Abstract
This study investigated the impact of metabolic syndrome (MetS) and its components on prostate volume (PV) in the general Chinese population. In total, 43 455 participants in The First Medical Center of the Chinese PLA General Hospital (Beijing, China) from January 1, 2012, to December 31, 2022, undergoing health examinations were included in the study. Participants were categorized into four groups according to PV quartiles: Q1 (PV ≤24.94 ml), Q2 (PV >24.94 ml and ≤28.78 ml), Q3 (PV >28.78 ml and ≤34.07 ml), and Q4 (PV >34.07 ml), with Q1 serving as the reference group. Logistic regression analyses were used to examine the association between MetS and PV, with subgroup analyses conducted by age. Among the participants, 18 787 (43.2%) were diagnosed with MetS. In the multivariate analysis model, a significant correlation between MetS and PV was observed, with odds ratios (ORs) increasing as PV increased (Q2, OR = 1.203, 95% confidence interval [CI]: 1.139–1.271; Q3, OR = 1.300, 95% CI: 1.230–1.373; and Q4, OR = 1.556, 95% CI: 1.469–1.648). Analysis of MetS components revealed that all components were positively associated with PV, with abdominal obesity showing the most significant effect. The number of MetS components was identified as a dose-dependent risk factor for elevated PV. The impact of MetS, its components, and component count on PV exhibited a decreasing trend with advancing age. Overall, the influence of MetS, its components, and component count on PV was predominantly observed in the age groups of 40–49 years and 50–59 years. Early intervention targeting MetS can significantly alleviate the increase in PV, particularly benefiting individuals aged 40–59 years who have abdominal obesity.
Keywords: age, benign prostatic hyperplasia, metabolic syndrome, prostate volume
INTRODUCTION
Benign prostatic hyperplasia (BPH) is a common condition among middle-aged and elderly men. Enlargement of the prostate gland can cause bladder outlet obstruction, leading to a range of urinary symptoms, including frequency, urgency, and nocturia. BPH is a major contributor to lower urinary tract symptoms (LUTS). In China, the overall prevalence of LUTS/BPH is estimated to be 10.66%, with a higher prevalence of 14.67% among those aged 70 years and older.1 The Global Burden of Disease analysis in 2019 revealed that compared with those in 1990, the incidence and prevalence of BPH in China had increased by 122.38% and 125.06%, respectively.2 With the aging population, the disease burden of BPH is expected to rise further, underscoring the importance of identifying risk factors for prostate enlargement.
Epidemiological studies have identified several potential risk factors for BPH, including lifestyle factors, obesity, dietary habits, smoking, alcohol consumption, and sedentary behavior3 as well as metabolic syndrome (MetS).4 Among these, MetS has drawn significant attention from researchers.4,5,6 MetS is a multifaceted disorder with significant socioeconomic impact, now recognized as a global epidemic, and encompasses a range of metabolic abnormalities including central obesity, dyslipidemia, hypertension, insulin resistance with compensatory hyperinsulinemia, and impaired glucose tolerance.7 Research from various countries, including the USA, Italy, Türkiye, South Korea, and China, has indicated a strong association between MetS and both the onset of BPH and an increase in prostate volume (PV).4,8,9,10,11
Despite extensive research in this area, most studies have focused on individuals already diagnosed with BPH8,9,10,11 or have used the occurrence of BPH/LUTS as the primary outcome.4,12 This approach often neglects the prediagnostic state of PV, which could be crucial for understanding the etiology of BPH. Additionally, studies specific to the Chinese population are limited, often involving smaller sample sizes and a predominance of participants from southern China.11,13,14 Previous researches have not definitively determined whether the impact of MetS on PV varies with age. Therefore, this study was performed to analyze the effect of MetS on PV in a general health examination population from northern China, rather than focusing solely on patients with BPH, and to explore how this effect varies across different age groups, thereby providing stronger evidence for the prevention of BPH.
PARTICIPANTS AND METHODS
Study participants
This retrospective analysis involved individuals who underwent routine health examinations at The First Medical Center of the Chinese PLA General Hospital (Beijing, China) from January 1, 2012, to December 31, 2022. After excluding female participants and those without abdominal ultrasound examinations, 44 405 individuals were included in this cross-sectional study. The dataset used for analysis was provided by the Data Center of The First Medical Center and was anonymized to protect patient confidentiality. Because no identifiable patient information was included, informed consent was not required from the study participants. Ethical approval for this study was obtained from the Medical Ethics Committee of the Chinese PLA General Hospital (Approval No. S2022-700-02). The study was conducted in accordance with the principles outlined in the Declaration of Helsinki.
Diagnostic criteria for MetS
The diagnostic criteria for MetS were based on the 2020 edition of the “Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes”, which are specifically designed for the Chinese population.15 The diagnostic criteria are as follows: (1) abdominal obesity (central obesity, waist circumference of ≥90 cm for men and ≥85 cm for women); (2) elevated blood glucose (fasting plasma glucose of ≥6.1 mmol l−1 or a prior diagnosis of diabetes with ongoing treatment); (3) elevated blood pressure (blood pressure of ≥130/85 mmHg or a prior diagnosis of hypertension with ongoing treatment); (4) elevated fasting triglycerides (TG) of ≥1.70 mmol l−1; and (5) low fasting high-density lipoprotein cholesterol (HDL-C) of <1.04 mmol l−1. To meet the criteria for MetS, a patient must fulfill at least three of the five specified conditions.
Because of the lack of a glucose tolerance test in the routine health examination, the definition of elevated blood glucose in this study did not include the criterion of postprandial 2 h glucose of ≥7.8 mmol l−1.
The International Diabetes Federation (IDF) defines MetS slightly differently.16 According to the IDF, abdominal obesity is defined as a waist circumference of >94 cm for men and >80 cm for women, while hyperglycemia is defined as a fasting blood glucose level of ≥5.6 mmol l−1. Additionally, the IDF considers abdominal obesity to be a mandatory criterion for the diagnosis of MetS.
Clinical information collection
The study participants were instructed to abstain from food from the morning of the examination day. Blood samples for biochemical analysis were collected in a fasting state. We examined a range of serum biochemical indicators, including serum glucose, total cholesterol, TG, HDL-C, low-density lipoprotein cholesterol (LDL-C), prostate-specific antigen (PSA), and free PSA (fPSA). All laboratory analyses were performed by the Department of Laboratory Medicine at The First Medical Center of the Chinese PLA General Hospital.
Anthropometric measurements, including height, weight, blood pressure, waist circumference, and waist–hip ratio, were also part of the examination process. Waist circumference was measured at the midpoint between the lower edge of the costal arch and the iliac crest along the midaxillary line, in accordance with the standards established by the National Health and Family Planning Commission of the People’s Republic of China in 2013 and as detailed in the “Health Industry Standards of the People’s Republic of China for Adult Weight Determination” (Standard No. WS/T428-2013).15 These measurements were conducted by the center’s specialized medical personnel.
Each participant’s medical history, including smoking and drinking habits and histories of hypertension and diabetes, was carefully collected by the internists at the Physical Examination Center. However, self-reported information, such as smoking history, alcohol consumption, and medical history, was noted to be subject to potential information bias.
PV was measured by professional sonographers using transabdominal ultrasound and calculated with the ellipsoid formula (PV = anteroposterior diameter × transverse diameter × superior–inferior diameter × 0.52).17 Although transabdominal ultrasound has lower accuracy than magnetic resonance imaging and transrectal ultrasound, it is preferred for large-scale screenings because of its affordability, simplicity, and minimal invasiveness.17
Statistical analyses
Continuous variables were presented as mean (standard deviation [s.d.]), and categorical variables were shown as count (percentage). Given that the rate of missing data for any variable was <3%, all participants with missing data (n = 950) were excluded from the analysis. Outliers among continuous variables were addressed using the quartile method for imputation.
