Comparative Evaluation of Non-HDL Cholesterol and the Triglyceride-Glucose Index for Predicting Acute Myocardial Infarction Risk in a Nepalese Hospital Population: A Cross-Sectional Study

Comparative Evaluation of Non-HDL Cholesterol and the Triglyceride-Glucose Index for Predicting Acute Myocardial Infarction Risk in a Nepalese Hospital Population: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Evaluation of Non-HDL Cholesterol and the Triglyceride-Glucose Index for Predicting Acute Myocardial Infarction Risk in a Nepalese Hospital Population: A Cross-Sectional Study Bijay Kumar Gupta, Rachana Pandey, Nisha Thapa, Banjita Neupane, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9141505/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide, with South Asian populations facing a disproportionately high burden. In Nepal, the incidence of MI has been rising, and recent data suggest that cardiovascular diseases constitute a significant percentage of total deaths annually. While non-high-density lipoprotein cholesterol (non-HDL-C) is established in long-term cardiovascular risk stratification, newer metabolic markers such as the triglyceride-glucose (TyG) index have emerged as promising predictors. However, comparative evidence regarding their diagnostic and predictive roles in the acute setting, particularly in Nepal, is lacking. Methods This hospital-based, cross-sectional study enrolled adult individuals presenting with and without acute MI. Demographic, lifestyle, and clinical data were collected, and biochemical profiles — including fasting lipid profile, fasting glucose, and derived indices (non-HDL-C, TyG, Atherogenic Index of Plasma, and lipid ratios) — were analyzed. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of non-HDL-C and the TyG index. Univariate and multivariate logistic regression identified independent MI risk factors. Correlation analyses were used to explore relationships among biochemical parameters. Results A total of 200 participants (100 MI and 100 non-MI) were included (mean age for MI: 59.9 ± 13.0 years; non-MI: 45.0 ± 9.3 years). MI patients were predominantly male and more likely to be current smokers. Non-HDL-C did not significantly differ between the MI and non-MI groups (median 125.0 mg/dL, p = 0.880) and demonstrated poor diagnostic performance (AUC 0.49, 95% CI 0.41–0.57). In contrast, the TyG index was significantly higher in MI patients (8.91 ± 0.61 vs. 8.63 ± 0.45, p < 0.001) and showed modestly better discrimination (AUC 0.64, 95% CI 0.56–0.72). Traditional risk factors such as older age, male sex, smoking, low HDL-C, higher BMI, and fasting glucose remained strong independent predictors of MI. Lipid ratios (non-HDL/HDL, TC/HDL, TG/HDL) and the Atherogenic Index of Plasma were also elevated in MI patients (p < 0.01). Spearman correlation revealed strong associations among non-HDL-C, TG, and TyG index, while HDL-C was inversely related to atherogenic markers. Conclusion In this first Nepalese study comparing non-HDL-C and the TyG index for acute MI diagnosis, the TyG index emerged as a more effective marker of metabolic risk, though its incremental diagnostic utility was modest. Non-HDL-C did not enhance acute MI detection. Traditional risk factors continue to dominate MI prediction in this population. These findings suggest that while non-HDL-C and the TyG index are valuable for long-term risk assessment, their roles in acute MI diagnosis are limited. Integrating the TyG index with existing regional lipid-management and acute coronary syndrome protocols could potentially refine risk stratification for better clinical outcomes. Larger, multi-center studies are warranted to validate the clinical utility of the TyG index, especially in high-risk and metabolically diverse South Asian populations. Myocardial infarction Non-HDL cholesterol TyG index South Asia Cardiovascular risk Acute coronary syndrome Lipid indices Lipid ratios Insulin resistance Nepal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Myocardial infarction (MI) has a substantial impact on public health, causing mortality worldwide. 1 Atherosclerosis is the primary factor contributing to myocardial infarction progression. 2 Cholesterol (in free and esterified forms) is one of the key components of atherosclerotic plaque. Dyslipidemia is an established independent major risk factor for myocardial infarction. 3 The prevalence of dyslipidemia is increasing due to an unhealthy diet and lifestyle. 4 It plays a key role in both initiating and worsening atherosclerotic cardiovascular disease (ASCVD) and MI. 5 Dyslipidemia — characterized by high total cholesterol, high LDL cholesterol, high triglycerides, and low HDL cholesterol — disrupts normal endothelial function, promotes arterial damage, increases oxidative stress, and renders atherosclerotic plaques more vulnerable to rupture. 6,7 These pathological changes precipitate plaque destabilization, which can trigger acute MI and worsen patient outcomes. Despite improvements in lifestyle modification and pharmacological therapy including statins, ezetimibe, and PCSK9 inhibitors, cardiovascular disease and MI continue to rise globally. 8 From 1990 to 2019, cardiovascular disease (CVD) cases nearly doubled, rising from 271 million to 523 million, with related deaths increasing from 12 million to 18.6 million. 9 CVD caused approximately 17.9 million deaths globally and is projected to result in over 23 million deaths by 2030, with 82% occurring in low- and middle-income countries. 10 Traditionally, LDL cholesterol has been the primary focus for dyslipidemia management and cardiovascular risk assessment. However, emerging evidence highlights its limitations — LDL cholesterol does not capture all atherogenic lipid fractions. 11 Non-high-density lipoprotein cholesterol (non-HDL-C), calculated as total cholesterol minus HDL cholesterol, encompasses the cholesterol content of all atherogenic apolipoprotein B-containing lipoproteins, including VLDL, IDL, LDL, chylomicron remnants, and Lp(a). 12 The National Lipid Association Expert Panel has identified non-HDL-C elevation as a primary contributor to clinical coronary heart disease events. 13 Major guidelines now recommend routine use of non-HDL-C for cardiovascular risk stratification, with a threshold of ≥ 130 mg/dL indicating elevated risk. 14 The landscape of lipid testing is rapidly evolving, including new approaches to estimating traditional lipid parameters and novel lipid markers. The recently established Sampson–National Institutes of Health (NIH) LDL-C equation has demonstrated superiority over prior calculations. 15 LDL-C can be estimated as: TC/0.948 minus HDL-C/0.971 minus (TG/8.56 plus [TG × non-HDL-C]/2140 minus TG 2 /16100) minus 9.44; applicable when TG < 800 mg/dL. 16 The triglyceride–glucose (TyG) index was originally introduced to assess insulin resistance (IR) and guide diabetic patient management. 17 Insulin resistance is a critical mechanism in the pathogenesis of diabetes mellitus and has been extensively demonstrated as a potent risk factor for CVD, as it promotes atherosclerosis, aneurysms, and small vessel disease. 18 Numerous studies have found a positive correlation between the TyG index and cardiovascular risk markers, including arterial stiffness, carotid atherosclerosis, coronary artery calcification, coronary artery stenosis, symptomatic coronary artery disease, hypertension, and metabolic syndrome. 19,20 Although the hyperinsulinemic-euglycemic clamp remains the gold standard for measuring IR, it is impractical in clinical settings for logistical, ethical, and financial reasons. 21 Therefore, there is an urgent need for simple, reliable surrogate markers of IR such as the TyG index. 22 South Asians, including Nepalese, carry a disproportionately high burden of CVD and MI, compounded by genetic predisposition, central adiposity, metabolic syndrome, and suboptimal lifestyle habits. 23,24 Nepal faces additional challenges including limited access to preventive healthcare, delayed diagnosis, and inadequate risk factor management. National health insurance covers less than 10% of the population, with over 50% of health expenditure being out-of-pocket. Structural health system weaknesses further undermine CVD prevention efficacy. 25 Environmental factors such as air pollution, dietary patterns, and healthcare gaps further amplify MI risk in this region. Heart attacks in South Asians often occur at a younger age and present with greater severity compared to Western populations. 26 Despite their promise, few studies have rigorously compared non-HDL-C and the TyG index as predictors of acute MI in Nepal and South Asia. Most existing studies fail to adequately account for confounding variables or have limited generalizability. 19,27 The interplay between traditional lipid tests and novel markers in MI risk prediction remains poorly characterized in the Nepalese context. To address these gaps, we conducted a cross-sectional study at Dhulikhel Hospital, Nepal, enrolling 200 participants (with and without MI). Rigorous statistical analyses, including multivariable regression and ROC curve analysis, were applied to assess predictive accuracy and identify independent risk factors. This study aims to evaluate the effectiveness of non-HDL-C and the TyG index in predicting MI risk among the Nepalese population, with the intent of informing clinical decision-making and supporting cost-effective risk assessment tools in resource-limited settings. Methods Study Design and Setting This was a hospital-based, observational cross-sectional study conducted at the Department of Clinical Biochemistry, Kathmandu University Hospital, Dhulikhel Hospital, Dhulikhel, Kavre, Nepal, from March 2024 to April 2025. Study Population The study population comprised adult patients (aged ≥ 18 years) attending the emergency or outpatient departments during the study period. Participants were divided into two groups: MI group Patients diagnosed with myocardial infarction based on clinical assessment, ECG changes, and elevated cardiac biomarkers (troponin I and CK-MB), presenting to the emergency department. Non-MI group Individuals attending the outpatient department for routine check-ups or non-cardiac consultations with no clinical, biochemical, or electrocardiographic evidence of MI. Sample Size The required sample size was calculated using the formula for comparing two independent means: n = {2 x (Za/2 + Zb)2 x s2} / d2, where Za/2 = 1.96 (95% confidence), Zb = 0.84 (80% power), s = pooled standard deviation, and d = expected mean difference. Based on published data for non-HDL-C (s = 1.2 mmol/L, d = 0.8 mmol/L, n = 63 per group) 34 and the TyG index (s = 1.0 unit, d = 0.7 units, n = 65 per group), 35 100 participants per group (total N = 200) were enrolled to provide adequate statistical power for subgroup analyses and to account for potential dropouts. Sampling Technique A purposive sampling technique was employed. MI patients were selected from the emergency department based on clinical symptoms, ECG findings, and elevated cardiac biomarkers. Non-MI participants were selected from individuals visiting the laboratory for routine investigations without evidence of MI. Participants meeting the inclusion criteria and providing informed consent were included until the required sample size was achieved. The two groups were not matched for age or sex, as the study aimed to reflect real-world patient characteristics. Inclusion and Exclusion Criteria Inclusion criteria: age 18–90 years, willingness to provide informed consent, and, for the MI group, a first-time diagnosis of acute MI. Exclusion criteria: known diabetes mellitus (excluded to eliminate confounding from glucose-lowering medications and pre-existing insulin resistance that would directly inflate TyG index values), unwillingness to participate, age 90 years, loss to follow-up, and pregnancy or lactation. Data Collection Structured case report forms were used to collect sociodemographic data (age, sex, occupation, education), lifestyle habits (smoking, alcohol use, dietary pattern), and clinical variables (blood pressure, BMI, family