Associations of different insulin resistance indices with the erectile dysfunction incidence: a cross-sectional analysis of the NHANES 2001–2004

Introduction

Erectile dysfunction (ED) is a common condition that substantially impairs men’s quality of life worldwide. It is defined as the consistent inability to achieve or maintain an erection sufficient for satisfactory sexual performance (1). ED shows an age-related prevalence increase. According to the Massachusetts Male Aging Study (MMAS), 52% of men aged 40–70 years experience mild to moderate ED, and nearly all men over 70 years are affected (2). Despite advancements in medical treatments, ED remains a complex issue due to its multifactorial etiology, which includes physiological, psychological, and lifestyle factors such as obesity, physical inactivity, and smoking, as well as metabolic conditions like diabetes, hypertension, hyperlipidemia, and depression (3-6). Early recognition and intervention of risk factors are crucial for enhancing sexual function and overall quality of life. Diabetes is one of the most common risk factors for ED, tripling its risk by damaging vascular and neural systems. Studies confirm that both established and newly diagnosed diabetes, as well as impaired glucose metabolism, are linked to more severe ED (7,8). Insulin resistance (IR), characterized by a diminished cellular response to insulin, results in impaired glucose metabolism and elevated blood sugar levels, and has emerged as a significant factor in the pathogenesis of ED (9). Various studies have established a link between IR and ED, suggesting that metabolic disturbances associated with IR can lead to endothelial dysfunction and disrupt the subcellular signaling pathways necessary for nitric oxide (NO) production (10). Understanding this connection is vital for early identification and treatment of men at risk of ED, particularly those with metabolic syndrome and type 2 diabetes mellitus (DM).

However, the current gold method for assessing IR is the hyperinsulinemic-euglycemic clamp; nevertheless, its invasive nature and high cost restrict its widespread clinical application (11). So, more convenient and non-invasive indices have been developed to evaluate IR. The Homeostatic Model Assessment of IR (HOMA-IR) index, for instance, has been proposed as an early predictive marker of both endothelial dysfunction and non-organic ED, even when fasting plasma insulin and glucose levels are within normal ranges (12). Nonetheless, circulating insulin concentrations are not routinely measured in primary care, prompting the development of simpler and more feasible alternative indices such as the triglyceride glucose (TyG) index (13), TyG-body mass index (TyG-BMI) index (14), and estimated glucose disposal rate (eGDR) (15). Among these indices, each reflects distinct aspects of glucose and lipid metabolism, insulin sensitivity, and body composition, thereby offering complementary perspectives on metabolic health. In particular, these indices provide an opportunity to assess the metabolic contribution to ED beyond the presence of overt diabetes, capturing early subclinical alterations in insulin signaling and endothelial function. Recent evidence supports the TyG index as an economical and convenient marker for the diagnosis and monitoring of ED (16,17). However, there has been limited research on the relationships between TyG-BMI, eGDR, and ED, and a lack of comparative assessments of different IR surrogates within the same population. This study sought to investigate the links between the prevalence of ED and various IR surrogate markers using data from the National Health and Nutrition Examination Survey (NHANES) 2001–2004. The primary objective was to identify valuable predictors of ED incidence. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-665/rc).

Methods

Study design and participants

The NHANES project biennially chooses the typical U.S. adults for assessing and evaluating their nutritional and health status. It gathers extensive data, including demographic and dietary details, questionnaire responses, laboratory tests, and medical examinations.

As NHANES erectile function questionnaire data were only available during 2001–2004, our work focused on this timeframe, including 4,954 males (≥20 years). Following excluding subjects without information on the eGDR record, ED information, and covariates, 1,737 participants were used for the following analysis (Figure 1). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Figure 1 Flow chart of the study participants. ED, erectile dysfunction; IR, insulin resistance; NHANES, National Health and Nutrition Examination Survey.

