Beyond BMI: central obesity measures and cardiovascular risk in late life

Abstract

Background

In older adults, age-related changes in body composition may limit the predictive value of traditional obesity measures such as body mass index (BMI). The “obesity paradox,” in which higher BMI appears protective, further complicates cardiovascular risk stratification in this population.

Aims

To assess the predictive value of various anthropometric indices for ischemic heart disease (IHD) in older adults.

Methods

This cross-sectional observational study included 1174 community-dwelling adults aged ≥ 65 years evaluated at the university geriatrics outpatient clinic. Anthropometric measures included BMI, waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), body adiposity index (BAI), relative fat mass (RFM), body fat percentage, and skeletal muscle mass. Multivariate logistic regression analyses were conducted to assess associations with IHD. Receiver operating characteristics (ROC) curve analyses were used to assess discriminatory power.

Results

The mean age was 75.6 ± 6.9 years; 68.8% were female. IHD was present in 20.3% of participants. WHR(OR = 1.839; 95% CI:1.255–2.695; p = 0.002), WHtR (OR = 1.746; 95% CI:1.250–2.437; p = 0.001), WC (OR = 1.463; 95% CI:1.060–2.020; p = 0.021), and RFM (OR = 1.740; 95% CI:1.257–2.408; p = 0.001), were independently associated with IHD, while BMI, BAI and fat percentage were not. WHR (AUC = 0.611, p < 0.001), WHtR (AUC = 0.561, p = 0.005), and RFM (AUC = 0.555, p = 0.010) demonstrated significant discriminatory performance.

Discussion

In this geriatric cohort, central obesity measures such as WHR, WHtR and RFM appeared to be more predictive of IHD than BMI. However, the modest discriminatory ability observed suggests these indices may be more valuable when incorporated into multifactorial cardiovascular risk models rather than as standalone screening tools. While BMI’s limitations in older adults are increasingly recognized, our findings provide direct comparative evidence supporting the clinical utility of fat distribution measures in cardiovascular risk assessment.

Conclusion

These findings support the use of fat distribution indices as part of comprehensive cardiovascular risk assessment in older adults.

Introduction

Cardiovascular diseases (CVD) cause approximately one-third of deaths worldwide. Among all CVD, ischemic heart disease (IHD) are the leading cause of death, especially in older adults [1, 2]. With increasing life expectancies and lifestyle-related conditions such as hypertension, diabetes mellitus and obesity, become more prevalent, the burden of CVD continues to rise. Given the chronic and multifactorial nature of these diseases, effective risk stratification and early intervention strategies are essential components of cardiovascular care.

Obesity has long been regarded as one of the key reversible determinants of cardiovascular risk in adults [3–5]. Body mass index (BMI) has traditionally been the most commonly used anthropometric measure to define obesity due to its ease of use and strong epidemiological associations with adverse health outcomes. Although, BMI is a poor measure of body composition since it fails to differentiate between fat and lean tissue and does not provide information about the distribution of adipose tissue [6, 7]. BMI fails to reflect visceral adiposity, which poses a greater risk to cardiovascular and metabolic health than peripheral fat. Individuals with high visceral fat—regardless of BMI—tend to exhibit insulin resistance, elevated triglycerides, increased apolipoprotein B, lower HDL cholesterol, and a predominance of small dense LDL and HDL particles, a pattern known as atherogenic dyslipidemia [7–9].

Recent studies have also brought into focus the importance of alternative anthropometric indices that better reflect central obesity. Waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and Relative Fat Mass (RFM) have emerged as valuable tools for assessing visceral fat accumulation [10]. Furthermore, these measures may retain their predictive power across various ages and ethnicities, offering potential advantages over BMI in various clinical settings.

Despite growing interest in these alternative indices, evidence of their relative utility in the prediction of IHD in older adults is sparse and controversial. Furthermore, in older individuals, use of anthropometric indices poses unique challenges due to the changes in body composition with aging, such as sarcopenia, increased visceral fat, and height loss. These physiologic alterations may complicate traditional obesity classifications, making it essential to establish which indices provide useful clinical information in geriatric settings. Furthermore, so-called “obesity paradox”—where higher BMI is paradoxically corresponds with better survival outcomes in older adults—raises questions about the utility of conventional obesity markers in predicting cardiovascular risk in this population.