history of diabetes). Standardized physical examinations were performed following established clinical protocols. Sample Collection and Processing Venous blood samples were collected from both groups. For MI patients, cardiac biomarkers (troponin I and CK-MB) were collected at emergency admission. Fasting blood samples for lipid profile analysis were obtained the following morning from ICU-admitted MI patients. For non-MI participants, fasting samples were collected at the time of their laboratory visit. All samples were allowed to clot for 30 minutes at room temperature, then centrifuged at 4,000 rpm for 10 minutes to separate serum for analysis. Laboratory Analysis Troponin I was analyzed using the Dialab Troponin Kit, and CK-MB was measured using the Vitros 5600 analyzer. Serum fasting samples were analyzed for total cholesterol (TC), HDL-C, LDL-C, triglycerides (TG), and fasting blood glucose. Non-HDL-C was calculated as TC minus HDL-C. The TyG index was calculated as ln[fasting TG (mg/dL) × fasting glucose (mg/dL) / 2]. The Atherogenic Index of Plasma (AIP) was calculated as log(TG/HDL-C). Biochemical analyses were performed in the Clinical Biochemistry Laboratory of Dhulikhel Hospital using calibrated analyzers, with daily internal quality control procedures ensuring accuracy and consistency. Statistical Analysis Data were entered into Microsoft Excel 2013 and analyzed using R software version 4.4.1. Normality was assessed using the Shapiro–Wilk test, supplemented by visual inspection of histograms and Q-Q plots. Normally distributed continuous variables are presented as mean ± standard deviation (SD) with 95% confidence intervals; non-normally distributed variables are presented as median with interquartile range (IQR). Categorical variables are expressed as frequencies and percentages. Between-group comparisons were performed using the independent samples t-test for normally distributed variables, the Mann–Whitney U test for non-normally distributed variables, and Chi-square or Fisher's exact test for categorical variables. Spearman correlation analysis assessed relationships among biochemical parameters. Univariate and multivariate logistic regression identified independent predictors of MI, adjusting for clinically relevant confounders. Diagnostic performance of non-HDL-C and the TyG index was evaluated using ROC curve analysis with AUC and 95% CI. A p-value of < 0.05 was considered statistically significant. Ethical Considerations Ethical approval was granted by the Institutional Review Committee of Kathmandu University School of Medical Sciences (KUSMS) (IRC approval letter no: 18/24). Written informed consent was obtained from all participants prior to data or sample collection. Participants were informed of the study's purpose and their right to withdraw at any time without impact on their clinical care. All personal data were coded and kept strictly confidential. Blood samples were collected using standard biosafety and aseptic procedures. The study adhered to the ethical principles of the Declaration of Helsinki (2013). Results Baseline Characteristics of Participants A total of 200 participants were enrolled, comprising 100 MI patients and 100 non-MI controls. The mean age of non-MI participants was 45.0 ± 9.3 years, while MI patients were significantly older, with a mean age of 59.9 ± 13.0 years. The MI group was predominantly male (60%), whereas females comprised 63% of the non-MI group. Occupational distribution was comparable across groups, with agriculture being the most common occupation. Current smoking (39% vs. 10%) and current alcohol use (35% vs. 9%) were markedly higher among MI patients. Most participants in both groups consumed a non-vegetarian diet (MI: 89%; non-MI: 93%). Family history of diabetes was reported in 34% of non-MI and 21% of MI participants. Mean BMI was slightly higher in the non-MI group (26.2 ± 4.0 kg/m²) compared to the MI group (24.5 ± 3.5 kg/m²). Median systolic blood pressure was slightly higher in MI patients (124.0 mmHg, IQR 120.0–130.0 vs. 120.0 mmHg, IQR 110.0–122.5). Table 1 Baseline characteristics of MI and non-MI participants (n = 200) Variable Non-MI (n = 100) MI (n = 100) Age, years (Mean ± SD) 45.0 ± 9.3 59.9 ± 13.0 Sex, n (%) Female: 63 (63%) Female: 40 (40%) Male: 37 (37%) Male: 60 (60%) Smoking, n (%) Current: 10 (10%) Former: 7 (7%) Never: 83 (83%) Current: 39 (39%) Former: 39 (39%) Never: 22 (22%) Alcohol, n (%) Current: 9 (9%) Former: 19 (19%) Never: 72 (72%) Current: 35 (35%) Former: 39 (39%) Never: 26 (26%) Systolic BP, mmHg Median 120.0 (IQR 110.0–122.5) Median 124.0 (IQR 120.0–130.0) Diastolic BP, mmHg Median 80.0 (IQR 80.0–81.5) Median 82.0 (IQR 80.0–90.0) Food type, n (%) Non-veg: 93 (93%) Veg: 7 (7%) Non-veg: 89 (89%) Veg: 11 (11%) Family history of Diabetes, n (%) No: 66 (66%) Yes: 34 (34%) No: 79 (79%) Yes: 21 (21%) BMI, kg/m² (Mean ± SD) 26.2 ± 4.0 24.5 ± 3.5 Note: Continuous variables expressed as mean ± SD or median (IQR) as appropriate. Categorical variables expressed as n (%). Figures 1 and 2 illustrate the distribution of smoking status and alcohol consumption among MI and non-MI participants, respectively. Figure 3 shows the age distribution, confirming that MI participants had a significantly higher median age. Figure 4 displays BMI distribution, showing that non-MI participants had slightly higher median BMI. Biochemical Parameters Table 2 presents biochemical parameters of study participants. HDL-C was significantly lower in MI patients (36.6 ± 11.1 vs. 45.5 ± 13.8 mg/dL; p < 0.001). Fasting glucose was markedly higher in MI patients (median 110.0 vs. 92.0 mg/dL; p < 0.001). The TyG index was significantly elevated in MI patients (8.91 ± 0.61 vs. 8.63 ± 0.45; p < 0.001). Non-HDL-C (median 125.0 mg/dL in both groups; p = 0.880), total cholesterol (p = 0.172), LDL-C (p = 0.843), and triglycerides (p = 0.369) showed no significant differences between groups. Table 2 Biochemical parameters of the study participants, MI vs. non-MI (n = 200) Variable Non-MI (n = 100) MI (n = 100) p-value Total cholesterol (mg/dL) 171.2 ± 32.7 (95% CI: 164.8–177.7) 163.1 ± 49.2 (95% CI: 153.5–172.8) 0.172† HDL-C (mg/dL) 45.5 ± 13.8 (95% CI: 42.8–48.2) 36.6 ± 11.1 (95% CI: 34.4–38.7) < 0.001† LDL-C (mg/dL) 99.3 ± 26.5 (95% CI: 94.1–104.5) 98.3 ± 39.7 (95% CI: 90.5–106.1) 0.843† Non-HDL-C (mg/dL) Median 125.0 (IQR 100.5–150.5) Median 125.0 (IQR 91.5–156.5) 0.880* Fasting glucose (mg/dL) Median 92.0 (IQR 85.0–96.0) Median 110.0 (IQR 99.5–129.3) < 0.001* Triglycerides (mg/dL) Median 123.5 (IQR 84.8–169.3) Median 120.5 (IQR 85.0–183.5) 0.369* TyG index 8.63 ± 0.45 (95% CI: 8.54–8.71) 8.91 ± 0.61 (95% CI: 8.79–9.03) < 0.001† *Mann–Whitney U test (non-normal distribution); †Independent samples t-test (normal distribution). Bold values indicate p < 0.05. Sociodemographic Characteristics with p-values Table 3 presents formal statistical comparisons of sociodemographic and clinical characteristics between groups. Statistically significant differences were observed for age (p < 0.001), sex (p = 0.002), smoking (p < 0.001), alcohol consumption (p < 0.001), BMI (p = 0.003), and systolic blood pressure (p = 0.007). Occupation, food type, family history of diabetes, and diastolic blood pressure showed no significant differences. Table 3 Sociodemographic characteristics with p-values between MI and non-MI groups (n = 200) Variable Non-MI (n = 100) MI (n = 100) p-value Age, years 45.0 ± 9.3 59.9 ± 13.0 < 0.001* Sex (Female/Male) 63 (63%) / 37 (37%) 40 (40%) / 60 (60%) 0.002† Alcohol Current: 9 (9%) Former: 19 (19%) Never: 72 (72%) Current: 35 (35%) Former: 39 (39%) Never: 26 (26%) < 0.001† Smoking Current: 10 (10%) Former: 7 (7%) Never: 83 (83%) Current: 39 (39%) Former: 39 (39%) Never: 22 (22%) < 0.001† Systolic BP (mmHg) Median 120.0 (IQR 110.0–122.5) Median 124.0 (IQR 120.0–130.0) 0.007* Diastolic BP (mmHg) Median 80.0 (IQR 80.0–81.5) Median 82.0 (IQR 80.0–90.0) 0.233* Food type Non-veg: 93 (93%) Veg: 7 (7%) Non-veg: 89 (89%) Veg: 11 (11%) 0.459† Family history of Diabetes No: 66 (66%) Yes: 34 (34%) No: 79 (79%) Yes: 21 (21%) 0.057† BMI (kg/m²) 26.2 ± 4.0 24.5 ± 3.5 0.003* *Mann–Whitney U test (non-normal distribution); †Chi-square or Fisher's exact test. Bold p-values indicate statistical significance (p < 0.05). Lipid and Metabolic Ratios Table 4 shows that atherogenic and metabolic ratios were significantly higher in MI patients compared to controls. Non-HDL/HDL (3.66 vs. 2.95), TC/HDL (4.66 vs. 3.95), TyG index (8.81 vs. 8.64), and TyG/HDL (0.252 vs. 0.200) all demonstrated p < 0.001. TG/HDL (p = 0.005) and the Atherogenic Index of Plasma (0.551 vs. 0.451; p = 0.005) were also significantly elevated. TyG-BMI did not differ significantly between groups (222 vs. 224; p = 0.271). Table 4 Comparison of lipid and metabolic ratios between MI and non-MI groups (n = 200) Marker Median Non-MI Median MI p-value Non-HDL/HDL 2.95 3.66 < 0.001 TC/HDL 3.95 4.66 < 0.001 TG/HDL 2.82 3.56 0.005 Atherogenic Index of Plasma (AIP) 0.451 0.551 0.005 TyG index 8.64 8.81 < 0.001 TyG/HDL 0.200 0.252 < 0.001 TyG-BMI 224 222 0.271 Medians reported; p-values calculated using Wilcoxon rank-sum test. Bold values indicate p < 0.05. Spearman Correlation Analysis A Spearman correlation analysis was performed to evaluate relationships among biochemical parameters. Strong positive correlations were observed between TC and non-HDL-C (ρ = 0.95), TG and TyG index (ρ = 0.89), and non-HDL-C and TyG index (ρ = 0.53). Moderate correlations included TC–TG (ρ = 0.48), TC–TyG index (ρ = 0.44), and TG–non-HDL-C (ρ = 0.56). HDL-C showed weak negative correlations with TG (ρ = −0.16) and TyG index (ρ = −0.20), confirming its inverse relationship with atherogenic markers. Fasting blood sugar showed generally weak correlations with lipid parameters. Univariate Logistic Regression Table 5 presents the univariate logistic regression analysis. Age was strongly associated with MI (OR = 1.12, 95% CI: 1.09–1.16, p < 0.001). Male sex showed more than double the odds of MI compared to females (OR = 2.55, 95% CI: 1.45–4.55, p = 0.0013). Never smoking (OR = 0.07, 95% CI: 0.03–0.16, p < 0.001) and never drinking alcohol (OR = 0.09, 95% CI: 0.04–0.22, p < 0.001) were strongly protective. BMI was significantly associated with MI (OR = 1.12, 95% CI: 1.04–1.21, p = 0.003). HDL-C was protective (OR = 0.94, 95% CI: 0.91–0.96, p < 0.001). Fasting blood sugar (OR = 1.09, 95% CI: 1.06–1.12, p < 0.001) and TyG index (OR = 2.72, 95% CI: 1.56–4.76, p < 0.001) were strongly associated with MI. Traditional lipid measures including total cholesterol, LDL-C, non-HDL-C, and triglycerides were not significantly associated with MI. Table 5 Univariate logistic regression analysis — MI status and sociodemographic and biochemical parameters (n = 200) Variable OR 95% CI p-value Age 1.12 1.09–1.16 < 0.001 Sex (Male vs. Female) 2.55 1.45–4.55 0.001 Alcohol (Never vs. Current) 0.09 0.04–0.22 < 0.001 Alcohol (Former vs. Current) 0.56 0.22–1.41 0.213 Smoking (Never vs. Current) 0.07 0.03–0.16 < 0.001 Smoking (Former vs. Current) 1.43 0.50–4.17 0.511 Family history of Diabetes (Yes vs. No) 0.49 0.26–0.93 0.030 BMI (kg/m²) 1.12 1.04–1.21 0.003 Total cholesterol (mg/dL) 0.99 0.99–1.01 0.172 HDL-C (mg/dL) 0.94 0.91–0.96 < 0.001 LDL-C (mg/dL) 1.00 0.99–1.01 0.842 Non-HDL-C (mg/dL) 1.00 0.99–1.01 0.879 Triglycerides (mg/dL) 1.01 1.00–1.01 0.368 Fasting blood sugar (mg/dL) 1.09 1.06–1.12 < 0.001 TyG index 2.72 1.56–4.76 < 0.001 Systolic BP (mmHg) 1.04 1.01–1.07 0.009 Diastolic BP (mmHg) 1.02 0.99–1.06 0.234 OR = odds ratio; CI = confidence interval. Reference category for sex: Female; for smoking and alcohol: Current. Non-MI participants served as the reference group. Multivariate Logistic Regression Tables 6 and 7 present the adjusted multivariate logistic regression analyses. Age remained a strong and independent predictor of MI (AOR = 1.14, 95% CI: 1.09–1.19, p < 0.001). Former smokers had significantly lower odds compared to current smokers (AOR = 0.20, 95% CI: 0.06–0.67, p = 0.009). Sex, alcohol consumption, and family history of diabetes were not significant after adjustment. Among biochemical markers, HDL-C