Evaluation of Triglyceride-Glucose (TyG) Index in Individuals Living with HIV Under Antiretroviral Therapy (ART)

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There are very limited number of studies evaluating TyG index in individuals living with HIV, where the incidence of these diseases is increased. This study was conducted to determine the change in TyG index during the first year of antiretroviral therapy and the relationship between TyG index and CD4/CD8 ratio. Methods Data of patients living with HIV who were followed up between 2011 and 2024 were retrospectively analyzed. 348 ART-naive, non-diabetic patients who completed their first year of follow-up under ART were included in the study. TyG index was calculated according to the formula Ln [fasting triglyceride (mg/dL) x fasting glucose (mg/dL)/2]. Patients with TG values ​​>150 mg/dl at the time of diagnosis constituted the hypertriglyceridemic (HTG) group, and patients with TG values ​​≤150 mg/dl at the time of diagnosis constituted the non-hypertriglyceridemic (NHTG) group. Results Hypertriglyceridemia was detected in 146 (42%) of our patients in the initial evaluation at the time of diagnosis. No correlation was found between TyG index and lymphocyte subgroups at the time of diagnosis (p > 0.05). However, a positive linear correlation was found between TyG index and CD8 + T cell count and percentage (r = 0.192, p < 0.001; r = 0.118, p < 0.001, respectively) and a negative linear correlation was found between TyG index and CD4/CD8 ratio (r=-0.092, p = 0.007) during the first year of ART. TyG index did not show any significant change in the months after starting ART in the NHTG group, while it decreased statistically significantly in the HTG group (p < 0.001). Conclusion The decrease in TyG index with control of immune activation under ART is promising as an important follow-up parameter. Therefore, data from new prospective studies are needed to analyze conditions such as CVD and metabolic syndrome that will occur in the longer term under ART. HIV Triglyceride-Glucose index TyG index Figures Figure 1 Figure 2 Figure 3 Introduction Thanks to effective antiretroviral therapy (ART), the incidence and mortality of HIV infection have decreased significantly worldwide since 2010, while life expectancy has increased, almost approaching that of the general population ( 1 , 2 ). On the other hand, the prevalence of non-infectious chronic diseases, especially cardiovascular disease (CVD), diabetes mellitus (DM), and osteoporosis, is increasing in individuals living with HIV and they occur at a younger age compared to the general population ( 2 , 3 ). The risk of CVD and DM is twice as high compared to the general population, and insulin resistance is also more common ( 3 – 5 ). Insulin resistance poses a serious risk for DM and CVD. The fasting triglyceride-glucose (TyG) index, which indirectly evaluates insulin resistance with a very simple method, is used as an important risk parameter in various diseases such as type 2 DM, obesity, stroke, CVD, and cancer ( 6 ). However, there are very limited studies on the TyG index in individuals living with HIV, where the incidence of these chronic diseases is increased ( 7 – 9 ). This study was conducted to determine the prevalence of insulin resistance in non-diabetic individuals living with HIV before ART using the TyG index, to determine the change in the TyG index during the first year of ART, and to determine the relationship between the TyG index and the CD4 + T cell count, CD8 + T cell count and CD4/CD8 ratio, which are considered indicators of immune activation. Patients and methods Data collection The files and hospital automation records of patients followed up in our hospital's adult (over 18 years old) HIV clinic between 2011–2024 were retrospectively reviewed. Demographic data, age, gender, body mass index (BMI), fasting triglyceride (TG), glucose (Glu), HIV-RNA, CD4 + T cell count and percentage, CD8 + T cell count and percentage, CD4/CD8 ratio, hemoglobin A1c (HbA1c), triglyceride-glucose index (TyG index) at diagnosis and in the 1st, 3rd, 6th and 12th months of ART were recorded in the excel file. BMI was calculated according to the formula weight (kg) / height (m) 2 . TyG index was calculated according to the formula Ln [fasting triglyceride (mg/dL) x fasting glucose (mg/dL)/2]. Patients ART naive patients who completed the first year of follow-up under ART were included in the study. Patients with hepatitis B or hepatitis C coinfection and patients diagnosed with DM were excluded. Thus, 348 patients were included in the study and their data were analyzed. Patients with TG values ​​>150 mg/dl at the time of diagnosis constituted the hypertriglyceridemic (HTG) group, while those with TG values ​​≤150 mg/dl at the time of diagnosis constituted the non-hypertriglyceridemic (NHTG) group. Statistical analysis Gender, age, BMI, laboratory values ​​at diagnosis and during treatment were analyzed using SPSS 29.0 (SPSS Inc., Chicago, IL, USA) statistical program. Median and interquartile range (IQR) were calculated for numerical continuous variables, Mann-Whitney U test was used to compare these variables between cases with and without hypertriglyceridemia, and Kruskal-Wallis and Friedman’s Two-Way tests were used to compare these numerical variables between months of treatment. Categorical variables were compared using the x 2 test . Correlation of numerical variables with each other was evaluated using Pearson and Spearman’s correlation coefficients . Results The median age of the 348 patients included in the study was 36 (IQR 29–45), and 309 (88.8%) were male. Hypertriglyceridemia was detected in 146 (42%) of our cases in the initial evaluation at the time of diagnosis. The median age was higher in the HTG group than in the other group [40 (IQR 32–47) vs. 33 (IQR 27–42), p ≤ 0.001]. There was no statistically significant difference between the gender distribution in both groups (p > 0.005). The median BMI of all cases at diagnosis was 23.8 (IQR 21.3–26.1), which was higher in the HTG group [24.6 (IQR 21.7–27) vs. 22.9 (IQR 21.1–25.3), p ≤ 0.001] (Table 1 ). Table 1 Demographic and some laboratory characteristics at the time of diagnosis Total HTG group NHTG group p N (% of total patients) 348 (100) 146 (42) 202 (58) Age (Years), median (IQR) 36 (29–45) 40 (32–47) 33 (27–42) < 0.001 Sex, n (%) .826 Female 39 (11.2) 17 ( 12 ) 22 ( 11 ) Male 309 (88.8) 129 (88) 180 (89) BMI 23.8 (21.3–26.1) 24.6 (21.7–27.0) 22.9 (21.1–25.3) < 0.001 HIV-RNA (copy/mL), median (IQR) 101.737 (29.677-465.181) 127.469 (29.860-634.396) 95.197 (29.664–320.440) .459 CD4/mm3, median (IQR) 343 (180–485) 356 (170–494) 316 (188–476) .443 CD4%, median (IQR) 17 ( 11 – 24 ) 18 (10–25) 17 ( 12 – 24 ) .669 CD8/mm3, median (IQR) 876 (595–1326) 902 (603–1416) 840 (593–1288) .253 CD8%, median (IQR) 52 (42–63) 55 (47–64) 51 (40–62) .007 CD4/CD8, median (IQR) 0.33 (0.19–0.56) 0.32 (0.15–0.55) 0.32 (0.20–0.56) .562 TG (mg/dL), median (IQR) 127 (86–195) 202 (174–266) 92 (72–117) < 0.001 Glu (mg/dL), median (IQR) 91 (85–99) 94 (88–100) 90 (83–97) 0.001 HbA1c %, median (IQR) 5.4 (5.2–5.7) 5.4 (5.2–5.7) 5.4 (5.1–5.6) .599 TyG index, median (IQR) 8.68 (8.24–9.11) 9.16 (8.99–9.45) 8.31 (8.09–8.59) < 0.001 At the time of diagnosis, the median CD4 count of all cases was 343 (IQR 180–485) and there was no statistically significant difference between HTG and NHTG groups. Similarly, there was no significant difference between HIV-RNA value, CD4 count and percentage, CD8 count, CD4/CD8 ratio (p > 0.05) (Table 1 ). Only CD8 percentage was statistically significantly higher in the HTG group [55 (IQR 47–64) vs. 51 (IQR 40–62), p = 0.007]. At the time of diagnosis, the median fasting Glu values ​​of all cases were 91 (IQR 85–99) mg/dL, the median fasting TG values ​​were 127 (IQR 86–195) mg/dL and the median TyG index was 8.68 (IQR 8.24–9.11). All three values ​​were higher in the HTG group [TG values ​​were median 202 (IQR 174–266) mg/dl vs. 92 (IQR 72–117) mg/dl; Glu values ​​were median 94 (IQR 88–100) mg/dl vs. 90 (IQR 83–97) mg/dl; TyG index was median 9.16 (IQR 8.99–9.45) vs. 8.31 (IQR 8.09–8.59); p < 0.001 for all three]. The median HbA1c level at the time of diagnosis was 5.4% (IQR 5.2–5.7) and there was no statistically significant difference between the groups (p = 0.599) (Table 1 ). Integrase Strand Transfer Inhibitor (INSTI) and nucleoside reverse transcriptase inhibitor (NRTI) combinations were started as ART in 310 (89%) patients. Dolutegravir was preferred as the integrase inhibitor in 133 (38%) patients. When all numerical variables were compared, no correlation was found between TyG index and lymphocyte subsets at diagnosis (p > 0.05) (Table 2 ). However, a positive linear correlation was found between the TyG index and CD8 count and percentage (r = 0.192, p < 0.001; r = 0.118, p < 0.001, respectively), and a negative linear correlation was found between the TyG index and the CD4/CD8 ratio (r=-0.092, p = 0.007) during the 12 months after starting ART (Table 3 ) (Fig. 1 ). Table 2 Correlation analysis of TyG index with age, BMI and lymphocyte subsets at the time of diagnosis. Pearson Correlation Sig. (2-tailed) 95% Confidence Intervals (2-tailed) a Lower Upper Age – TyG index .278 < .001 .177 .372 BMI – TyG index .215 < .001 .112 .312 CD4 – TyG index .092 .085 − .013 .196 CD4% - TyG index .071 .186 − .035 .175 CD8 - TyG index − .004 .945 − .102 .109 CD8% - TyG index .044 .418 − .062 .148 CD4/CD8 - TyG index .034 .529 − .072 .139 TG – TyG index .924 < .001 .906 .938 GLU - TyG index .312 < .001 .213 .403 HbA1c - TyG index .136 .025 .017 .251 a. Estimation is based on Fisher's r-to-z transformation with bias adjustment. Table 3 Correlation analysis of TyG index with lymphocyte subsets during the first year of ART. Pearson Correlation Sig. (2-tailed) 95% Confidence Intervals (2-tailed) a Lower Upper CD4 - TyG index .047 .167 − .020 .113 CD4% - TyG index − .065 .056 − .131 .002 CD8 - TyG index .192 < .001 .127 .255 CD8% - TyG index .118 < .001 .052 .184 CD4/CD8 - TyG index − .092 .007 − .157 − .025 a. Estimation is based on Fisher's r-to-z transformation with bias adjustment. At the time of diagnosis, there was a positive significant correlation between each of age, BMI, TyG index, TG, Glu and Hb1c values ​​(p 0.05). A very weak correlation was found between HbA1c and CD4 count (r=-0.130, p = 0.033), CD4 percentage (r=-0.157, p = 0.010), CD8 percentage (r = 0.135, p = 0.027) and CD4/CD8 ratio (r=-0.159, p = 0.009). CD4 levels and CD4/CD8 ratios of all patients increased significantly over the months in the 12-month period after starting ART (p < 0.001) (Fig. 2 ). While the TyG index did not show a significant change over months in the NHTG group, it decreased statistically significantly over months in the HTG group (p < 0.001) (Fig. 3 ). Discussion In our study, no significant correlation was detected between TyG index and lymphocyte subgroups in treatment-naive individuals living with HIV without a history of DM, whereas a positive linear correlation was found between TyG index and CD8 + T cell count and percentage and a negative linear correlation with CD4/CD8 ratio in the period after starting ART. While CD4/CD8 ratios increased by months under ART, TyG index significantly decreased by month, especially in the HTG group. This suggests that insulin resistance and, indirectly, risks such as CVD and metabolic syndrome are reduced with ART. Recent studies have shown that the TyG index is an alternative biomarker of insulin resistance with high sensitivity and specificity. Some high-quality clinical studies have highlighted the importance of the TyG index in various medical conditions and diseases such as DM, CVD, cerebrovascular diseases, obesity, fatty liver, kidney diseases, osteoporosis, cancer, and reproductive system diseases ( 6 ). It has been shown that the TyG index, which is calculated according to fasting TG and Glu levels and indirectly indicates insulin resistance, is better than the homeostatic model assessment of insulin resistance (HOMA-IR) method in estimating the prevalence of type 2 DM, with a predictive value of 0.784, and will be more useful in the early detection of type 2 DM ( 10 ). It is also a sensitive and specific indicator in the screening of metabolic syndrome ( 11 ). High TyG index has also been found to be associated with the risk of subclinical atherosclerosis ( 12 ). It also has a potential role in risk stratification for ischemic stroke. At the same time, high TyG index has been shown to be associated with high mortality and stroke recurrence ( 13 ). In a large-scale cohort study of critically ill patients in the intensive care unit (ICU), TyG index was shown to be