As such, identifying the most reliable and clinically relevant anthropometric predictors of IHD in this population is both timely and necessary. This study addresses these critical gaps by evaluating a range of obesity-related indices in relation to IHD in a large geriatric outpatient cohort. The aim of the this study is to examine whether there is a correlation between several anthropometric indices—including BMI, WC, WHR, WHtR, Body adiposity index (BAI) and RFM—and IHD in a geriatric outpatient population. Additionally, we determined whether these indices, alongside body composition variables measured via bioelectrical impedance analysis (BIA) (fat percentage and skeletal muscle mass), provide superior predictive value compared to BMI for cardiovascular risk stratification in older adults.

Methods

Study design and population

This cross-sectional observational study was conducted at the Geriatrics Outpatient Clinic of Istanbul University, Istanbul Medical Faculty, between November 2012 and June 2024. Older adults aged 65 years and above who were evaluated during this period and had complete clinical, anthropometric, and cardiovascular risk data were included. Exclusion criteria comprised patients with missing anthropometric data, incomplete cardiovascular risk profiles, or conditions that may significantly alter body composition (e.g., advanced malignancy, cachexia, or limb amputation) and patients with technical factors that could interfere with accurate assessments using BIA, such as the presence of edema or an implantable pacemaker. We obtained written informed consent from all participants. The study was approved by the ethics committee of Istanbul University, Istanbul Medical Faculty.

Data Collection- measurements

Sociodemographic and clinical information including age, gender, history of IHD, diabetes mellitus (DM), hypertension (HT), dyslipidemia, and smoking were recorded. Anthropometric assessments were conducted during routine outpatient evaluation. All measurements were taken by a single health professional—a geriatric physiotherapist with certification and training in anthropometric measurement. Height and weight were measured using a regularly calibrated stadiometer. WC was measured at the midpoint between the lower edge of the last palpable rib and the top of the iliac crest using a non-elastic tape. The subject stood with arms relaxed at the sides, feet positioned close together, and weight evenly distributed. WC was recorded at the end of a normal expiration, after the subject was allowed to relax and take a few deep, natural breaths to minimize inward retraction of the abdominal contents during the measurement. Hip circumference (HC) was measured at the widest part of the buttocks [11]. Two measurements of waist and hip circumference were taken to the nearest 0.5 cm, and the average value was taken into consideration for analysis.

The following indices were calculated from these measurements:

Body mass index (BMI)

weight (kg)/height² (m²).

Waist-to-Hip ratio (WHR)

WC/HC.

Waist-to-Height ratio (WHtR)

WC/height.

Body adiposity index (BAI): [HC (cm)/height (m)^1.5] – 18 [12].

Relative fat mass (RFM): 64 - [20 x (height/waist)] + (12 x sex) (where sex was coded as 0 for men and 1 for women) [13].

The following cut-off values were used to categorize risk based on anthropometric indices: Men with a WC ≥ 102.0 cm and women with a WC ≥ 88.0 cm [14]. Men with a WHR ≥ 1.00 and women with a WHR ≥ 0.85 [15]. WHtR: ≥0.5 for men, ≥ 0.5 for women [16]. BAI: ≥28 for men and ≥ 36 for older women [17]. RFM: ≥30 for men and ≥ 40 for women [18].

Skeletal muscle mass (SMM) was assessed using BIA with the Tanita BC-532 body composition analyzer, following an overnight fasting period of at least 8 h. Fat-free mass (FFM) was derived from the BIA measurements, and SMM was subsequently calculated using the validated equation: SMM (kg) = 0.566 × FFM [19]. Low muscle mass (LMM) thresholds were assessed according to national data that is defined by lower than two standard deviation of young reference population [20] and were as follows: muscle mass adjusted by weight for women < 33.6%, for men 37.4% [21].

Body fat percentage was assessed using BIA, and the presence of excess adiposity was defined based on population-specific thresholds: >27% for men and > 41% for women in community-dwelling older adults in Turkiye [22].

Outcome variable

The primary outcome was the presence of IHD, defined as a prior history of myocardial infarction, coronary revascularization, or documented coronary artery disease on diagnostic testing.

