking was conducted through Web of Science and Google Scholar. All search procedures and protocol amendments were documented in compliance with PROSPERO (International Prospective Register of Systematic Reviews) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standards.

Eligibility Criteria

Studies were considered eligible if they met the following criteria: (1) Population: Adult men diagnosed with BPH based on clinical and/or imaging criteria. (2) Exposure: Diagnosis of MetS using standardized criteria. (3) Comparator: BPH patients without MetS. (4) Outcomes: Reported at least one of the following—IPSS, PV, maximum urinary flow rate (Qmax), or the prevalence of MetS among BPH patients. (5) Design: Observational studies (cross-sectional, case–control, or cohort). (6) Data: Provided sufficient quantitative information to derive effect estimates.

Exclusion criteria included reviews, case reports, conference abstracts, animal or laboratory research, studies lacking extractable outcome data, and duplicate or overlapping cohorts.

Study Selection

All retrieved records were imported into EndNote for de-duplication. Two reviewers independently screened titles/abstracts and subsequently assessed full texts using prespecified inclusion and exclusion criteria. Any discrepancies were resolved through consensus or adjudication by a third reviewer. The study selection workflow followed the PRISMA 2020 guidelines.

Data Extraction

Data extraction was performed independently by two investigators using a piloted and standardized extraction template. The following variables were collected from each study: general study information (author, year, country/region, study design), sample size of MetS and non-MetS groups, participant characteristics (mean age), diagnostic criteria for MetS, outcome data: IPSS, PV, Qmax, and prevalence of MetS. Continuous outcomes were extracted as means with standard deviations; proportion data were extracted as event counts. When required, corresponding authors were contacted to clarify missing or ambiguous information.

Quality Assessment

The methodological quality of all included observational studies was evaluated using the Newcastle–Ottawa Scale (NOS), adapted for cross-sectional and cohort designs (Stang, 2010). The NOS assesses three methodological domains: (1) selection of participants, (2) comparability of exposure groups, and (3) outcome or exposure ascertainment. Each study could receive up to 10 points, and, consistent with commonly accepted thresholds in high-impact epidemiologic literature, studies were categorized as follows: high quality: NOS ≥7 points; moderate quality: NOS 4–6 points; low quality: NOS 0–3 points (none met this criterion).

Special attention was given to critical indicators that influence internal validity, including explicit diagnostic criteria for BPH and MetS, representativeness of cohorts, adjustment for confounders (e.g., age, body mass index [BMI], comorbidities), and validated outcome measurement (such as TRUS for PV and standardized IPSS scoring).

Two reviewers independently conducted the quality assessment. Discrepancies were resolved through discussion or adjudication by a third senior reviewer. Quality assessments informed interpretation of findings but were not used to exclude studies.

Statistical Analysis

Meta-analyses were conducted using random-effects models (DerSimonian–Laird estimator) to accommodate expected clinical and methodological heterogeneity across study settings, populations, and measurement techniques. For continuous outcomes (IPSS and PV), standardized mean differences (SMDs) were calculated when measurement scales varied, and mean differences (MDs) were used for comparable units. For prevalence outcomes, pooled estimates were derived using the inverse variance method, applying appropriate variance-stabilizing transformations when required.

Assessment of heterogeneity: Between-study heterogeneity was quantified using Cochran’s Q and expressed as I², with interpretation aligned with Cochrane and major epidemiologic guidelines: low heterogeneity: I² < 30%; moderate heterogeneity: 30% to 60%; substantial heterogeneity: 60% to 75%; considerable (high) heterogeneity: I² > 75%. Tau-squared (τ²) was also calculated to evaluate absolute heterogeneity. Sources of heterogeneity were further explored using subgroup analyses (based on sample size categories: small <200, medium 200–499, large ≥500) and univariable meta-regression examining study-level covariates (mean age, sample size, publication year).

Sensitivity and publication bias analysis: To assess the robustness of summary estimates, leave-one-out sensitivity analyses were performed for each outcome. Potential publication bias was evaluated visually using funnel plots, interpreted with attention to asymmetry patterns commonly associated with small-study effects or selective reporting.

Software and significance thresholds: All analyses were performed using R (version 4.1.2) with the meta and metafor packages. Statistical significance was defined as a two-sided p < .05, except in heterogeneity testing where p-values for Q were interpreted cautiously given low power in small meta-analyses.