associated with poor prognosis in acute coronary syndrome, ischemic stroke and heart failure, and to be a useful parameter in predicting mortality in the ICU and hospital ( 14 ). The prevalence of non-infectious chronic diseases, especially CVD, DM, osteoporosis and insulin resistance, is higher in individuals living with HIV compared to the general population and occurs at an earlier age ( 2 – 5 ). The prevalence of hyperlipidemia, which is most closely related to CVD, in individuals living with HIV varies between 28% and 80% in different studies, and hypertriglyceridemia is the most common lipid abnormality ( 15 ). In our study, hypertriglyceridemia was detected in 42% of our ART-naïve patients without a diagnosis of DM. A meta-analysis evaluating the global burden of CVD risk in individuals living with HIV showed that it is 1.5-2 times higher than in the normal population and that this risk increased 3-fold between 1990 and 2015, and this increase is especially evident in Sub-Saharan Africa ( 5 ). Although the prevalence of DM in individuals living with HIV is 2% in Sub-Saharan Africa, insulin resistance was reported as 47.3% and 68.2% in two different studies ( 16 , 17 ). There is no clear cut-off value for the TyG index, which we used as an indicator of insulin resistance in our study. In a very recent study from our country, insulin resistance was investigated in 147 individuals living with HIV and a positive linear correlation was found between HOMA-IR and TyG index (r = 0.628, p < 0.001), and the TyG index cut-off value for predicting insulin resistance was found to be 8.25 ( 7 ). Accordingly, the initial TyG index was above this value in 74.4% of our cases. In a large cohort study by Luo et al., 16,122 treatment-naïve individuals living with HIV were followed for an average of 70 months between 2005 and 2022, and 214 of them developed CVD during follow-up. They created 4 groups according to the baseline TyG index, and the highest index group had a 2.92-fold increased risk of CVD. The cut-off level for the initial TyG index in indicating CVD risk was found to be 8.479 ( 8 ). Accordingly, the initial TyG index was above this value in 62.4% of our cases. As it is an expected result in the normal population, higher BMI and age in the HTG group (p < 0.001 for both) were not found to be associated with HIV in our study. Although we did not include cases with DM, the higher fasting glucose values ​​in the HTG group (p = 0.001) indicate a significant relationship between TG and Glu values. The higher CD8 + T cell percentage in the ART-naïve HTG group (p = 0.007) indicates that inflammation is greater in this group. In fact, it has been shown that the CD8 + T cell count is more associated with disease progression in individuals living with HIV compared to the CD4 + T cell count and allows the prediction of serious non-infectious events ( 18 ). Both HIV infection itself and ART play a role in the increased risk of CVD and metabolic syndrome in individuals living with HIV ( 19 ). High viral load and low CD4/CD8 ratio are associated with CVD risk ( 18 , 20 – 21 ). A study by Tiozzo et al. showed that high viral load, low CD4 count and duration of infection are associated with increased DM incidence ( 22 ). On the other hand, this relationship could not be demonstrated in a study by Montes et al. because it consisted of patients who were diagnosed more recently and started ART ( 23 ). The main reason for this difference is that protease inhibitor (PI)-based antiretroviral drugs, which were preferred in previous years, and non-nucleoside reverse transcriptase inhibitor (NNRTI)-based antiretroviral drugs such as efavirenz, have negative effects on lipid metabolism and this negative effect has been eliminated with the more recently preferred INSTI-based regimens ( 19 ). The conclusion here is that the most effective approach to reducing the risk of CVD and metabolic syndrome in individuals living with HIV is to start treatment as soon as possible with antiretrovirals that do not have negative effects on these risks and to prevent disease progression. Therefore, in our study, we examined the correlation between TyG index and lymphocyte subgroups under ART. As in our study, in the study conducted by Yan et al., it was shown that TyG index and CD8 + T cell count under ART were positively correlated, and high TyG index was associated with low CD4/CD8 ratio ( 9 ). This situation shows that metabolic risks can be reduced by controlling inflammation. In our study, 89% of our patients were started on INSTI and NRTI-based ART. It was shown that this combination provided greater improvement in CD4/CD8 ratio compared to PI-based regimens ( 24 ). The most important limitations of our study are that the follow-up data of our cases after the first year of ART were not included, risk assessment and comorbidities that emerged during follow-up were not analyzed, and comparisons were not made according to the antiretrovirals initiated. Conclusion There is a significant correlation between the TyG index and the CD8+ T cell count and CD4/CD8 ratio under ART in individuals living with HIV. The TyG index which has high sensitivity and specificity in assessing insulin resistance, metabolic syndrome and CVD risk, as well as being simple, fast and cost-effective, is promising as a follow-up parameter for individuals living with HIV. For this reason, data from new prospective studies are needed to analyse conditions such as CVD and metabolic syndrome that will occur in the longer term under ART. Declarations Scientific responsibility statement: The authors accept that they are responsible for the scientific content of the article during the planning of the study, data collection, analysis and interpretation of the data, writing, scientific review of the content, and approval of the final version. Animal and human rights statement: All procedures performed in this study are in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki declaration. No animal or human studies were performed in this article. Funding: None Conflict of interest: The authors have not received any financial support that would create a conflict of interest regarding the article. 