Statistical analysis

All statistical procedures were conducted using IBM SPSS Statistics version 21.0 (IBM Corp., Armonk, NY, USA). P-value of < 0.05 was considered indicative of statistical significance. The normality of continuous variables was evaluated using the Kolmogorov–Smirnov test and visual assessments including histograms and Q-Q plots. Data were presented as mean ± standard deviation (SD) for normally distributed variables, and as median with interquartile range (IQR) for non-normally distributed variables. Categorical variables were expressed as counts and percentages. Group comparisons between participants with and without IHD were performed using the independent samples t-test or Mann–Whitney U test for continuous variables, depending on distribution. Categorical variables were compared using the Chi-square test or Fisher’s exact test, as appropriate. To determine the independent associations between anthropometric parameters and IHD, eight separate multivariate logistic regression models were constructed—each including one anthropometric index (BMI, WC, WHR, WHtR, BAI, RFM, SMM/weight, body fat percentage) as the primary independent variable. All models were adjusted for age, gender, HT, DM, dyslipidemia, and current smoking status. This approach was employed to avoid multicollinearity and to evaluate the isolated predictive value of each anthropometric measure. Multicollinearity among covariates was assessed using correlation analyses and variance inflation factor (VIF) thresholds; variables with strong collinearity (r > 0.7 or VIF > 5) were not included in the same model. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Model calibration was evaluated using the Hosmer–Lemeshow goodness-of-fit test. In addition, receiver operating characteristic (ROC) curve analyses were performed to assess the discriminatory ability of each anthropometric index for IHD, with area under the curve (AUC) values reported.

Results

A total of 1174 community-dwelling older adults (mean age: 75.6 ± 6.9 years; 68.8% female) who were evaluated at the geriatrics outpatient clinic were included in the study. Among these, 238 patients (20.3%) had a documented history of IHD. In addition, the distribution of Key cardiovascular risk factors was as follows: 386 patients (32.9%) had DM, 849 (72.3%) had HT, 337 (28.7%) had dyslipidemia, and 83 patients (7.2%) reported current smoking.

Anthropometric characteristics are summarized in Table 1. The mean BMI was 29.6 ± 5.5 while the mean WC was 99.4 ± 12.8 cm. Hip circumference mean was 107.1 ± 10.7 cm. The mean WHR, WHtR, BAI and RFM were 0.93 ± 0.09, 0.64 ± 0.08, 37.1 ± 7.5 and 40.3 ± 9.4 respectively.

Table 1.

Demographic and clinical characteristics of the patients

Abbreviations: IHD: Ischemic heart disease; SD: standart deviation; BMI: Body Mass Index, WC: waist circumference; WHR: Waist/Hip Ratio, WHtR: Waist/Height Ratio; BAI: Body Adiposity Index, RFM: Relative Fat Mass, SMM: Skeletal Muscle Mass, SMMI: Skeletal muscle mass index.

Patients with IHD were significantly older, more likely to be male, and had higher WHR (p < 0.001). The prevalence of HT, DM, dyslipidemia, and active smoking was also significantly higher in the IHD group compared to those without IHD.

The prevalence of obesity (BMI ≥ 30) and high WC did not differ significantly between groups (p = 0.382 and p = 0.110, respectively). However, high WHR (42% vs. 20%), high WHtR (70% vs. 57%), high BAI (51.7% vs. 43.7%) and high RFM (60.5% vs. 48.6%) were significantly more common in the IHD group. High fat percentage was more prevalent in the IHD group (34% vs. 30%) but not statistically significant (p = 0.284). In the subset with available muscle mass data (n = 1132), low SMMI was observed in 43% of participants with IHD compared to 50% without IHD, with the difference reaching statistical significance (p = 0.005).

In the multivariate logistic regression analysis, adjusted for age, gender, smoking status, HT, DM, and dyslipidemia, WC was independently associated with increased odds of IHD (OR = 1.463; 95% CI: 1.060–2.020; p 0.021). Similarly, WHR (OR = 1.839; 95% CI: 1.255–2.695; p = 0.002), WHtR (OR = 1.746; 95% CI: 1.250–2.437; p = 0.001), and RFM (OR = 1.740; 95% CI: 1.257–2.408; p = 0.001) also showed significant associations. In contrast, BMI ≥ 30 kg/m² was not significantly associated with IHD in the adjusted model (OR = 0.994; 95% CI: 0.718–1.378; p = 0.973). These findings indicate that central obesity indices—particularly WHtR, WHR, and RFM—are stronger predictors of IHD than BMI and BAI in older adults. The results are detailed in Table 2.

Table 2.