remained protective (AOR = 0.93, 95% CI: 0.88–0.98, p = 0.006), the TyG index was a strong independent predictor (AOR = 3.85, 95% CI: 1.18–14.29, p = 0.026), and BMI was modestly associated with MI (AOR = 1.03, 95% CI: 1.00–1.29, p = 0.022). Total cholesterol was not significant after adjustment. Table 6 Multivariate logistic regression — sociodemographic predictors of MI (n = 200) Characteristic Unadjusted OR Adjusted OR 95% CI p-value Age 1.12 1.14 1.09–1.19 < 0.001 Sex (Male vs. Female) 2.56 1.49 0.56–4.17 0.446 Alcohol (Former vs. Current) 0.56 0.96 0.26–3.70 0.951 Alcohol (Never vs. Current) 0.09 0.51 0.13–2.00 0.330 Smoking (Former vs. Current) 1.43 1.85 0.46–7.69 0.387 Smoking (Never vs. Current) 0.07 0.20 0.06–0.67 0.009 Family history of Diabetes (Yes vs. No) 0.49 0.52 0.17–1.64 0.262 Table 7 Multivariate logistic regression — biochemical predictors of MI (n = 200) Variable Unadjusted OR Adjusted OR 95% CI p-value Total cholesterol (mg/dL) 0.99 1.00 0.98–1.02 0.614 HDL-C (mg/dL) 0.94 0.93 0.88–0.98 0.006 TyG index 2.72 3.85 1.18–14.29 0.026 BMI (kg/m²) 1.12 1.03 1.00–1.29 0.022 OR = odds ratio; AOR = adjusted odds ratio; CI = confidence interval. Diagnostic Performance: ROC Curve Analysis Table 8 presents the diagnostic performance of non-HDL-C and the TyG index for predicting acute MI. Non-HDL-C alone demonstrated poor discriminative ability (AUC = 0.49, 95% CI: 0.41–0.57), with low sensitivity (16–17%) and high specificity (96–97%). The TyG index performed modestly better (AUC = 0.64, 95% CI: 0.56–0.72), with sensitivity of 35% and specificity of 90%. The baseline clinical model achieved excellent discrimination (AUC = 0.92, 95% CI: 0.88–0.96). Addition of non-HDL-C (AUC 0.92, p = 0.24) or the TyG index (AUC 0.93, p = 0.21) provided no statistically significant incremental improvement. Table 8 Diagnostic performance of non-HDL cholesterol and TyG index for predicting acute MI (n = 200) Model/Marker AUC Cut-off Sensitivity (%) Specificity (%) 95% CI Non-HDL alone 0.49 172–175 16–17 96–97 0.41–0.57 TyG alone 0.64 9.14 35 90 0.56–0.72 Baseline model 0.92 — — — 0.88–0.96 Baseline + non-HDL 0.92 0.52 85 86 0.88–0.96 Baseline + TyG 0.93 0.52–0.56 84–85 87–88 0.89–0.96 Discussion This is the first study in Nepal to compare non-HDL-C and the TyG index as predictors of acute MI using hospital-based data. Our results demonstrate that these two biomarkers behave distinctly in this population: non-HDL-C did not differ significantly between MI and non-MI groups, while the TyG index was significantly elevated in MI patients and outperformed non-HDL-C in ROC analysis. Importantly, patients with known diabetes were excluded from this study to eliminate confounding from glucose-lowering medications and pre-existing metabolic derangements; this design choice isolates the TyG index's predictive performance in a non-diabetic population, though it limits direct generalizability to diabetic individuals. The observed age difference between groups (MI: 59.9 years; non-MI: 45.0 years) reflects real-world hospital practice and was adjusted for in all multivariate models; however, readers should consider this imbalance when interpreting unadjusted between-group comparisons. The mean age of MI patients was 59.9 ± 13.0 years, compared with 45.0 ± 9.3 years in non-MI patients (p < 0.001), consistent with findings from Iran and Australia 36 , 37 and confirming age as a fundamental non-modifiable cardiovascular risk factor. 38,39 Males comprised 60% of MI patients, likely reflecting the protective role of estrogen in premenopausal women and sex differences in lipid metabolism and atherosclerosis. 40 Smoking differed significantly between groups (p < 0.001), with 39% current and 39% former smokers among MI patients, compared to 83% never-smokers in the non-MI group. Alcohol consumption was also notably higher in MI patients. Both smoking and alcohol are established modifiable risk factors for MI. 41 Blood pressure differences were observed but did not reach statistical significance, possibly due to limited sample size or antihypertensive medication use, consistent with South Asian cohort data. 42 Dietary habits, occupational patterns, and family history of diabetes showed no statistically significant between-group differences. Nevertheless, sedentary occupational patterns, 43 non-vegetarian dietary habits, 44 and family history of diabetes 45 are recognized cardiovascular risk factors that may carry residual confounding influence in this population and deserve consideration in future studies. The MI group had a mean BMI of 24.5 ± 3.5 kg/m², compared to 26.2 ± 4.0 in the non-MI group, 46 consistent with the 'obesity paradox,' which suggests that overweight or mildly obese patients may paradoxically experience better short-term outcomes in certain conditions. 47 Total cholesterol and LDL-C levels were similar in both groups, suggesting that traditional lipid measures alone may not reliably identify acute MI risk, particularly in populations with widespread use of lipid-lowering therapy. 7 HDL-C was significantly lower in MI patients (36.6 ± 11.1 vs. 45.5 ± 13.8 mg/dL; p < 0.001), supporting its established protective role against atherosclerosis 48 , 49 through reverse cholesterol transport and plaque stabilization. Non-HDL-C showed no significant difference between groups, suggesting that other metabolic markers may be more informative for acute MI. 50 Fasting glucose was markedly higher in MI patients (p < 0.001), 51 and the TyG index was significantly elevated (8.91 ± 0.61 vs. 8.63 ± 0.45; p < 0.001), reinforcing the role of insulin resistance in cardiovascular risk beyond traditional lipid measurements. 30,52,53,54 Several lipid ratios were elevated in MI patients. The non-HDL/HDL ratio (3.66 vs. 2.95; p < 0.001) and TC/HDL ratio (4.66 vs. 3.95; p < 0.001) are consistent with published literature. 55 The TG/HDL ratio above 3 has been linked to a twofold increase in coronary artery disease risk. 3 The AIP was significantly higher in MI patients (0.551 vs. 0.451; p = 0.005), consistent with Dobiásová and Frohlich. 56 The TyG/HDL ratio was also significantly elevated (p < 0.001), as described by Jin et al. 57 TyG-BMI did not differ significantly (p = 0.271), suggesting it may be less sensitive in this population, 58,59 with further subgroup analysis warranted. Spearman correlation analysis demonstrated a very strong positive correlation between TC and non-HDL-C (ρ = 0.95), consistent with Millán et al. 55 A strong correlation was found between TG and TyG index (ρ = 0.89), matching Simental-Mendía et al. 30 Correlations between TG–non-HDL-C (ρ = 0.56) and non-HDL-C–TyG index (ρ = 0.53) were consistent with findings in a PCI cohort. 32 Moderate correlations between TC–TG (ρ = 0.48) and TC–TyG index (ρ = 0.44) are biologically plausible given the shared metabolic pathways linking triglyceride-rich lipoproteins and overall cholesterol burden. HDL-C showed weak negative correlations with TG (ρ = −0.16) and TyG index (ρ = −0.20), consistent with Dobiásová and Frohlich, 56 confirming its inverse relationship with atherogenic markers. In univariate analysis, each one-year increase in age raised MI odds by 12% (OR = 1.12; p < 0.001). 61 Male sex was associated with more than double the odds of MI (OR = 2.55; p = 0.0013), matching global evidence. 62 Never smoking (OR = 0.07; p < 0.001) and never drinking (OR = 0.09; p < 0.001) were strongly protective. 63,64 Higher BMI was linked to increased MI risk (OR = 1.12; p = 0.003), 65 and HDL-C was protective (OR = 0.94; p < 0.001). 48,49 Fasting blood sugar (OR = 1.09; p < 0.001) and TyG index (OR = 2.72; p < 0.001) were strongly associated with MI. 30,51,52 Systolic blood pressure was also a significant predictor (OR = 1.04; p = 0.009), consistent with longstanding evidence. 66 Traditional lipid measures were not significantly predictive, possibly reflecting statin use or population-specific confounding. 30,49 The unexpected protective association of family history of diabetes may reflect greater health awareness in this subgroup, warranting further investigation. 33 Multivariate analysis confirmed age (AOR = 1.14; p < 0.001) 61 and never smoking (AOR = 0.20; p = 0.009) as independent MI predictors. 63,64 These findings carry direct public health relevance: smoking cessation remains one of the most impactful modifiable interventions, with an estimated 80 fewer MI cases per 1,000 people who quit — underscoring the critical importance of population-level cessation programmes. Male sex lost significance after adjustment (AOR = 1.49; p = 0.446), 62 suggesting that its univariate association was partially explained by confounding from age and lifestyle factors. Alcohol consumption was also non-significant in the adjusted model, differing from some meta-analytic evidence, 67,68 possibly due to the relatively small sample or differences in drinking patterns and beverage types in this population. Among biochemical markers, HDL-C remained protective (AOR = 0.93; p = 0.006), consistent with the Framingham Heart Study. 69 The TyG index was a strong independent predictor (AOR = 3.85; p = 0.026), similar to findings by Sánchez-Íñigo et al., 52 reinforcing its utility as a simple, cost-effective marker of metabolic risk. BMI was modestly significant (AOR = 1.03; p = 0.022), consistent with the Emerging Risk Factors Collaboration. 70 Total cholesterol was not significant after adjustment, 41 likely due to statin use or population-specific factors. The forest plot confirmed age, 69,71 BMI, 70,72 TyG index, 52 and HDL-C 48,69,73 as independent MI predictors. In ROC analysis, non-HDL-C demonstrated poor predictive performance (AUC = 0.49), consistent with its established utility for long-term rather than acute MI risk assessment. 74,75 The low sensitivity (16–17%) confirms that non-HDL-C alone is insufficient for acute prediction, likely due to acute-phase lipid changes and prior statin use, both of which attenuate the discriminative value of cholesterol-based markers in the immediate post-event period. Notably, the near-chance AUC for non-HDL-C (0.49) should not be interpreted as a 'lipid paradox' but rather as a methodological consequence of cross-sectional, single time-point sampling in an acutely ill population. The TyG index performed modestly better (AUC = 0.64), with pooled AUCs in the literature ranging from 0.60 to 0.72 52,78 and cut-offs of 8.8 to 9.2 linked to higher risk across diverse populations. The baseline clinical model achieved excellent discrimination (AUC = 0.92). Neither non-HDL-C nor the TyG index provided statistically significant incremental value beyond the established baseline model (p > 0.05 for both), though the TyG index may still help identify metabolically at-risk cases — particularly individuals with insulin resistance — missed by standard clinical predictors. 