Result

Study Selection

The initial database search yielded a total of 1,383 records, including PubMed (n = 317), Web of Science (n = 281), Embase (n = 410), Scopus (n = 350), and the Cochrane Library (n = 25). Before screening, 324 records were removed, comprising 306 duplicate entries and 18 records that were automatically identified as ineligible by the EndNotes reference management software. A total of 1,059 records proceeded to title and abstract screening, during which 876 were excluded for being unrelated to BPH, MetS, or lacking relevant clinical outcomes. The remaining 183 reports were sought for full-text retrieval; four reports could not be obtained. A total of 179 full-text articles were assessed for eligibility. Of these, 169 were excluded, including 82 without extractable quantitative data, 49 with irrelevant outcomes, 23 review/editorial/case-report publications, and 15 studies involving duplicate or overlapping cohorts. Ultimately, 10 studies met all inclusion criteria and were incorporated into the quantitative synthesis. A detailed depiction of the screening and selection process is provided in Figure 1.

Figure 1.

PRISMA Flow Diagram of Study Selection.

Study Characteristics

Characteristics of the 10 included studies are summarized in Table 1. Studies were published between 1998 and 2025, and the core characteristics were as follows: the total sample size was 3,947 patients with BPH; all studies adopted the NCEP-ATP III to diagnose MetS, reporting of outcome indicators: eight studies reported PV, eight studies reported the IPSS, and 10 studies reported the prevalence of MetS; the study types were all cross-sectional studies or cohort studies; the age range of the study subjects was 50 to 80 years; geographical distribution: five studies in Asia, four studies in Europe, and one study in the Middle East.

Table 1.

Study Characteristics.

Study ID	Country/region	Study design	Sample size (MetS/non-MetS)	Mean age (years)	MetS definition	Outcomes reported
Daher et al. (2023)	Syria	Observational (cross-sectional/cohort)	197/229	63	NCEP-ATP III	IPSS; prostate volume; MetS prevalence
Pan et al. (2014)	China	Observational (cross-sectional/cohort)	418/634	66	NCEP-ATP III	IPSS; prostate volume; MetS prevalence
Zhang et al. (2014)	China	Observational (cross-sectional/cohort)	222/179	64	NCEP-ATP III	IPSS; prostate volume; MetS prevalence
Russo et al. (2018)	Italy	Observational (cross-sectional/cohort)	46/178	67	NCEP-ATP III	IPSS; prostate volume; MetS prevalence
Ozden et al. (2007)	Turkey	Observational (cross-sectional/cohort)	38/40	60	NCEP-ATP III	IPSS; prostate volume; MetS prevalence
Gacci et al. (2013)	Italy	Observational (cross-sectional/cohort)	86/185	69	NCEP-ATP III	IPSS; prostate volume; MetS prevalence
Gacci et al. (2017)	Italy	Observational (cross-sectional/cohort)	140/239	70	NCEP-ATP III	IPSS; prostate volume; MetS prevalence
Urgesa et al. (2025)	Ethiopia	Observational (cross-sectional/cohort)	61/97	60	NCEP-ATP III	IPSS; prostate volume; MetS prevalence
Jan et al. (2024)	Pakistan	Observational (cross-sectional/cohort)	320/480	60	NCEP-ATP III	MetS prevalence
Hammarsten et al. (1998)	Sweden	Observational (cross-sectional/cohort)	128/30	70	NCEP-ATP III	MetS prevalence

Quality Assessment

All included observational studies were evaluated using the NOS for cross-sectional and cohort studies. The NOS assesses three major domains: selection of participants, comparability between exposure groups, and outcome/exposure assessment. Across the 10 included studies, seven were rated as high quality (NOS ≥ 7 points) and three were rated as moderate quality (NOS 5–6 points). No study was classified as low quality (<5 points). Most studies clearly defined BPH and MetS using standardized diagnostic criteria (NCEP ATP III), employed validated outcome measures such as IPSS and transrectal ultrasound for PV, and used appropriate statistical methods. However, common methodological limitations included inadequate reporting of nonrespondents, limited adjustment for confounding factors (such as age, BMI, or comorbidities), and lack of blinding in outcome ascertainment. The detailed NOS scoring of each study is presented in Table 2.

