Triglyceride Glucose Index as a Predictor of Cardiovascular and Metabolic Risk in an Urban Population in Kenya

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Insulin resistance (IR) plays a key role in the development of these conditions, but its assessment is often limited by resource constraints. The triglyceride glucose (TyG) index incorporates triglyceride and glucose measurements and has emerged as a simple and cost-effective surrogate marker for IR. This study evaluated the use of Tyg index as a predictor of cardiovascular and metabolic risk and determined the optimal cutoffs for predicting CVD risk and MetS in a Kenyan population. Methods Data were analyzed from 528 healthy Black African adults (255 males, 273 females) recruited for a global reference interval study. CVD risk was estimated using the Framingham Risk Score, while MetS was diagnosed using the 2009 harmonized criteria. The TyG index was calculated, and its ability to predict CVD risk and MetS was assessed using receiver operating characteristic (ROC) curve analysis. Results Males had significantly higher TyG index values, blood pressure, and waist circumference compared to females (p < 0.05). ROC analysis showed that the TyG index was a strong predictor of intermediate-to-high CVD risk (AUC = 0.840) and MetS (AUC = 0.858). The optimal TyG index cutoff for predicting CVD risk was 4.74 (sensitivity 74.1%, specificity 81.2%), while the best cutoff for MetS was 4.64 (sensitivity 74.8%, specificity 84.5%). Conclusion The TyG index demonstrates strong potential as a screening tool for cardiovascular and metabolic risk in seemingly healthy adults. Its ease of measurement and diagnostic accuracy make it particularly valuable in resource-limited settings. Further studies are needed to assess its real-world application in SSA and explore its role in early prevention strategies. Triglyceride glucose index cardiovascular disease metabolic syndrome insulin resistance Framingham Risk Score Sub-Saharan Africa Figures Figure 1 Figure 2 INTRODUCTION Non-communicable diseases (NCDs) are now the leading cause of death around the world, responsible for about 40.5 million deaths each year—roughly 71% of all global deaths, according to the World Health Organization (2018). These conditions cause more deaths than HIV, tuberculosis, and malaria combined. Among them, four groups—cardiovascular diseases (CVDs), cancer, chronic respiratory illnesses, and diabetes—account for 87% of all NCD-related deaths( 1 ). Of these, heart and blood vessel diseases (CVDs) alone are linked to nearly 18 million deaths, or around 45% of the total NCD burden( 1 )( 2 ). In Sub-Saharan Africa, the number of people affected by CVDs is expected to rise, along with the risk factors that lead to them. This trend is largely driven by shifts in diet—especially the growing consumption of high-calorie, low-nutrient foods—as well as the move toward more sedentary lifestyles, both of which are linked to rapid urbanization( 2 )( 3 ). To help tackle the growing impact of NCDs, one effective approach is early detection. Using simple, reliable screening tools to identify people at risk of developing CVDs can play a key role in prevention and easing the overall disease burden( 4 ) Insulin resistance (IR) plays a major role in the onset and progression of metabolic syndrome—a group of conditions that raise the risk of developing heart disease and other cardiovascular issues( 5 ).Managing insulin resistance through healthy lifestyle changes, and sometimes with medication, is a crucial part of preventing and controlling metabolic syndrome and its related health problems. However, detecting insulin resistance in many parts of Sub-Saharan Africa is not easy. The most accurate test—the hyperinsulinemic-euglycemic clamp—is expensive, takes a lot of time, and requires specialized equipment and training, making it impractical for routine use( 6 )( 7 )A more practical alternative is the HOMA-IR (Homeostatic Model Assessment for Insulin Resistance), which can estimate insulin resistance using fasting insulin and glucose levels. But even this method has its challenges, as insulin testing isn’t commonly available in many local clinics or labs across the region ( 8 ). The triglyceride-glucose (TyG) index has recently gained attention as a reliable and practical way to estimate insulin resistance ( 9 ). People with insulin resistance often have problems with how their bodies handle fats and sugars. One common issue is increased fat breakdown, which raises the level of free fatty acids in the blood. This leads to the liver producing more triglycerides and very low-density lipoproteins (VLDL), both of which are linked to poor heart health ( 10 ). As a result of these changes, individuals with insulin resistance typically show an unhealthy lipid profile—high triglycerides, low levels of "good" HDL cholesterol, and an increase in smaller, denser LDL particles, which are more harmful to blood vessels( 11 , 12 ). The TyG index, which is calculated using just triglyceride and glucose levels, offers a simple way to identify insulin resistance. Since both of these measures are commonly available and inexpensive, the TyG index is a practical tool, especially in resource-limited settings ( 13 ). In fact, research suggests it may be even better than HOMA-IR at predicting metabolic syndrome. People with higher TyG index scores are also more likely to go on to develop metabolic syndrome over time ( 14 ). The TyG index has been widely studied as a reliable stand-in for insulin resistance and has shown strong links to a range of cardiovascular diseases ( 6 , 7 , 15 ). Higher TyG values have been connected to a greater risk of serious health problems like carotid artery disease, coronary artery disease, metabolic syndrome, and type 2 diabetes ( 14 , 16 ). In fact, research shows that people with elevated TyG index scores are more likely to experience cardiovascular events and even premature death. What makes the TyG index especially useful is that it performs well as a predictor—studies report sensitivity rates between 67% and 96%, and specificity ranging from 32.5–85% ( 17 , 18 ). These figures show that it can be a valuable tool in identifying people at risk. Many of these studies used the Framingham Risk Score to estimate a person's 10-year risk of developing cardiovascular disease. This score combines