IR surrogates

This study utilized several surrogates for IR, including HOMA-IR, eGDR, TyG index, and TyG-BMI. HOMA-IR was derived using the formula: [fasting glucose (mmol/L) × fasting insulin (µU/mL)]/22.5 (18). The eGDR (mg/kg/min) was calculated as 21.158 − [0.09 × waist circumference (cm)] − [3.407 × hypertension status (present =1; absent =0)] − [0.551 × glycated hemoglobin (%)] (19). Hypertension was defined as either: (I) systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg; (II) use of antihypertensive medication; or (III) self-reported hypertension diagnosis (20). TyG index was computed using the equation: ln[fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2] (20). Additionally, TyG-BMI was obtained by multiplying the TyG index value by BMI (kg/m²) (21). These indices were selected because they represented different metabolic dimensions of IR. HOMA-IR reflects hepatic insulin sensitivity and β-cell function, TyG index and TyG-BMI are lipid-glucose composite indices that capture peripheral IR, and eGDR incorporates glycemic control, blood pressure, and central adiposity to estimate whole-body insulin sensitivity.

Outcome variable

ED was assessed using a single-item questionnaire adapted from the MMAS (22), which asked, “How would you describe your ability to get and keep an erection adequate for satisfactory intercourse?”. The response options were “always or almost always”, “usually”, “sometimes”, or “never”. Participants who answered “sometimes” or “never” were classified into the ED group, while those who responded “usually” or “always or almost always” were placed in the non-ED group. This method’s reliability is comparable to the International Index of Erectile Function (IIEF) (22).

Covariate

Participants provided data on age, race (classified as Mexican American, non-Hispanic White, non-Hispanic Black, other Hispanic, or other), marital status, education level (below, equivalent to, or above high school), family poverty-to-income ratio (PIR), alcohol intake (non-drinker, 1–<5, 5–<10, or 10+ drinks/month), smoking status (defined by having smoked at least 100 cigarettes in their lifetime), and physical activity (defined by having moderate or vigorous activity) through surveys. BMI was measured during the survey examination. Laboratory tests were performed on serum samples collected at the mobile examination center and analyzed at the National Center for Environmental Health. DM was defined by (I) hemoglobin A1c (HbA1c) level ≥6.5%; (II) fasting plasma glucose ≥126 mg/dL; (III) use of antidiabetic medications; or (IV) a diabetes diagnosis. Cardiovascular disease (CVD) was defined by meeting any of the following criteria: (I) diagnosis of congestive heart failure; (II) diagnosis of coronary heart disease (CHD); (III) diagnosis of angina pectoris; (IV) diagnosis of myocardial infarction; or (V) diagnosis of stroke, all based on affirmative responses to being informed of these conditions by a health professional. Hyperlipidemia was defined by triglyceride levels ≥150 mg/dL, high-density lipoprotein (HDL) levels <40 mg/dL, or a history of hyperlipidemia diagnosis and/or use of lipid-lowering medications (23).

Statistical analysis

In accordance with NHANES’s complex survey design and NCHS recommendations, we used appropriate weighting techniques to ensure accurate results. Baseline characteristics were classified into two groups: with or without ED. Continuous data were presented as mean ± standard deviation (SD). Categorical data were represented as counts (weighted proportions). To control the confounders, three weighted logistic regression models were applied: Model 1 (unadjusted), Model 2 (adjusted for age, race, marital status, PIR, and education level), and Model 3 (adjusted for age, race, marital status, PIR, education level, BMI, smoking status, alcohol intake, DM, CVD, hyperlipidemia, and physical activity). Weighted logistic regression analyses were conducted to evaluate the association of IR surrogates with ED, with results presented as odds ratios (ORs) and 95% confidence intervals (CIs). Three nodes, representing the 25^th, 50^th, and 75^th percentiles of eGDR distribution, were selected. Subgroup analyses were performed to evaluate effect modification in the eGDR levels and ED association. Statistical significance was set at P<0.05 (two-tailed). Analyses were performed using R version 4.3.0.

Results

Baseline characteristics

Altogether 1,737 participants were included in this study. Basic subject features are shown in Table 1. About 480 (19%) of the participants had ED. Notably, participants with ED were older, more likely to have a partner, possess a lower education level, exhibit an increased BMI, and be smokers. Additionally, those with ED tended to have higher incidences of DM, CVD, and hyperlipidemia, engage in no physical activity, have lower eGDR level and higher levels of HOMA-IR, TyG index, and TyG-BMI.