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Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 17 Jul, 2025 Reviews received at journal 17 Jul, 2025 Reviews received at journal 09 Jul, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 12 May, 2025 Editor assigned by journal 03 Apr, 2025 Submission checks completed at journal 03 Apr, 2025 First submitted to journal 26 Mar, 2025 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. 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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-6314815","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434464397,"identity":"28084a05-0274-4367-b01f-a3a4288a2d4d","order_by":0,"name":"Bülent Kaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACAzCCEIyPwULMzA1EaEkAa2E2BnOYGYnXwiYNtQ2/FnP2wxsf/PxhZ8w/I/lZdUHFn2j+dqCWHxXbcGqx7EkrNuxJSDaTuJFmdnvGGYPcGYcZGxh7ztzG7bADOWYSPAnMNgw3Esxu87YZ5DYAtTAztuHRcv6N+c8/CfU28jfSvxWDtMwnqOVGjhkzT8JhMzADpGUDIS2WM54VS8ukHTc2PPOmWJrnjHHuRqCWg/j8Ys6fvPHjG5tqw3nH0zd+5qmQy513/vDBBz8qcGtBAIEEBPsAEeqBgJ9IdaNgFIyCUTDyAACYo1pA1Q73yAAAAABJRU5ErkJggg==","orcid":"","institution":"Dr Lütfi Kırdar Kartal Eğitim ve Araştırma Hastanesi","correspondingAuthor":true,"prefix":"","firstName":"Bülent","middleName":"","lastName":"Kaya","suffix":""},{"id":434464398,"identity":"44f8e725-ebfe-4b9f-93ec-ea535315e721","order_by":1,"name":"Suzan Şahin","email":"","orcid":"","institution":"Dr Lütfi Kırdar Kartal Eğitim ve Araştırma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Suzan","middleName":"","lastName":"Şahin","suffix":""},{"id":434464399,"identity":"1fdb95b2-3f0f-4a14-9a96-febaccd08997","order_by":2,"name":"Serap Gençer","email":"","orcid":"","institution":"Acıbadem University","correspondingAuthor":false,"prefix":"","firstName":"Serap","middleName":"","lastName":"Gençer","suffix":""}],"badges":[],"createdAt":"2025-03-26 18:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6314815/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6314815/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-12011-0","type":"published","date":"2025-11-12T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79577826,"identity":"4dd33c65-3a33-4808-a586-ca5b6c3a8526","added_by":"auto","created_at":"2025-03-31 11:28:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56595,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between TyG index and (a) CD8+ T cells, (b) CD4/CD8 ratios during the first year of ART.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6314815/v1/c443a53d7652a7ad41342b2f.jpg"},{"id":79578627,"identity":"79c671d1-424b-4bd6-b460-2989018b1d1d","added_by":"auto","created_at":"2025-03-31 11:36:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29493,"visible":true,"origin":"","legend":"\u003cp\u003eIncrease of CD4/CD8 ratios during ART (p\u0026lt;0.001) (all cases).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6314815/v1/3a80d35c363550fd1ad8a258.jpg"},{"id":79577829,"identity":"6f50af65-cf11-4620-9c53-31e4cbe8c034","added_by":"auto","created_at":"2025-03-31 11:28:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42012,"visible":true,"origin":"","legend":"\u003cp\u003eDecrease of TyG index during ART in hypertrigliseridemic group (HTG) in compared to non-hypertrigliseridemic group (NHTG) (p\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6314815/v1/f0e6de315169fd9f7f396538.jpg"},{"id":96105055,"identity":"acbb846b-a36a-4d76-bf2f-e0ac8406772f","added_by":"auto","created_at":"2025-11-17 16:07:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":752364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6314815/v1/5c698cff-999e-4aa1-9237-4d739c4ff689.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Triglyceride-Glucose (TyG) Index in Individuals Living with HIV Under Antiretroviral Therapy (ART)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThanks to effective antiretroviral therapy (ART), the incidence and mortality of HIV infection have decreased significantly worldwide since 2010, while life expectancy has increased, almost approaching that of the general population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). On the other hand, the prevalence of non-infectious chronic diseases, especially cardiovascular disease (CVD), diabetes mellitus (DM), and osteoporosis, is increasing in individuals living with HIV and they occur at a younger age compared to the general population (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The risk of CVD and DM is twice as high compared to the general population, and insulin resistance is also more common (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInsulin resistance poses a serious risk for DM and CVD. The fasting triglyceride-glucose (TyG) index, which indirectly evaluates insulin resistance with a very simple method, is used as an important risk parameter in various diseases such as type 2 DM, obesity, stroke, CVD, and cancer (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, there are very limited studies on the TyG index in individuals living with HIV, where the incidence of these chronic diseases is increased (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study was conducted to determine the prevalence of insulin resistance in non-diabetic individuals living with HIV before ART using the TyG index, to determine the change in the TyG index during the first year of ART, and to determine the relationship between the TyG index and the CD4\u0026thinsp;+\u0026thinsp;T cell count, CD8\u0026thinsp;+\u0026thinsp;T cell count and CD4/CD8 ratio, which are considered indicators of immune activation.\u003c/p\u003e"},{"header":"Patients and methods","content":"\u003cp\u003e \u003cstrong\u003eData collection\u003c/strong\u003e \u003cp\u003e The files and hospital automation records of patients followed up in our hospital's adult (over 18 years old) HIV clinic between 2011\u0026ndash;2024 were retrospectively reviewed. Demographic data, age, gender, body mass index (BMI), fasting triglyceride (TG), glucose (Glu), HIV-RNA, CD4\u0026thinsp;+\u0026thinsp;T cell count and percentage, CD8\u0026thinsp;+\u0026thinsp;T cell count and percentage, CD4/CD8 ratio, hemoglobin A1c (HbA1c), triglyceride-glucose index (TyG index) at diagnosis and in the 1st, 3rd, 6th and 12th months of ART were recorded in the excel file. BMI was calculated according to the formula weight (kg) / height (m)\u003csup\u003e2\u003c/sup\u003e. TyG index was calculated according to the formula Ln [fasting triglyceride (mg/dL) x fasting glucose (mg/dL)/2].