several key risk factors—like age, blood pressure, cholesterol levels, and smoking status—to give a clearer picture of someone’s future heart health. It’s a practical, cost-effective way to figure out who might benefit most from early preventive care and who can be reassured that their risk is low ( 19 ). While the TyG index has been widely studied as a marker for insulin resistance and its link to cardiovascular disease in Asia and other parts of the world, there’s limited information about this relationship in Sub-Saharan Africa ( 15 ). To help fill this gap, the current study aimed to assess how well the TyG index can predict cardiovascular risk—using the Framingham Risk Score—as well as the risk of metabolic syndrome, based on harmonized diagnostic criteria. The study also sought to identify the most appropriate TyG index cut-off values for predicting both cardiovascular risk and the presence of metabolic syndrome in this population. METHODS STUDY POPULATION This study used data from a global reference interval project led by the Committee of Reference Intervals and Decision Limits (C-RIDL), part of the International Federation of Clinical Chemistry (IFCC). In Kenya, 533 healthy Black African adults between the ages of 18 and 65 were recruited between January and October 2015. Participants were invited through posters placed in public spaces such as churches, universities, colleges, hospitals, and workplaces. DATA COLLECTION The data used in this study was not collected as part of a new investigation but was drawn from a larger global study organized by the Committee of Reference Intervals and Decision Limits (C-RIDL), under the International Federation of Clinical Chemistry. The details of participant recruitment, data and sample collection, and laboratory analysis were previously published by Omuse et al( 20 ) In brief, participants provided informed consent and completed a questionnaire before any samples were taken. All participants fasted overnight, and their blood samples were collected in line with the study protocol. The samples were processed within four hours—centrifuged and then stored at − 80°C before being shipped on dry ice to a reference lab in South Africa. All laboratory testing, including biochemical and immunoassay analyses, was carried out at the ISO 15189-accredited Pathcare Reference Laboratory in Cape Town. Importantly, samples were thawed only once before testing ( 20 ). To ensure accuracy and consistency across all participating sites in the global reference interval study, laboratories used a standardized panel of sera with known values to adjust for any measurement differences. In addition to blood sampling, participants also had their blood pressure, waist circumference, and body mass index (BMI) measured. DATA DEFINITIONS Metabolic syndrome diagnosis The 2009 harmonized criteria was used to diagnose MetS which requires the presence of any 3 of the following: increased WC (men: ≥ 94 cm, women: ≥ 80 cm), low HDL-C (men: <1 mmol/l, women: <1.3 mmol/l), hypertriglyceridemia ≥ 1.7 mmol/l, elevated BP (systolic BP ≥ 130 mmHg and/or diastolic ≥ 85 mmHg or drug treatment for hypertension) and elevated blood sugar (FPG ≥ 5.6 mmol/l) or diabetes mellitus ( 20 , 21 ) CVD risk calculation The 10-year CVD risk was calculated using the Framingham risk calculation. Family history of CVD was not included as this information wasn’t captured in the study questionnaire ( 20 , 22 ) TyG index calculation TyG index = Ln (fasting triglycerides [mmol/L] x 88.57 x fasting glucose [mmol/L] x 18)/2) ( 23 ) STATISTICAL ANALYSIS Categorical data were presented as frequencies, while quantitative data were reported as medians and interquartile ranges. To compare variables based on sex, a Mann-Whitney test was applied. A receiver operating characteristic (ROC) curve was created and area under the curve (AUC) along with its 95% confidence interval determined to evaluate the ability of the TyG index to predict cardiovascular risk and MetS. A Youden index was also computed and used to determine the appropriate cutoff values. All analysis was conducted using the Statistical Package for Social Sciences (SPSS, ® 22, Chicago, IL, USA). RESULTS Out of 533 participants, 5 didn’t have a fasting plasma glucose measurement and were subsequently excluded leaving a total of 528 participants (255 males and 273 females) included in the analysis. Males had significantly higher TyG index, systolic and diastolic BP, and WC compared to females, while females had higher HDL levels as shown in Table 1 . Table 1 Showing Baseline characteristics of the study population Gender N Median IQR Minimum Maximum P value Age (Years) Male 255 38.0 18.0 20.0 65.0 0.867 Female 273 39.0 20.0 18.0 64.0 Triglyceride (mmol/l) Male 255 1.10 0.79 0.320 10.51 0.067 Female 273 0.99 0.71 0.420 9.55 Triglyceride Index Male 255 4.54 0.40 3.84 5.98 0.025 Female 273 4.46 0.38 3.95 5.65 Dystolic BP (mmHg) Male 255 81.0 12.0 56.0 101.0 0.003 Female 273 79.0 14.0 57.0 112.0 Systolic BP (mmHg) Male 255 127.0 18.0 94.0 167.0 < 0.001 Female 273 118.0 20.0 77.0 194.0 Total Cholesterol (mmol/l) Male 255 4.70 1.20 2.30 8.20 0.237 Female 273 4.60 1.20 2.60 7.70 HDL (mmol/l) Male 255 1.10 0.30 0.50 2.00 < 0.001 Female 273 1.20 0.30 0.30 2.40 Waist Circumference (cm) Male 255 90.0 15.0 65.0 124.0 < 0.001 Female 273 86.0 16.0 64.0 115.0 BP: Blood pressure, HDL: High density lipoproteins The ROC analysis comparing TyG index and parameters of the Framingham criteria in determining cardiovascular risk found that TyG index was the best predictor with an AUC of 0.840 with a 95% confidence interval of (0.772–0.909) as shown in Fig. 1 . The optimal cut-off for the TyG index was 4.74 with a sensitivity of 74.1% and specificity of 81.2% for identifying individuals with intermediate to high CVD risk. TyG index was also compared to other parameters of the harmonized criteria in diagnosing MetS and had the highest diagnostic accuracy with an AUC of 0.858 with a 95% confidence interval of (0.817-0.900) as shown in Fig. 2 . A cutoff of 4.64 was found to be the best predictor with a sensitivity of 74.8% and specificity of 84.5%. DISCUSSION TyG index was found to be a robust marker for determining CVD risk and MetS compared to other parameters in a healthy adult population in Kenya. It demonstrated strong diagnostic performance (AUC of 0.840) for CVD risk