Триглицерид-глюкозный индекс как предиктор тяжести и летальности при COVID-19: систематический обзор и метаанализ

• Алвианто Стивен
• Готама Й.
• Вангиджаджа О.
• Йонатан Э.Р.
• Джусни Л.Ф.Д.
• Кахьяди А.

Резюме

Инсулинорезистентность (ИР) все чаще признается одним из основных факторов риска ухудшения клинических исходов коронавирусной инфекции (COVID-19). Триглицерид-глюкозный индекс (TyG) стал надежным маркером для оценки ИР.

Цель исследования - метаанализ публикаций с оценкой прогностической значимости индекса TyG у пациентов с COVID-19.

Материал и методы. Исследование было проведено в 2024 г. в соответствии с рекомендациями PRISMA. Статьи были получены из MedLine, Cochrane, ScienceDirect, ProQuest и Google Scholar. Для анализа отобраны все обсервационные исследования, в которых изучался индекс TyG при COVID-19. Для оценки качества исследования применяли шкалу Ньюкасла-Оттавы (NOS), а для проведения метаанализа - Review Manager 5.4.

Результаты. Для проведения метаанализа отобраны 5 публикаций. Установлено, что при тяжелом течении COVID-19 уровни индекса TyG были значительно выше по сравнению с более легкими случаями [MD 0,29; 95% доверительный интервал (ДИ) 0,13-0,45, p=0,0003]. Умершие пациенты с COVID-19 также имели более высокий индекс TyG по сравнению с выжившими (MD 0,31, 95% ДИ 0,03-0,58, p=0,03). Было показано, что прогностическая ценность индекса TyG для прогнозирования тяжести и летального исхода от COVID-19 [отношение шансов (ОШ) 1,88; 95% ДИ 1,33-2,67, p=0,0004; и ОШ 1,86; 95% ДИ 1,03-3,33, p=0,04, соответственно] статистически значима.

Заключение. Более высокие уровни индекса TyG были у пациентов с тяжелым течением и умерших от COVID-19. Этот индекс также обладает потенциалом в качестве ценного маркера для определения тяжести и летальных исходов у пациентов с COVID-19.

Ключевые слова: метаанализ; триглицерид-глюкозный индекс; летальность; COVID-19

The Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has emerged as a highly contagious and deadly pandemic, significantly impacting global health, economies, and social structures [1]. COVID-19 manifests in varying degrees of severity, from mild symptoms to cases requiring intensive care in hospitals [2]. Epidemiological studies have shown that 75% of hospitalized COVID-19 patients have at least one comorbidity, with the most common being hypertension, diabetes, cancer, neurodegenerative diseases, cardiovascular diseases, obesity, and kidney diseases [3].

One area of significant focus in COVID-19 research is the relationship between SARS-CoV-2 infection and insulin resistance (IR), a condition that worsens patient prognosis, especially for those with pre-existing metabolic issues [4]. The triglyceride-glucose index (TyG index) was initially developed as a simple parameter for measuring IR. It is calculated based on fasting glucose concentration and serum triglycerides, two indicators closely associated with lipid and glucose metabolism [5]. This index was first proposed in 2008, and it was identified as an optimal tool for the assessment of IR, with high sensitivity (96.5%) and specificity (85%) compared to the gold standard in 2010 [5].

Currently, no specific methods are available for the precise determination of IR. The challenges in assessing IR stem from the time-consuming, invasive and expensive gold standard method, the hyperinsulinemic-euglycemic clamp technique and intravenous glucose tolerance testing, as well as the lack of standardized, accessible, and cost-effective alternatives like homeostatic model assessment for insulin resistance (HOMA-IR), fasting glucose-insulin ratio (FG-IR), and quantitative insulin sensitivity check index (QUICKI) [6, 7]. The TyG index has recently emerged as a dependable marker for assessing IR [8]. The TyG index is a simple and cost-effective method for estimating IR and can be helpful in assessing COVID-19 severity [6]. Multiple studies and meta-analyses have demonstrated that TyG index is a reliable tool for evaluating IR in various chronic disease conditions, such as cardiovascular disease, type 2 diabetes mellitus, and non-alcoholic fatty liver disease (NAFLD) [9-11].

Given the link between COVID-19 and IR and the findings of previous studies showing the effectiveness of TyG index in other metabolic diseases, it is crucial to evaluate the role of TyG index in the context of COVID-19. Therefore, this study aims to explore the potential of TyG index as a risk marker in COVID-19 patients.