PV was stratified into quartiles, resulting in four groups: Q1 (PV ≤24.94 ml), Q2 (PV >24.94 ml and ≤28.78 ml), Q3 (PV >28.78 ml and ≤34.07 ml), and Q4 (PV >34.07 ml). The Kruskal–Wallis test was employed to compare these groups. The analysis included two distinct models: Model 1, an unadjusted model; and Model 2, which adjusted for potential confounders such as age, PSA, fPSA, smoking history, and alcohol consumption.
Multiple logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (95% CI), assessing the influence of MetS, MetS components, and the number of MetS components on PV. In all logistic regression models, the Q1 group served as the reference category. For subgroup analysis, we further divided the participants into age groups of <40 years, 40–49 years, 50–59 years, 60–69 years, and ≥70 years (ensuring no overlap between adjacent groups) and repeated the analysis for each age group.
Statistical significance was defined as a two-tailed P < 0.05. Data management and baseline characteristics analysis were performed using R software, version 4.3.3 (R Foundation for Statistical Computing, Vienna, Austria). The construction and execution of multiple logistic regression models were conducted using the SPSS software, version 27.0 (IBM Corp., Armonk, NY, USA).
RESULTS
The clinical characteristics of the study cohort are summarized in Table 1. The mean age of the participants was 52.4 (s.d.: 7.7) years, the mean body mass index was 25.82 (s.d.: 3.13) kg m−2, and the mean waist circumference was 91.81 (s.d.: 8.33) cm. The rates of current smoking and drinking were 42.3% and 73.9%, respectively. The prevalence rates for hypertension, diabetes, and MetS were 47.1%, 17.9%, and 43.2%, respectively. The mean serum lipid levels were as follows: total cholesterol, 4.68 (s.d.: 0.90) mmol l−1; TG, 1.87 (s.d.: 1.38) mmol l−1; HDL-C, 1.19 (s.d.: 0.29) mmol l−1; and LDL-C, 3.01 (s.d.: 0.80) mmol l−1.
Table 1.
Baseline characteristics of the study population
| Characteristic | Total | Q1a | Q2a | Q3a | Q4a |
|---|---|---|---|---|---|
| Participant (n) | 43 455 | 10 895 | 10 841 | 10 871 | 10 848 |
| Age (year), mean (s.d.) | 52.4 (7.7) | 50.7 (7.4) | 51.12 (7.3) | 52.3 (7.4) | 55.4 (7.6) |
| BMI (kg m−2), mean (s.d.) | 25.82 (3.13) | 25.44 (3.20) | 25.81 (3.08) | 25.95 (3.14) | 26.09 (3.06) |
| Smoking status, n (%) | |||||
| Never | 20 537 (47.3) | 5116 (47.0) | 5039 (46.5) | 5105 (47.0) | 5277 (48.6) |
| Current | 18 394 (42.3) | 4786 (43.9) | 4740 (43.7) | 4606 (42.4) | 4262 (39.3) |
| Previous | 4524 (10.4) | 993 (9.1) | 1062 (9.8) | 1160 (10.7) | 1309 (12.1) |
| Alcohol status, n (%) | |||||
| Never | 10 538 (24.3) | 2503 (23.0) | 2516 (23.2) | 2566 (23.6) | 2953 (27.2) |
| Current | 32 115 (73.9) | 8217 (75.4) | 8142 (75.1) | 8100 (74.5) | 7656 (70.6) |
| Previous | 802 (1.9) | 175 (1.6) | 183 (1.70) | 205 (1.90) | 239 (2.2) |
| Diabetes status, n (%) | 7770 (17.9) | 1735 (15.9) | 1909 (17.6) | 1927 (17.7) | 2199 (20.3) |
| Hypertension status, n (%) | 20 466 (47.1) | 4681 (43.0) | 4924 (45.4) | 5166 (47.5) | 5695 (52.5) |
| SBP (mmHg), mean (s.d.) | 125.24 (14.91) | 124.22 (14.81) | 124.75 (14.75) | 125.44 (14.81) | 126.54 (15.17) |
| DBP (mmHg), mean (s.d.) | 85.15 (10.86) | 84.75 (11.01) | 84.98 (10.79) | 85.35 (10.86) | 85.54 (10.74) |
| Waist circumference (cm), mean (s.d.) | 91.81 (8.33) | 90.71 (8.45) | 91.60 (8.21) | 92.17 (8.35) | 92.78 (8.17) |
| WHR, mean (s.d.) | 0.94 (0.05) | 0.93 (0.05) | 0.94 (0.05) | 0.94 (0.05) | 0.95 (0.05) |
| TC (mmol l−1), mean (s.d.) | 4.68 (0.90) | 4.71 (0.90) | 4.70 (0.90) | 4.67 (0.91) | 4.63 (0.90) |
| TG (mmol l−1), mean (s.d.) | 1.87 (1.38) | 1.88 (1.41) | 1.90 (1.39) | 1.88 (1.40) | 1.81 (1.30) |
| HDL-C (mmol l−1), mean (s.d.) | 1.19 (0.29) | 1.21 (0.30) | 1.19 (0.29) | 1.19 (0.29) | 1.19 (0.28) |
| LDL-C (mmol l−1), mean (s.d.) | 3.01 (0.80) | 3.03 (0.80) | 3.03 (0.80) | 3.01 (0.81) | 2.98 (0.80) |
| FPG (mmol l−1), mean (s.d.) | 6.03 (1.51) | 5.97 (1.52) | 6.03 (1.52) | 6.04 (1.51) | 6.09 (1.50) |
| PSA (ng ml−1), mean (s.d.) | 1.85 (9.99) | 0.99 (1.07) | 1.22 (1.39) | 1.10 (1.08) | 2.07 (19.87) |
| fPSA (ng ml−1), mean (s.d.) | 0.36 (1.67) | 0.29 (0.17) | 0.31 (0.18) | 0.34 (0.23) | 0.51 (3.33) |
| PV (ml), mean (s.d.) | 30.96 (9.90) | 22.51 (1.76) | 26.82 (1.09) | 31.15 (1.52) | 43.39 (11.95) |
| MetS, n (%) | 18 787 (43.2) | 4322 (39.7) | 4688 (43.2) | 4799 (44.1) | 4978 (45.9) |
| Elevated BP, n (%) | 27 151 (62.5) | 6407 (58.8) | 6601 (60.9) | 6844 (63.0) | 7299 (67.3) |
| Hyperglycemia, n (%) | 12 728 (29.3) | 2873 (26.4) | 3188 (29.4) | 3139 (28.9) | 3528 (32.5) |
| High TG, n (%) | 17 873 (41.1) | 4476 (41.1) | 4575 (42.2) | 4587 (42.2) | 4235 (39.0) |
| Low HDL-C, n (%) | 13 807 (31.8) | 3292 (30.2) | 3518 (32.5) | 8580 (32.9) | 3417 (31.5) |
| Central obesity, n (%) | 18 787 (43.2) | 6055 (55.6) | 6437 (59.4) | 6758 (62.2) | 7127 (65.7) |
aThe Kruskal–Wallis test was used for group comparisons, and there were significant differences among the groups (P<0.001). BMI: body mass index; BP: blood pressure; SBP: systolic BP; DBP: diastolic BP; WHR: waist-to-hip ratio; TC: total cholesterol; TG: triglyceride; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; FPG: fasting plasma glucose; PSA: prostate-specific antigen; fPSA: free PSA; MetS: metabolic syndrome; s.d.: standard deviation; PV: prostate volume
The mean PV for the study population was 30.96 (s.d.: 9.90) ml, with quartile group volumes as follows: Q1, 22.51 (s.d.: 1.76) ml; Q2, 26.82 (s.d.: 1.09) ml; Q3, 31.15 (s.d.: 1.52) ml; and Q4, 43.39 (s.d.: 11.95) ml. Statistically significant differences were observed among the four quartile groups for the abovementioned indicators (all P < 0.001).