prediction, outperforming other variables such as total cholesterol and systolic BP. This aligns with other studies, largely carried out in non-African populations, which have shown that the TyG index is a reliable predictor of CVD, including coronary artery disease and myocardial infarction, across diverse populations and settings. The findings from the analysis of the TyG Index, alongside sex-based comparisons of various health metrics, offer important insights into its utility as a predictive tool for MetS and CVD risk. The significant differences observed between males and females in key health indicators such as FPG, systolic BP, and WC highlight the importance of considering sex-specific factors in health assessments. These disparities, as seen in other studies ( 24 , 25 ), suggest that sex may play a role in the development of metabolic disorders, reinforcing the need for tailored prevention and treatment strategies that address these differences. Notably, the current findings also underscore the TyG index's utility in diagnosing MetS, with an AUC of 0.858, higher than FPG and fasting triglyceride. Previous research corroborates these results, demonstrating the index's ability to accurately classify individuals with MetS across different demographics, while variables like blood pressure and waist circumference have shown moderate or minimal predictive ability ( 26 , 27 ). The optimal cutoff of 4.74 for CVD risk (sensitivity 74.1%, specificity 81.2%) and 4.64 for MetS (sensitivity 74.8%, specificity 84.5%) aligns with similar thresholds reported in published studies. A Brazilian study identified TyG cutoffs of 4.52 and 4.55 (logarithmic scale) for MetS diagnosed using a different criterion, with sensitivity of ~ 84% and specificity around 76–80%—like our described thresholds for MetS and CVD risk ( 28 )Another study found a TyG index cutoff of 4.49 with sensitivity and specificity exceeding 80%, indicating its potential utility in predicting MetS and insulin resistance ( 28 ). These consistent results highlight the TyG index's applicability for identifying high-risk individuals and reinforces its possible role in identifying subjectively health individuals at an intermediate or high risk for CVD hence providing an opportunity for early prevention strategies. These findings collectively support the conclusion that the TyG index is a practical tool for screening presumably healthy adults and identifying those at intermediate or high risk of cardiovascular and metabolic disorders in our setting. The reliance on only two simple tests in the computation of TyG index makes it particularly valuable for widespread clinical application in SSA population. Future research should explore its implementation in a real world setting in SSA to evaluate its effectiveness in identifying high risk individuals and reducing CVD incidence when coupled with appropriate interventions. STRENGTHS AND LIMITATIONS OF THIS STUDY The study's primary strength lies in its inclusion of a diverse population of healthy adults, which enhances the generalizability of the findings to similar populations. The diversity ensures that the results are representative and applicable to a broad demographic, making them valuable for clinical recommendations. However, a notable limitation is the cross-sectional design of the study, which captures data at a single point in time and limits the ability to establish causality or observe changes over time. A longitudinal study would have been ideal for examining trends, assessing causative relationships, and understanding the dynamic processes within individuals over a more extended period. Despite these limitations, the study provides valuable insights into the potential utility of TyG index in an adult urban population in Kenya. Declarations Ethical Approval and consent to participate. This study involves only secondary analysis of anonymized data from a previously approved research study conducted under the ethical oversight of the Aga Khan University, Nairobi Health Research Ethics Committee (Approval No. 2014/REC-46) and does not include direct participant interaction, new data collection, or additional risks, it qualifies for a waiver of ethical approval. All analysis was conducted in accordance with ethical guidelines for research involving human participants, ensuring the confidentiality and privacy of the data. Furthermore, this study adhered to the principles outlined in the Declaration of Helsinki. Should any ethical concerns arise, we remain committed to consulting the relevant ethics review board for guidance. Consent to Publication Not applicable Data Availability statement The datasets generated and analysed during the current study are not publicly available due to study participant privacy. However, it can be availed to reviewers if needed. Conflict of Interest Disclosure None Funding No funding from any funding agency in the public, commercial or not-for-profit sectors. Acknowledgment Not applicable Author contributions MM: conceptualization, resources, analysis interpretation, writing of manuscript. TS: resources, writing (review and editing). DM: conceptualization, writing (review and editing), analysis interpretation, supervision. GO: conceptualization, resources, writing, analysis interpretation, supervision. 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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-6594101","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468111254,"identity":"7b187f78-64f1-437e-a8a8-83635b9044b7","order_by":0,"name":"Matthews Madakwa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYJACZhhDguEAAwM/AxuKIDYNjM0oWiQbSNZicICAFt3288cfF1QctmcQO/zwxo8zh+WNjx9Lk2CosE5swKHF7EwyY/OMM4cTG6TTjC17bhw23HYm7ZgEw5l03FoOALXwtt1OYJBOMJPg+XCbcduB9DYJxrbDuLWcfwzU8u+2PYN0+jfJPx9u22/ufw7U8g+PlhsgWxpuMzZI55hJ89y4nbhBAugwxgZ8Wh4bzuY59j+xTTqn2FrmzP/kGTeeJVskHEs3xu2wxAefeWrS7Pml0zfefHMszba/P83wxocaa1lcWuCADYWXQEj5KBgFo2AUjAK8AAA362E7Bb7TmwAAAABJRU5ErkJggg==","orcid":"","institution":"Aga Khan University Nairobi","correspondingAuthor":true,"prefix":"","firstName":"Matthews","middleName":"","lastName":"Madakwa","suffix":""},{"id":468111255,"identity":"0f0fefbf-b8c1-4246-afc9-9b8ec2ddb2be","order_by":1,"name":"Tandzile Simelane","email":"","orcid":"","institution":"Aga Khan University