Table 2.

NOS Scoring of Each Study.

Study ID	Selection (0–5)	Comparability (0–2)	Outcome/exposure (0–3)	Total score (0–10)	Quality
Daher et al. (2023)	4	1	2	7	High
Pan et al. (2014)	5	1	2	8	High
Zhang et al. (2014)	4	1	2	7	High
Russo et al. (2018)	4	1	2	7	High
Ozden et al. (2007)	3	1	2	6	Moderate
Gacci et al. (2013)	4	2	2	8	High
Gacci et al. (2017)	3	1	2	6	Moderate
Urgesa et al. (2025)	3	1	2	6	Moderate
Jan et al. (2024)	4	1	2	7	High
Hammarsten et al. (1998)	4	1	2	7	High

International Prostate Symptom Score

Eight studies reported continuous IPSS values, involving 1,208 patients with MetS and 1,781 without MetS. The pooled SMD was 0.36 (95% confidence interval [CI] = [−0.24, 0.95]) (Figure 2), suggesting a trend toward more severe LUTS in the MetS group, although the CI crossed zero. Heterogeneity was extremely high (I² = 98.1%, τ² = 0.7175). Notably, Pan et al. (2014) demonstrated the largest effect (SMD = 1.87), whereas several studies (Daher et al., 2023; Urgesa et al., 2025; Zhang et al., 2014) reported minimal between-group differences.

Figure 2.

Forest Plot for IPSS SMD Between MetS and Non-MetS Groups.

Prevalence of MetS Among BPH Patients

A total of 10 studies (n = 3,947) reported the prevalence of MetS among patients with BPH. As shown in Figure 3, the pooled prevalence of MetS in BPH patients was 43.49% (95% CI = [36.41, 50.85]) with marked heterogeneity (I² = 94.7%, τ² = 0.2083, p < .0001). Individual study estimates ranged from 20.54% (Russo et al., 2018) to 81.01% (Hammarsten et al., 1998), demonstrating considerable variability across regions and study periods.

Figure 3.

Pooled Prevalence of Metabolic Syndrome Among BPH Patients.

Prostate Volume

Eight studies reported PV, totaling the same patient groups as IPSS analysis. The pooled MD indicated that BPH patients with MetS had significantly larger prostates than those without MetS, with an MD of 10.17 mL (95% CI = [5.46, 14.87]) (Figure 4). Substantial heterogeneity was observed (I² = 88.5%, τ² = 33.4722, p < .0001). The largest differences were seen in Russo et al. (2018; +20.8 mL) and Pan et al. (2014; +11.75 mL), whereas Daher et al. (2023) showed no difference between groups.

Figure 4.

Forest Plot for Prostate Volume MD Between MetS and Non-MetS Groups.

Publication Bias

IPSS

The funnel plot (Figure 5A) demonstrated mild asymmetry, with small studies scattering widely and large studies clustering near the right upper quadrant, suggesting potential small-study effects.

Figure 5.

Funnel Plots (A: IPSS; B: MetS Prevalence; C: Prostate Volume).

MetS Prevalence

The funnel plot for prevalence (Figure 5B) revealed marked asymmetry, with high-prevalence studies clustering to the right, whereas small-sample, low-prevalence studies appeared in the lower left. This pattern indicates possible publication bias and strong between-study heterogeneity.

Prostate Volume

The PV funnel plot (Figure 5C) showed moderate asymmetry, indicating potential influence from sample size, region, or measurement variation.

Subgroup Analysis

Subgroup analysis stratified by sample size revealed a significant between-group difference (χ² = 219.89, p < .0001). Large-sample studies consistently reported substantially higher SMDs, with estimates such as Pan et al. (2014) showing SMD = 1.87, whereas small and medium studies demonstrated only minimal effects (Supplemental Figure S1A). However, despite these apparent differences in point estimates, the 95% CIs of the pooled effects in all sample-size subgroups crossed the null value (SMD = 0), indicating that none of the subgroup-specific pooled effects reached statistical significance. Therefore, the observed pattern should be interpreted cautiously, suggesting that study precision and statistical power may influence effect magnitude estimates, but without providing definitive evidence for a statistically significant association between MetS and IPSS within any subgroup.