Results of multivariate logistic regression analyses for ischemic heart disease

Abbreviations: CI: Confidence Interval, OR: Odds Ratio, BMI: Body Mass Index, WC: Waist Circumference, WHR: Waist/Hip Ratio, WHtR: Waist/Height Ratio; SMM: Skeletal Muscle Mass, BAI: Body Adiposity Index, RFM: Relative Fat Mass.

ROC analysis showed that WHR (AUC = 0.611, p < 0.001), WHtR (AUC = 0.561, p = 0.005), and RFM (AUC = 0.555, p = 0.010) had significant discriminative ability for IHD, whereas BMI, BAI and WC did not show significant performance. (Fig. 1).

Fig. 1.

ROC curves for anthropometric and body composition indices in predicting ischemic heart disease

Discussion

In this cross-sectional study of older adults, we examined the correlations between several anthropometric indices and IHD. We realized that indicators of central obesity, namely WHR, WHtR, and RFM were independently associated with increased odds of IHD, whereas BMI, BAI and fat percentage were not significant predictors after multivariate adjustment. ROC analysis further confirmed that WHR, WHtR and RFM had statistically significant discriminatory ability compared to both BMI, BAI and WC. These results together highlight the importance of fat distribution, rather than overall adiposity assessing cardiovascular risk among older adults.

WHR, WHtR, and RFM were statistically significant predictors of IHD; however, their AUC values (0.611, 0.561 and 0.555 respectively) indicate only modest discriminatory ability. These findings are consistent with the large-scale meta-analysis by Ashwell et al., which reported that while central adiposity indices such as WHR and WHtR outperform BMI in cardiovascular risk prediction, their discriminatory power remains limited [23]. This reinforces the need to incorporate these measures into multifactorial cardiovascular risk models rather than relying on them in isolation.

Although BMI is the widely used measure of obesity, it inadequately reflects body fat distribution and fails to differentiate between lean and fat mass [6, 7]. In older adults, age-related alterations in body composition—such as declines in FFM, increases in fat mass, and loss of stature—can distort BMI’s accuracy [24, 25]. Especially, visceral abdominal fat tends to increase in older adult men [26] and women [27]—even without any notable changes in BMI. Relying solely on BMI may lead to underestimation of adiposity, particularly in individuals who have experienced significant muscle mass loss.

On the other hand several large-scale studies and meta-analyses have indicated that in older adults, the lowest mortality risk is observed at BMI ranges higher than those identified in younger populations, typically between 27–30 kg/m², conforming a right-shifted J- or U-shaped relationship between mortality and BMI. While low BMI consistently predicts increased mortality, moderate overweight does not appear to confer excess risk and may even be associated with improved survival in geriatric cohorts [28–31]. The same has been reported for patients with coronary artery disease, particularly those undergoing PCI or CABG. Several studies suggest that overweight and obesity may be associated with lower mortality compared to normal weight. These observations have been interpreted as potential evidence of an ‘’obesity paradox’’ in cardiovascular populations [32–34].

Our findings align with this paradox, revealing that BMI lacked predictive value for IHD, while WHR, WHtR, RFM - reflecting central adiposity- were significantly associated. This may be explained by protective metabolic reserves, greater muscle mass, and nutritional buffers in the overweight individuals, which together enhance resilience against catabolic stressors, acute illness, and frailty. Indeed, from a geriatric standpoint, mild to moderate adiposity—especially when not accompanied by significant central fat accumulation—might offer functional advantages, such as preserved mobility and reduced sarcopenia-related complication. Thus, while overall adiposity measured by BMI may sometimes appear protective in older adults, central obesity remains a relevant cardiovascular risk factor. These findings underscore the importance of differentiating between fat quantity and fat distribution in geriatric risk stratification among older adults.

Although waist-based indices such as WC, WHR, and WHtR are increasingly emphasized in cardiovascular risk assessment and metabolic syndrome diagnosis, their specific utility and comparative performance in older adults remain underexplored. Particularly, age-related shifts in body composition, including sarcopenia and fat redistribution, may alter the clinical interpretation of these indices in geriatric populations. In this context, our study adds value by directly comparing these anthropometric indices head-to-head in a well-defined geriatric cohort.