19,79,80,81 Several limitations of this study must be acknowledged. First, patients with known diabetes mellitus were excluded, which, while controlling for a major confounding condition, limits the generalizability of TyG index findings to non-diabetic populations only. Since the TyG index is primarily a surrogate for insulin resistance, future studies including diabetic patients — with appropriate subgroup stratification — are warranted to fully characterize its utility. Second, the MI and non-MI groups differed substantially in age (59.9 vs. 45.0 years) and sex distribution, as participants were not matched. Although multivariate analysis adjusted for these variables, residual confounding cannot be entirely excluded, and between-group differences in TyG index may partly reflect age-related metabolic changes rather than MI-specific pathophysiology. Third, the single-center design and modest sample size (n = 200) limit external validity. Fourth, the cross-sectional design precludes causal inference. Fifth, self-reported lifestyle data may introduce recall bias. Sixth, lipid profiles were measured after hospital admission, and acute-phase responses or pre-admission statin use may have attenuated the predictive value of cholesterol-based markers. Seventh, long-term follow-up data for cardiovascular outcomes were not available. Conclusions This is the first Nepalese study to compare non-HDL-C and the TyG index in acute MI patients using hospital data. Non-HDL-C did not significantly differ between MI and non-MI patients, demonstrated poor AUC and limited sensitivity, and did not improve acute MI diagnosis — likely due to high rates of lipid-lowering therapy and acute-phase effects. The TyG index, reflecting insulin resistance, was significantly elevated in MI patients and marginally improved clinical model accuracy, supporting its role as a complementary metabolic marker, particularly in patients with metabolic syndrome or diabetes. Traditional risk factors — including age, male sex, smoking, low HDL-C, higher BMI, and impaired fasting glucose — remained the strongest independent predictors of MI. A combined clinical, biochemical, and lifestyle risk-factor approach is recommended over reliance on any single biomarker. Non-HDL-C remains useful for long-term cardiovascular risk stratification, while the TyG index adds modest value in acute settings and warrants validation in larger, multicenter, prospective studies across diverse South Asian populations. Declarations Ethics Approval and Consent to Participate Ethical approval was granted by the Institutional Review Committee of Kathmandu University School of Medical Sciences (KUSMS) (IRC approval letter no: 18/24). Written informed consent was obtained from all participants prior to enrollment. The study adhered to the ethical principles of the Declaration of Helsinki (2013). 31 Consent for Publication Not applicable. Clinical trial number Not applicable Availability of Data and Materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests. Funding No external funding was received for this study. Authors' Contributions BKG conceived and designed the study, supervised data collection, and critically revised the manuscript. RP and NT contributed to data collection, laboratory analyses, and manuscript preparation. BN and SB performed laboratory analyses. PR provided academic supervision, institutional oversight, and final approval of the manuscript. All authors read and approved the final version for submission. Acknowledgements The authors gratefully acknowledge the staff of the Department of Medical Biochemistry and the Clinical Biochemistry Laboratory, Kathmandu University Hospital, Dhulikhel Hospital, Dhulikhel, Kavre, Nepal, for their technical support. We extend sincere thanks to all participants who volunteered their time for this study. References Sacco RL, Roth GA, Reddy KS, Arnett DK, Bonita R, Gaziano TA, et al. The heart of 25 by 25: achieving the goal of reducing global and regional premature deaths from cardiovascular diseases and stroke. 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Multimarker approach to risk stratification in non-ST elevation acute coronary syndromes. Circulation. 2002;105(15):1760–3. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9141505","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610358638,"identity":"cfa960cd-36d3-4f1a-9c8b-907d5570e564","order_by":0,"name":"Bijay Kumar Gupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACAxDB2CDBzMDAfMDgA5DDxk6MloNgLWwJhTNAWpiJ0wIieQw+84BoQlrM2Zufff64w4Kdf0aO4WabX9vk+ZgZGD98zMGtxbLnmPGMg2ckmCVupBUb5/bdNmxjZmCWnLkNj8NuJBgzHGwD+uVG8jbj3J7bjEAtbMy8+LTcf/4ZrEX+RoL5b8ue2/aEtdzggdhicCPFwJjhx+1EwlrO5BQznAX6xfDMswTD3obbyW3MjM34/XL8+GaGyh11yXLHkw8Y/Phz23Z+e/PBDx/xaIGBZAaBBGAyaAOxGRsIqwcCOwb+A0DqD1GKR8EoGAWjYIQBADIUVQtLx3xFAAAAAElFTkSuQmCC","orcid":"","institution":"Kathmandu University","correspondingAuthor":true,"prefix":"","firstName":"Bijay","middleName":"Kumar","lastName":"Gupta","suffix":""},{"id":610358639,"identity":"dd25c593-e503-4d9e-88bb-384f413330fd","order_by":1,"name":"Rachana Pandey","email":"","orcid":"","institution":"Kathmandu University","correspondingAuthor":false,"prefix":"","firstName":"Rachana","middleName":"","lastName":"Pandey","suffix":""},{"id":610358640,"identity":"049d4374-34db-4bfa-84f8-639874af2b8c","order_by":2,"name":"Nisha Thapa","email":"","orcid":"","institution":"Kathmandu University","correspondingAuthor":false,"prefix":"","firstName":"Nisha","middleName":"","lastName":"Thapa","suffix":""},{"id":610358641,"identity":"edea877c-350a-4653-b3e8-87e6cd14d302","order_by":3,"name":"Banjita Neupane","email":"","orcid":"","institution":"Kathmandu University","correspondingAuthor":false,"prefix":"","firstName":"Banjita","middleName":"","lastName":"Neupane","suffix":""},{"id":610358642,"identity":"c3efd776-581e-4197-8745-d228baf70d52","order_by":4,"name":"Swastika Bhattarai","email":"","orcid":"","institution":"Kathmandu University","correspondingAuthor":false,"prefix":"","firstName":"Swastika","middleName":"","lastName":"Bhattarai","suffix":""},{"id":610358643,"identity":"956f675d-8b2f-47e9-b833-cacf5aa8de88","order_by":5,"name":"Prabodh Risal","email":"","orcid":"","institution":"Kathmandu University","correspondingAuthor":false,"prefix":"","firstName":"Prabodh","middleName":"","lastName":"Risal","suffix":""}],"badges":[],"createdAt":"2026-03-16 19:54:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9141505/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9141505/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105310744,"identity":"d9ebc236-f9a3-4173-9532-4c5376fa9e38","added_by":"auto","created_at":"2026-03-24 15:12:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAge distribution among MI and non-MI participants (n = 200). MI participants had a higher median age compared to non-MI participants.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9141505/v1/54c2086f43ea3231d20261e7.png"},{"id":105310702,"identity":"9914a4ff-f2f7-4a75-967b-98bae6745c70","added_by":"auto","created_at":"2026-03-24 15:12:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBMI distribution among MI and non-MI participants (n = 200). Non-MI participants had slightly higher median BMI compared to MI participants.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9141505/v1/1cb776de434224c9b034d879.png"},{"id":105310626,"identity":"3aed1325-72d3-4637-8b31-f017f74affbd","added_by":"auto","created_at":"2026-03-24 15:11:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118789,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSystolic and diastolic blood pressure distribution among MI and non-MI participants (n = 200). Systolic blood pressure was significantly higher in the MI group (p = 0.014), whereas diastolic blood pressure did not differ significantly (p = 0.13).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9141505/v1/48e792328658acc8139284ec.png"},{"id":105310785,"identity":"b699bbc5-c224-49a3-82d7-ef72efe0f671","added_by":"auto","created_at":"2026-03-24 15:12:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpearman correlation heatmap showing relationships among biochemical parameters (n = 200). Blue indicates positive correlations and red indicates negative correlations. ρ values are shown within each cell.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9141505/v1/9827bed339fc6336001928b3.png"},{"id":105310624,"identity":"7e73a456-7e7d-41f1-93c5-bfd3881434e1","added_by":"auto","created_at":"2026-03-24 15:11:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":214821,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eForest plot of adjusted odds ratios (95% CI) for predictors of myocardial infarction from multivariable logistic regression (n = 200). Significant variables are highlighted. The red dashed vertical line at OR = 1 represents the null value.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9141505/v1/4f552e5ba433af943c72a097.png"},{"id":105310791,"identity":"fde581eb-300f-4353-ad4c-f6a9d4289ff6","added_by":"auto","created_at":"2026-03-24 15:12:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":134380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eROC curves comparing baseline model with addition of metabolic biomarkers (n = 200). The baseline model (AUC = 0.921) demonstrated excellent discrimination. Addition of non-HDL-C (AUC 0.920) or TyG index (AUC 0.929) resulted in minimal change, with all three curves demonstrating nearly identical performance.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9141505/v1/bfca71832649067e2240d0d7.png"},{"id":106961266,"identity":"c7aa7c1b-1d17-4dc2-88ad-b5c0f22c2c81","added_by":"auto","created_at":"2026-04-15 09:24:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1820463,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9141505/v1/0cdf5768-aea2-469c-b8cc-4946cb9d9ea7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Evaluation of Non-HDL Cholesterol and the Triglyceride-Glucose Index for Predicting Acute Myocardial Infarction Risk in a Nepalese Hospital Population: A Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eMyocardial infarction (MI) has a substantial impact on public health, causing mortality worldwide. \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAtherosclerosis is the primary factor contributing to myocardial infarction progression. \u003csup\u003e2\u003c/sup\u003e Cholesterol (in free and esterified forms) is one of the key components of atherosclerotic plaque. Dyslipidemia is an established independent major risk factor for myocardial infarction. \u003csup\u003e3\u003c/sup\u003e The prevalence of dyslipidemia is increasing due to an unhealthy diet and lifestyle. \u003csup\u003e4\u003c/sup\u003e It plays a key role in both initiating and worsening atherosclerotic cardiovascular disease (ASCVD) and MI. \u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDyslipidemia \u0026mdash; characterized by high total cholesterol, high LDL cholesterol, high triglycerides, and low HDL cholesterol \u0026mdash; disrupts normal endothelial function, promotes arterial damage, increases oxidative stress, and renders atherosclerotic plaques more vulnerable to rupture. \u003csup\u003e6,7\u003c/sup\u003e These pathological changes precipitate plaque destabilization, which can trigger acute MI and worsen patient outcomes. Despite improvements in lifestyle modification and pharmacological therapy including statins, ezetimibe, and PCSK9 inhibitors, cardiovascular disease and MI continue to rise globally. \u003csup\u003e8\u003c/sup\u003e From 1990 to 2019, cardiovascular disease (CVD) cases nearly doubled, rising from 271\u0026nbsp;million to 523\u0026nbsp;million, with related deaths increasing from 12\u0026nbsp;million to 18.6\u0026nbsp;million. \u003csup\u003e9\u003c/sup\u003e CVD caused approximately 17.9\u0026nbsp;million deaths globally and is projected to result in over 23\u0026nbsp;million deaths by 2030, with 82% occurring in low- and middle-income countries. \u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTraditionally, LDL cholesterol has been the primary focus for dyslipidemia management and cardiovascular risk assessment. However, emerging evidence highlights its limitations \u0026mdash; LDL cholesterol does not capture all atherogenic lipid fractions. \u003csup\u003e11\u003c/sup\u003e Non-high-density lipoprotein cholesterol (non-HDL-C), calculated as total cholesterol minus HDL cholesterol, encompasses the cholesterol content of all atherogenic apolipoprotein B-containing lipoproteins, including VLDL, IDL, LDL, chylomicron remnants, and Lp(a). \u003csup\u003e12\u003c/sup\u003e The National Lipid Association Expert Panel has identified non-HDL-C elevation as a primary contributor to clinical coronary heart disease events. \u003csup\u003e13\u003c/sup\u003e Major guidelines now recommend routine use of non-HDL-C for cardiovascular risk stratification, with a threshold of \u0026ge;\u0026thinsp;130 mg/dL indicating elevated risk. \u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe landscape of lipid testing is rapidly evolving, including new approaches to estimating traditional lipid parameters and novel lipid markers. The recently established Sampson\u0026ndash;National Institutes of Health (NIH) LDL-C equation has demonstrated superiority over prior calculations. \u003csup\u003e15\u003c/sup\u003e LDL-C can be estimated as: TC/0.948 minus HDL-C/0.971 minus (TG/8.56 plus [TG \u0026times; non-HDL-C]/2140 minus TG\u003csup\u003e2\u003c/sup\u003e/16100) minus 9.44; applicable when TG\u0026thinsp;\u0026lt;\u0026thinsp;800 mg/dL. \u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe triglyceride\u0026ndash;glucose (TyG) index was originally introduced to assess insulin resistance (IR) and guide diabetic patient management. \u003csup\u003e17\u003c/sup\u003e Insulin resistance is a critical mechanism in the pathogenesis of diabetes mellitus and has been extensively demonstrated as a potent risk factor for CVD, as it promotes atherosclerosis, aneurysms, and small vessel disease. \u003csup\u003e18\u003c/sup\u003e Numerous studies have found a positive correlation between the TyG index and cardiovascular risk markers, including arterial stiffness, carotid atherosclerosis, coronary artery calcification, coronary artery stenosis, symptomatic coronary artery disease, hypertension, and metabolic syndrome. \u003csup\u003e19,20\u003c/sup\u003e Although the hyperinsulinemic-euglycemic clamp remains the gold standard for measuring IR, it is impractical in clinical settings for logistical, ethical, and financial reasons. \u003csup\u003e21\u003c/sup\u003e Therefore, there is an urgent need for simple, reliable surrogate markers of IR such as the TyG index. \u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSouth Asians, including Nepalese, carry a disproportionately high burden of CVD and MI, compounded by genetic predisposition, central adiposity, metabolic syndrome, and suboptimal lifestyle habits. \u003csup\u003e23,24\u003c/sup\u003e Nepal faces additional challenges including limited access to preventive healthcare, delayed diagnosis, and inadequate risk factor management. National health insurance covers less than 10% of the population, with over 50% of health expenditure being out-of-pocket. Structural health system weaknesses further undermine CVD prevention efficacy. \u003csup\u003e25\u003c/sup\u003e Environmental factors such as air pollution, dietary patterns, and healthcare gaps further amplify MI risk in this region. Heart attacks in South Asians often occur at a younger age and present with greater severity compared to Western populations. \u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite their promise, few studies have rigorously compared non-HDL-C and the TyG index as predictors of acute MI in Nepal and South Asia. Most existing studies fail to adequately account for confounding variables or have limited generalizability. \u003csup\u003e19,27\u003c/sup\u003e The interplay between traditional lipid tests and novel markers in MI risk prediction remains poorly characterized in the Nepalese context.\u003c/p\u003e \u003cp\u003eTo address these gaps, we conducted a cross-sectional study at Dhulikhel Hospital, Nepal, enrolling 200 participants (with and without MI). Rigorous statistical analyses, including multivariable regression and ROC curve analysis, were applied to assess predictive accuracy and identify independent risk factors. This study aims to evaluate the effectiveness of non-HDL-C and the TyG index in predicting MI risk among the Nepalese population, with the intent of informing clinical decision-making and supporting cost-effective risk assessment tools in resource-limited settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis was a hospital-based, observational cross-sectional study conducted at the Department of Clinical Biochemistry, Kathmandu University Hospital, Dhulikhel Hospital, Dhulikhel, Kavre, Nepal, from March 2024 to April 2025.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study population comprised adult patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years) attending the emergency or outpatient departments during the study period. Participants were divided into two groups:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMI group\u003c/strong\u003e \u003cp\u003ePatients diagnosed with myocardial infarction based on clinical assessment, ECG changes, and elevated cardiac biomarkers (troponin I and CK-MB), presenting to the emergency department.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNon-MI group\u003c/strong\u003e \u003cp\u003eIndividuals attending the outpatient department for routine check-ups or non-cardiac consultations with no clinical, biochemical, or electrocardiographic evidence of MI.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eSample Size\u003c/h3\u003e\n\u003cp\u003eThe required sample size was calculated using the formula for comparing two independent means: n = {2 x (Za/2\u0026thinsp;+\u0026thinsp;Zb)2 x s2} / d2, where Za/2\u0026thinsp;=\u0026thinsp;1.96 (95% confidence), Zb\u0026thinsp;=\u0026thinsp;0.84 (80% power), s\u0026thinsp;=\u0026thinsp;pooled standard deviation, and d\u0026thinsp;=\u0026thinsp;expected mean difference. Based on published data for non-HDL-C (s\u0026thinsp;=\u0026thinsp;1.2 mmol/L, d\u0026thinsp;=\u0026thinsp;0.8 mmol/L, n\u0026thinsp;=\u0026thinsp;63 per group) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and the TyG index (s\u0026thinsp;=\u0026thinsp;1.0 unit, d\u0026thinsp;=\u0026thinsp;0.7 units, n\u0026thinsp;=\u0026thinsp;65 per group), \u003csup\u003e35\u003c/sup\u003e 100 participants per group (total N\u0026thinsp;=\u0026thinsp;200) were enrolled to provide adequate statistical power for subgroup analyses and to account for potential dropouts.\u003c/p\u003e\n\u003ch3\u003eSampling Technique\u003c/h3\u003e\n\u003cp\u003eA purposive sampling technique was employed. MI patients were selected from the emergency department based on clinical symptoms, ECG findings, and elevated cardiac biomarkers. Non-MI participants were selected from individuals visiting the laboratory for routine investigations without evidence of MI. Participants meeting the inclusion criteria and providing informed consent were included until the required sample size was achieved. The two groups were not matched for age or sex, as the study aimed to reflect real-world patient characteristics.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003e Inclusion criteria: age 18\u0026ndash;90 years, willingness to provide informed consent, and, for the MI group, a first-time diagnosis of acute MI. Exclusion criteria: known diabetes mellitus (excluded to eliminate confounding from glucose-lowering medications and pre-existing insulin resistance that would directly inflate TyG index values), unwillingness to participate, age\u0026thinsp;\u0026lt;\u0026thinsp;18 or \u0026gt;\u0026thinsp;90 years, loss to follow-up, and pregnancy or lactation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eStructured case report forms were used to collect sociodemographic data (age, sex, occupation, education), lifestyle habits (smoking, alcohol use, dietary pattern), and clinical variables (blood pressure, BMI, family history of diabetes). Standardized physical examinations were performed following established clinical protocols.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Collection and Processing\u003c/h3\u003e\n\u003cp\u003eVenous blood samples were collected from both groups. For MI patients, cardiac biomarkers (troponin I and CK-MB) were collected at emergency admission. Fasting blood samples for lipid profile analysis were obtained the following morning from ICU-admitted MI patients. For non-MI participants, fasting samples were collected at the time of their laboratory visit. All samples were allowed to clot for 30 minutes at room temperature, then centrifuged at 4,000 rpm for 10 minutes to separate serum for analysis.\u003c/p\u003e\n\u003ch3\u003eLaboratory Analysis\u003c/h3\u003e\n\u003cp\u003eTroponin I was analyzed using the Dialab Troponin Kit, and CK-MB was measured using the Vitros 5600 analyzer. Serum fasting samples were analyzed for total cholesterol (TC), HDL-C, LDL-C, triglycerides (TG), and fasting blood glucose. Non-HDL-C was calculated as TC minus HDL-C. The TyG index was calculated as ln[fasting TG (mg/dL) \u0026times; fasting glucose (mg/dL) / 2]. The Atherogenic Index of Plasma (AIP) was calculated as log(TG/HDL-C). Biochemical analyses were performed in the Clinical Biochemistry Laboratory of Dhulikhel Hospital using calibrated analyzers, with daily internal quality control procedures ensuring accuracy and consistency.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were entered into Microsoft Excel 2013 and analyzed using R software version 4.4.1. Normality was assessed using the Shapiro\u0026ndash;Wilk test, supplemented by visual inspection of histograms and Q-Q plots. Normally distributed continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) with 95% confidence intervals; non-normally distributed variables are presented as median with interquartile range (IQR). Categorical variables are expressed as frequencies and percentages.\u003c/p\u003e \u003cp\u003eBetween-group comparisons were performed using the independent samples t-test for normally distributed variables, the Mann\u0026ndash;Whitney U test for non-normally distributed variables, and Chi-square or Fisher's exact test for categorical variables. Spearman correlation analysis assessed relationships among biochemical parameters. Univariate and multivariate logistic regression identified independent predictors of MI, adjusting for clinically relevant confounders. Diagnostic performance of non-HDL-C and the TyG index was evaluated using ROC curve analysis with AUC and 95% CI. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was granted by the Institutional Review Committee of Kathmandu University School of Medical Sciences (KUSMS) (IRC approval letter no: 18/24). Written informed consent was obtained from all participants prior to data or sample collection. Participants were informed of the study's purpose and their right to withdraw at any time without impact on their clinical care. All personal data were coded and kept strictly confidential. Blood samples were collected using standard biosafety and aseptic procedures. The study adhered to the ethical principles of the Declaration of Helsinki (2013).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics of Participants\u003c/h2\u003e \u003cp\u003e A total of 200 participants were enrolled, comprising 100 MI patients and 100 non-MI controls. The mean age of non-MI participants was 45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 years, while MI patients were significantly older, with a mean age of 59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0 years. The MI group was predominantly male (60%), whereas females comprised 63% of the non-MI group. Occupational distribution was comparable across groups, with agriculture being the most common occupation. Current smoking (39% vs. 10%) and current alcohol use (35% vs. 9%) were markedly higher among MI patients. Most participants in both groups consumed a non-vegetarian diet (MI: 89%; non-MI: 93%). Family history of diabetes was reported in 34% of non-MI and 21% of MI participants. Mean BMI was slightly higher in the non-MI group (26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 kg/m\u0026sup2;) compared to the MI group (24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 kg/m\u0026sup2;). Median systolic blood pressure was slightly higher in MI patients (124.0 mmHg, IQR 120.0\u0026ndash;130.0 vs. 120.0 mmHg, IQR 110.0\u0026ndash;122.5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of MI and non-MI participants (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-MI (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMI (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale: 63 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale: 40 (40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale: 37 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale: 60 (60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent: 10 (10%) Former: 7 (7%) Never: 83 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent: 39 (39%) Former: 39 (39%) Never: 22 (22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent: 9 (9%) Former: 19 (19%) Never: 72 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent: 35 (35%) Former: 39 (39%) Never: 26 (26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian 120.0 (IQR 110.0\u0026ndash;122.