Across sample size categories, no statistically significant subgroup differences were detected (χ² = 1.61, p = .4466). Although prevalence appeared numerically higher in smaller studies, the differences did not reach significance (Supplemental Figure S1B). These findings indicate that sample size is unlikely to be a major source of heterogeneity for prevalence estimates.

A significant subgroup effect was observed for PV (χ² = 46.46, p < .0001). Large studies showed stronger associations (SMD = 1.34) compared with medium (0.59) and small studies (0.35), indicating that larger datasets may capture more stable and pronounced MetS-related differences (Supplemental Figure S1C).

Sensitivity Analysis

To evaluate the stability of the association between MetS and IPSS, a leave-one-out sensitivity analysis was performed. Sequential omission of each study generated SMD estimates ranging from 0.33 to 0.44, and no single study changed the direction or magnitude of the overall effect (Supplemental Figure S2A). The narrow fluctuation range suggests that the pooled estimate was not disproportionately influenced by any individual dataset, confirming the high robustness of the primary findings.

Sensitivity analysis for the pooled prevalence of MetS among BPH patients showed similarly stable results. Excluding studies one at a time produced prevalence estimates ranging from 39.38% to 46.27%, which closely aligned with the main pooled rate (Supplemental Figure S2B). The consistency across iterations indicates that the observed prevalence was not driven by outliers or low-quality studies, further strengthening the reliability of the prevalence estimate.

For PV, leave-one-out analysis revealed minor variations, with MD values fluctuating only between 0.44 and 0.73. None of the exclusions meaningfully altered the effect size or led to directional changes (Supplemental Figure S2C). This pattern demonstrates that the association between MetS and increased PV remained structurally stable, providing no indication of undue influence from any single study.

Meta-Regression

Sample size showed a strong positive linear relationship with IPSS effect estimates (p < .001; R² = 9,355.1%). Larger studies consistently yielded higher SMDs, reinforcing the sensitivity of IPSS outcomes to sample size and statistical power (Supplemental Figure S3A).

Publication year significantly predicted MetS prevalence (p = .0032; R² = 420.5%). More recent studies tended to report lower prevalence, suggesting temporal shifts in diagnostic criteria, lifestyle factors, or population characteristics (Supplemental Figure S3B).

Meta-regression demonstrated that mean age significantly contributed to heterogeneity in PV differences (β > 0; p = .0488; R² = 2,307.3%). Studies with older participants reported larger MetS-associated increases in PV, indicating an age-related amplifying effect (Supplemental Figure S3C).

Discussion

In this systematic review and meta-analysis, we synthesized current evidence regarding the association between MetS and clinical features of BPH. The findings demonstrate that MetS is associated with higher IPSS, increased PV, and a substantial prevalence of MetS among patients with BPH. These results align with an expanding body of literature, suggesting that metabolic dysregulation contributes to prostatic overgrowth and LUTS through multiple endocrine, inflammatory, and vascular mechanisms (Calmasini et al., 2020; Gacci et al., 2017; Sachdeva et al., 2022).

Several pathophysiological pathways may explain the observed associations. Hyperinsulinemia and insulin resistance may promote prostate stromal proliferation via activation of the Insulin-like Growth Factor-1 (IGF-1) axis (Gong et al., 2022; Sharkey et al., 2025), while visceral obesity-related inflammation increases local cytokine production, fostering tissue remodeling and enlargement (Colado-Velázquez et al., 2023; Urgesa et al., 2025; Xia & Li, 2017). Dyslipidemia has also been implicated in altering sex hormone metabolism, resulting in androgen–estrogen imbalances that stimulate prostatic hyperplasia (Wang et al., 2025). These mechanisms support the notion that MetS acts not only as a comorbidity but also as a potential driver of LUTS progression.

Our results are consistent with previous epidemiologic studies that reported larger prostate size and more severe LUTS among men with MetS (Bratchikov et al., 2020; Jan et al., 2024; Urgesa et al., 2025). However, the considerable heterogeneity observed across studies warrants careful interpretation. Differences in sample size, measurement techniques (e.g., TRUS vs. digital estimation), population age structure, and regional prevalence of obesity likely contributed to the variation. Meta-regression findings indicating publication year, age-, and sample-size effects further support these possibilities. In addition, variations in diagnostic rigor for MetS and lifestyle patterns across geographic regions may explain discrepancies among studies (Cai et al., 2025; Sharkey et al., 2025).