The higher levels of overall adiposity, particularly when defined by BMI, may not confer increased cardiovascular risk and may even be associated with improved survival- possibly due to protective metabolic reserves or confounding effect of frailty [35]. These results further support the view that BMI is an inadequate marker of cardiovascular risk in geriatric populations, reinforcing the importance of regional fat distribution over total adiposity [9, 15]. In line with this, our study demonstrated that BMI, BAI and body fat percentage were not associated with IHD, whereas WHR, WHtR and RFM—markers more reflective of visceral adiposity—showed significant associations. These results underscore the clinical relevance of shifting from traditional weight-based obesity assessments to functionally oriented and distribution-focused indices when evaluating older adults.

Our findings are consistent with those of de Koning et al., who conducted a comprehensive meta-regression analysis and identified that both WC and WHR are significantly associated with cardiovascular events, with WHR exhibiting a slightly stronger association than WC [36]. This finding is particularly significant as WHR not only for abdominal fat but also for protective gluteofemoral fat, that may help explain its higher discriminatory power in predicting adverse cardiovascular outcomes. Similarly, our findings demonstrate that even after controlling for conventional major cardiovascular risk factors like DM, HT, and dyslipidemia, WHR remained significant. Consistently, recent evidence has demonstrated that the Relative Fat Mass (RFM) index is significantly associated with cardiovascular morbidity and mortality, showing predictive accuracy comparable to other abdominal adiposity indices [37–40].

These findings are further supported by large, multiethnic cohorts such as NHANES, where RFM was independently linked to prevalent cardiovascular disease and long-term mortality. Likewise, prospective data from the PREVEND cohort associated higher RFM with incident heart failure, highlighting its prognostic relevance across age groups, particularly in older adults [37].

The superiority of WHtR over other indices has also been established by some recent studies [16]. For example, the systematic review by Tewari et al. highlighted WHtR as a superior predictor of cardiovascular risk to BMI in diabetic patients, emphasizing its independence from gender, age, and ethnicity [41]. Another large cross-sectional study among Chinese diabetic patients demonstrated that, after adjusting for all the anthropometric indices simultaneously, only WHtR was independently associated with cardio-cerebrovascular events [42]. These results corroborate this evidence, reinforcing the potential utility of WHtR as a practical and effective screening tool in older persons, who are particularly vulnerable to the cardiometabolic consequences of visceral adiposity.

We also aimed to evaluate the usefulness of BAI as a cardiovascular health risk marker. Although BAI was initially proposed as a simple surrogate for body fat percentage, its utility in predicting cardiovascular outcomes remains controversial. Our finding is consistent with previous studies reporting that BAI is less strongly correlated with cardiometabolic and cardiovascular risk factors compared with waist-based indices or BMI [43, 44]. Unlike waist-based indices that directly reflect central adiposity, BAI is mainly determined by hip circumference, which captures gluteofemoral fat together with muscle mass rather than visceral fat. Since cardiovascular risk is more strongly driven by central obesity, this peripheral emphasis may explain the weak discriminatory performance of BAI in our cohort. The higher proportion of elevated BAI observed in our results may also partly reflect muscle contribution, which paradoxically represents a “protective” component and could have weakened the association.

In our geriatric cohort, WC, WHR, WHtR, and RFM all showed associations with risk of IHD, even after adjustment for confounders. This finding may reflect the cumulative effect over time of central adiposity over time, and its association with metabolic abnormalities such as insulin resistance, low-grade systemic inflammation, and endothelial dysfunction—all of which are well-established contributors to atherosclerosis [9]. Furthermore, the fact that WHtR remained significant beyond WC might be an indicator of its ability to normalize central fat to body stature, a feature particularly relevant in older adults who often lose height with age.

In addition to conventional anthropometric indices, we also evaluated muscle mass and body fat percentage as markers of body composition. Remarkably, while body fat percentage was not independently associated with IHD, while WC and central obesity indices (WHR, WHtR and RFM) continued to be significantly associated. This finding is reflective of the importance of fat distribution over total adiposity in cardiovascular risk stratification.

In addition to anthropometric indices, other determinants shape obesity-related cardiovascular risk. The 2021 American Heart Association (AHA) Scientific Statement underscores obesity’s multifactorial nature and highlights the prognostic importance of visceral adiposity and metabolic heterogeneity [45]. Genetic factors also contribute by influencing adiposity patterns, lipid handling, and cardiometabolic risk; large-scale genomic studies link common variation with serum lipids and obesity susceptibility, supporting inherited contributions beyond anthropometry [46, 47]. Gut microbiota adds another layer, with altered composition and metabolites affecting energy balance, lipid absorption, inflammation, and cardiovascular events, offering plausible pathways from obesity to atherosclerotic risk [48, 49]. Together, these data suggest that risk stratification in older adults may be improved by integrating anthropometric measures with genetic and microbial determinants.