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePatients\u003c/strong\u003e \u003cp\u003eART naive patients who completed the first year of follow-up under ART were included in the study. Patients with hepatitis B or hepatitis C coinfection and patients diagnosed with DM were excluded. Thus, 348 patients were included in the study and their data were analyzed. Patients with TG values ​​\u0026gt;150 mg/dl at the time of diagnosis constituted the hypertriglyceridemic (HTG) group, while those with TG values ​​\u0026le;150 mg/dl at the time of diagnosis constituted the non-hypertriglyceridemic (NHTG) group.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical analysis\u003c/strong\u003e \u003cp\u003eGender, age, BMI, laboratory values ​​at diagnosis and during treatment were analyzed using SPSS 29.0 (SPSS Inc., Chicago, IL, USA) statistical program. Median and interquartile range (IQR) were calculated for numerical continuous variables, \u003cem\u003eMann-Whitney U test\u003c/em\u003e was used to compare these variables between cases with and without hypertriglyceridemia, and \u003cem\u003eKruskal-Wallis\u003c/em\u003e and \u003cem\u003eFriedman\u0026rsquo;s Two-Way tests\u003c/em\u003e were used to compare these numerical variables between months of treatment. Categorical variables were compared using the \u003cem\u003ex\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003etest\u003c/em\u003e. Correlation of numerical variables with each other was evaluated using \u003cem\u003ePearson\u003c/em\u003e and \u003cem\u003eSpearman\u0026rsquo;s correlation coefficients\u003c/em\u003e.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe median age of the 348 patients included in the study was 36 (IQR 29\u0026ndash;45), and 309 (88.8%) were male. Hypertriglyceridemia was detected in 146 (42%) of our cases in the initial evaluation at the time of diagnosis. The median age was higher in the HTG group than in the other group [40 (IQR 32\u0026ndash;47) vs. 33 (IQR 27\u0026ndash;42), p\u0026thinsp;\u0026le;\u0026thinsp;0.001]. There was no statistically significant difference between the gender distribution in both groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.005). The median BMI of all cases at diagnosis was 23.8 (IQR 21.3\u0026ndash;26.1), which was higher in the HTG group [24.6 (IQR 21.7\u0026ndash;27) vs. 22.9 (IQR 21.1\u0026ndash;25.3), p\u0026thinsp;\u0026le;\u0026thinsp;0.001] (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\u003eDemographic and some laboratory characteristics at the time of diagnosis\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHTG group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNHTG group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (% of total patients)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (29\u0026ndash;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (32\u0026ndash;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (27\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309 (88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.8 (21.3\u0026ndash;26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.6 (21.7\u0026ndash;27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.9 (21.1\u0026ndash;25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV-RNA (copy/mL), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.737 (29.677-465.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.469 (29.860-634.396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.197 (29.664\u0026ndash;320.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4/mm3, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343 (180\u0026ndash;485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e356 (170\u0026ndash;494)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e316 (188\u0026ndash;476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4%, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (10\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8/mm3, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e876 (595\u0026ndash;1326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e902 (603\u0026ndash;1416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e840 (593\u0026ndash;1288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8%, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (42\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (47\u0026ndash;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (40\u0026ndash;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4/CD8, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33 (0.19\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32 (0.15\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (0.20\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (86\u0026ndash;195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (174\u0026ndash;266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (72\u0026ndash;117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu (mg/dL), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (85\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (88\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (83\u0026ndash;97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c %, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4 (5.2\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4 (5.2\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4 (5.1\u0026ndash;5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.68 (8.24\u0026ndash;9.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.16 (8.99\u0026ndash;9.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.31 (8.09\u0026ndash;8.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eAt the time of diagnosis, the median CD4 count of all cases was 343 (IQR 180\u0026ndash;485) and there was no statistically significant difference between HTG and NHTG groups. Similarly, there was no significant difference between HIV-RNA value, CD4 count and percentage, CD8 count, CD4/CD8 ratio (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Only CD8 percentage was statistically significantly higher in the HTG group [55 (IQR 47\u0026ndash;64) vs. 51 (IQR 40\u0026ndash;62), p\u0026thinsp;=\u0026thinsp;0.007].