Impact of MetS and its components on PV
In the crude analysis model, MetS demonstrated a positive correlation with PV, with this correlation becoming more pronounced as the PV quartiles increased (Q2, OR = 1.159, 95% CI: 1.098–1.223, P < 0.001; Q3, OR = 1.202, 95% CI: 1.139–1.269, P < 0.001; and Q4, OR = 1.290, 95% CI: 1.222–1.361, P < 0.001). After adjusting for multiple factors, the positive influence of MetS on PV was further amplified (Q2, OR = 1.203, 95% CI: 1.139–1.271, P < 0.001; Q3, OR = 1.300, 95% CI: 1.230–1.373, P < 0.001; and Q4, OR = 1.556, 95% CI: 1.469–1.648, P < 0.001), as shown in Table 2.
Table 2.
Impact of metabolic syndrome and its components on prostate volume
| Type | Participant, n (%) | Model 1 | Model 2 | ||
|---|---|---|---|---|---|
|
|
|
||||
| OR (95% CI) | P | OR (95% CI) | P | ||
| Q2 (total=10 841) | |||||
| MetS | 4688 (43.2) | 1.159 (1.098–1.223) | <0.001 | 1.203 (1.139–1.271) | <0.001 |
| Elevated BP | 6601 (60.9) | 1.091 (1.033–1.151) | 0.002 | 1.091 (1.033–1.153) | 0.002 |
| Hyperglycemia | 3188 (29.4) | 1.163 (1.096–1.234) | <0.001 | 1.187 (1.117–1.261) | <0.001 |
| High triglycerides | 4575 (42.2) | 1.047 (0.992–1.105) | 0.095 | 1.080 (1.022–1.141) | 0.006 |
| Low HDL-C | 3518 (32.5) | 1.110 (1.048–1.175) | <0.001 | 1.144 (1.080–1.213) | 0.003 |
| Central obesity | 6437 (59.4) | 1.168 (1.107–1.233) | <0.001 | 1.213 (1.149–1.282) | <0.001 |
| Q3 (total=10 871) | |||||
| MetS | 4799 (44.1) | 1.202(1.139–1.269) | <0.001 | 1.300 (1.230–1.373) | <0.001 |
| Elevated BP | 6844 (63.0) | 1.190 (1.127–1.257) | <0.001 | 1.161 (1.098–1.228) | <0.001 |
| Hyperglycemia | 3139 (28.9) | 1.134 (1.068–1.203) | 0.096 | 1.140 (1.073–1.212) | <0.001 |
| High triglycerides | 4587 (42.2) | 1.047 (0.992–1.105) | <0.001 | 1.142 (1.080–1.207) | <0.001 |
| Low HDL-C | 8580 (32.9) | 1.134 (1.071–1.201) | <0.001 | 1.231 (1.161–1.305) | <0.001 |
| Central obesity | 6758 (62.2) | 1.313 (1.244–1.386) | <0.001 | 1.424 (1.347–1.505) | <0.001 |
| Q4 (total=10 848) | |||||
| MetS | 4978 (45.9) | 1.290 (1.222–1.361) | <0.001 | 1.556 (1.469–1.648) | <0.001 |
| Elevated BP | 7299 (67.3) | 1.441 (1.363–1.523) | <0.001 | 1.316 (1.241–1.397) | <0.001 |
| Hyperglycemia | 3528 (32.5) | 1.346 (1.269–1.427) | <0.001 | 1.338 (1.256–1.425) | <0.001 |
| High triglycerides | 4235 (39.0) | 0.918 (0.870–0.970) | 0.918 | 1.161 (1.095–1.231) | <0.001 |
| Low HDL-C | 3417 (31.5) | 1.062 (1.003–1.125) | 0.041 | 1.294 (1.216–1.376) | <0.001 |
| Central obesity | 7127 (65.7) | 1.531 (1.449–1.617) | <0.001 | 1.857 (1.750–1.970) | <0.001 |
Model 1: unadjusted model; Model 2: adjusted for age, PSA, fPSA, smoking status, and alcohol status. Q2: PV >24.94 ml and ≤28.78 ml, Q3: PV >28.78 ml and ≤34.07 ml, and Q4: PV >34.07 ml. OR: odds ratio; CI: confidence interval; TG: triglyceride; HDL-C: high-density lipoprotein cholesterol; PSA: prostate-specific antigen; fPSA: free PSA; MetS: metabolic syndrome; BP: blood pressure; PV: prostate volume
A subsequent analysis of the five individual components of MetS, elevated blood pressure, hyperglycemia, high TG, low HDL-C, and central obesity revealed that each component exhibited an effect similar to that of overall MetS. Central obesity had the most significant impact on PV (Q2, OR = 1.213, 95% CI: 1.149–1.282, P < 0.001; Q3, OR = 1.424, 95% CI: 1.347–1.505, P < 0.001; and Q4, OR = 1.857, 95% CI: 1.750–1.970, P < 0.001). By contrast, high TG had the least impact (Q2, OR = 1.080, 95% CI: 1.022–1.141, P = 0.006; Q3, OR = 1.142, 95% CI: 1.080–1.207, P < 0.001; and Q4, OR = 1.161, 95% CI: 1.095–1.231, P < 0.001), as shown in Table 2.
Impact of number of MetS components on PV
In both the unadjusted and adjusted models, the absence of any MetS components was used as the reference point. The analysis showed that in Q2, Q3, and Q4, the presence of one or more MetS components was significantly associated with an increase in PV, as illustrated in Figure 1. Additionally, there was a progressive increase in the OR as the number of MetS components increased. This effect was particularly pronounced in the highest quartile of PV (Q4), with the OR values and their respective 95% CI as follows: for one MetS component, 1.364 (95% CI: 1.225–1.519, P < 0.001); for two components, 1.734 (95% CI: 1.563–1.923, P < 0.001); for three components, 2.113 (95% CI: 1.902–2.346, P < 0.001); for four components, 2.307 (95% CI: 2.056–2.588, P < 0.001); and for five components, 2.634 (95% CI: 2.259–3.071, P < 0.001).
Figure 1.
Impact of MetS components number on prostate volume. Model 1: unadjusted model. Model 2: adjusted for age, PSA, fPSA, smoking status, and alcohol status. TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; PSA: prostate-specific antigen; fPSA: free PSA; MetS: metabolic syndrome; BP: blood pressure; OR: odds ratio; aOR: adjusted OR.
Impact of MetS, its components, and component count on PV across different ages
Figure 2a displays the fitted curves illustrating the relationship between age and PV, revealing that PV increases with age, with this trend becoming more pronounced over time. Significant differences in PV were observed within the different age subgroups (Figure 2b). Repeated analyses across these age groups were performed, with the results presented in Supplementary Table 1 and 2.
Figure 2.
Age-related trends in prostate volume. (a) Fitted curves between age and prostate volume. (b) Prostate volume in different age subgroups.
Supplementary Table 1.