In the present study, a WHR cut-off of ≥ 1.00 for men and ≥ 0.85 for women was used to define increased cardiometabolic risk. Whilst a cut-off of ≥ 0.90 has been put forward by he World Health Organization for men, recent large-scale epidemiological studies have demonstrated that WHR values above 1.00 in men are more strongly associated with adverse cardiovascular outcomes, such as myocardial infarction, insulin resistance, and atherogenic dyslipidemia [15]. Therefore, we opted for this higher threshold to enhance the specificity of the association in our elderly male cohort, where central adiposity tends to accumulate with age. Importantly, using a higher cut-off may better distinguish individuals at highest risk within an aging population, where gluteofemoral fat loss and abdominal fat accumulation are more pronounced. Nevertheless, this choice should be considered when comparing our results with studies using lower WHR thresholds, as it may influence prevalence estimates and effect sizes.

Strength

An important strength of our study is the relatively large cohort of older adults assessed in a real-world geriatric outpatient setting, which enhances the clinical relevance and generalizablity of our findings. Standardized measurement of anthropometric data by a trained geriatric physiotherapist reduce measurement bias. In addition, adjusting for key cardiovascular risk factors helped better identify the independent associations between anthropometric indices and IHD.

Limitations

This study has several limitations that should be acknowledged. First, its cross-sectional design limits causal inference. Second, IHD diagnosis relied on clinical documentation and history, without standardized diagnostic re-confirmation (e.g., angiography or imaging-based verification). Another limitation is the cross-sectional design, which precludes establishing temporal relationships. Reverse causation is a plausible concern in older adults. Established IHD and its treatments may precipitate unintentional weight loss and loss of skeletal muscle, thereby lowering BMI and waist measures and potentially biasing associations toward the null or producing paradoxical patterns. Although we considered body composition (skeletal muscle mass and fat percentage) in our models, our study design cannot determine temporality; therefore, illness-related weight or muscle loss may have influenced the observed relationships. These findings should be interpreted with this caveat in mind. Another important issue to be considered in geriatric cohorts is polypharmacy, particularly the use of antihypertensive drugs. Experimental studies have demonstrated that ACE inhibitors and ARBs may differentially influence body composition and muscle quality, suggesting a potential confounding effect on anthropometric indices [50]. In addition, lifestyle and nutritional interventions also play a significant role; a recent meta-analysis confirmed that protein supplementation combined with resistance exercise improves body composition and physical function in older adults [51]. These findings indicate that medication use and lifestyle factors, which were not captured in our study, should be taken into account when interpreting the associations observed. Finally, while WHR, WHtR and RFM were statistically significant predictors of IHD, their AUC values indicated only modest discriminatory ability, which may limit their clinical utility for prediction at an individual-level.

Conclusion

Central obesity indices, WHR and WHtR, were independently associated with IHD in older adults but not with BMI and fat percentage. These findings emphasize the clinical relevance of fat distribution as a cardiovascular risk assessment tool. Prospective studies within diverse populations, however, are needed to verify these associations and determine their predictive utility over time.

Author contributions

OE: conception, design, interpretation of the data, drafting of the manuscript TE: design, data analysis, interpretation of the data, drafting of the manuscript NHÖ: data gathering, data analysis SÖ, GB: interpretation of the data, critical revision of the manuscript for intellectual content GB, MAK: supervision of the project. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work.

Funding

This study did not receive financial support from any governmental, commercial, or non-profit funding bodies.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

There are no relationships/conditions/circumstances that present a potential conflict of interest.

Ethical approval

All study procedures involving human subjects complied with the ethical principles of the Istanbul University Ethics Committee, as well as with the Declaration of Helsinki (1964) and its subsequent revisions or equivalent ethical standards.

Informed consent

Informed consent was obtained from all participants who were prospectively enrolled in the study. For retrospectively included individuals, data were collected from medical records and handled anonymously. No procedures beyond standard clinical assessments or routine laboratory tests were performed as part of this study.