Nairobi","correspondingAuthor":false,"prefix":"","firstName":"Tandzile","middleName":"","lastName":"Simelane","suffix":""},{"id":468111256,"identity":"bf740637-230b-46af-b808-1fde8dc3dc2f","order_by":2,"name":"Daniel Maina","email":"","orcid":"","institution":"Aga Khan University Nairobi","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Maina","suffix":""},{"id":468111257,"identity":"64aa8e22-40a6-43a7-a866-4267f23f9ea8","order_by":3,"name":"Geoffrey Omuse","email":"","orcid":"","institution":"Aga Khan University Nairobi","correspondingAuthor":false,"prefix":"","firstName":"Geoffrey","middleName":"","lastName":"Omuse","suffix":""}],"badges":[],"createdAt":"2025-05-05 11:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6594101/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6594101/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84201197,"identity":"5842ac9a-20ce-4a19-9093-ecf55b30b39e","added_by":"auto","created_at":"2025-06-09 08:28:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":357250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC of the TyG index to predict intermediate to high cardiovascular risk in ten years assessed by the Framingham score, Cut offs, sensitivity and specificity\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6594101/v1/5e747a94a334152f7110dd1b.jpeg"},{"id":84204442,"identity":"e018a655-12f6-4af8-9165-84daa5eb00e2","added_by":"auto","created_at":"2025-06-09 08:52:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":453159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTyg index compared to other parameters of the 2009 harmonized criteria. AUCs, optimal cut offs, specificity and sensitivity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6594101/v1/b43eb879dd75c681414871d2.jpeg"},{"id":109620833,"identity":"27c915cc-4021-4162-9ae6-35a8c234f747","added_by":"auto","created_at":"2026-05-20 09:11:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1051595,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6594101/v1/b8714551-b7d6-46b3-82bb-500d7f800bd1.pdf"},{"id":84203651,"identity":"b2daa0ef-7dcb-4f80-a0a5-40f0589e1c7b","added_by":"auto","created_at":"2025-06-09 08:44:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":125539,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-6594101/v1/8d78c044aa07f8b94a79609a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eTriglyceride Glucose Index as a Predictor of Cardiovascular and Metabolic Risk in an Urban Population in Kenya\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNon-communicable diseases (NCDs) are now the leading cause of death around the world, responsible for about 40.5\u0026nbsp;million deaths each year\u0026mdash;roughly 71% of all global deaths, according to the World Health Organization (2018). These conditions cause more deaths than HIV, tuberculosis, and malaria combined. Among them, four groups\u0026mdash;cardiovascular diseases (CVDs), cancer, chronic respiratory illnesses, and diabetes\u0026mdash;account for 87% of all NCD-related deaths(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Of these, heart and blood vessel diseases (CVDs) alone are linked to nearly 18\u0026nbsp;million deaths, or around 45% of the total NCD burden(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Sub-Saharan Africa, the number of people affected by CVDs is expected to rise, along with the risk factors that lead to them. This trend is largely driven by shifts in diet\u0026mdash;especially the growing consumption of high-calorie, low-nutrient foods\u0026mdash;as well as the move toward more sedentary lifestyles, both of which are linked to rapid urbanization(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo help tackle the growing impact of NCDs, one effective approach is early detection. Using simple, reliable screening tools to identify people at risk of developing CVDs can play a key role in prevention and easing the overall disease burden(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eInsulin resistance (IR) plays a major role in the onset and progression of metabolic syndrome\u0026mdash;a group of conditions that raise the risk of developing heart disease and other cardiovascular issues(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).Managing insulin resistance through healthy lifestyle changes, and sometimes with medication, is a crucial part of preventing and controlling metabolic syndrome and its related health problems.\u003c/p\u003e \u003cp\u003eHowever, detecting insulin resistance in many parts of Sub-Saharan Africa is not easy. The most accurate test\u0026mdash;the hyperinsulinemic-euglycemic clamp\u0026mdash;is expensive, takes a lot of time, and requires specialized equipment and training, making it impractical for routine use(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)A more practical alternative is the HOMA-IR (Homeostatic Model Assessment for Insulin Resistance), which can estimate insulin resistance using fasting insulin and glucose levels. But even this method has its challenges, as insulin testing isn\u0026rsquo;t commonly available in many local clinics or labs across the region (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe triglyceride-glucose (TyG) index has recently gained attention as a reliable and practical way to estimate insulin resistance (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). People with insulin resistance often have problems with how their bodies handle fats and sugars. One common issue is increased fat breakdown, which raises the level of free fatty acids in the blood. This leads to the liver producing more triglycerides and very low-density lipoproteins (VLDL), both of which are linked to poor heart health (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). As a result of these changes, individuals with insulin resistance typically show an unhealthy lipid profile\u0026mdash;high triglycerides, low levels of \"good\" HDL cholesterol, and an increase in smaller, denser LDL particles, which are more harmful to blood vessels(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe TyG index, which is calculated using just triglyceride and glucose levels, offers a simple way to identify insulin resistance. Since both of these measures are commonly available and inexpensive, the TyG index is a practical tool, especially in resource-limited settings (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In fact, research suggests it may be even better than HOMA-IR at predicting metabolic syndrome. People with higher TyG index scores are also more likely to go on to develop metabolic syndrome over time (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe TyG index has been widely studied as a reliable stand-in for insulin resistance and has shown strong links to a range of cardiovascular diseases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Higher TyG values have been connected to a greater risk of serious health problems like carotid artery disease, coronary artery disease, metabolic syndrome, and type 2 diabetes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In fact, research shows that people with elevated TyG index scores are more likely to experience cardiovascular events and even premature death.