Impact of metabolic syndrome and its components on prostate volume in different age group
| PV | Type | ORa | 2.50% | 97.50% | P | aORb | 2.50% | 97.50% | P |
|---|---|---|---|---|---|---|---|---|---|
| <40 years | |||||||||
| Q2 | MetS | 1.267 | 0.982 | 1.634 | 0.068 | 1.256 | 0.966 | 1.633 | 0.089 |
| Elevated BP | 1.261 | 0.985 | 1.616 | 0.066 | 1.250 | 0.971 | 1.609 | 0.083 | |
| Hyperglycemia | 1.248 | 0.862 | 1.806 | 0.241 | 1.212 | 0.834 | 1.763 | 0.313 | |
| High TG | 1.107 | 0.863 | 1.421 | 0.422 | 1.071 | 0.829 | 1.384 | 0.600 | |
| Low HDL-C | 1.082 | 0.840 | 1.395 | 0.541 | 1.080 | 0.833 | 1.399 | 0.561 | |
| Central obesity | 1.405 | 1.094 | 1.804 | 0.008 | 1.395 | 1.077 | 1.806 | 0.012 | |
| Q3 | MetS | 1.682 | 1.277 | 2.216 | 0.000 | 1.691 | 1.270 | 2.251 | 0.000 |
| Elevated BP | 1.551 | 1.181 | 2.037 | 0.002 | 1.569 | 1.186 | 2.075 | 0.002 | |
| Hyperglycemia | 1.132 | 0.750 | 1.708 | 0.555 | 1.112 | 0.732 | 1.689 | 0.620 | |
| High TG | 1.720 | 1.309 | 2.261 | 0.000 | 1.648 | 1.243 | 2.185 | 0.001 | |
| Low HDL-C | 1.243 | 0.943 | 1.639 | 0.122 | 1.260 | 0.950 | 1.673 | 0.109 | |
| Central obesity | 1.732 | 1.310 | 2.290 | 0.000 | 1.728 | 1.293 | 2.309 | 0.000 | |
| Q4 | MetS | 1.499 | 1.020 | 2.203 | 0.039 | 1.634 | 1.093 | 2.443 | 0.017 |
| Elevated BP | 1.553 | 1.059 | 2.277 | 0.024 | 1.655 | 1.114 | 2.460 | 0.013 | |
| Hyperglycemia | 0.773 | 0.404 | 1.476 | 0.435 | 0.802 | 0.416 | 1.547 | 0.511 | |
| High TG | 1.542 | 1.053 | 2.259 | 0.026 | 1.532 | 1.031 | 2.277 | 0.035 | |
| Low HDL-C | 1.142 | 0.775 | 1.684 | 0.502 | 1.232 | 0.827 | 1.837 | 0.305 | |
| Central obesity | 1.953 | 1.306 | 2.920 | 0.001 | 2.104 | 1.383 | 3.200 | 0.001 | |
| 40–49 years | |||||||||
| Q2 | MetS | 1.174 | 1.076 | 1.282 | 0.000 | 1.239 | 1.133 | 1.354 | 0.000 |
| Elevated BP | 1.063 | 0.976 | 1.159 | 0.162 | 1.094 | 1.003 | 1.194 | 0.043 | |
| Hyperglycemia | 1.090 | 0.983 | 1.208 | 0.103 | 1.144 | 1.030 | 1.270 | 0.012 | |
| High TG | 1.112 | 1.020 | 1.212 | 0.016 | 1.149 | 1.053 | 1.254 | 0.002 | |
| Low HDL-C | 1.187 | 1.083 | 1.301 | 0.000 | 1.212 | 1.104 | 1.330 | 0.000 | |
| Central obesity | 1.186 | 1.088 | 1.293 | 0.000 | 1.249 | 1.144 | 1.364 | 0.000 | |
| Q3 | MetS | 1.299 | 1.187 | 1.422 | 0.000 | 1.399 | 1.276 | 1.535 | 0.000 |
| Elevated BP | 1.169 | 1.068 | 1.278 | 0.001 | 1.204 | 1.099 | 1.318 | 0.000 | |
| Hyperglycemia | 1.071 | 0.963 | 1.193 | 0.206 | 1.132 | 1.015 | 1.262 | 0.026 | |
| High TG | 1.136 | 1.039 | 1.242 | 0.005 | 1.189 | 1.086 | 1.302 | 0.000 | |
| Low HDL-C | 1.310 | 1.192 | 1.439 | 0.000 | 1.361 | 1.236 | 1.499 | 0.000 | |
| Central obesity | 1.471 | 1.343 | 1.611 | 0.000 | 1.587 | 1.446 | 1.741 | 0.000 | |
| Q4 | MetS | 1.352 | 1.219 | 1.501 | 0.000 | 1.555 | 1.397 | 1.732 | 0.000 |
| Elevated BP | 1.247 | 1.124 | 1.384 | 0.000 | 1.299 | 1.168 | 1.446 | 0.000 | |
| Hyperglycemia | 1.225 | 1.085 | 1.382 | 0.001 | 1.342 | 1.185 | 1.519 | 0.000 | |
| High TG | 1.064 | 0.960 | 1.180 | 0.239 | 1.170 | 1.052 | 1.301 | 0.004 | |
| Low HDL-C | 1.291 | 1.158 | 1.440 | 0.000 | 1.409 | 1.259 | 1.575 | 0.000 | |
| Central obesity | 1.635 | 1.470 | 1.820 | 0.000 | 1.877 | 1.681 | 2.096 | 0.000 | |
| 50–59 years | |||||||||
| Q2 | MetS | 1.138 | 1.050 | 1.233 | 0.002 | 1.172 | 1.081 | 1.272 | 0.000 |
| Elevated BP | 1.077 | 0.991 | 1.170 | 0.079 | 1.080 | 0.994 | 1.175 | 0.070 | |
| Hyperglycemia | 1.202 | 1.103 | 1.309 | 0.000 | 1.237 | 1.134 | 1.348 | 0.000 | |
| High TG | 1.007 | 0.929 | 1.093 | 0.858 | 1.019 | 0.939 | 1.106 | 0.649 | |
| Low HDL-C | 1.086 | 0.997 | 1.184 | 0.059 | 1.115 | 1.022 | 1.216 | 0.014 | |
| Central obesity | 1.141 | 1.052 | 1.238 | 0.001 | 1.174 | 1.081 | 1.274 | 0.000 | |
| Q3 | MetS | 1.150 | 1.062 | 1.246 | 0.001 | 1.247 | 1.150 | 1.352 | 0.000 |
| Elevated BP | 1.105 | 1.018 | 1.199 | 0.017 | 1.110 | 1.021 | 1.206 | 0.014 | |
| Hyperglycemia | 1.107 | 1.017 | 1.206 | 0.019 | 1.180 | 1.082 | 1.287 | 0.000 | |
| High TG | 1.046 | 0.965 | 1.133 | 0.275 | 1.090 | 1.005 | 1.182 | 0.039 | |
| Low HDL-C | 1.068 | 0.981 | 1.162 | 0.131 | 1.146 | 1.051 | 1.250 | 0.002 | |
| Central obesity | 1.246 | 1.149 | 1.350 | 0.000 | 1.347 | 1.241 | 1.463 | 0.000 | |
| Q4 | MetS | 1.270 | 1.174 | 1.374 | 0.000 | 1.506 | 1.387 | 1.637 | 0.000 |
| Elevated BP | 1.306 | 1.203 | 1.417 | 0.000 | 1.343 | 1.232 | 1.464 | 0.000 | |
| Hyperglycemia | 1.181 | 1.086 | 1.284 | 0.000 | 1.339 | 1.226 | 1.462 | 0.000 | |
| High TG | 1.014 | 0.936 | 1.097 | 0.737 | 1.112 | 1.023 | 1.209 | 0.012 | |
| Low HDL-C | 1.047 | 0.962 | 1.139 | 0.287 | 1.184 | 1.083 | 1.293 | 0.000 | |
| Central obesity | 1.504 | 1.388 | 1.631 | 0.000 | 1.783 | 1.637 | 1.942 | 0.000 | |
| 60–69 years | |||||||||
| Q2 | MetS | 1.130 | 0.963 | 1.326 | 0.133 | 1.167 | 0.994 | 1.371 | 0.06 |
| Elevated BP | 1.061 | 0.897 | 1.254 | 0.489 | 1.056 | 0.891 | 1.250 | 0.531 | |
| Hyperglycemia | 1.060 | 0.901 | 1.247 | 0.483 | 1.112 | 0.944 | 1.311 | 0.204 | |
| High TG | 1.067 | 0.900 | 1.265 | 0.454 | 1.083 | 0.912 | 1.285 | 0.363 | |
| Low HDL-C | 1.046 | 0.874 | 1.251 | 0.626 | 1.074 | 0.896 | 1.287 | 0.441 | |
| Central obesity | 1.108 | 0.947 | 1.296 | 0.200 | 1.144 | 0.976 | 1.340 | 0.097 | |
| Q3 | MetS | 1.058 | 0.910 | 1.230 | 0.464 | 1.140 | 0.978 | 1.328 | 0.093 |
| Elevated BP | 1.085 | 0.926 | 1.270 | 0.313 | 1.084 | 0.924 | 1.272 | 0.323 | |
