The relationship between the triglyceride glucose-waist height ratio and benign prostatic hyperplasia in middle-aged and elderly adults: a nationwide cohort study

Abstract

Objective

Currently, evidence regarding the relationship between variations in the Triglyceride Glucose-Waist-to-Height Ratio (TyG-WHtR) and benign prostatic hyperplasia (BPH) remains scarce. This study aimed to examine the potential association between alterations in TyG-WHtR and the risk of developing BPH.

Methods

This study enrolled 3,296 male participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study (CHARLS). Multivariable logistic regression analysis combined with restricted cubic spline models was employed to explore the potential relationship between TyG-WHtR variation and the risk of developing BPH.

Results

Over a 4-year follow-up period, 267 individuals were diagnosed with BPH. Elevated TyG-WHtR values were significantly associated with a higher risk of BPH (OR = 1.20, 95% CI: 1.02–1.43, p = 0.031), demonstrating a clear dose–response trend (p = 0.01). Furthermore, subgroup analyses revealed that this positive correlation between TyG-WHtR and BPH risk was consistently observed across multiple stratifications.

Conclusion

These findings highlight the potential metabolic links between TyG-WHtR and BPH, and underscore the need for future longitudinal studies to explore whether targeting these pathways may aid in BPH prevention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-025-01844-1.

Introduction

Benign prostatic hyperplasia (BPH) is highly prevalent among middle-aged and elderly populations [1–5]. According to 2019 data, there were an astonishing 11.26 million new cases worldwide that year [6]. BPH is an age-related disease, and its incidence increases as life expectancy rises. A study from Spain reported that the incidence rate in patients over 80 exceeded 95%. In China, the incidence rate is over 50% in men aged 60 and reaches 83% in men aged 80 [7]. BPH not only severely impacts patients'quality of life and mental health but also imposes a significant social and economic burden [8–10]. Early identification and prevention in high-risk populations are therefore essential.

The Triglyceride Glucose (TyG) Index, derived from triglycerides and fasting glucose, is a reliable marker of insulin resistance (IR). A 2019 Korean study introduced the concept of combining the TyG index with an obesity marker, the waist-to-height ratio (WHtR), to better reflect and predict IR. The study also demonstrated that this index could serve as a surrogate marker for IR in clinical settings [11]. As research on TyG-WHtR expands, its association with a variety of metabolic and cardiovascular diseases has become increasingly evident. TyG-WHtR has been correlated with coronary heart disease and angina [12, 13], and demonstrates significant associations with all-cause and cardiovascular mortality, chronic heart failure, and myocardial infarction [14–16]. It also predicts the onset of hypertension [17] and type 2 diabetes, with higher TyG-WHtR quartiles linked to increased incidence across diverse populations [18]. These associations highlight the role of TyG-WHtR as a surrogate marker for insulin resistance and metabolic dysfunction—mechanisms that may also contribute to benign prostatic hyperplasia (BPH). The relationship between the TyG-WHtR index and BPH remains unclear. Therefore, using data from the China Health and Retirement Longitudinal Study (CHARLS), we conducted a retrospective study to explore the association between the TyG-WHtR index and the risk of developing BPH.

Study population

The data used in this research originated from the CHARLS, which is publicly available at http://charls.pku.edu.cn/. The current analysis included baseline data collected in 2011 and follow-up data from 2015. CHARLS is a comprehensive, nationally representative longitudinal study designed to collect multidisciplinary data from participants aged 45 years and older. The study covered participants residing in 450 villages or communities across 28 provinces in China, capturing extensive information, including demographics, family composition, health status, healthcare utilization, insurance coverage, employment status, income levels, expenditure patterns, housing conditions, and laboratory test results.

Participants provided fasting venous blood samples after at least 12 h of fasting. Complete blood counts were performed immediately at the site of collection, while the remaining blood samples were preserved at 4 °C and subsequently transported to a central laboratory. There, glucose and triglyceride (TG) levels were measured using an enzymatic colorimetric method. The diagnosis of BPH was extracted from the 2015 follow-up dataset.

Ethical approval for CHARLS was obtained from the Ethical Review Committee of Peking University in 2008 (Approval No.: IRB00001052-11015). This study adhered strictly to the ethical guidelines stipulated by CHARLS, with informed consent obtained from all participants prior to data collection. Eligible participants met the following inclusion criteria: age ≥ 45 years, availability of complete socio-demographic data (education, marital status, and residential area), and comprehensive baseline and follow-up records from 2011 and 2015, respectively. Ultimately, 3,296 participants met these criteria and were included in our analyses (Fig. 1).