\u003c/p\u003e \u003cp\u003eWhat makes the TyG index especially useful is that it performs well as a predictor\u0026mdash;studies report sensitivity rates between 67% and 96%, and specificity ranging from 32.5\u0026ndash;85% (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These figures show that it can be a valuable tool in identifying people at risk.\u003c/p\u003e \u003cp\u003eMany of these studies used the Framingham Risk Score to estimate a person's 10-year risk of developing cardiovascular disease. This score combines several key risk factors\u0026mdash;like age, blood pressure, cholesterol levels, and smoking status\u0026mdash;to give a clearer picture of someone\u0026rsquo;s future heart health. It\u0026rsquo;s a practical, cost-effective way to figure out who might benefit most from early preventive care and who can be reassured that their risk is low (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the TyG index has been widely studied as a marker for insulin resistance and its link to cardiovascular disease in Asia and other parts of the world, there\u0026rsquo;s limited information about this relationship in Sub-Saharan Africa (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). To help fill this gap, the current study aimed to assess how well the TyG index can predict cardiovascular risk\u0026mdash;using the Framingham Risk Score\u0026mdash;as well as the risk of metabolic syndrome, based on harmonized diagnostic criteria. The study also sought to identify the most appropriate TyG index cut-off values for predicting both cardiovascular risk and the presence of metabolic syndrome in this population.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSTUDY POPULATION\u003c/h2\u003e \u003cp\u003eThis study used data from a global reference interval project led by the Committee of Reference Intervals and Decision Limits (C-RIDL), part of the International Federation of Clinical Chemistry (IFCC). In Kenya, 533 healthy Black African adults between the ages of 18 and 65 were recruited between January and October 2015. Participants were invited through posters placed in public spaces such as churches, universities, colleges, hospitals, and workplaces.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDATA COLLECTION\u003c/h3\u003e\n\u003cp\u003eThe data used in this study was not collected as part of a new investigation but was drawn from a larger global study organized by the Committee of Reference Intervals and Decision Limits (C-RIDL), under the International Federation of Clinical Chemistry. The details of participant recruitment, data and sample collection, and laboratory analysis were previously published by Omuse et al(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e In brief, participants provided informed consent and completed a questionnaire before any samples were taken. All participants fasted overnight, and their blood samples were collected in line with the study protocol. The samples were processed within four hours\u0026mdash;centrifuged and then stored at \u0026minus;\u0026thinsp;80\u0026deg;C before being shipped on dry ice to a reference lab in South Africa. All laboratory testing, including biochemical and immunoassay analyses, was carried out at the ISO 15189-accredited Pathcare Reference Laboratory in Cape Town. Importantly, samples were thawed only once before testing (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo ensure accuracy and consistency across all participating sites in the global reference interval study, laboratories used a standardized panel of sera with known values to adjust for any measurement differences. In addition to blood sampling, participants also had their blood pressure, waist circumference, and body mass index (BMI) measured.\u003c/p\u003e\n\u003ch3\u003eDATA DEFINITIONS\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic syndrome diagnosis\u003c/h2\u003e \u003cp\u003eThe 2009 harmonized criteria was used to diagnose MetS which requires the presence of any 3 of the following: increased WC (men: \u0026ge; 94 cm, women: \u0026ge; 80 cm), low HDL-C (men: \u0026lt;1 mmol/l, women: \u0026lt;1.3 mmol/l), hypertriglyceridemia\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/l, elevated BP (systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg and/or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg or drug treatment for hypertension) and elevated blood sugar (FPG\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mmol/l) or diabetes mellitus (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCVD risk calculation\u003c/h3\u003e\n\u003cp\u003eThe 10-year CVD risk was calculated using the Framingham risk calculation. Family history of CVD was not included as this information wasn\u0026rsquo;t captured in the study questionnaire (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTyG index calculation\u003c/h2\u003e \u003cp\u003eTyG index\u0026thinsp;=\u0026thinsp;Ln (fasting triglycerides [mmol/L] x 88.57 x fasting glucose [mmol/L] x 18)/2) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSTATISTICAL ANALYSIS\u003c/h2\u003e \u003cp\u003eCategorical data were presented as frequencies, while quantitative data were reported as medians and interquartile ranges. To compare variables based on sex, a Mann-Whitney test was applied.