\u003c/p\u003e \u003cp\u003eAt the time of diagnosis, the median fasting Glu values ​​of all cases were 91 (IQR 85\u0026ndash;99) mg/dL, the median fasting TG values ​​were 127 (IQR 86\u0026ndash;195) mg/dL and the median TyG index was 8.68 (IQR 8.24\u0026ndash;9.11). All three values ​​were higher in the HTG group [TG values ​​were median 202 (IQR 174\u0026ndash;266) mg/dl vs. 92 (IQR 72\u0026ndash;117) mg/dl; Glu values ​​were median 94 (IQR 88\u0026ndash;100) mg/dl vs. 90 (IQR 83\u0026ndash;97) mg/dl; TyG index was median 9.16 (IQR 8.99\u0026ndash;9.45) vs. 8.31 (IQR 8.09\u0026ndash;8.59); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all three]. The median HbA1c level at the time of diagnosis was 5.4% (IQR 5.2\u0026ndash;5.7) and there was no statistically significant difference between the groups (p\u0026thinsp;=\u0026thinsp;0.599) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIntegrase Strand Transfer Inhibitor (INSTI) and nucleoside reverse transcriptase inhibitor (NRTI) combinations were started as ART in 310 (89%) patients. Dolutegravir was preferred as the integrase inhibitor in 133 (38%) patients.\u003c/p\u003e \u003cp\u003eWhen all numerical variables were compared, no correlation was found between TyG index and lymphocyte subsets at diagnosis (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, a positive linear correlation was found between the TyG index and CD8 count and percentage (r\u0026thinsp;=\u0026thinsp;0.192, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.118, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively), and a negative linear correlation was found between the TyG index and the CD4/CD8 ratio (r=-0.092, p\u0026thinsp;=\u0026thinsp;0.007) during the 12 months after starting ART (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eCorrelation analysis of TyG index with age, BMI and lymphocyte subsets at the time of diagnosis.\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% Confidence Intervals (2-tailed)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge \u0026ndash; TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI \u0026ndash; TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4 \u0026ndash; TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4% - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8 - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8% - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4/CD8 - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG \u0026ndash; TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea. Estimation is based on Fisher's r-to-z transformation with bias adjustment.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eCorrelation analysis of TyG index with lymphocyte subsets during the first year of ART.\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% Confidence Intervals (2-tailed)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4 - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4% - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8 - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8% - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4/CD8 - TyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea. Estimation is based on Fisher's r-to-z transformation with bias adjustment.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the time of diagnosis, there was a positive significant correlation between each of age, BMI, TyG index, TG, Glu and Hb1c values ​​(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for each), but there was no significant correlation between each of them except HbA1c and lymphocyte subgroups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A very weak correlation was found between HbA1c and CD4 count (r=-0.130, p\u0026thinsp;=\u0026thinsp;0.033), CD4 percentage (r=-0.157, p\u0026thinsp;=\u0026thinsp;0.010), CD8 percentage (r\u0026thinsp;=\u0026thinsp;0.135, p\u0026thinsp;=\u0026thinsp;0.027) and CD4/CD8 ratio (r=-0.159, p\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003eCD4 levels and CD4/CD8 ratios of all patients increased significantly over the months in the 12-month period after starting ART (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While the TyG index did not show a significant change over months in the NHTG group, it decreased statistically significantly over months in the HTG group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, no significant correlation was detected between TyG index and lymphocyte subgroups in treatment-naive individuals living with HIV without a history of DM, whereas a positive linear correlation was found between TyG index and CD8\u0026thinsp;+\u0026thinsp;T cell count and percentage and a negative linear correlation with CD4/CD8 ratio in the period after starting ART. While CD4/CD8 ratios increased by months under ART, TyG index significantly decreased by month, especially in the HTG group. This suggests that insulin resistance and, indirectly, risks such as CVD and metabolic syndrome are reduced with ART.\u003c/p\u003e \u003cp\u003eRecent studies have shown that the TyG index is an alternative biomarker of insulin resistance with high sensitivity and specificity. Some high-quality clinical studies have highlighted the importance of the TyG index in various medical conditions and diseases such as DM, CVD, cerebrovascular diseases, obesity, fatty liver, kidney diseases, osteoporosis, cancer, and reproductive system diseases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt has been shown that the TyG index, which is calculated according to fasting TG and Glu levels and indirectly indicates insulin resistance, is better than the homeostatic model assessment of insulin resistance (HOMA-IR) method in estimating the prevalence of type 2 DM, with a predictive value of 0.784, and will be more useful in the early detection of type 2 DM (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). It is also a sensitive and specific indicator in the screening of metabolic syndrome (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh TyG index has also been found to be associated with the risk of subclinical atherosclerosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). It also has a potential role in risk stratification for ischemic stroke. At the same time, high TyG index has been shown to be associated with high mortality and stroke recurrence (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a large-scale cohort study of critically ill patients in the intensive care unit (ICU), TyG index was shown to be associated