Fig. 1.

Flowchart of participants selection; Note: 7 participants were missing both TyG and BPH data and are counted once in total exclusions

Measurements

TyG index was calculated using fasting TG and fasting glucose (FG) levels. The TyG index was calculated as ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2], a formula validated in previous metabolic studies [19]. Waist circumference (WC) was determined by measuring at the midpoint between the lowest rib and the iliac crest after normal exhalation. Subsequently, the TyG-Waist-to-Height Ratio (TyG-WHtR) was computed by combining the TyG index and WC divided by height in centimeters, specifically as TyG-WHtR = TyG × WC/height (cm). Some participants were excluded due to missing both TyG and BPH data simultaneously (n = 7), and were counted only once in the exclusion total.

BPH data

Cases of BPH were identified among male participants who had no prior diagnosis of BPH at the baseline survey conducted in 2011 but received a new BPH diagnosis during the 2015 follow-up. BPH was defined based on self-reported physician diagnosis from the 2015 follow-up survey, in response to the question, “Has a doctor ever told you that you have benign prostatic hyperplasia (BPH)?”. Covariates included sociodemographic characteristics, lifestyle factors, and physical examination parameters. Sociodemographic information encompassed age, education (categorized as middle school or below, high school, or college and above), residential location (urban or rural), and marital status (married or single/divorced/widowed). Lifestyle variables involved smoking habits (never or current smoker), frequency of alcohol consumption (never, less than once per month, or once per month or more), and sleep duration, all obtained from self-reported questionnaires administered by trained interviewers. Physical examination variables included systolic blood pressure (SBP) and diastolic blood pressure (DBP), each measured three times using an Omron HEM-7200 electronic monitor, with the mean of these measurements utilized for analysis.

Statistical analysis

Continuous variables were reported as means ± standard deviations for normally distributed data and medians (interquartile range) for data with skewed distributions. Categorical variables were summarized using frequencies and percentages. Baseline characteristics and the incidence of BPH across quartiles of TyG-WHtR (Q1–Q4) were compared using one-way analysis of variance (ANOVA), Kruskal–Wallis H tests, or chi-square tests as appropriate.

We constructed three logistic regression models to evaluate the relationship between TyG-WHtR and BPH risk. TyG-WHtR was analyzed both continuously (per standard deviation increment) and categorically (by quartiles). Model 1 was unadjusted; Model 2 was adjusted for sociodemographic variables including age, education, residence, and marital status; Model 3 further incorporated adjustments for lifestyle factors and physiological parameters, including smoking status, alcohol intake frequency, sleep duration, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Associations were quantified using odds ratios (OR) and corresponding 95% confidence intervals (CI).

To assess potential modifications of the relationship between TyG-WHtR and BPH by sociodemographic and lifestyle factors, interaction terms [TyG-WHtR × (covariate)] were included in the models. Restricted cubic spline analyses were also conducted to explore nonlinear dose–response relationships between TyG-WHtR and BPH risk.

All analyses were performed with R software version 4.3.1, using the "rms" package for constructing restricted cubic splines. The"rcs"function defined spline terms for the independent variables and facilitated model fitting. All statistical tests were two-sided, and a significance level of α = 0.05 was used. Statistical significance was set at a two-tailed p-value of < 0.05.

Result

Baseline characteristics

A total of 3,296 men were included in this study (mean age: 59.4 ± 8.8 years), of whom 267 developed BPH during follow-up. The median TyG-WHtR for the overall population was 4.4 (3.9, 4.9), with a baseline median of 4.5 (4.0, 5.0) for those who did not develop BPH and 4.3 (3.9, 4.9) for those who did. BPH patients had significantly different baseline characteristics compared to non-BPH participants, characterized by older age, lower education levels, more rural residency, and higher BMI and waist circumference (Table 1).

Table 1.