| Hyperglycemia | 0.971 | 0.832 | 1.132 | 0.705 | 1.073 | 0.918 | 1.255 | 0.375 | |
| High TG | 0.991 | 0.843 | 1.164 | 0.910 | 1.024 | 0.870 | 1.205 | 0.778 | |
| Low HDL-C | 1.097 | 0.927 | 1.297 | 0.282 | 1.142 | 0.963 | 1.355 | 0.127 | |
| Central obesity | 1.139 | 0.983 | 1.320 | 0.084 | 1.236 | 1.064 | 1.437 | 0.006 | |
| Q4 | MetS | 1.270 | 1.107 | 1.457 | 0.001 | 1.514 | 1.308 | 1.752 | 0.000 |
| Elevated BP | 1.183 | 1.023 | 1.367 | 0.023 | 1.145 | 0.981 | 1.336 | 0.086 | |
| Hyperglycemia | 1.070 | 0.930 | 1.231 | 0.342 | 1.322 | 1.138 | 1.535 | 0.000 | |
| High TG | 1.029 | 0.888 | 1.192 | 0.704 | 1.106 | 0.946 | 1.293 | 0.206 | |
| Low HDL-C | 1.176 | 1.009 | 1.371 | 0.038 | 1.322 | 1.123 | 1.557 | 0.001 | |
| Central obesity | 1.483 | 1.295 | 1.700 | 0.000 | 1.795 | 1.550 | 2.078 | 0.000 | |
| ≥70 years | |||||||||
| Q2 | MetS | 1.084 | 0.583 | 2.017 | 0.799 | 1.127 | 0.597 | 2.125 | 0.712 |
| Elevated BP | 0.895 | 0.433 | 1.848 | 0.764 | 0.984 | 0.466 | 2.078 | 0.967 | |
| Hyperglycemia | 1.266 | 0.679 | 2.362 | 0.457 | 1.323 | 0.700 | 2.501 | 0.389 | |
| High TG | 0.694 | 0.352 | 1.365 | 0.289 | 0.699 | 0.351 | 1.389 | 0.307 | |
| Low HDL-C | 0.695 | 0.339 | 1.423 | 0.319 | 0.665 | 0.317 | 1.395 | 0.281 | |
| Central obesity | 1.318 | 0.695 | 2.500 | 0.398 | 1.357 | 0.707 | 2.604 | 0.359 | |
| Q3 | MetS | 0.893 | 0.493 | 1.618 | 0.709 | 0.933 | 0.508 | 1.712 | 0.822 |
| Elevated BP | 1.069 | 0.528 | 2.165 | 0.854 | 1.162 | 0.562 | 2.401 | 0.685 | |
| Hyperglycemia | 0.762 | 0.416 | 1.395 | 0.378 | 0.794 | 0.428 | 1.473 | 0.464 | |
| High TG | 0.840 | 0.447 | 1.579 | 0.588 | 0.868 | 0.457 | 1.649 | 0.665 | |
| Low HDL-C | 1.129 | 0.591 | 2.157 | 0.713 | 1.174 | 0.600 | 2.298 | 0.639 | |
| Central obesity | 0.914 | 0.502 | 1.663 | 0.768 | 0.908 | 0.493 | 1.672 | 0.757 | |
| Q4 | MetS | 0.705 | 0.422 | 1.179 | 0.183 | 1.082 | 0.990 | 1.529 | 0.632 |
| Elevated BP | 0.976 | 0.535 | 1.782 | 0.938 | 1.134 | 0.581 | 2.213 | 0.713 | |
| Hyperglycemia | 0.799 | 0.477 | 1.338 | 0.393 | 0.971 | 0.552 | 1.709 | 0.919 | |
| High TG | 0.631 | 0.365 | 1.090 | 0.099 | 0.743 | 0.408 | 1.352 | 0.331 | |
| Low HDL-C | 0.785 | 0.444 | 1.386 | 0.403 | 1.074 | 0.574 | 2.012 | 0.822 | |
| Central obesity | 1.006 | 0.601 | 1.687 | 0.980 | 1.206 | 0.683 | 2.130 | 0.518 |
aThe unadjusted model. bAdjusted covariates age, PSA, fPSA, smoking status, and alcohol status. TG: triglyceride; HDL-C: high-density lipoprotein cholesterol; PSA: prostate-specific antigen; fPSA: free PSA; MetS: Metabolic syndrome; BP: blood pressure; OR: odds ratio; aOR: adjusted OR; PV: prostate volume
Supplementary Table 2.
Impact of metabolic syndrome components number on prostate volume in different age groups
| PV | Type | ORa | 2.50% | 97.50% | P | aORb | 2.50% | 97.50% | P |
|---|---|---|---|---|---|---|---|---|---|
| <40 years | |||||||||
| Q2 | Five components | 1.302 | 0.687 | 2.468 | 0.418 | 1.274 | 0.666 | 2.435 | 0.464 |
| Four components | 1.715 | 1.101 | 2.670 | 0.017 | 1.678 | 1.067 | 2.638 | 0.025 | |
| Three components | 1.451 | 0.983 | 2.142 | 0.061 | 1.406 | 0.943 | 2.096 | 0.094 | |
| Two components | 1.376 | 0.929 | 2.038 | 0.111 | 1.313 | 0.881 | 1.957 | 0.180 | |
| One component | 1.241 | 0.845 | 1.824 | 0.270 | 1.218 | 0.826 | 1.794 | 0.319 | |
| Q3 | Five components | 1.404 | 0.689 | 2.860 | 0.350 | 1.393 | 0.674 | 2.879 | 0.371 |
| Four components | 2.550 | 1.593 | 4.082 | 0.000 | 2.591 | 1.596 | 4.206 | 0.000 | |
| Three components | 1.748 | 1.136 | 2.690 | 0.011 | 1.701 | 1.090 | 2.653 | 0.019 | |
| Two components | 1.719 | 1.115 | 2.651 | 0.014 | 1.642 | 1.055 | 2.554 | 0.028 | |
| One component | 0.850 | 0.532 | 1.359 | 0.497 | 0.840 | 0.522 | 1.351 | 0.472 | |
| Q4 | Five components | 0.551 | 0.121 | 2.522 | 0.443 | 0.615 | 0.132 | 2.866 | 0.536 |
| Four components | 3.640 | 1.898 | 6.982 | 0.000 | 3.185 | 2.124 | 8.245 | 0.000 | |
| Three components | 1.629 | 0.844 | 3.142 | 0.146 | 1.744 | 0.884 | 3.443 | 0.109 | |
| Two components | 1.907 | 1.004 | 3.620 | 0.048 | 1.886 | 0.977 | 3.642 | 0.059 | |
| One component | 1.422 | 0.740 | 2.734 | 0.291 | 1.449 | 0.742 | 2.830 | 0.277 | |
| 40–49 years | |||||||||
| Q2 | Five components | 1.384 | 1.087 | 1.763 | 0.008 | 1.546 | 1.211 | 1.975 | 0.000 |
| Four components | 1.412 | 1.203 | 1.658 | 0.000 | 1.549 | 1.316 | 1.823 | 0.000 | |
| Three components | 1.228 | 1.065 | 1.417 | 0.005 | 1.330 | 1.150 | 1.538 | 0.000 | |
| Two components | 1.167 | 1.014 | 1.342 | 0.031 | 1.225 | 1.064 | 1.411 | 0.005 | |
| One component | 1.130 | 0.976 | 1.308 | 0.101 | 1.163 | 1.004 | 1.347 | 0.044 | |
| Q3 | Five components | 1.988 | 1.564 | 2.527 | 0.000 | 2.298 | 1.801 | 2.932 | 0.000 |
| Four components | 1.686 | 1.428 | 1.992 | 0.000 | 1.907 | 1.609 | 2.260 | 0.000 | |
| Three components | 1.339 | 1.152 | 1.557 | 0.000 | 1.489 | 1.277 | 1.737 | 0.000 | |
| Two components | 1.267 | 1.093 | 1.470 | 0.002 | 1.345 | 1.158 | 1.563 | 0.000 | |
| One component | 1.178 | 1.008 | 1.375 | 0.039 | 1.215 | 1.039 | 1.422 | 0.015 | |
| Q4 | Five components | 2.526 | 1.923 | 3.318 | 0.000 | 3.250 | 2.457 | 4.299 | 0.000 |
| Four components | 1.806 | 1.478 | 2.208 | 0.000 | 2.254 | 1.833 | 2.772 | 0.000 | |
| Three components | 1.680 | 1.402 | 2.014 | 0.000 | 2.038 | 1.691 | 2.455 | 0.000 | |
| Two components | 1.532 | 1.281 | 1.831 | 0.000 | 1.700 | 1.416 | 2.041 | 0.000 | |