\u003c/p\u003e \u003cp\u003eA receiver operating characteristic (ROC) curve was created and area under the curve (AUC) along with its 95% confidence interval determined to evaluate the ability of the TyG index to predict cardiovascular risk and MetS. A Youden index was also computed and used to determine the appropriate cutoff values. All analysis was conducted using the Statistical Package for Social Sciences (SPSS, \u0026reg; 22, Chicago, IL, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOut of 533 participants, 5 didn\u0026rsquo;t have a fasting plasma glucose measurement and were subsequently excluded leaving a total of 528 participants (255 males and 273 females) included in the analysis. Males had significantly higher TyG index, systolic and diastolic BP, and WC compared to females, while females had higher HDL levels as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eShowing Baseline characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e64.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e10.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e9.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e101.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e57.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e112.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e127.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e167.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e118.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e194.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e124.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e64.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e115.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eBP: Blood pressure, HDL: High density lipoproteins\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ROC analysis comparing TyG index and parameters of the Framingham criteria in determining cardiovascular risk found that TyG index was the best predictor with an AUC of 0.840 with a 95% confidence interval of (0.772\u0026ndash;0.909) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The optimal cut-off for the TyG index was 4.74 with a sensitivity of 74.1% and specificity of 81.2% for identifying individuals with intermediate to high CVD risk.\u003c/p\u003e \u003cp\u003eTyG index was also compared to other parameters of the harmonized criteria in diagnosing MetS and had the highest diagnostic accuracy with an AUC of 0.858 with a 95% confidence interval of (0.817-0.900) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A cutoff of 4.64 was found to be the best predictor with a sensitivity of 74.8% and specificity of 84.5%.\u003c/p\u003e "},{"header":"DISCUSSION","content":"\u003cp\u003eTyG index was found to be a robust marker for determining CVD risk and MetS compared to other parameters in a healthy adult population in Kenya. It demonstrated strong diagnostic performance (AUC of 0.840) for CVD risk prediction, outperforming other variables such as total cholesterol and systolic BP. This aligns with other studies, largely carried out in non-African populations, which have shown that the TyG index is a reliable predictor of CVD, including coronary artery disease and myocardial infarction, across diverse populations and settings.\u003c/p\u003e \u003cp\u003eThe findings from the analysis of the TyG Index, alongside sex-based comparisons of various health metrics, offer important insights into its utility as a predictive tool for MetS and CVD risk. The significant differences observed between males and females in key health indicators such as FPG, systolic BP, and WC highlight the importance of considering sex-specific factors in health assessments. These disparities, as seen in other studies (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), suggest that sex may play a role in the development of metabolic disorders, reinforcing the need for tailored prevention and treatment strategies that address these differences. Notably, the current findings also underscore the TyG index's utility in diagnosing MetS, with an AUC of 0.858, higher than FPG and fasting triglyceride. Previous research corroborates these results, demonstrating the index's ability to accurately classify individuals with MetS across different demographics, while variables like blood pressure and waist circumference have shown moderate or minimal predictive ability (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe optimal cutoff of 4.74 for CVD risk (sensitivity 74.1%, specificity 81.2%) and 4.64 for MetS (sensitivity 74.8%, specificity 84.5%) aligns with similar thresholds reported in published studies. A Brazilian study identified TyG cutoffs of 4.52 and 4.55 (logarithmic scale) for MetS diagnosed using a different criterion, with sensitivity of ~\u0026thinsp;84% and specificity around 76\u0026ndash;80%\u0026mdash;like our described thresholds for MetS and CVD risk (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)Another study found a TyG index cutoff of 4.49 with sensitivity and specificity exceeding 80%, indicating its potential utility in predicting MetS and insulin resistance (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). These consistent results highlight the TyG index's applicability for identifying high-risk individuals and reinforces its possible role in identifying subjectively health individuals at an intermediate or high risk for CVD hence providing an opportunity for early prevention strategies.\u003c/p\u003e \u003cp\u003eThese findings collectively support the conclusion that the TyG index is a practical tool for screening presumably healthy adults and identifying those at intermediate or high risk of cardiovascular and metabolic disorders in our setting. The reliance on only two simple tests in the computation of TyG index makes it particularly valuable for widespread clinical application in SSA population. Future research should explore its implementation in a real world setting in SSA to evaluate its effectiveness in identifying high risk individuals and reducing CVD incidence when coupled with appropriate interventions.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSTRENGTHS AND LIMITATIONS OF THIS STUDY\u003c/h2\u003e \u003cp\u003eThe study's primary strength lies in its inclusion of a diverse population of healthy adults, which enhances the generalizability of the findings to similar populations. The diversity ensures that the results are representative and applicable to a broad demographic, making them valuable for clinical recommendations. However, a notable limitation is the cross-sectional design of the study, which captures data at a single point in time and limits the ability to establish causality or observe changes over time. A longitudinal study would have been ideal for examining trends, assessing causative relationships, and understanding the dynamic processes within individuals over a more extended period. Despite these limitations, the study provides valuable insights into the potential utility of TyG index in an adult urban population in Kenya.