Associations between different IR indices and ED

Table 2 presents the association between IR indices and ED by the logistic regression analysis with three adjusted models. HOMA-IR, TyG index, and TyG-BMI were significantly associated with ED in Models 1 and 2, but not in Model 3. eGDR was inversely associated with ED risk. The protective effect weakened across models but remained significant in Model 3 (P=0.03). This result demonstrated that eGDR had a persistent protective effect of ED. When eGDR was divided into quartiles, the ED risk of subjects in quartile 3 (Q3) decreased by approximately 41% (95% CI: 0.37–0.95; P=0.03) in comparison with quartile 1 (Q1). Figure 2 illustrates the relation of eGDR with the risk of ED using RCS curves, indicating a significant nonlinear relationship (P<0.001).

Figure 2 Restricted cubic spline curve illustrating the association between eGDR and the risk of ED. The red lines depict the ORs, while the blue shaded areas indicate the 95% CIs. The model adjustments include age, race, marital status, PIR, education level, BMI, smoking status, alcohol intake, DM, CVD, hyperlipidemia, and physical activity. BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; DM, diabetes mellitus; ED, erectile dysfunction; eGDR, estimated glucose disposal rate; OR, odd ratio; PIR, poverty-to-income ratio.

Subgroup analysis

Subgroup analysis was conducted to evaluate the consistency of the association between eGDR and ED across different subgroups, including age, race, marital status, PIR, education level, BMI, smoking status, alcohol intake, DM, CVD, hyperlipidemia, and physical activity. The results indicated that, within each subgroup, participants with higher eGDR levels consistently exhibited a lower risk of ED (Figure 3). Additionally, significant interactions were observed across the subgroups of education level and physical activity.

Figure 3 Stratified associations between eGDR and ED. BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; ED, erectile dysfunction; eGDR, estimated glucose disposal rate; OR, odds ratio; PIR, poverty-to-income ratio.

Discussion

This is the first study to evaluate the association between the various IR indices and ED. Multivariate regression models adjusted for confounders demonstrated that the eGDR was associated with a lower risk of ED. Subgroup analyses further indicated participants with higher eGDR levels consistently exhibited a lower risk of ED in each subgroup.

Penile erection represents a multifaceted neurovascular process that depends on the coordinated function of vascular, neurological, psychological, hormonal, and interpersonal factors (24). Vascular factors significantly impact ED due to the specific vascular network of the penis (25). Consequently, ED is often linked not only to age but also to conditions such as obesity, physical inactivity, diabetes, hypertension, dyslipidemia, and CVD (26,27). IR plays a pivotal role in the development of ED by inducing endothelial dysfunction and structural vascular abnormalities (28,29). Studies in insulin-resistant obese animal models have shown that IR upregulates endothelin-b receptor expression, thereby promoting increased production of reactive oxygen species, endothelial dysfunction, and vasoconstriction in erectile tissue (30,31). Endothelial dysfunction reduces NO production, which results in ED (32). Previous studies have demonstrated that IR patients exhibit lower scores on the IIEF, predicting ED after adjusting for confounding factors (33).

Although the glucose clamp technique is considered the gold standard for assessing IR, its clinical utility is limited due to its invasive nature and high cost. Surrogate markers such as the HOMA-IR, which uses fasting glucose and insulin levels, show strong correlation with clamp-measured IR (34). Studies have shown that HOMA-IR has a predictive ability for ED with an AUC of 0.759 (12). Multivariate logistic regression analysis identified HOMA-IR ≥3 as a strong predictor of ED (33). Our study confirmed significant differences in HOMA-IR between ED and non-ED populations.

However, the main limitation of HOMA-IR might be the challenges in insulin testing. Recent studies have proposed the TyG index, which is determined by fasting glucose and triglyceride levels, may be a better alternative IR marker to HOMA-IR (35,36). Systematic reviews and meta-analyses further support its value as a diagnostic and prognostic tool across a range of cardiovascular and metabolic conditions (37,38). Yilmaz et al. reported that TyG had a significant inverse correlation with IIEF-5 scores, noting elevated TyG index values among individuals with ED (17). Another study found significantly higher TyG index levels in ED cases compared to controls, associating each unit increase in TyG index with a 25% increase in ED prevalence (39,40). Consistent with these findings, our analysis also confirmed higher TyG index levels in the ED group.