with poor prognosis in acute coronary syndrome, ischemic stroke and heart failure, and to be a useful parameter in predicting mortality in the ICU and hospital (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevalence of non-infectious chronic diseases, especially CVD, DM, osteoporosis and insulin resistance, is higher in individuals living with HIV compared to the general population and occurs at an earlier age (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The prevalence of hyperlipidemia, which is most closely related to CVD, in individuals living with HIV varies between 28% and 80% in different studies, and hypertriglyceridemia is the most common lipid abnormality (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In our study, hypertriglyceridemia was detected in 42% of our ART-na\u0026iuml;ve patients without a diagnosis of DM. A meta-analysis evaluating the global burden of CVD risk in individuals living with HIV showed that it is 1.5-2 times higher than in the normal population and that this risk increased 3-fold between 1990 and 2015, and this increase is especially evident in Sub-Saharan Africa (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the prevalence of DM in individuals living with HIV is 2% in Sub-Saharan Africa, insulin resistance was reported as 47.3% and 68.2% in two different studies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). There is no clear cut-off value for the TyG index, which we used as an indicator of insulin resistance in our study. In a very recent study from our country, insulin resistance was investigated in 147 individuals living with HIV and a positive linear correlation was found between HOMA-IR and TyG index (r\u0026thinsp;=\u0026thinsp;0.628, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the TyG index cut-off value for predicting insulin resistance was found to be 8.25 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Accordingly, the initial TyG index was above this value in 74.4% of our cases.\u003c/p\u003e \u003cp\u003eIn a large cohort study by Luo et al., 16,122 treatment-na\u0026iuml;ve individuals living with HIV were followed for an average of 70 months between 2005 and 2022, and 214 of them developed CVD during follow-up. They created 4 groups according to the baseline TyG index, and the highest index group had a 2.92-fold increased risk of CVD. The cut-off level for the initial TyG index in indicating CVD risk was found to be 8.479 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Accordingly, the initial TyG index was above this value in 62.4% of our cases.\u003c/p\u003e \u003cp\u003eAs it is an expected result in the normal population, higher BMI and age in the HTG group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both) were not found to be associated with HIV in our study. Although we did not include cases with DM, the higher fasting glucose values ​​in the HTG group (p\u0026thinsp;=\u0026thinsp;0.001) indicate a significant relationship between TG and Glu values. The higher CD8\u0026thinsp;+\u0026thinsp;T cell percentage in the ART-na\u0026iuml;ve HTG group (p\u0026thinsp;=\u0026thinsp;0.007) indicates that inflammation is greater in this group. In fact, it has been shown that the CD8\u0026thinsp;+\u0026thinsp;T cell count is more associated with disease progression in individuals living with HIV compared to the CD4\u0026thinsp;+\u0026thinsp;T cell count and allows the prediction of serious non-infectious events (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth HIV infection itself and ART play a role in the increased risk of CVD and metabolic syndrome in individuals living with HIV (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). High viral load and low CD4/CD8 ratio are associated with CVD risk (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A study by Tiozzo et al. showed that high viral load, low CD4 count and duration of infection are associated with increased DM incidence (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). On the other hand, this relationship could not be demonstrated in a study by Montes et al. because it consisted of patients who were diagnosed more recently and started ART (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The main reason for this difference is that protease inhibitor (PI)-based antiretroviral drugs, which were preferred in previous years, and non-nucleoside reverse transcriptase inhibitor (NNRTI)-based antiretroviral drugs such as efavirenz, have negative effects on lipid metabolism and this negative effect has been eliminated with the more recently preferred INSTI-based regimens (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The conclusion here is that the most effective approach to reducing the risk of CVD and metabolic syndrome in individuals living with HIV is to start treatment as soon as possible with antiretrovirals that do not have negative effects on these risks and to prevent disease progression. Therefore, in our study, we examined the correlation between TyG index and lymphocyte subgroups under ART. As in our study, in the study conducted by Yan et al., it was shown that TyG index and CD8\u0026thinsp;+\u0026thinsp;T cell count under ART were positively correlated, and high TyG index was associated with low CD4/CD8 ratio (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This situation shows that metabolic risks can be reduced by controlling inflammation. In our study, 89% of our patients were started on INSTI and NRTI-based ART. It was shown that this combination provided greater improvement in CD4/CD8 ratio compared to PI-based regimens (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most important limitations of our study are that the follow-up data of our cases after the first year of ART were not included, risk assessment and comorbidities that emerged during follow-up were not analyzed, and comparisons were not made according to the antiretrovirals initiated.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThere is a significant correlation between the TyG index and the CD8+ T cell count and CD4/CD8 ratio under ART in individuals living with HIV. The TyG index which has high sensitivity and specificity in assessing insulin resistance, metabolic syndrome and CVD risk, as well as being simple, fast and cost-effective, is promising as a follow-up parameter for individuals living with HIV. For this reason, data from new prospective studies are needed to analyse conditions such as CVD and metabolic syndrome that will occur in the longer term under ART.