| One component | 1.359 | 1.126 | 1.641 | 0.001 | 1.428 | 1.179 | 1.730 | 0.000 | |
| 50–59 years | |||||||||
| Q2 | Five components | 1.407 | 1.143 | 1.732 | 0.001 | 1.487 | 1.206 | 1.833 | 0.000 |
| Four components | 1.282 | 1.094 | 1.501 | 0.002 | 1.330 | 1.134 | 1.560 | 0.000 | |
| Three components | 1.289 | 1.115 | 1.490 | 0.001 | 1.325 | 1.145 | 1.533 | 0.000 | |
| Two components | 1.204 | 1.044 | 1.390 | 0.011 | 1.217 | 1.054 | 1.405 | 0.008 | |
| One component | 1.156 | 0.996 | 1.342 | 0.056 | 1.150 | 0.991 | 1.335 | 0.066 | |
| Q3 | Five components | 1.358 | 1.101 | 1.674 | 0.004 | 1.562 | 1.264 | 1.932 | 0.000 |
| Four components | 1.410 | 1.205 | 1.650 | 0.000 | 1.557 | 1.327 | 1.826 | 0.000 | |
| Three components | 1.380 | 1.194 | 1.594 | 0.000 | 1.483 | 1.281 | 1.718 | 0.000 | |
| Two components | 1.314 | 1.139 | 1.515 | 0.000 | 1.351 | 1.169 | 1.561 | 0.000 | |
| One component | 1.199 | 1.033 | 1.391 | 0.017 | 1.181 | 1.016 | 1.373 | 0.030 | |
| Q4 | Five components | 1.794 | 1.456 | 2.210 | 0.000 | 2.396 | 1.925 | 2.982 | 0.000 |
| Four components | 1.799 | 1.533 | 2.111 | 0.000 | 2.224 | 1.879 | 2.632 | 0.000 | |
| Three components | 1.793 | 1.546 | 2.079 | 0.000 | 2.112 | 1.807 | 2.468 | 0.000 | |
| Two components | 1.619 | 1.398 | 1.875 | 0.000 | 1.727 | 1.480 | 2.015 | 0.000 | |
| One component | 1.411 | 1.211 | 1.645 | 0.000 | 1.379 | 1.174 | 1.620 | 0.000 | |
| 60–69 years | |||||||||
| Q2 | Five components | 0.973 | 0.607 | 1.560 | 0.909 | 1.029 | 0.641 | 1.652 | 0.906 |
| Four components | 1.222 | 0.888 | 1.682 | 0.219 | 1.305 | 0.946 | 1.800 | 0.105 | |
| Three components | 1.234 | 0.922 | 1.652 | 0.157 | 1.262 | 0.941 | 1.692 | 0.121 | |
| Two components | 1.122 | 0.845 | 1.489 | 0.426 | 1.142 | 0.859 | 1.520 | 0.361 | |
| One component | 1.031 | 0.773 | 1.374 | 0.835 | 1.030 | 0.771 | 1.374 | 0.843 | |
| Q3 | Five components | 1.338 | 0.880 | 2.036 | 0.173 | 1.507 | 0.986 | 2.303 | 0.058 |
| Four components | 0.990 | 0.729 | 1.343 | 0.946 | 1.135 | 0.833 | 1.547 | 0.421 | |
| Three components | 1.182 | 0.899 | 1.554 | 0.230 | 1.259 | 0.954 | 1.661 | 0.104 | |
| Two components | 1.165 | 0.894 | 1.516 | 0.258 | 1.215 | 0.929 | 1.588 | 0.155 | |
| One component | 1.002 | 0.766 | 1.311 | 0.988 | 0.999 | 0.761 | 1.312 | 0.995 | |
| Q4 | Five components | 1.677 | 1.134 | 2.482 | 0.010 | 2.171 | 1.435 | 3.283 | 0.000 |
| Four components | 1.438 | 1.085 | 1.905 | 0.012 | 1.913 | 1.416 | 2.583 | 0.000 | |
| Three components | 1.696 | 1.314 | 2.190 | 0.000 | 1.921 | 1.462 | 2.525 | 0.000 | |
| Two components | 1.483 | 1.156 | 1.901 | 0.002 | 1.557 | 1.193 | 2.032 | 0.001 | |
| One component | 1.152 | 0.894 | 1.484 | 0.274 | 1.125 | 0.858 | 1.476 | 0.393 | |
| ≥70 years | |||||||||
| Q2 | Five components | 0.714 | 0.132 | 3.868 | 0.696 | 0.756 | 0.137 | 4.168 | 0.749 |
| Four components | 0.900 | 0.194 | 4.165 | 0.893 | 0.923 | 0.196 | 4.351 | 0.919 | |
| Three components | 1.687 | 0.422 | 6.743 | 0.459 | 1.716 | 0.425 | 6.933 | 0.448 | |
| Two components | 1.263 | 0.318 | 5.011 | 0.740 | 1.277 | 0.316 | 5.155 | 0.732 | |
| One component | 1.059 | 0.260 | 4.318 | 0.936 | 1.039 | 0.252 | 4.287 | 0.957 | |
| Q3 | Five components | 0.536 | 0.112 | 2.553 | 0.433 | 0.581 | 0.120 | 2.822 | 0.501 |
| Four components | 1.125 | 0.289 | 4.377 | 0.865 | 1.192 | 0.300 | 4.740 | 0.803 | |
| Three components | 0.859 | 0.237 | 3.121 | 0.818 | 0.904 | 0.246 | 3.326 | 0.879 | |
| Two components | 1.020 | 0.291 | 3.576 | 0.976 | 1.065 | 0.298 | 3.803 | 0.923 | |
| One component | 0.919 | 0.257 | 3.292 | 0.897 | 0.927 | 0.255 | 3.367 | 0.909 | |
| Q4 | Five components | 0.524 | 0.147 | 1.870 | 0.319 | 1.254 | 0.316 | 4.984 | 0.748 |
| Four components | 0.483 | 0.147 | 1.587 | 0.231 | 0.671 | 0.181 | 2.488 | 0.550 | |
| Three components | 0.646 | 0.216 | 1.930 | 0.434 | 0.900 | 0.270 | 3.004 | 0.864 | |
| Two components | 0.947 | 0.327 | 2.748 | 0.921 | 1.245 | 0.385 | 4.030 | 0.714 | |
| One component | 0.598 | 0.201 | 1.776 | 0.355 | 0.767 | 0.231 | 2.543 | 0.664 |
aThe unadjusted model. bAdjusted covariates age, PSA, fPSA, smoking status, and alcohol status. PSA: prostate-specific antigen; fPSA: free PSA; PV: prostate volume; OR: odds ratio; aOR: adjusted OR
After multivariable adjustment (Figure 3 and 4), the impact of MetS, its components, and the number of components on PV exhibited a decreasing trend with advancing age. Specifically, in individuals aged ≥70 years, neither MetS nor its components appeared to influence PV. Overall, the influence of MetS, its components, and the component count on PV was most prominent in the age groups of 40–49 years and 50–59 years. For the Q3 and Q4 subgroups, the positive effects of MetS, abdominal obesity, elevated blood pressure, and hyperglycemia on PV remained significant in individuals aged <40 years (Figure 3).
Figure 3.
Impact of MetS and its components on PV varies with age. Odds ratios were calculated based on a multivariate-adjusted logistic regression model (adjusted for age, prostate-specific antigen, free prostate-specific antigen, smoking status, and alcohol status). Represents (a) the Q2 group, (b) the Q3 group, and (c) the Q4 group of the PV group. Q2: PV >24.94 ml and ≤28.78 ml, Q3: PV >28.78 ml and ≤34.07 ml, and Q4: PV >34.07 ml. MetS: metabolic syndrome; PV: prostate volume.