From a clinical perspective, these findings underscore the importance of metabolic health assessment in men presenting with BPH symptoms. Addressing modifiable metabolic disturbances may provide dual benefits in improving systemic risk profiles and mitigating LUTS severity. Emerging interventional studies suggest that weight reduction, glycemic control, and lipid optimization may help attenuate prostate growth and improve urinary symptoms, although evidence remains preliminary (O’Mahony et al., 2025; Wu et al., 2025).

This review has several strengths, including comprehensive database coverage, rigorous methodological assessment, and multiple sensitivity analyses confirming the robustness of findings. Nonetheless, limitations must be acknowledged. All included studies were observational, limiting causal inference. Residual confounding—particularly from lifestyle factors, medication use, or unmeasured metabolic markers—cannot be excluded. High heterogeneity, although explored through subgroup and meta-regression analyses, may still reflect unaccounted population-level differences. Finally, the reliance on cross-sectional designs in several studies restricts insights into temporal relationships between MetS and BPH progression.

Conclusion

In summary, MetS appears to be significantly associated with increased PV and more severe LUTS, highlighting an important metabolic–urological interface. Future large-scale prospective studies are needed to clarify causality and evaluate whether metabolic interventions can modify the clinical trajectory of BPH.

Supplemental Material

sj-jpg-1-jmh-10.1177_15579883261436335 – Supplemental material for The Association Between Metabolic Syndrome and Benign Prostatic Hyperplasia: A Systematic Review and Meta-Analysis

Supplemental material, sj-jpg-1-jmh-10.1177_15579883261436335 for The Association Between Metabolic Syndrome and Benign Prostatic Hyperplasia: A Systematic Review and Meta-Analysis by Wenfeng Gao, Yingbo Guo, Xudong Yu, Tao Zhang, Huijie Gong and Yaosheng Zhang in American Journal of Men's Health

Supplemental Material

sj-jpg-2-jmh-10.1177_15579883261436335 – Supplemental material for The Association Between Metabolic Syndrome and Benign Prostatic Hyperplasia: A Systematic Review and Meta-Analysis

Supplemental material, sj-jpg-2-jmh-10.1177_15579883261436335 for The Association Between Metabolic Syndrome and Benign Prostatic Hyperplasia: A Systematic Review and Meta-Analysis by Wenfeng Gao, Yingbo Guo, Xudong Yu, Tao Zhang, Huijie Gong and Yaosheng Zhang in American Journal of Men's Health

Supplemental Material

sj-jpg-3-jmh-10.1177_15579883261436335 – Supplemental material for The Association Between Metabolic Syndrome and Benign Prostatic Hyperplasia: A Systematic Review and Meta-Analysis

Supplemental material, sj-jpg-3-jmh-10.1177_15579883261436335 for The Association Between Metabolic Syndrome and Benign Prostatic Hyperplasia: A Systematic Review and Meta-Analysis by Wenfeng Gao, Yingbo Guo, Xudong Yu, Tao Zhang, Huijie Gong and Yaosheng Zhang in American Journal of Men's Health

Footnotes

ORCID iD

Yaosheng Zhang

Funding

The authors disclosed receipt of the following financial sup port for the research, authorship, and/or publication of this article: This work was supported in part by the National High-level TCM Hospitals Clinical Research Fund (DZMG-ZLZX-25027), and the Beijing Traditional Chinese Medicine “Torch Inheritance 3+3 Project”.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

References

Bertocchi

Foglietta

Collotta

Eva

Brancaleone

Thiemermann

Collino

(2020). The hidden role of NLRP3 inflammasome in obesity-related COVID-19 exacerbations: Lessons for drug repurposing. British Journal of Pharmacology, 177(21), 4921–4930. https://doi.org/10.1111/bph.15229

Bratchikov

O. I.

Artishchev

S. O.

Tyuzikov

I. A.

(2018). Vitamin D deficiency, metabolic syndrome, and prostate adenoma: Current epidemiological trends and pathophysiological mechanisms of interaction. Urologiia, 4, 179–185.