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian 124.0 (IQR 120.0\u0026ndash;130.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian 80.0 (IQR 80.0\u0026ndash;81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian 82.0 (IQR 80.0\u0026ndash;90.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood type, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-veg: 93 (93%) Veg: 7 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-veg: 89 (89%) Veg: 11 (11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of Diabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo: 66 (66%) Yes: 34 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo: 79 (79%) Yes: 21 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2; (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote: Continuous variables expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) as appropriate. Categorical variables expressed as n (%).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrate the distribution of smoking status and alcohol consumption among MI and non-MI participants, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the age distribution, confirming that MI participants had a significantly higher median age. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays BMI distribution, showing that non-MI participants had slightly higher median BMI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical Parameters\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents biochemical parameters of study participants. HDL-C was significantly lower in MI patients (36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 vs. 45.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Fasting glucose was markedly higher in MI patients (median 110.0 vs. 92.0 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The TyG index was significantly elevated in MI patients (8.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 vs. 8.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Non-HDL-C (median 125.0 mg/dL in both groups; p\u0026thinsp;=\u0026thinsp;0.880), total cholesterol (p\u0026thinsp;=\u0026thinsp;0.172), LDL-C (p\u0026thinsp;=\u0026thinsp;0.843), and triglycerides (p\u0026thinsp;=\u0026thinsp;0.369) showed no significant differences between groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiochemical parameters of the study participants, MI vs. non-MI (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-MI (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMI (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171.2\u0026thinsp;\u0026plusmn;\u0026thinsp;32.7 (95% CI: 164.8\u0026ndash;177.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163.1\u0026thinsp;\u0026plusmn;\u0026thinsp;49.2 (95% CI: 153.5\u0026ndash;172.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8 (95% CI: 42.8\u0026ndash;48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 (95% CI: 34.4\u0026ndash;38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5 (95% CI: 94.1\u0026ndash;104.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.3\u0026thinsp;\u0026plusmn;\u0026thinsp;39.7 (95% CI: 90.5\u0026ndash;106.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.843\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian 125.0 (IQR 100.5\u0026ndash;150.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian 125.0 (IQR 91.5\u0026ndash;156.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.880*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian 92.0 (IQR 85.0\u0026ndash;96.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian 110.0 (IQR 99.5\u0026ndash;129.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian 123.5 (IQR 84.8\u0026ndash;169.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian 120.5 (IQR 85.0\u0026ndash;183.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.369*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 (95% CI: 8.54\u0026ndash;8.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 (95% CI: 8.79\u0026ndash;9.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Mann\u0026ndash;Whitney U test (non-normal distribution); \u0026dagger;Independent samples t-test (normal distribution). Bold values indicate p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic Characteristics with p-values\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents formal statistical comparisons of sociodemographic and clinical characteristics between groups. Statistically significant differences were observed for age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sex (p\u0026thinsp;=\u0026thinsp;0.002), smoking (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alcohol consumption (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (p\u0026thinsp;=\u0026thinsp;0.003), and systolic blood pressure (p\u0026thinsp;=\u0026thinsp;0.007). Occupation, food type, family history of diabetes, and diastolic blood pressure showed no significant differences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics with p-values between MI and non-MI groups (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-MI (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMI (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Female/Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (63%) / 37 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (40%) / 60 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent: 9 (9%) Former: 19 (19%) Never: 72 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent: 35 (35%) Former: 39 (39%) Never: 26 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent: 10 (10%) Former: 7 (7%) Never: 83 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent: 39 (39%) Former: 39 (39%) Never: 22 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian 120.0 (IQR 110.0\u0026ndash;122.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian 124.0 (IQR 120.0\u0026ndash;130.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian 80.0 (IQR 80.0\u0026ndash;81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian 82.0 (IQR 80.0\u0026ndash;90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.233*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-veg: 93 (93%) Veg: 7 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-veg: 89 (89%) Veg: 11 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.459\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo: 66 (66%) Yes: 34 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo: 79 (79%) Yes: 21 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Mann\u0026ndash;Whitney U test (non-normal distribution); \u0026dagger;Chi-square or Fisher's exact test. Bold p-values indicate statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLipid and Metabolic Ratios\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that atherogenic and metabolic ratios were significantly higher in MI patients compared to controls. Non-HDL/HDL (3.66 vs. 2.95), TC/HDL (4.66 vs. 3.95), TyG index (8.81 vs. 8.64), and TyG/HDL (0.252 vs. 0.200) all demonstrated p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. TG/HDL (p\u0026thinsp;=\u0026thinsp;0.005) and the Atherogenic Index of Plasma (0.551 vs. 0.451; p\u0026thinsp;=\u0026thinsp;0.005) were also significantly elevated. TyG-BMI did not differ significantly between groups (222 vs. 224; p\u0026thinsp;=\u0026thinsp;0.271).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of lipid and metabolic ratios between MI and non-MI groups (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian Non-MI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian MI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtherogenic Index of Plasma (AIP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMedians reported; p-values calculated using Wilcoxon rank-sum test. Bold values indicate p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSpearman Correlation Analysis\u003c/h2\u003e \u003cp\u003eA Spearman correlation analysis was performed to evaluate relationships among biochemical parameters. Strong positive correlations were observed between TC and non-HDL-C (ρ\u0026thinsp;=\u0026thinsp;0.95), TG and TyG index (ρ\u0026thinsp;=\u0026thinsp;0.89), and non-HDL-C and TyG index (ρ\u0026thinsp;=\u0026thinsp;0.53). Moderate correlations included TC\u0026ndash;TG (ρ\u0026thinsp;=\u0026thinsp;0.48), TC\u0026ndash;TyG index (ρ\u0026thinsp;=\u0026thinsp;0.44), and TG\u0026ndash;non-HDL-C (ρ\u0026thinsp;=\u0026thinsp;0.56). HDL-C showed weak negative correlations with TG (ρ = \u0026minus;0.16) and TyG index (ρ = \u0026minus;0.20), confirming its inverse relationship with atherogenic markers. Fasting blood sugar showed generally weak correlations with lipid parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate Logistic Regression\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the univariate logistic regression analysis. Age was strongly associated with MI (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.09\u0026ndash;1.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Male sex showed more than double the odds of MI compared to females (OR\u0026thinsp;=\u0026thinsp;2.55, 95% CI: 1.45\u0026ndash;4.55, p\u0026thinsp;=\u0026thinsp;0.0013). Never smoking (OR\u0026thinsp;=\u0026thinsp;0.07, 95% CI: 0.03\u0026ndash;0.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and never drinking alcohol (OR\u0026thinsp;=\u0026thinsp;0.09, 95% CI: 0.04\u0026ndash;0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were strongly protective. BMI was significantly associated with MI (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.04\u0026ndash;1.21, p\u0026thinsp;=\u0026thinsp;0.003). HDL-C was protective (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.91\u0026ndash;0.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Fasting blood sugar (OR\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 1.06\u0026ndash;1.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TyG index (OR\u0026thinsp;=\u0026thinsp;2.72, 95% CI: 1.56\u0026ndash;4.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were strongly associated with MI. Traditional lipid measures including total cholesterol, LDL-C, non-HDL-C, and triglycerides were not significantly associated with MI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate logistic regression analysis \u0026mdash; MI status and sociodemographic and biochemical parameters (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male vs. Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.45\u0026ndash;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (Never vs. Current)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026ndash;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (Former vs. Current)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (Never vs. Current)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026ndash;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (Former vs. Current)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u0026ndash;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of Diabetes (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026ndash;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026ndash;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting blood sugar (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56\u0026ndash;4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval. Reference category for sex: Female; for smoking and alcohol: Current. Non-MI participants served as the reference group.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Logistic Regression\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e present the adjusted multivariate logistic regression analyses. Age remained a strong and independent predictor of MI (AOR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.09\u0026ndash;1.