A more recently developed metric, the eGDR, offers a non-invasive and clinically feasible alternative for assessing IR (41). Derived from waist circumference, HbA1c, and hypertension status, eGDR demonstrates strong agreement with the hyperinsulinemic-euglycemic clamp technique (42,43) and has been associated with adverse outcomes, including coronary artery disease, stroke, and all-cause mortality (44,45). In the present study, eGDR emerged as a significant predictor of ED, integrating both biochemical and clinical parameters for a holistic risk evaluation. To our knowledge, this is the first investigation to establish a link between eGDR and ED.

Subgroup analyses revealed consistent negative associations between eGDR and ED risk, with significant interactions observed in education level and physical activity subgroups. Higher education levels are associated with a reduced risk of ED (46,47), potentially due to lower obesity rates among the highly educated (48-50). Physical activity has been linked to significant improvements in cardiovascular health and erectile function, with low and high activity levels reducing ED risk by over 20% in men aged 40 years and older (51-54). Physical activity improves sexual function by controlling cardiovascular risk factors, increasing endothelial-derived NO bioavailability, enhancing insulin sensitivity, reducing proinflammatory cytokines, and boosting testosterone levels (55,56).

There are several notable strengths in this study. First, it leveraged a large, nationally representative cross-sectional sample from NHANES to examine and compare the associations between multiple surrogate markers of IR and the incidence of ED. This is the first study to report on the relationship between eGDR and ED. Furthermore, the analysis was rigorously adjusted for relevant confounding variables through multivariable logistic regression models. Finally, subgroup analyses were conducted to verify the consistency and stability of the association between eGDR and ED across different population strata.

However, there were limitations. The cross-sectional design prevented causal inference. The observed associations should be interpreted as hypothesis-generating. Future longitudinal studies or randomized controlled trials are essential to confirm a causal relationship. Self-reported variables like erectile function, smoking, alcohol intake, and physical activity might introduce reporting biases. Some individuals with erectile issues might have been unaware of their condition, leading to potential misclassification. Information on the duration of diabetes was not available in the NHANES 2001–2004 public dataset; Thus, we were unable to explore the potential relationship between diabetes duration and ED risk and clarify the impact of diabetes chronicity on erectile function. Additionally, variables were assessed at baseline, lacking longitudinal data. Despite adjusting for many ED risk factors, residual confounders might remain due to database limitations and missing value exclusions. Lastly, the NHANES database represented the U.S. population, requiring further studies to verify these findings in other populations.

Conclusions

This study identified a significant inverse association between eGDR and ED, indicating that lower eGDR values were strongly linked to a higher likelihood of ED.

Acknowledgments

We would like to thank the data collection team and NHANES administration for making the data available through the NHANES website.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-665/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-665/prf

Funding: This study was supported by the Medicine and Health Project of Zhejiang Province (No. 2025KY1284), the Zhejiang Medical Association Clinical Medical Research Special Fund Project (No. 2024ZYC-Z82), the Natural Science Foundation of Ningbo Municipality (No. 2024J346), the Key Cultivating Discipline of Lihuili Hospital (No. 2022-P09), and the Ningbo Key Clinical Specialty Construction Project (No. 2023-BZZ).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-665/coif). The authors have no conflicts of interest to declare.