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthical Approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involves only secondary analysis of anonymized data from a previously approved research study conducted under the ethical oversight of the Aga Khan University, Nairobi Health Research Ethics Committee (Approval No. 2014/REC-46) and does not include direct participant interaction, new data collection, or additional risks, it qualifies for a waiver of ethical approval. All analysis was conducted in accordance with ethical guidelines for research involving human participants, ensuring the confidentiality and privacy of the data. Furthermore, this study adhered to the principles outlined in the Declaration of Helsinki. Should any ethical concerns arise, we remain committed to consulting the relevant ethics review board for guidance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to study participant privacy. However, it can be availed to reviewers if needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding from any funding agency in the public, commercial or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eMM: conceptualization, resources, analysis interpretation, writing of manuscript.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eTS: resources, writing (review and editing).\u003c/li\u003e\n \u003cli\u003eDM: conceptualization, writing (review and editing), analysis interpretation, supervision.\u003c/li\u003e\n \u003cli\u003eGO: conceptualization, resources, writing, analysis interpretation, supervision.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990\u0026ndash;2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2224\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChikafu H, Chimbari MJ. Cardiovascular Disease Healthcare Utilization in Sub-Saharan Africa: A Scoping Review. Int J Environ Res Public Health. 2019;16(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImamura F, Micha R, Khatibzadeh S, Fahimi S, Shi P, Powles J, et al. Dietary quality among men and women in 187 countries in 1990 and 2010: a systematic assessment. Lancet Glob Health. 2015;3(3):e132\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrong K, Wald N, Miller A, Alwan A. Current concepts in screening for noncommunicable disease: World Health Organization Consultation Group Report on methodology of noncommunicable disease screening. J Med Screen. 2005;12(1):12\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo E, Wang D, Yan G, Qiao Y, Liu B, Hou J, et al. High triglyceride\u0026ndash;glucose index is associated with poor prognosis in patients with acute ST-elevation myocardial infarction after percutaneous coronary intervention. Cardiovasc Diabetol. 2019;18(1):150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNam KW, Kwon HM, Jeong HY, Park JH, Kwon H, Jeong SM. High triglyceride-glucose index is associated with subclinical cerebral small vessel disease in a healthy population: a cross-sectional study. Cardiovasc Diabetol. 2020;19(1):53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao S, Yu S, Chi C, Fan X, Tang J, Ji H, et al. Association between macro- and microvascular damage and the triglyceride glucose index in community-dwelling elderly individuals: the Northern Shanghai Study. Cardiovasc Diabetol. 2019;18(1):95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurniawan LB. Triglyceride-Glucose Index As A Biomarker Of Insulin Resistance, Diabetes Mellitus, Metabolic Syndrome, And Cardiovascular Disease: A Review. EJIFCC. 2024;35(1):44\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJian S, Su-Mei N, Xue C, Jie Z, Xue-sen W. Association and interaction between triglyceride\u0026ndash;glucose index and obesity on risk of hypertension in middle-aged and elderly adults. Clin Exp Hypertens. 2017;39(8):732\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrmazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zu\u0026ntilde;iga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu WY, Chen SC, Huang YT, Huang JC, Wu PY, Hsu WH, et al. Comparison of the Effects of Fasting Glucose, Hemoglobin A1c, and Triglyceride\u0026ndash;Glucose Index on Cardiovascular Events in Type 2 Diabetes Mellitus. Nutrients. 2019;11(11):2838.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAra\u0026uacute;jo SP, Juvanhol LL, Bressan J, Hermsdorff HHM. Triglyceride glucose index: A new biomarker in predicting cardiovascular risk. Prev Med Rep. 2022;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon DH, Lee HS, Lee YJ, Lee JH, Han JH. Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome. Nutr Metabolism Cardiovasc Dis. 2022;32(3):596\u0026ndash;604.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez-Jaramillo P, Gomez-Arbelaez D, Martinez-Bello D, Abat MEM, Alhabib KF, Avezum \u0026Aacute;, et al. Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. Lancet Healthy Longev. 2023;4(1):e23\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eda Silva A, Caldas APS, Rocha DMUP, Bressan J. Triglyceride-glucose index predicts independently type 2 diabetes mellitus risk: A systematic review and meta-analysis of cohort studies. Prim Care Diabetes. 2020;14(6):584\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eda Silva A, Caldas APS, Rocha DMUP, Bressan J. Triglyceride-glucose index predicts independently type 2 diabetes mellitus risk: A systematic review and meta-analysis of cohort studies. Prim Care Diabetes. 2020;14(6):584\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026aacute;nchez-Garc\u0026iacute;a A, Rodr\u0026iacute;guez-Guti\u0026eacute;rrez R, Mancillas-Adame L, Gonz\u0026aacute;lez-Nava V, D\u0026iacute;az Gonz\u0026aacute;lez-Colmenero A, Solis RC, et al. Diagnostic Accuracy of the Triglyceride and Glucose Index for Insulin Resistance: A Systematic Review. Int J Endocrinol. 2020;2020:4678526.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDada AS, Ajayi DD, Areo PO, Raimi TH, Emmanuel EE, Odu OO et al. Metabolic Syndrome and Framingham Risk Score: Observation from Screening of Low-Income Semi-Urban African Women. Med (Basel). 