Baseline characteristics

Characteristic	Total (n = 3296)	Non-BPH (n = 3029)	BPH (n = 267)	p
Age	59.4 ± 8.8	59.2 ± 8.8	61.4 ± 8.5	< 0.001
Education				< 0.001
Primary school or below	2867 (87.0)	2653 (87.6)	214 (80.1)
High school	376 (11.4)	336 (11.1)	40 (15)
College or above	53 (1.6)	40 (1.3)	13 (4.9)
Marital				0.337
Married	2929 (88.9)	2690 (88.7)	214 (80.1)
Non-married	367 (11.1)	342 (11.3)	25 (9.4)
Location				0.001
City/Town	225 (6.8)	194 (6.4)	31 (11.6)
Village	3075 (93.2)	2838 (93.6)	236 (88.4)
Smoking				0.054
Smoking	803 (24.4)	725 (23.9)	78 (29.2)
Non-smoker	2493 (75.6)	2304 (76.1)	189 (70.8)
Drinking				0.355
Drink more than once a month	1548 (47.0)	1433 (47.3)	115 (43.1)
Drink but less than once a month	373 (11.3)	338 (11.1)	35 (13.1)
None of these	1375 (41.7)	1258 (41.5)	117 (43.8)
Sleep time	7.0 (5.0, 8.0)	7.0 (5.0, 8.0)	7.0 (5.0, 8.0)	0.567
SBP	127.0 (115.0, 141.0)	127.0 (115.0, 140.7)	128.7 (115.7, 140.7)	0.493
DBP	75.3 (67.3, 83.7)	75.3 (67.3, 83.3)	74.3 (67.0, 84.7)	0.787
BMI (kg/m2)	22.5 (20.4, 25.0)	22.5 (20.4, 24.9)	23.3 (21.2, 26.1)	< 0.001
WC (cm)	83.7 (77.1, 91.0)	83.2 (77.0, 91.0)	86.4 (79.3, 93.8)	< 0.001
Glucose (mg/dl)	102.4 (94.1, 113.6)	102.4 (94.1, 113.6)	102.1 (95.0, 113.0)	0.952
HDL-C (mg/dl)	48.3 (39.4, 59.9)	48.7 (39.4, 59.9)	47.6 (37.5, 59.2)	0.117
TyG-WHtR	4.4 (3.9, 4.9)	4.3 (3.9, 4.9)	4.5 (4.0, 5.0)	0.005
Quartiles of TyG-WHtR				0.045
Q1	824 (25.0)	772 (25.5)	52 (19.5)
Q2	824 (25.0)	765 (25.2)	61 (22.8)
Q3	824 (25.0)	751 (24.8)	73 (27.3)
Q4	824 (25.0)	743 (24.5)	81 (30.3)

BPH benign prostatic hyperplasia, SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, WC waist circumference, TG triglyceride, HDL-C high-density lipoprotein cholesterol, TyG-WHtR Triglyceride glucose-waist height ratio index

Linear relationship between TyG-WHtR and BPH

Three logistic regression models were developed with different sets of adjustments for confounding variables. In the fully adjusted Model 3, TyG-WHtR exhibited a significant positive relationship with BPH risk, showing an adjusted odds ratio (OR) of 1.61 (95% CI: 1.10–2.35). Additionally, when analyzed as a continuous variable per standard deviation increase, TyG-WHtR maintained its positive correlation with BPH risk (OR = 1.20, 95% CI: 1.02–1.43). The analysis also demonstrated an increasing risk of BPH with higher TyG-WHtR quartiles (p = 0.031; Table 2, Fig. 2).

Table 2.

Association of TyG-WHtR with the risk of BPH in the CHARLS

	Model 1	P value	Model 2	P value	Model 3	P value
TyG-WHtR	1.20 (1.02 ~ 1.40)	0.027	1.21 (1.03 ~ 1.42)	0.024	1.20 (1.02 ~ 1.43)	0.031
Quartiles of TyG-WHtR
Q1	Ref		Ref		Ref
Q2	1.26 (0.87 ~ 1.82)	0.381	11.19 (0.81 ~ 1.75)	0.372	1.2 (0.82 ~ 1.76)	0.310
Q3	1.44 (1.00 ~ 2.09)	0.052	1.41 (0.97 ~ 2.04)	0.107	1.43 (0.98 ~ 2.09)	0.061
Q4	1.62 (1.13 ~ 2.33)	0.009	1.62 (1.12 ~ 2.35)	0.010	1.61 (1.10 ~ 2.35)	0.014
P for trend	0.005		0.006		0.010

Model 1 was crude model. Model 2 was adjusted for age, education level, location, and marital status. Model 3 was adjusted for age, education level, location and marital status, smoking status, drinking status, sleep time, SBP, and DBP

TyG-WHtR Triglyceride glucose-waist height ratio index, BPH benign prostatic hyperplasia, CHARLS China Health and Retirement Longitudinal Study, SBP systolic blood pressure DBP diastolic blood pressure

Fig. 2.