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

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

References

1. NIH Consensus Conference. Impotence. NIH Consensus Development Panel on Impotence. JAMA 1993;270:83-90.
2. Johannes CB, Araujo AB, Feldman HA, et al. Incidence of erectile dysfunction in men 40 to 69 years old: longitudinal results from the Massachusetts male aging study. J Urol 2000;163:460-3.
3. Wei M, Macera CA, Davis DR, et al. Total cholesterol and high density lipoprotein cholesterol as important predictors of erectile dysfunction. Am J Epidemiol 1994;140:930-7. [Crossref] [PubMed]
4. Roumeguère T, Wespes E, Carpentier Y, et al. Erectile dysfunction is associated with a high prevalence of hyperlipidemia and coronary heart disease risk. Eur Urol 2003;44:355-9. [Crossref] [PubMed]
5. Feldman HA, Goldstein I, Hatzichristou DG, et al. Impotence and its medical and psychosocial correlates: results of the Massachusetts Male Aging Study. J Urol 1994;151:54-61. [Crossref] [PubMed]
6. Bacon CG, Mittleman MA, Kawachi I, et al. Sexual function in men older than 50 years of age: results from the health professionals follow-up study. Ann Intern Med 2003;139:161-8. [Crossref] [PubMed]
7. Mazzilli R, Zamponi V, Olana S, et al. Erectile dysfunction as a marker of endocrine and glycemic disorders. J Endocrinol Invest 2022;45:1527-34. [Crossref] [PubMed]
8. Defeudis G, Mazzilli R, Scandurra C, et al. Diabetes and erectile dysfunction: The relationships with health literacy, treatment adherence, unrealistic optimism, and glycaemic control. Diabetes Metab Res Rev 2023;39:e3629. [Crossref] [PubMed]
9. Echouffo-Tcheugui JB, Zhang S, McEvoy JW, et al. Insulin Resistance and N-Terminal Pro-B-Type Natriuretic Peptide Among Healthy Adults. JAMA Cardiol 2023;8:989-95. [Crossref] [PubMed]
10. Trussell JC, Legro RS. Erectile dysfunction: does insulin resistance play a part? Fertil Steril 2007;88:771-8. [Crossref] [PubMed]
11. Xiao D, Sun H, Chen L, et al. Assessment of six surrogate insulin resistance indexes for predicting cardiometabolic multimorbidity incidence in Chinese middle-aged and older populations: Insights from the China health and retirement longitudinal study. Diabetes Metab Res Rev 2024;40:e3764. [Crossref] [PubMed]
12. Yao F, Liu L, Zhang Y, et al. Erectile dysfunction may be the first clinical sign of insulin resistance and endothelial dysfunction in young men. Clin Res Cardiol 2013;102:645-51. [Crossref] [PubMed]
13. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord 2008;6:299-304. [Crossref] [PubMed]
14. Er LK, Wu S, Chou HH, et al. Triglyceride Glucose-Body Mass Index Is a Simple and Clinically Useful Surrogate Marker for Insulin Resistance in Nondiabetic Individuals. PLoS One 2016;11:e0149731. [Crossref] [PubMed]
15. Orchard TJ, Olson JC, Erbey JR, et al. Insulin resistance-related factors, but not glycemia, predict coronary artery disease in type 1 diabetes: 10-year follow-up data from the Pittsburgh Epidemiology of Diabetes Complications Study. Diabetes Care 2003;26:1374-9. [Crossref] [PubMed]
16. Sambel M, Erdogan A, Caglayan V, et al. Can atherogenic indices and the triglyceride-glucose index be used to predict erectile dysfunction? Sex Med 2023;11:qfad069. [Crossref] [PubMed]
17. Yilmaz M, Karaaslan M, Tonyali S, et al. Triglyceride-Glucose Index (TyG) is associated with erectile dysfunction: A cross-sectional study. Andrology 2021;9:238-44. [Crossref] [PubMed]
18. Petrelli A, Cugnata F, Carnovale D, et al. HOMA-IR and the Matsuda Index as predictors of progression to type 1 diabetes in autoantibody-positive relatives. Diabetologia 2024;67:290-300. [Crossref] [PubMed]
19. Epstein EJ, Osman JL, Cohen HW, et al. Use of the estimated glucose disposal rate as a measure of insulin resistance in an urban multiethnic population with type 1 diabetes. Diabetes Care 2013;36:2280-5. [Crossref] [PubMed]