Figure 4.
Impact of MetS components number on PV varies with age. Odds ratios were calculated based on a multivariate-adjusted logistic regression model (adjusted for age, prostate-specific antigen, free prostate-specific antigen, smoking status, and alcohol status). Represents (a) the Q2 group, (b) the Q3 group, and (c) the Q4 group of the PV group. Q2: PV >24.94 ml and ≤28.78 ml, Q3: PV >28.78 ml and ≤34.07 ml, and Q4: PV >34.07 ml. MetS: metabolic syndrome; PV: prostate volume.
However, in the analysis of the impact of the number of MetS components on PV in individuals aged <40 years, only two, three, and four components in the Q3 group and only four components in the Q4 group were associated with increased PV, while no number of components in the Q2 group was related to a PV increase (Figure 4). In the age group of 60–69 years, there was no association between MetS, its components, or the number of components and increased PV in the Q2 group. In the Q3 group, only abdominal obesity was associated with increased PV. In the Q4 group, high TG, elevated blood pressure, and individual MetS components were not associated with increased PV, indicating a less pronounced effect compared with the age groups of 40–49 years and 50–59 years.
Additionally, similar to the overall population model, abdominal obesity showed the strongest correlation with PV across most age groups (Figure 3).
DISCUSSION
As the population ages in China and worldwide, the disease burden associated with BPH is escalating. The results of our study indicate that MetS is a significant risk factor associated with PV, with this relationship becoming stronger as PV increases. Notably, among the components of MetS, central obesity has the strongest association with PV. Additionally, our findings demonstrate a positive correlation between the number of MetS components and the increase in PV, suggesting a dose–response relationship. These effects were primarily observed in the age groups of 40–49 years and 50–59 years.
A cross-sectional study involving 426 individuals from Iraq showed no significant association between MetS and PV.18 By contrast, studies from China have reported different outcomes, demonstrating that MetS is a risk factor for both increased PV and the annual growth rate of PV in populations with BPH.13,14 However, these studies predominantly included participants from southern China and had smaller sample sizes. Our study, which primarily focused on a northern Chinese population and involved a substantially larger sample size, provides more robust evidence for the association between MetS and PV in the Chinese demographic.
A population-based study using the China Health and Retirement Longitudinal Study cohort in China showed that individuals with MetS were 1.60 times more likely to develop LUTS/BPH than individuals without MetS (95% CI: 1.24–2.06). Additionally, elevated blood pressure showed no correlation with the incidence of LUTS/BPH, while other MetS components were associated with an increased risk after propensity score matching.12 Similarly, large-scale studies from other countries align with these findings. For instance, Suarez Arbelaez et al.4 used the TriNetX research network to analyze 36 911 824 individuals, revealing a 10-fold increase in BPH risk for those with MetS (95% CI: 9.80–10.20). Additionally, a South Korean study involving 130 454 participants showed that all MetS components were associated with a higher prevalence of BPH requiring treatment.5 In these large-scale studies, the primary outcome was BPH rather than PV. Current studies indicate that the diagnosis of BPH extends beyond histological assessment to include LUTS attributed to BPH.19 However, it is known that the severity of LUTS does not necessarily correlate with PV.20,21 Thus, focusing on BPH/LUTS populations or using BPH/LUTS as an endpoint often overlooks individuals with enlarged PV but minimal LUTS, potentially introducing the selection bias. By examining the impact of MetS on PV in a general health examination cohort, our study reduces this bias, enhancing the objectivity of our findings.
Sebastianelli and Gacci22 noted a growing consensus on the positive link between MetS and increased PV, despite the conflicting outcomes in studies examining MetS and LUTS. Notably, abdominal obesity emerges as a key MetS component strongly associated with greater PV,22 a finding consistent with our results. MetS is characterized as a systemic inflammatory state with mechanisms leading to a pro-inflammatory condition involving elevated levels of inflammatory markers such as interleukin-6 and C-reactive protein.23 Furthermore, the oxidative stress from insulin resistance and obesity triggers inflammatory responses that can result in tissue fibrosis and atherosclerosis.24 In prostate hyperplasia, inflammation is also a key factor, with chronic inflammation commonly coexisting with BPH histology.25 Foundational studies suggest that dihydrotestosterone in the hypertrophied prostate may provoke chronic inflammation, increase the expression of inflammatory mediators, and enhance prostate tissue proliferation.26 De Nunzio et al.27 proposed that MetS, comprising obesity, hypertension, dyslipidemia, and insulin resistance, could foster a pro-inflammatory prostatic environment, leading to uncontrolled cell proliferation. Consequently, the inflammatory state induced by MetS, combined with the pelvic ischemia from atherosclerotic changes, is believed to contribute significantly to prostate gland enlargement.
Previous research has shown that the rate of PV increase accelerates with age,28 which aligns with our findings. Specifically, the peripheral zone volume growth peaks between 60 years and 70 years of age, while the transition zone volume continues to grow across all age groups without a clear peak.29 This growth pattern further confirms an accelerated mode of PV enlargement with advancing age. Our results suggest that the influence of MetS on PV diminishes with age, possibly because of the increasing impact of age itself on PV expansion. Additionally, the low incidence of BPH in individuals aged <40 years may have led to a smaller sample size in this age group, which might explain the lack of observed effects in patients of this age.
This large-sample cross-sectional study, focused on the Chinese population, adds valuable evidence to the prevention and management of BPH in China. Given the rising burden of BPH, our research holds significant clinical relevance. However, the study has its limitations. Being a cross-sectional study, it cannot establish a causal relationship between MetS and PV; it can only indicate correlations. Future prospective cohort studies or Mendelian randomization studies are needed to further validate these findings in the Chinese population. Additionally, the reliance on self-reported data for smoking, alcohol consumption, and medical history may introduce recall bias. Finally, some potential risk factors for BPH, such as diet and sex hormone levels, were not considered in this study.30 More comprehensive studies are needed to verify our conclusions.
CONCLUSION
The findings of this study suggest that proactive management of MetS, such as weight loss and active control of diabetes and hypertension, in middle-aged and elderly individuals may help reduce prostatic tissue enlargement, thereby lowering the risk of developing BPH and slowing its progression. This effect is particularly significant in individuals aged 40 to 59 years with abdominal obesity, highlighting the importance of targeted interventions in this subgroup. These strategies are crucial for reducing the burden of BPH in our population and decreasing associated healthcare costs.
AUTHOR CONTRIBUTIONS
GRY designed the study protocol, organized and analyzed the data, and was responsible for drafting and revising the manuscript. CL acquired the data and participated in data analysis. KKL participated in data analysis and drafted the manuscript. YYW contributed to drafting and revising the manuscript. XWH was involved in the study design. QY participated in the study design, data acquisition, and manuscript writing and revision. TS was responsible for study design and guidance throughout the research process. All authors read and approved the final manuscript.
COMPETING INTEREST
All authors declare no competing interests.
ACKNOWLEDGMENTS
This work was supported by the Beijing NOVA Program (grant No. 20220484230), and National Key Research and Development Program of China (grant No. 2023YFC3605305).
Supplementary Information is linked to the online version of the paper on the Asian Journal of Andrology website.