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Former smokers had significantly lower odds compared to current smokers (AOR\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.06\u0026ndash;0.67, p\u0026thinsp;=\u0026thinsp;0.009). Sex, alcohol consumption, and family history of diabetes were not significant after adjustment. Among biochemical markers, HDL-C remained protective (AOR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.88\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.006), the TyG index was a strong independent predictor (AOR\u0026thinsp;=\u0026thinsp;3.85, 95% CI: 1.18\u0026ndash;14.29, p\u0026thinsp;=\u0026thinsp;0.026), and BMI was modestly associated with MI (AOR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.00\u0026ndash;1.29, p\u0026thinsp;=\u0026thinsp;0.022). Total cholesterol was not significant after adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression \u0026mdash; sociodemographic predictors of MI (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male vs. Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u0026ndash;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (Former vs. Current)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u0026ndash;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (Never vs. Current)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u0026ndash;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (Former vs. Current)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u0026ndash;7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (Never vs. Current)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u0026ndash;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of Diabetes (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u0026ndash;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression \u0026mdash; biochemical predictors of MI (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18\u0026ndash;14.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOR\u0026thinsp;=\u0026thinsp;odds ratio; AOR\u0026thinsp;=\u0026thinsp;adjusted odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Performance: ROC Curve Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the diagnostic performance of non-HDL-C and the TyG index for predicting acute MI. Non-HDL-C alone demonstrated poor discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.49, 95% CI: 0.41\u0026ndash;0.57), with low sensitivity (16\u0026ndash;17%) and high specificity (96\u0026ndash;97%). The TyG index performed modestly better (AUC\u0026thinsp;=\u0026thinsp;0.64, 95% CI: 0.56\u0026ndash;0.72), with sensitivity of 35% and specificity of 90%. The baseline clinical model achieved excellent discrimination (AUC\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.88\u0026ndash;0.96). Addition of non-HDL-C (AUC 0.92, p\u0026thinsp;=\u0026thinsp;0.24) or the TyG index (AUC 0.93, p\u0026thinsp;=\u0026thinsp;0.21) provided no statistically significant incremental improvement.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of non-HDL cholesterol and TyG index for predicting acute MI (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel/Marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172\u0026ndash;175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96\u0026ndash;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u0026ndash;0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u0026ndash;0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u0026thinsp;+\u0026thinsp;non-HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u0026thinsp;+\u0026thinsp;TyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026ndash;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87\u0026ndash;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study in Nepal to compare non-HDL-C and the TyG index as predictors of acute MI using hospital-based data. Our results demonstrate that these two biomarkers behave distinctly in this population: non-HDL-C did not differ significantly between MI and non-MI groups, while the TyG index was significantly elevated in MI patients and outperformed non-HDL-C in ROC analysis. Importantly, patients with known diabetes were excluded from this study to eliminate confounding from glucose-lowering medications and pre-existing metabolic derangements; this design choice isolates the TyG index's predictive performance in a non-diabetic population, though it limits direct generalizability to diabetic individuals. The observed age difference between groups (MI: 59.9 years; non-MI: 45.0 years) reflects real-world hospital practice and was adjusted for in all multivariate models; however, readers should consider this imbalance when interpreting unadjusted between-group comparisons.\u003c/p\u003e \u003cp\u003eThe mean age of MI patients was 59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0 years, compared with 45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 years in non-MI patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with findings from Iran and Australia \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and confirming age as a fundamental non-modifiable cardiovascular risk factor. \u003csup\u003e38,39\u003c/sup\u003e Males comprised 60% of MI patients, likely reflecting the protective role of estrogen in premenopausal women and sex differences in lipid metabolism and atherosclerosis. \u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSmoking differed significantly between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with 39% current and 39% former smokers among MI patients, compared to 83% never-smokers in the non-MI group. Alcohol consumption was also notably higher in MI patients. Both smoking and alcohol are established modifiable risk factors for MI. \u003csup\u003e41\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBlood pressure differences were observed but did not reach statistical significance, possibly due to limited sample size or antihypertensive medication use, consistent with South Asian cohort data. \u003csup\u003e42\u003c/sup\u003e Dietary habits, occupational patterns, and family history of diabetes showed no statistically significant between-group differences. Nevertheless, sedentary occupational patterns, \u003csup\u003e43\u003c/sup\u003e non-vegetarian dietary habits, \u003csup\u003e44\u003c/sup\u003e and family history of diabetes \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e are recognized cardiovascular risk factors that may carry residual confounding influence in this population and deserve consideration in future studies.\u003c/p\u003e \u003cp\u003eThe MI group had a mean BMI of 24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 kg/m\u0026sup2;, compared to 26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 in the non-MI group, \u003csup\u003e46\u003c/sup\u003e consistent with the 'obesity paradox,' which suggests that overweight or mildly obese patients may paradoxically experience better short-term outcomes in certain conditions. \u003csup\u003e47\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTotal cholesterol and LDL-C levels were similar in both groups, suggesting that traditional lipid measures alone may not reliably identify acute MI risk, particularly in populations with widespread use of lipid-lowering therapy. \u003csup\u003e7\u003c/sup\u003e HDL-C was significantly lower in MI patients (36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 vs. 45.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting its established protective role against atherosclerosis \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e through reverse cholesterol transport and plaque stabilization.\u003c/p\u003e \u003cp\u003eNon-HDL-C showed no significant difference between groups, suggesting that other metabolic markers may be more informative for acute MI. \u003csup\u003e50\u003c/sup\u003e Fasting glucose was markedly higher in MI patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003csup\u003e51\u003c/sup\u003e and the TyG index was significantly elevated (8.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 vs. 8.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reinforcing the role of insulin resistance in cardiovascular risk beyond traditional lipid measurements. \u003csup\u003e30,52,53,54\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSeveral lipid ratios were elevated in MI patients. The non-HDL/HDL ratio (3.66 vs. 2.95; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TC/HDL ratio (4.66 vs. 3.95; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are consistent with published literature. \u003csup\u003e55\u003c/sup\u003e The TG/HDL ratio above 3 has been linked to a twofold increase in coronary artery disease risk. \u003csup\u003e3\u003c/sup\u003e The AIP was significantly higher in MI patients (0.551 vs. 0.451; p\u0026thinsp;=\u0026thinsp;0.005), consistent with Dobi\u0026aacute;sov\u0026aacute; and Frohlich. \u003csup\u003e56\u003c/sup\u003e The TyG/HDL ratio was also significantly elevated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as described by Jin et al. \u003csup\u003e57\u003c/sup\u003e TyG-BMI did not differ significantly (p\u0026thinsp;=\u0026thinsp;0.271), suggesting it may be less sensitive in this population, \u003csup\u003e58,59\u003c/sup\u003e with further subgroup analysis warranted.\u003c/p\u003e \u003cp\u003eSpearman correlation analysis demonstrated a very strong positive correlation between TC and non-HDL-C (ρ\u0026thinsp;=\u0026thinsp;0.95), consistent with Mill\u0026aacute;n et al. \u003csup\u003e55\u003c/sup\u003e A strong correlation was found between TG and TyG index (ρ\u0026thinsp;=\u0026thinsp;0.89), matching Simental-Mend\u0026iacute;a et al. \u003csup\u003e30\u003c/sup\u003e Correlations between TG\u0026ndash;non-HDL-C (ρ\u0026thinsp;=\u0026thinsp;0.56) and non-HDL-C\u0026ndash;TyG index (ρ\u0026thinsp;=\u0026thinsp;0.53) were consistent with findings in a PCI cohort. \u003csup\u003e32\u003c/sup\u003e Moderate correlations between TC\u0026ndash;TG (ρ\u0026thinsp;=\u0026thinsp;0.48) and TC\u0026ndash;TyG index (ρ\u0026thinsp;=\u0026thinsp;0.44) are biologically plausible given the shared metabolic pathways linking triglyceride-rich lipoproteins and overall cholesterol burden. HDL-C showed weak negative correlations with TG (ρ = \u0026minus;0.16) and TyG index (ρ = \u0026minus;0.20), consistent with Dobi\u0026aacute;sov\u0026aacute; and Frohlich, \u003csup\u003e56\u003c/sup\u003e confirming its inverse relationship with atherogenic markers.\u003c/p\u003e \u003cp\u003eIn univariate analysis, each one-year increase in age raised MI odds by 12% (OR\u0026thinsp;=\u0026thinsp;1.12; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003csup\u003e61\u003c/sup\u003e Male sex was associated with more than double the odds of MI (OR\u0026thinsp;=\u0026thinsp;2.55; p\u0026thinsp;=\u0026thinsp;0.0013), matching global evidence. \u003csup\u003e62\u003c/sup\u003e Never smoking (OR\u0026thinsp;=\u0026thinsp;0.07; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and never drinking (OR\u0026thinsp;=\u0026thinsp;0.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were strongly protective. \u003csup\u003e63,64\u003c/sup\u003e Higher BMI was linked to increased MI risk (OR\u0026thinsp;=\u0026thinsp;1.12; p\u0026thinsp;=\u0026thinsp;0.003), \u003csup\u003e65\u003c/sup\u003e and HDL-C was protective (OR\u0026thinsp;=\u0026thinsp;0.94; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003csup\u003e48,49\u003c/sup\u003e Fasting blood sugar (OR\u0026thinsp;=\u0026thinsp;1.