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eScientific responsibility statement:\u003c/strong\u003e The authors accept that they are responsible for the scientific content of the article during the planning of the study, data collection, analysis and interpretation of the data, writing, scientific review of the content, and approval of the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal and human rights statement:\u003c/strong\u003e All procedures performed in this study are in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki declaration. No animal or human studies were performed in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors have not received any financial support that would create a conflict of interest regarding the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Committee:\u003c/strong\u003e Kartal Dr. Lütfi Kırdar City Hospital (Decision no: 2022/514/239/7)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed equally to every stage of the writing of the article. All authors have reviewed the manuscript and declared it ready for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUNAIDS epidemiological estimates. 2024 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aidsinfo.unaids.org/\u003c/span\u003e\u003cspan address=\"https://aidsinfo.unaids.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWandeler G, Johnson LF, Egger M. Trends in life expectancy of HIV-positive adults on antiretroviral therapy across the globe: comparisons with general population. Curr Opin HIV AIDS. 2016;11(5):492\u0026ndash;500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePremaor MO, Compston JE. People living with HIV and fracture risk. 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PMID: 32763219.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, Triglyceride-Glucose index, TyG index","lastPublishedDoi":"10.21203/rs.3.rs-6314815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6314815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eFasting triglyceride-glucose (TyG) index, which indirectly evaluates insulin resistance, is an important risk parameter in various diseases, especially cardiovascular diseases (CVD). There are very limited number of studies evaluating TyG index in individuals living with HIV, where the incidence of these diseases is increased. This study was conducted to determine the change in TyG index during the first year of antiretroviral therapy and the relationship between TyG index and CD4/CD8 ratio.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData of patients living with HIV who were followed up between 2011 and 2024 were retrospectively analyzed. 348 ART-naive, non-diabetic patients who completed their first year of follow-up under ART were included in the study. TyG index was calculated according to the formula Ln [fasting triglyceride (mg/dL) x fasting glucose (mg/dL)/2]. Patients with TG values ​​\u0026gt;150 mg/dl at the time of diagnosis constituted the hypertriglyceridemic (HTG) group, and patients with TG values ​​\u0026le;150 mg/dl at the time of diagnosis constituted the non-hypertriglyceridemic (NHTG) group.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHypertriglyceridemia was detected in 146 (42%) of our patients in the initial evaluation at the time of diagnosis. No correlation was found between TyG index and lymphocyte subgroups at the time of diagnosis (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, a positive linear correlation was found between TyG index and CD8\u0026thinsp;+\u0026thinsp;T cell count and percentage (r\u0026thinsp;=\u0026thinsp;0.192, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.118, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively) and a negative linear correlation was found between TyG index and CD4/CD8 ratio (r=-0.092, p\u0026thinsp;=\u0026thinsp;0.007) during the first year of ART. TyG index did not show any significant change in the months after starting ART in the NHTG group, while it decreased statistically significantly in the HTG group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe decrease in TyG index with control of immune activation under ART is promising as an important follow-up parameter. Therefore, data from new prospective studies are needed to analyze conditions such as CVD and metabolic syndrome that will occur in the longer term under ART.\u003c/p\u003e","manuscriptTitle":"Evaluation of Triglyceride-Glucose (TyG) Index in Individuals Living with HIV Under Antiretroviral Therapy (ART)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 11:28:22","doi":"10.21203/rs.3.rs-6314815/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-17T12:57:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-17T09:12:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-09T09:59:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239261070886186452272872694479538144990","date":"2025-06-23T15:56:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170899314895445277981532307934027805190","date":"2025-06-23T04:39:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T18:05:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232032302923634772615260332551404280579","date":"2025-05-14T18:14:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-12T17:53:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-03T11:46:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-03T11:44:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-03-26T18:24:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6644ef37-52cb-479e-84da-0eb1520189a3","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:02:39+00:00","versionOfRecord":{"articleIdentity":"rs-6314815","link":"https://doi.org/10.1186/s12879-025-12011-0","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-11-12 15:58:11","publishedOnDateReadable":"November 12th, 2025"},"versionCreatedAt":"2025-03-31 11:28:22","video":"","vorDoi":"10.1186/s12879-025-12011-0","vorDoiUrl":"https://doi.org/10.1186/s12879-025-12011-0","workflowStages":[]},"version":"v1","identity":"rs-6314815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6314815","identity":"rs-6314815","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}