REFERENCES
- 1.Zhang W, Zhang X, Li H, Wu F, Wang H, et al. Prevalence of lower urinary tract symptoms suggestive of benign prostatic hyperplasia (LUTS/BPH) in China:results from the China health and retirement longitudinal study. BMJ Open. 2019;9:e022792. doi: 10.1136/bmjopen-2018-022792. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Liu D, Li C, Li Y, Zhou L, Li J, et al. Benign prostatic hyperplasia burden comparison between China and United States based on the Global Burden of Disease Study 2019. World J Urol. 2023;41:3629–34. doi: 10.1007/s00345-023-04658-8. [DOI] [PubMed] [Google Scholar]
- 3.Wang YB, Yang L, Deng YQ, Yan SY, Luo LS, et al. Causal relationship between obesity, lifestyle factors and risk of benign prostatic hyperplasia:a univariable and multivariable Mendelian randomization study. J Transl Med. 2022;20:495. doi: 10.1186/s12967-022-03722-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Suarez Arbelaez MC, Nackeeran S, Shah K, Blachman-Braun R, Bronson I, et al. Association between body mass index, metabolic syndrome and common urologic conditions:a cross-sectional study using a large multi-institutional database from the United States. Ann Med. 2023;55:2197293. doi: 10.1080/07853890.2023.2197293. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Yoo S, Oh S, Park J, Cho SY, Cho MC, et al. The impacts of metabolic syndrome and lifestyle on the prevalence of benign prostatic hyperplasia requiring treatment:historical cohort study of 130 454 men. BJU Int. 2019;123:140–8. doi: 10.1111/bju.14528. [DOI] [PubMed] [Google Scholar]
- 6.Li J, Peng L, Cao D, Gou H, Li Y, et al. The association between metabolic syndrome and benign prostatic hyperplasia:a systematic review and meta-analysis. Aging Male. 2020;23:1388–99. doi: 10.1080/13685538.2020.1771552. [DOI] [PubMed] [Google Scholar]
- 7.De Nunzio C, Aronson W, Freedland SJ, Giovannucci E, Parsons JK. The correlation between metabolic syndrome and prostatic diseases. Eur Urol. 2012;61:560–70. doi: 10.1016/j.eururo.2011.11.013. [DOI] [PubMed] [Google Scholar]
- 8.Gacci M, Sebastianelli A, Salvi M, De Nunzio C, Vignozzi L, et al. Benign prostatic enlargement can be influenced by metabolic profile:results of a multicenter prospective study. BMC Urol. 2017;17:22. doi: 10.1186/s12894-017-0211-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ozden C, Ozdal OL, Urgancioglu G, Koyuncu H, Gokkaya S, et al. The correlation between metabolic syndrome and prostatic growth in patients with benign prostatic hyperplasia. Eur Urol. 2007;51:199–203. doi: 10.1016/j.eururo.2006.05.040. [DOI] [PubMed] [Google Scholar]
- 10.Park YW, Kim SB, Kwon H, Kang HC, Cho K, et al. The relationship between lower urinary tract symptoms/benign prostatic hyperplasia and the number of components of metabolic syndrome. Urology. 2013;82:674–9. doi: 10.1016/j.urology.2013.03.047. [DOI] [PubMed] [Google Scholar]
- 11.Zhang X, Zeng X, Liu Y, Dong L, Zhao X, et al. Impact of metabolic syndrome on benign prostatic hyperplasia in elderly Chinese men. Urol Int. 2014;93:214–9. doi: 10.1159/000357760. [DOI] [PubMed] [Google Scholar]
- 12.Xiong Y, Zhang Y, Tan J, Qin F, Yuan J. The association between metabolic syndrome and lower urinary tract symptoms suggestive of benign prostatic hyperplasia in aging males:evidence based on propensity score matching. Transl Androl Urol. 2021;10:384–96. doi: 10.21037/tau-20-1127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Yin Z, Yang JR, Rao JM, Song W, Zhou KQ. Association between benign prostatic hyperplasia, body mass index, and metabolic syndrome in Chinese men. Asian J Androl. 2015;17:826–30. doi: 10.4103/1008-682X.148081. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Pan JG, Liu M, Zhou X. Relationship between lower urinary tract symptoms and metabolic syndrome in a Chinese male population. J Endocrinol Invest. 2014;37:339–44. doi: 10.1007/s40618-013-0018-9. [DOI] [PubMed] [Google Scholar]
- 15.Chinese Diabetes Society. Zhu D. [Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition) Chin J Diabetes Millitus. 2021;13:315–409. [Article in Chinese] [Google Scholar]
- 16.Wassink AM, van der Graaf Y, Olijhoek JK, Visseren FL, Group SS. Metabolic syndrome and the risk of new vascular events and all-cause mortality in patients with coronary artery disease, cerebrovascular disease, peripheral arterial disease or abdominal aortic aneurysm. Eur Heart J. 2008;29:213–23. doi: 10.1093/eurheartj/ehm582. [DOI] [PubMed] [Google Scholar]
- 17.Song Y, Gu Y, Guo H, Yang H, Wang X, et al. Association between mean platelet volume and benign prostatic hyperplasia:a population study from the TCLSIH cohort study. J Inflamm Res. 2023;16:3259–69. doi: 10.2147/JIR.S416404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Daher M, Saqer T, Jabr M, Al-Mousa S. Benign prostatic hyperplasia and metabolic syndrome;prevalence and association:a cross-sectional study in Syria. BMC Urol. 2023;23:187. doi: 10.1186/s12894-023-01365-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Sandhu JS, Bixler BR, Dahm P, Goueli R, Kirkby E, et al. Management of lower urinary tract symptoms attributed to benign prostatic hyperplasia (BPH):AUA guideline amendment 2023. J Urol. 2024;211:11–9. doi: 10.1097/JU.0000000000003698. [DOI] [PubMed] [Google Scholar]
- 20.Wang JY, Liu M, Zhang YG, Zeng P, Ding Q, et al. Relationship between lower urinary tract symptoms and objective measures of benign prostatic hyperplasia:a Chinese survey. Chin Med J (Engl) 2008;121:2042–5. [PubMed] [Google Scholar]
- 21.Ezz el Din K, Kiemeney LA, de Wildt MJ, Debruyne FM, de la Rosette JJ. Correlation between uroflowmetry, prostate volume, postvoid residue, and lower urinary tract symptoms as measured by the International Prostate Symptom Score. Urology. 1996;48:393–7. doi: 10.1016/S0090-4295(96)00206-3. [DOI] [PubMed] [Google Scholar]
- 22.Sebastianelli A, Gacci M. Current status of the relationship between metabolic syndrome and lower urinary tract symptoms. Eur Urol Focus. 2018;4:25–7. doi: 10.1016/j.euf.2018.03.007. [DOI] [PubMed] [Google Scholar]
- 23.Fahed G, Aoun L, Bou Zerdan M, Allam S, Bou Zerdan M, et al. Metabolic syndrome:updates on pathophysiology and management in 2021. Int J Mol Sci. 2022;23:786. doi: 10.3390/ijms23020786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444:860–7. doi: 10.1038/nature05485. [DOI] [PubMed] [Google Scholar]
- 25.Madersbacher S, Sampson N, Culig Z. Pathophysiology of benign prostatic hyperplasia and benign prostatic enlargement:a mini-review. Gerontology. 2019;65:458–64. doi: 10.1159/000496289. [DOI] [PubMed] [Google Scholar]
- 26.Tong Y, Zhou RY. Review of the roles and interaction of androgen and inflammation in benign prostatic hyperplasia. Mediators Inflamm 2020. 2020:7958316. doi: 10.1155/2020/7958316. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.De Nunzio C, Presicce F, Tubaro A. Inflammatory mediators in the development and progression of benign prostatic hyperplasia. Nat Rev Urol. 2016;13:613–26. doi: 10.1038/nrurol.2016.168. [DOI] [PubMed] [Google Scholar]
- 28.Zhang SJ, Qian HN, Zhao Y, Sun K, Wang HQ, et al. Relationship between age and prostate size. Asian J Androl. 2013;15:116–20. doi: 10.1038/aja.2012.127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sharkey C, Long X, Wang Z, Al-Faouri R, Gershman B, et al. Zonal growth pattern of the prostate is affected by age and body mass index. J Urol. 2022;207:876–84. doi: 10.1097/JU.0000000000002332. [DOI] [PubMed] [Google Scholar]
- 30.Chughtai B, Forde JC, Thomas DD, Laor L, Hossack T, et al. Benign prostatic hyperplasia. Nat Rev Dis Primers. 2016;2:16031. doi: 10.1038/nrdp.2016.31. [DOI] [PubMed] [Google Scholar]