20. Zhao Y, Gu Y, Zhang B. Associations of triglyceride-glucose (TyG) index with chest pain incidence and mortality among the U.S. population. Cardiovasc Diabetol 2024;23:111. [Crossref] [PubMed]
21. Zhou Z, Liu Q, Zheng M, et al. Comparative study on the predictive value of TG/HDL-C, TyG and TyG-BMI indices for 5-year mortality in critically ill patients with chronic heart failure: a retrospective study. Cardiovasc Diabetol 2024;23:213. [Crossref] [PubMed]
22. Derby CA, Araujo AB, Johannes CB, et al. Measurement of erectile dysfunction in population-based studies: the use of a single question self-assessment in the Massachusetts Male Aging Study. Int J Impot Res 2000;12:197-204. [Crossref] [PubMed]
23. Zhang K, Cheng M, Yang P, et al. Association between serum neurofilament light chains and depression: A cross-sectional study based on NHANES 2013-2014 database. J Affect Disord 2025;368:591-8. [Crossref] [PubMed]
24. Petrone L, Mannucci E, Corona G, et al. Structured interview on erectile dysfunction (SIEDY): a new, multidimensional instrument for quantification of pathogenetic issues on erectile dysfunction. Int J Impot Res 2003;15:210-20. [Crossref] [PubMed]
25. Solomon H, Man JW, Jackson G. Erectile dysfunction and the cardiovascular patient: endothelial dysfunction is the common denominator. Heart 2003;89:251-3. [Crossref] [PubMed]
26. Feldman HA, Johannes CB, Derby CA, et al. Erectile dysfunction and coronary risk factors: prospective results from the Massachusetts male aging study. Prev Med 2000;30:328-38. [Crossref] [PubMed]
27. Thompson IM, Tangen CM, Goodman PJ, et al. Erectile dysfunction and subsequent cardiovascular disease. JAMA 2005;294:2996-3002. [Crossref] [PubMed]
28. Weinberg AE, Eisenberg M, Patel CJ, et al. Diabetes severity, metabolic syndrome, and the risk of erectile dysfunction. J Sex Med 2013;10:3102-9. [Crossref] [PubMed]
29. Blick C, Ritchie RW, Sullivan ME. Is Erectile Dysfunction an Example of Abnormal Endothelial Function? Curr Vasc Pharmacol 2016;14:163-7. [Crossref] [PubMed]
30. Contreras C, Sánchez A, Martínez P, et al. Impaired endothelin calcium signaling coupled to endothelin type B receptors in penile arteries from insulin-resistant obese Zucker rats. J Sex Med 2013;10:2141-53. [Crossref] [PubMed]
31. Sánchez A, Contreras C, Martínez MP, et al. Role of neural NO synthase (nNOS) uncoupling in the dysfunctional nitrergic vasorelaxation of penile arteries from insulin-resistant obese Zucker rats. PLoS One 2012;7:e36027. [Crossref] [PubMed]
32. Andersson KE. Erectile physiological and pathophysiological pathways involved in erectile dysfunction. J Urol 2003;170:S6-13; discussion S13-4. [Crossref] [PubMed]
33. Russo GI, Cimino S, Fragalà E, et al. Insulin resistance is an independent predictor of severe lower urinary tract symptoms and of erectile dysfunction: results from a cross-sectional study. J Sex Med 2014;11:2074-82. [Crossref] [PubMed]
34. Matthews DR, Hosker JP, Rudenski AS, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-9. [Crossref] [PubMed]
35. Kang B, Yang Y, Lee EY, et al. Triglycerides/glucose index is a useful surrogate marker of insulin resistance among adolescents. Int J Obes (Lond) 2017;41:789-92. [Crossref] [PubMed]
36. Mazidi M, Kengne AP, Katsiki N, et al. Lipid accumulation product and triglycerides/glucose index are useful predictors of insulin resistance. J Diabetes Complications 2018;32:266-70. [Crossref] [PubMed]
37. Khalaji A, Behnoush AH, Khanmohammadi S, et al. Triglyceride-glucose index and heart failure: a systematic review and meta-analysis. Cardiovasc Diabetol 2023;22:244. [Crossref] [PubMed]
38. Sánchez-Íñigo L, Navarro-González D, Fernández-Montero A, et al. The TyG index may predict the development of cardiovascular events. Eur J Clin Invest 2016;46:189-97. [Crossref] [PubMed]