2016;3(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmuse G, Maina D, Hoffman M, Mwangi J, Wambua C, Kagotho E et al. Metabolic syndrome and its predictors in an urban population in Kenya: A cross sectional study. BMC Endocr Disord. 2017;17(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing Metabolic Syndrome Circulation. 2009;120(16):1640\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosomworth NJ. Practical use of the Framingham risk score in primary prevention: Canadian perspective. Can Fam Physician. 2011;57(4):417\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerrero-Romero F, Simental-Mend\u0026iacute;a LE, Gonz\u0026aacute;lez-Ortiz M, Mart\u0026iacute;nez-Abundis E, Ramos-Zavala MG, Hern\u0026aacute;ndez-Gonz\u0026aacute;lez SO, et al. The Product of Triglycerides and Glucose, a Simple Measure of Insulin Sensitivity. Comparison with the Euglycemic-Hyperinsulinemic Clamp. J Clin Endocrinol Metab. 2010;95(7):3347\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadaegh F, Abdi A, Kohansal K, Hadaegh P, Azizi F, Tohidi M. Gender differences in the impact of 3-year status changes of metabolic syndrome and its components on incident type 2 diabetes mellitus: a decade of follow-up in the Tehran Lipid and Glucose Study. Front Endocrinol (Lausanne). 2023;14:1164771.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerreault L, Ma Y, Dagogo-Jack S, Horton E, Marrero D, Crandall J, et al. Sex Differences in Diabetes Risk and the Effect of Intensive Lifestyle Modification in the Diabetes Prevention Program. Diabetes Care. 2008;31(7):1416\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNayak SS, Kuriyakose D, Polisetty LD, Patil AA, Ameen D, Bonu R, et al. Diagnostic and prognostic value of triglyceride glucose index: a comprehensive evaluation of meta-analysis. Cardiovasc Diabetol. 2024;23(1):310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Tan Z, Huang Y, Zhao H, Liu M, Yu P, et al. Relationship between the triglyceride-glucose index and risk of cardiovascular diseases and mortality in the general population: a systematic review and meta-analysis. Cardiovasc Diabetol. 2022;21(1):124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira JRS, Zandonade E, de Paula Alves Bezerra OM, Salaroli LB. Cutoff point of TyG index for metabolic syndrome in Brazilian farmers. Arch Endocrinol Metab. 2021.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Triglyceride glucose index, cardiovascular disease, metabolic syndrome, insulin resistance, Framingham Risk Score, Sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-6594101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6594101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardiovascular diseases (CVDs) and metabolic syndrome (MetS) are significant contributors to the global burden of non-communicable diseases (NCDs), with their prevalence rising in Sub-Saharan Africa (SSA). Insulin resistance (IR) plays a key role in the development of these conditions, but its assessment is often limited by resource constraints. The triglyceride glucose (TyG) index incorporates triglyceride and glucose measurements and has emerged as a simple and cost-effective surrogate marker for IR. This study evaluated the use of Tyg index as a predictor of cardiovascular and metabolic risk and determined the optimal cutoffs for predicting CVD risk and MetS in a Kenyan population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were analyzed from 528 healthy Black African adults (255 males, 273 females) recruited for a global reference interval study. CVD risk was estimated using the Framingham Risk Score, while MetS was diagnosed using the 2009 harmonized criteria. The TyG index was calculated, and its ability to predict CVD risk and MetS was assessed using receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMales had significantly higher TyG index values, blood pressure, and waist circumference compared to females (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ROC analysis showed that the TyG index was a strong predictor of intermediate-to-high CVD risk (AUC\u0026thinsp;=\u0026thinsp;0.840) and MetS (AUC\u0026thinsp;=\u0026thinsp;0.858). The optimal TyG index cutoff for predicting CVD risk was 4.74 (sensitivity 74.1%, specificity 81.2%), while the best cutoff for MetS was 4.64 (sensitivity 74.8%, specificity 84.5%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe TyG index demonstrates strong potential as a screening tool for cardiovascular and metabolic risk in seemingly healthy adults. Its ease of measurement and diagnostic accuracy make it particularly valuable in resource-limited settings. Further studies are needed to assess its real-world application in SSA and explore its role in early prevention strategies.\u003c/p\u003e","manuscriptTitle":"Triglyceride Glucose Index as a Predictor of Cardiovascular and Metabolic Risk in an Urban Population in Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 08:28:41","doi":"10.21203/rs.3.rs-6594101/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":"95cc7e21-40de-43cb-800a-93d4fb0a6f83","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-20T09:05:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T10:46:11+00:00","index":92,"fulltext":""},{"type":"reviewerAgreed","content":"310841566704300738475468228888934138368","date":"2026-05-14T10:44:06+00:00","index":91,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T09:21:13+00:00","index":90,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T16:52:58+00:00","index":89,"fulltext":""},{"type":"reviewerAgreed","content":"114525161680152910763754676190105295940","date":"2026-05-07T20:38:43+00:00","index":88,"fulltext":""},{"type":"reviewerAgreed","content":"15272273513061530104138781012909825512","date":"2026-05-06T10:16:27+00:00","index":87,"fulltext":""},{"type":"reviewerAgreed","content":"101336288246131134081416653483754269140","date":"2026-05-05T20:41:16+00:00","index":86,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T09:11:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 08:28:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6594101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6594101","identity":"rs-6594101","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}