Restricted cubic spline of the association between TyG-WHtR and the risk of BPH. The model was adjusted for age, education level, location and marital status, smoking status, drinking status, sleep time, SBP, and DBP. The plot shows a linear relationship between TyG-WHtR and the risk of BPH. TyG-WHtR, Triglyceride glucose-waist height ratio index; BPH, benign prostatic hyperplasia; The reference point is the median of all the data

Stratified analysis

To assess whether the relationship between TyG-WHtR and BPH risk varied across specific subgroups, we performed stratified analyses by age, education, marital status, residence, smoking status, alcohol consumption, and sleep duration. To assess potential selection bias, we conducted a sensitivity analysis comparing baseline characteristics between participants included in the analysis (n = 3,296) and those excluded (n = 14,409). The analysis revealed revealed that included participants differed markedly from excluded participants: they had substantially lower smoking rates (24.4% vs 68.9%), lower BMI (22.5 vs 23.3 kg/m²) and waist circumference (83.7 vs 85.0 cm), lower TyG-WHtR index (4.4 vs 4.8), and were more likely to be married and reside in rural areas. This suggests our study population may represent a relatively healthier subgroup with better metabolic profiles and different sociodemographic characteristics. These differences may influence the assessment of associations between TyG index and health outcomes and may limit the generalizability of our findings to broader populations (Table S1). The findings consistently indicated a positive relationship between increased TyG-WHtR levels and higher BPH risk across all evaluated subgroups, without any significant interaction effects. Thus, elevated TyG-WHtR was uniformly associated with a greater likelihood of BPH across all stratifications (Fig. 3).

Fig. 3.

Forest plot of stratified analysis of the association of TyG-WHtR with the risk of BPH. OR, odds ratio; CI, confidence intervals; TyG-WHtR, Triglyceride glucose-waist height ratio index

Discussion

The TyG-WHtR is a novel index that integrates triglycerides, glucose, and waist circumference, and research on it is growing. Initially, the TyG-WHtR was closely associated with insulin resistance [20], but subsequent studies have revealed new findings. Utilizing data from the CHARLS, our study explored the association between TyG-WHtR and BPH risk. The results revealed a significant and positive correlation between TyG-WHtR levels and the incidence of BPH, displaying a clear dose–response pattern. Specifically, as TyG-WHtR increased across quartiles, BPH risk progressively escalated, suggesting TyG-WHtR as an independent risk factor for BPH. Further subgroup analyses demonstrated the stability of this relationship across different groups based on age, educational background, and residence, with no notable effect modifications observed. In conclusion, this study is the first to establish the positive relationship between TyG-WHtR and BPH risk, suggesting a potential shared metabolic mechanism underlying the relationship between TyG-WHtR and BPH. While our results show an association, interventional or Mendelian randomization studies are needed to assess causality and the impact of modifying TyG-WHtR on BPH prevention.

The pathophysiological mechanisms underlying the development and progression of BPH remain incompletely understood, as it is a multifactorial and dynamic process. Established risk factors include aging and the presence of functional testes. Historical studies in ancient Chinese populations who underwent castration found that their prostates became undetectable or severely atrophic, emphasizing the key role of testicular hormones in prostate maintenance and growth [21]. In recent decades, a growing body of evidence has linked obesity—both general and sarcopenic—as well as metabolic obesity to increased BPH risk [22, 23]. These associations may be mediated through mechanisms related to obesity-induced metabolic syndrome, which promotes systemic inflammation and oxidative stress, contributing to prostatic tissue remodeling and hyperplasia [24–30]. Inflammatory cell infiltration within the prostate microenvironment has also been implicated in stromal proliferation and may represent a bridge between metabolic dysfunction and local tissue responses [31, 32]. In parallel, hormonal factors such as altered androgen and estrogen levels, particularly in aging men, play critical roles in BPH pathogenesis [33]. Moreover, recent pathway-based metabolomic studies have identified glycerophospholipid metabolism as a key metabolic pathway involved in the progression of BPH, suggesting a broader metabolic contribution to prostate disease beyond classical hormonal paradigms [34]. These findings collectively support the hypothesis that TyG-WHtR, a surrogate marker of insulin resistance and metabolic imbalance, may reflect a convergence of these underlying biological pathways.