39. Jalali S, Zareshahi N, Behnoush AH, et al. Association of insulin resistance surrogate indices and erectile dysfunction: a systematic review and meta-analysis. Reprod Biol Endocrinol 2024;22:148. [Crossref] [PubMed]
40. Li L, Yao H, Dai W, et al. A higher TyG index is related with a higher prevalence of erectile dysfunction in males between the ages 20-70 in the United States, according to a cross-sectional research. Front Endocrinol (Lausanne) 2022;13:988257. [Crossref] [PubMed]
41. Guo R, Tong J, Cao Y, et al. Association between estimated glucose disposal rate and cardiovascular mortality across the spectrum of glucose tolerance in the US population. Diabetes Obes Metab 2024;26:5827-35. [Crossref] [PubMed]
42. Zabala A, Darsalia V, Lind M, et al. Estimated glucose disposal rate and risk of stroke and mortality in type 2 diabetes: a nationwide cohort study. Cardiovasc Diabetol 2021;20:202. [Crossref] [PubMed]
43. Liu C, Liu X, Ma X, et al. Predictive worth of estimated glucose disposal rate: evaluation in patients with non-ST-segment elevation acute coronary syndrome and non-diabetic patients after percutaneous coronary intervention. Diabetol Metab Syndr 2022;14:145. [Crossref] [PubMed]
44. Zhang Z, Zhao L, Lu Y, et al. Insulin resistance assessed by estimated glucose disposal rate and risk of incident cardiovascular diseases among individuals without diabetes: findings from a nationwide, population based, prospective cohort study. Cardiovasc Diabetol 2024;23:194. [Crossref] [PubMed]
45. Lu Z, Xiong Y, Feng X, et al. Insulin resistance estimated by estimated glucose disposal rate predicts outcomes in acute ischemic stroke patients. Cardiovasc Diabetol 2023;22:225. [Crossref] [PubMed]
46. Martins FG, Abdo CHN. Erectile dysfunction and correlated factors in Brazilian men aged 18-40 years. J Sex Med 2010;7:2166-73. [Crossref] [PubMed]
47. Ettala OO, Syvänen KT, Korhonen PE, et al. High-intensity physical activity, stable relationship, and high education level associate with decreasing risk of erectile dysfunction in 1,000 apparently healthy cardiovascular risk subjects. J Sex Med 2014;11:2277-84. [Crossref] [PubMed]
48. Kim TJ, Roesler NM, von dem Knesebeck O. Causation or selection - examining the relation between education and overweight/obesity in prospective observational studies: a meta-analysis. Obes Rev 2017;18:660-72. [Crossref] [PubMed]
49. Ogden CL, Carroll MD, Fakhouri TH, et al. Prevalence of Obesity Among Youths by Household Income and Education Level of Head of Household - United States 2011-2014. MMWR Morb Mortal Wkly Rep 2018;67:186-9. [Crossref] [PubMed]
50. Able C, Liao B, Saffati G, et al. Prescribing semaglutide for weight loss in non-diabetic, obese patients is associated with an increased risk of erectile dysfunction: a TriNetX database study. Int J Impot Res 2025;37:315-9. [Crossref] [PubMed]
51. Cheng JY, Ng EM, Ko JS, et al. Physical activity and erectile dysfunction: meta-analysis of population-based studies. Int J Impot Res 2007;19:245-52. [Crossref] [PubMed]
52. Pitta RM, Kaufmann O, Louzada ACS, et al. The association between physical activity and erectile dysfunction: A cross-sectional study in 20,789 Brazilian men. PLoS One 2022;17:e0276963. [Crossref] [PubMed]
53. Silva AB, Sousa N, Azevedo LF, et al. Physical activity and exercise for erectile dysfunction: systematic review and meta-analysis. Br J Sports Med 2017;51:1419-24. [Crossref] [PubMed]
54. Allen MS, Walter EE. Health-Related Lifestyle Factors and Sexual Dysfunction: A Meta-Analysis of Population-Based Research. J Sex Med 2018;15:458-75. [Crossref] [PubMed]
55. La Vignera S, Condorelli R, Vicari E, et al. Physical activity and erectile dysfunction in middle-aged men. J Androl 2012;33:154-61. [Crossref] [PubMed]
56. Leoni LA, Fukushima AR, Rocha LY, et al. Physical activity on endothelial and erectile dysfunction: a literature review. Aging Male 2014;17:125-30. [Crossref] [PubMed]