Bratchikov

O. I.

Tyuzikov

I. A.

Artishchev

S. O.

Shumakova

E. A.

(2020). The role of obesity in the pathogenesis of benign prostatic hyperplasia. Urologiia, 2, 101–106.

Cai

Ceccato

Botti

Kranz

Verze

Bonkat

. . . Bjerklund Johansen

T. E.

(2025). Impact of lifestyle and the microbiome on male lower urinary tract symptoms due to benign prostatic hyperplasia. European Urology Focus, 11(4), 557–559. https://doi.org/10.1016/j.euf.2025.04.013

Calmasini

F. B.

McCarthy

C. G.

Wenceslau

C. F.

Priviero

F. B. M.

Antunes

Webb

R. C.

(2020). Toll-like receptor 9 regulates metabolic profile and contributes to obesity-induced benign prostatic hyperplasia in mice. Pharmacological Reports, 72(1), 179–187. https://doi.org/10.1007/s43440-019-00010-3

Chiarot

Minhas

de Maat

N. M.

Doan

Nilsson

M. I.

Hettinga

B. P.

. . .Tarnopolsky

M. A.

(2025). A multi-ingredient supplement improves body re-composition, ovarian aging markers, and reproductive success in young and middle-aged female mice. Biomolecules, 15(9), 1258. https://doi.org/10.3390/biom15091258

Colado-Velázquez

J. I.

Mailloux-Salinas

Arias-Chávez

D. J.

Ledesma-Aparicio

Gómez-Viquez

N. L.

Cano-Europa

. . .Bravo

(2023). Lipidic extract of whole tomato reduces hyperplasia, oxidative stress and inflammation on testosterone-induced BPH in obese rats. International Urology and Nephrology, 55(3), 529–539. https://doi.org/10.1007/s11255-022-03383-2

Daher

Saqer

Jabr

Al-Mousa

(2023). Benign prostatic hyperplasia and metabolic syndrome; prevalence and association: A cross-sectional study in Syria. BMC Urology, 23(1), Article 187. https://doi.org/10.1186/s12894-023-01365-9

Feng

Jin

Zheng

Xia

. . .Li

(2025). FGF1(ΔHBS) ameliorates DSS-induced ulcerative colitis by reducing neutrophil recruitment through the MAPK pathway. British Journal of Pharmacology, 182(20), 5057–5070. https://doi.org/10.1111/bph.70049

10.

Gacci

Sebastianelli

Salvi

De Nunzio

Vignozzi

Corona

. . .Serni

(2017). Benign prostatic enlargement can be influenced by metabolic profile: Results of a multicenter prospective study. BMC Urology, 17(1), Article 22. https://doi.org/10.1186/s12894-017-0211-9

11.

Gacci

Vignozzi

Sebastianelli

Salvi

Giannessi

De Nunzio

. . .Maggi

(2013). Metabolic syndrome and lower urinary tract symptoms: The role of inflammation. Prostate Cancer and Prostatic Diseases, 16(1), 101–106. https://doi.org/10.1038/pcan.2012.44

12.

Gong

Y. M.

Wang

Liu

X. C.

Huang

(2022). Simvastatin inhibits prostatic hyperplasia in rats with metabolic syndrome. International Urology and Nephrology, 54(9), 2125–2131. https://doi.org/10.1007/s11255-022-03227-z

13.

Hammarsten

Högstedt

Holthuis

Mellström

(1998). Components of the metabolic syndrome-risk factors for the development of benign prostatic hyperplasia. Prostate Cancer and Prostatic Diseases, 1(3), 157–162. https://doi.org/10.1038/sj.pcan.4500221

14.

Pan

Liu

Wang

. . .Wang

(2025). Association between metabolic syndrome and risk of benign prostatic hyperplasia: A prospective cohort study of 163 975 participants. Journal of Global Health, 15, 04275. https://doi.org/10.7189/jogh.15.04275

15.

Jan

D. M. A.

Khan

Ur Rahman

S. Z.

Ahmad

Yousuf

M. H.

(2024). Prevalence and association of benign prostatic hyperplasia and metabolic syndrome: Findings from a cross-sectional study. Journal of Population Therapeutics and Clinical Pharmacology, 31(3), 1087–1096. https://doi.org/10.53555/jptcp.v31i3.5084

16.