The mechanisms underlying the development and progression of BPH remain unclear, as it is a complex process. Known risk factors include aging and functional testes. Studies on ancient Chinese populations who underwent castration revealed that the prostate becomes undetectable or significantly atrophied, suggesting the involvement of testes [21]. The relationship between obesity and BPH, including sarcopenic and metabolic obesity, is well established, with both increasing BPH risk [22, 23]. This may be related to metabolic syndrome induced by obesity, which elevates inflammatory factors and oxidative stress, contributing to BPH onset [24–30]. Extensive infiltration by immune cells may contribute to prostate enlargement, potentially increasing BPH risk [31, 32]. Previous research has also demonstrated that steroid hormones and aging play critical roles in the pathogenesis of BPH [33]. Moreover, glycerophospholipid (GP) metabolism has been identified through topological pathway analysis as an essential metabolic pathway involved in BPH progression [34].

Our findings demonstrate the significant predictive value of TyG-WHtR in determining BPH risk. Higher TyG-WHtR levels correlate with increased BPH susceptibility, possibly attributable to the higher prevalence of metabolic syndrome associated with elevated TyG-WHtR. This condition could enhance inflammatory responses and oxidative stress. To improve result accuracy and maintain comparability across variables, all data underwent standardization procedures.

The risk of BPH increases as TyG-WHtR rises. While TyG-WHtR is a risk factor for BPH, it is important to manage triglycerides, blood glucose, and BMI within healthy limits [35]. Triglycerides are the main form of fat in the blood and the body’s primary energy storage source. Stored in fat cells, triglycerides provide energy during times of need, such as fasting or high energy demands [36]. They also serve to insulate the body and protect internal organs, helping to maintain body temperature and prevent physical injury. Triglycerides also facilitate the absorption and transport of fat-soluble vitamins (A, D, E, and K), which are essential for maintaining physiological functions. Blood glucose is a key energy source for the body, especially for the brain and red blood cells [37]. It serves as the immediate energy source for cellular metabolism, supplying quick and efficient energy to high-demand organs like the brain and muscles. Stable blood glucose levels are crucial for normal functions such as nerve conduction, muscle contraction, and immune responses. Blood glucose levels are regulated by hormones like insulin and glucagon, ensuring energy balance in the body and preventing hypoglycemic symptoms such as dizziness, fatigue, and confusion. While TyG-WHtR is not intended as a direct target for intervention, it may serve as a valuable risk stratification tool if proven superior to its individual components. Therefore, we recommend that TyG-WHtR be maintained within a proper range to optimize health management, paving the way for more scientific and reasonable health interventions [38].

Limitations

This study has some limitations. The diagnosis of BPH was based on self-reported physician diagnosis, which may introduce recall or misclassification bias. Although we adjusted for multiple covariates, there may still be other potential confounding factors, such as dietary structure, physical activity, family history of BPH, and circulating hormone levels (e.g., testosterone, estrogen), due to their unavailability in the CHARLS dataset. This may have introduced residual confounding. The follow-up duration of four years may not be sufficient to assess the long-term association between TyG-WHtR and BPH development. Our study sample was limited to participants aged 45 and older, and further research is needed to determine whether the findings apply to younger populations. Moreover, the study population consisted exclusively of middle-aged and elderly Chinese adults, which may limit the generalizability of the results to other ethnic groups. Lastly, The study population included only 20% of the original cohort, potentially introducing selection bias. Excluded participants differed significantly in baseline demographics, which may limit generalizability.

Conclusion

This study provides the first evidence of a linear, positive association between TyG-WHtR and BPH, characterized by a distinct dose–response relationship. Our findings highlight the potential metabolic links between TyG-WHtR and BPH, and underscore the need for future longitudinal studies to explore whether targeting these pathways may aid in BPH prevention.

Authors’ contributions

LB and ZZQ conceptualized and designed the study, collected and interpreted data, and drafted the manuscript. LXQ and LJP were responsible for creating figures and tables and drafting the manuscript. All authors contributed to and approved the final version of the manuscript.

Funding

None.

Data availability

The statement clearly indicates that the data used in our study is publicly available via the CHARLS database, accessible at http://charls.pku.edu.cn/.

Declarations

Ethics approval and consent to participate

This study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.