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TyG index (OR\u0026thinsp;=\u0026thinsp;2.72; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were strongly associated with MI. \u003csup\u003e30,51,52\u003c/sup\u003e Systolic blood pressure was also a significant predictor (OR\u0026thinsp;=\u0026thinsp;1.04; p\u0026thinsp;=\u0026thinsp;0.009), consistent with longstanding evidence. \u003csup\u003e66\u003c/sup\u003e Traditional lipid measures were not significantly predictive, possibly reflecting statin use or population-specific confounding. \u003csup\u003e30,49\u003c/sup\u003e The unexpected protective association of family history of diabetes may reflect greater health awareness in this subgroup, warranting further investigation. \u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMultivariate analysis confirmed age (AOR\u0026thinsp;=\u0026thinsp;1.14; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e and never smoking (AOR\u0026thinsp;=\u0026thinsp;0.20; p\u0026thinsp;=\u0026thinsp;0.009) as independent MI predictors. \u003csup\u003e63,64\u003c/sup\u003e These findings carry direct public health relevance: smoking cessation remains one of the most impactful modifiable interventions, with an estimated 80 fewer MI cases per 1,000 people who quit \u0026mdash; underscoring the critical importance of population-level cessation programmes. Male sex lost significance after adjustment (AOR\u0026thinsp;=\u0026thinsp;1.49; p\u0026thinsp;=\u0026thinsp;0.446), \u003csup\u003e62\u003c/sup\u003e suggesting that its univariate association was partially explained by confounding from age and lifestyle factors. Alcohol consumption was also non-significant in the adjusted model, differing from some meta-analytic evidence, \u003csup\u003e67,68\u003c/sup\u003e possibly due to the relatively small sample or differences in drinking patterns and beverage types in this population. Among biochemical markers, HDL-C remained protective (AOR\u0026thinsp;=\u0026thinsp;0.93; p\u0026thinsp;=\u0026thinsp;0.006), consistent with the Framingham Heart Study. \u003csup\u003e69\u003c/sup\u003e The TyG index was a strong independent predictor (AOR\u0026thinsp;=\u0026thinsp;3.85; p\u0026thinsp;=\u0026thinsp;0.026), similar to findings by S\u0026aacute;nchez-\u0026Iacute;\u0026ntilde;igo et al., \u003csup\u003e52\u003c/sup\u003e reinforcing its utility as a simple, cost-effective marker of metabolic risk. BMI was modestly significant (AOR\u0026thinsp;=\u0026thinsp;1.03; p\u0026thinsp;=\u0026thinsp;0.022), consistent with the Emerging Risk Factors Collaboration. \u003csup\u003e70\u003c/sup\u003e Total cholesterol was not significant after adjustment, \u003csup\u003e41\u003c/sup\u003e likely due to statin use or population-specific factors. The forest plot confirmed age, \u003csup\u003e69,71\u003c/sup\u003e BMI, \u003csup\u003e70,72\u003c/sup\u003e TyG index, \u003csup\u003e52\u003c/sup\u003e and HDL-C \u003csup\u003e48,69,73\u003c/sup\u003e as independent MI predictors.\u003c/p\u003e \u003cp\u003eIn ROC analysis, non-HDL-C demonstrated poor predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.49), consistent with its established utility for long-term rather than acute MI risk assessment. \u003csup\u003e74,75\u003c/sup\u003e The low sensitivity (16\u0026ndash;17%) confirms that non-HDL-C alone is insufficient for acute prediction, likely due to acute-phase lipid changes and prior statin use, both of which attenuate the discriminative value of cholesterol-based markers in the immediate post-event period. Notably, the near-chance AUC for non-HDL-C (0.49) should not be interpreted as a 'lipid paradox' but rather as a methodological consequence of cross-sectional, single time-point sampling in an acutely ill population. The TyG index performed modestly better (AUC\u0026thinsp;=\u0026thinsp;0.64), with pooled AUCs in the literature ranging from 0.60 to 0.72 \u003csup\u003e52,78\u003c/sup\u003e and cut-offs of 8.8 to 9.2 linked to higher risk across diverse populations. The baseline clinical model achieved excellent discrimination (AUC\u0026thinsp;=\u0026thinsp;0.92). Neither non-HDL-C nor the TyG index provided statistically significant incremental value beyond the established baseline model (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for both), though the TyG index may still help identify metabolically at-risk cases \u0026mdash; particularly individuals with insulin resistance \u0026mdash; missed by standard clinical predictors. \u003csup\u003e19,79,80,81\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSeveral limitations of this study must be acknowledged. First, patients with known diabetes mellitus were excluded, which, while controlling for a major confounding condition, limits the generalizability of TyG index findings to non-diabetic populations only. Since the TyG index is primarily a surrogate for insulin resistance, future studies including diabetic patients \u0026mdash; with appropriate subgroup stratification \u0026mdash; are warranted to fully characterize its utility. Second, the MI and non-MI groups differed substantially in age (59.9 vs. 45.0 years) and sex distribution, as participants were not matched. Although multivariate analysis adjusted for these variables, residual confounding cannot be entirely excluded, and between-group differences in TyG index may partly reflect age-related metabolic changes rather than MI-specific pathophysiology. Third, the single-center design and modest sample size (n\u0026thinsp;=\u0026thinsp;200) limit external validity. Fourth, the cross-sectional design precludes causal inference. Fifth, self-reported lifestyle data may introduce recall bias. Sixth, lipid profiles were measured after hospital admission, and acute-phase responses or pre-admission statin use may have attenuated the predictive value of cholesterol-based markers. Seventh, long-term follow-up data for cardiovascular outcomes were not available.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis is the first Nepalese study to compare non-HDL-C and the TyG index in acute MI patients using hospital data. Non-HDL-C did not significantly differ between MI and non-MI patients, demonstrated poor AUC and limited sensitivity, and did not improve acute MI diagnosis \u0026mdash; likely due to high rates of lipid-lowering therapy and acute-phase effects. The TyG index, reflecting insulin resistance, was significantly elevated in MI patients and marginally improved clinical model accuracy, supporting its role as a complementary metabolic marker, particularly in patients with metabolic syndrome or diabetes.\u003c/p\u003e \u003cp\u003eTraditional risk factors \u0026mdash; including age, male sex, smoking, low HDL-C, higher BMI, and impaired fasting glucose \u0026mdash; remained the strongest independent predictors of MI. A combined clinical, biochemical, and lifestyle risk-factor approach is recommended over reliance on any single biomarker. Non-HDL-C remains useful for long-term cardiovascular risk stratification, while the TyG index adds modest value in acute settings and warrants validation in larger, multicenter, prospective studies across diverse South Asian populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the Institutional Review Committee of Kathmandu University School of Medical Sciences (KUSMS) (IRC approval letter no: 18/24). Written informed consent was obtained from all participants prior to enrollment. The study adhered to the ethical principles of the Declaration of Helsinki (2013).\u003csup\u003e\u0026nbsp;31\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBKG conceived and designed the study, supervised data collection, and critically revised the manuscript. RP and NT contributed to data collection, laboratory analyses, and manuscript preparation. BN and SB performed laboratory analyses. PR provided academic supervision, institutional oversight, and final approval of the manuscript. All authors read and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the staff of the Department of Medical Biochemistry and the Clinical Biochemistry Laboratory, Kathmandu University Hospital, Dhulikhel Hospital, Dhulikhel, Kavre, Nepal, for their technical support. We extend sincere thanks to all participants who volunteered their time for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSacco RL, Roth GA, Reddy KS, Arnett DK, Bonita R, Gaziano TA, et al. 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Circulation. 2002;105(15):1760\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Myocardial infarction, Non-HDL cholesterol, TyG index, South Asia, Cardiovascular risk, Acute coronary syndrome, Lipid indices, Lipid ratios, Insulin resistance, Nepal","lastPublishedDoi":"10.21203/rs.3.rs-9141505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9141505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMyocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide, with South Asian populations facing a disproportionately high burden. In Nepal, the incidence of MI has been rising, and recent data suggest that cardiovascular diseases constitute a significant percentage of total deaths annually. While non-high-density lipoprotein cholesterol (non-HDL-C) is established in long-term cardiovascular risk stratification, newer metabolic markers such as the triglyceride-glucose (TyG) index have emerged as promising predictors. However, comparative evidence regarding their diagnostic and predictive roles in the acute setting, particularly in Nepal, is lacking.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis hospital-based, cross-sectional study enrolled adult individuals presenting with and without acute MI. Demographic, lifestyle, and clinical data were collected, and biochemical profiles \u0026mdash; including fasting lipid profile, fasting glucose, and derived indices (non-HDL-C, TyG, Atherogenic Index of Plasma, and lipid ratios) \u0026mdash; were analyzed. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of non-HDL-C and the TyG index. Univariate and multivariate logistic regression identified independent MI risk factors. Correlation analyses were used to explore relationships among biochemical parameters.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 200 participants (100 MI and 100 non-MI) were included (mean age for MI: 59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0 years; non-MI: 45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 years). MI patients were predominantly male and more likely to be current smokers. Non-HDL-C did not significantly differ between the MI and non-MI groups (median 125.0 mg/dL, p\u0026thinsp;=\u0026thinsp;0.880) and demonstrated poor diagnostic performance (AUC 0.49, 95% CI 0.41\u0026ndash;0.57). In contrast, the TyG index was significantly higher in MI patients (8.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 vs. 8.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and showed modestly better discrimination (AUC 0.64, 95% CI 0.56\u0026ndash;0.72). Traditional risk factors such as older age, male sex, smoking, low HDL-C, higher BMI, and fasting glucose remained strong independent predictors of MI. Lipid ratios (non-HDL/HDL, TC/HDL, TG/HDL) and the Atherogenic Index of Plasma were also elevated in MI patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Spearman correlation revealed strong associations among non-HDL-C, TG, and TyG index, while HDL-C was inversely related to atherogenic markers.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn this first Nepalese study comparing non-HDL-C and the TyG index for acute MI diagnosis, the TyG index emerged as a more effective marker of metabolic risk, though its incremental diagnostic utility was modest. Non-HDL-C did not enhance acute MI detection. Traditional risk factors continue to dominate MI prediction in this population. These findings suggest that while non-HDL-C and the TyG index are valuable for long-term risk assessment, their roles in acute MI diagnosis are limited. Integrating the TyG index with existing regional lipid-management and acute coronary syndrome protocols could potentially refine risk stratification for better clinical outcomes. Larger, multi-center studies are warranted to validate the clinical utility of the TyG index, especially in high-risk and metabolically diverse South Asian populations.\u003c/p\u003e","manuscriptTitle":"Comparative Evaluation of Non-HDL Cholesterol and the Triglyceride-Glucose Index for Predicting Acute Myocardial Infarction Risk in a Nepalese Hospital Population: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 15:08:27","doi":"10.21203/rs.3.rs-9141505/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d16936d-e4c5-421e-8bed-ee3d17a7110f","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T10:27:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 15:08:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9141505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9141505","identity":"rs-9141505","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}