O’Mahony

C. J.

Arshad

M. A.

Norton

S. M.

(2025). Testosterone and lower urinary tract symptoms: A narrative review. Current Urology Reports, 26(1), 75. https://doi.org/10.1007/s11934-025-01307-y

17.

Ozden

Ozdal

O. L.

Urgancioglu

Koyuncu

Gokkaya

Memis

(2007). The correlation between metabolic syndrome and prostatic growth in patients with benign prostatic hyperplasia. European Urology, 51(1), 199–206. https://doi.org/10.1016/j.eururo.2006.05.040

18.

Pan

J. G.

Jiang

Luo

Zhou

(2014). Association of metabolic syndrome and benign prostatic hyperplasia in Chinese patients of different age decades. Urologia Internationalis, 93(1), 10–16. https://doi.org/10.1159/000354026

19.

Parums

D. V.

(2021). Review articles, systematic reviews, meta-analysis, and the updated preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 27, e934475–934471.

20.

Ren

Zhang

Xiong

Zhang

. . .Tian

(2024). Correlation between metabolic syndrome and periurethral prostatic fibrosis: Results of a prospective study. BMC Urology, 24(1), Article 38. https://doi.org/10.1186/s12894-024-01413-y

21.

Russo

G. I.

Regis

Spatafora

Frizzi

Urzì

Cimino

. . .Morgia

(2018). Association between metabolic syndrome and intravesical prostatic protrusion in patients with benign prostatic enlargement and lower urinary tract symptoms (MIPS Study). BJU International, 121(5), 799–804. https://doi.org/10.1111/bju.14007

22.

Sachdeva

Kaur

Kapoor

Singla

Thakur

Singhmar

(2022). Computational analysis of protein-protein interaction network of differentially expressed genes in benign prostatic hyperplasia. Molecular Cell Biology Research Communications, 11(2), 85–96. https://doi.org/10.22099/mbrc.2022.43721.1746

23.

Sharkey

Long

Tang

Patsatzis

. . .Wang

(2025). Stromal steroid 5 alpha-reductase 2 promotes prostate growth through WNT5A-lymphoid enhancer-binding factor 1-insulin-like growth factor 1 signaling in benign prostatic hyperplasia. American Journal of Pathology, 195(12), 2411–2430. https://doi.org/10.1016/j.ajpath.2025.08.011

24.

Stang

(2010). Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. European Journal of Epidemiology, 25, 603–605.

25.

Urgesa

H. E.

Tereffe

Z. A.

Leta

T. A.

Nasi

B. A.

Hurisa

S. G.

(2025). Metabolic syndrome and associated factors among benign prostatic hyperplasia patients at Jimma University Medical center, Jimma, Southwest Ethiopia. Cardiovascular Diabetology—Endocrinology Reports, 11(1), 25. https://doi.org/10.1186/s40842-025-00226-8

26.

Wang

Yang

Yin

Chen

(2025). Predictive factors of incidental prostate cancer in patients undergoing surgery for presumed benign prostatic hyperplasia: An updated systematic review and meta-analysis. Frontiers in Oncology, 15, Article 1561675. https://doi.org/10.3389/fonc.2025.1561675

27.

Yin

Yang

Gong

Wang

(2025). Beneficial effects of Akkermansia muciniphila on benign prostatic hyperplasia and metabolic syndrome. Archives of Biochemistry and Biophysics, 768, 110294. https://doi.org/10.1016/j.abb.2025.110294

28.

Xia

(2017). The role of perivascular adipose tissue in obesity-induced vascular dysfunction. British Journal of Pharmacology, 174(20), 3425–3442. https://doi.org/10.1111/bph.13650

29.

Yang

G. R.

K. K.

Y. Y.

Hao

X. W.

Yuan

Song

(2025). Age-related changes in the impact of metabolic syndrome on prostate volume: A cross-sectional study. Asian Journal of Andrology, 27(4), 475–481. https://doi.org/10.4103/aja2024101

30.

Zhang

Zeng

Liu

Dong

Zhao

(2014). Impact of metabolic syndrome on benign prostatic hyperplasia in elderly Chinese men. Urologia Internationalis, 93(2), 214–219. https://doi.org/10.1159/000357760

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