Methods

The methodology for this systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 statement guideline [12]. On August 28th, 2024, the protocol was registered with the registration number CRD42024583181 at the International Prospective Register of Systematic Reviews (PROSPERO).

Variable of interest

This study aimed to assess the TyG index in COVID-19 patients.

Eligibility criteria

Type of studies

This systematic review included all observational studies (cross sectional, case control, cohort) dating up to 2024 years of publication and written in English. In contrast, studies falling within the areas of in vitro or in silico studies, interventional studies and belonging to the category of reviews, case reports, case series, conference abstracts, book sections, and commentaries/ editorial were excluded.

Participants

The participants of the preliminary studies were patients diagnosed with COVID-19 and TyG index levels recorded. COVID-19 patients either severe or deceased and aged ≥18 years old were included. Participants being pregnant or breastfeeding were excluded.

Outcome of interest

The primary outcomes of interest were TyG index levels in severe and deceased COVID-19 patients. Meanwhile, the secondary outcome of interest was the prognostic ability of TyG index in determining severity and mortality of COVID-19 patients.

Search strategy and study selection

A comprehensive literature search was performed to identify relevant studies across multiple electronic databases, including MEDLINE, ProQuest, Google Scholar, ScienceDirect and Cochrane Library. Two independent authors executed this search using our predetermined keywords for “COVID-19” and “Triglyceride Glucose Index”, with studies included up to 2024 (Supplementary table 1). Inclusion was based on participant, intervention, comparator, outcomes, time, setting, and study design criteria, as outlined in Supplementary table 2.

All identified studies were imported into Zotero reference manager software for duplicate removal, followed by titles and abstracts screening. Two authors independently assessed the studies, excluding those whose title and/or abstract were not relevant for this review. Full-text assessments were then conducted using the aforementioned eligibility criteria. Any discrepancies were resolved by a majority agreement.

Data collection process

The following data of included studies will be extracted independently: the name of primary author, country of origin, study design, study period, population sample sizes, age and gender of participants, severity of COVID-19 criteria, cut-off, cut-off determination, and outcome odds ratio. Where possible, missing levels (e.g., standard deviation) will be calculated from the available data (p-levels, t-levels, confidence intervals or standard errors). Study authors may be contacted to obtain important missing data.

Summary measures

TyG index levels were measured and reported as mean differences (MDs). The prognostic ability of TyG index in predicting severity and mortality were measured and reported as odds ratios (OR).

Assessment of risk of bias/Quality assessment

Two independent reviewers assessed each study using an adapted version of the Newcastle-Ottawa Scale (NOS) suitable for cross-sectional, case-control, and cohort designs [13]. This tool evaluates studies across three domains: selection, comparability, and outcome. Studies were classified into three quality categories based on their NOS scores, reflecting the potential bias level: very high risk of bias (0-3 points), high risk of bias (4-6 points), and low risk of bias (7-9 points), with higher scores indicating superior methodological quality. Any discrepancies in evaluations were resolved through team discussion until a consensus was reached.

Confidence in cumulative evidence

The Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) method was employed to assess the confidence level in cumulative evidence for each outcome [14]. This approach evaluates evidence quality based on factors such as methodological quality (within-study bias), directness of evidence, variability (heterogeneity), precision of effect estimates, and risk of publication bias. The overall quality of evidence was then classified as high, moderate, low, or very low.

Synthesis of results and statistical analysis

Continuous outcomes were summarized as mean differences (MDs) with 95% confidence intervals (CIs) for comparisons between groups, while proportional outcomes were expressed as odds ratios (ORs). The pooled data, heterogeneity among studies, and statistical power were analyzed using Review Manager (RevMan, version 5.4; Cochrane Collaboration), with results displayed in forest plots. A random-effects model was applied to the meta-analysis to account for variations in outcome measurement methods. When heterogeneity was minimal (I²=0%), a fixed-effects model was used instead. Heterogeneity was quantified with the I² statistic, with values below 25% indicating no substantial heterogeneity, 25%-50% as low, 51%-75% as moderate, and above 75% as high heterogeneity [15]. Statistical significance was set at p<0.05.

Result

Study selection

The selection process for the studies, along with the results, is illustrated in Fig. 1. Initially, 665 relevant studies were found using the search strategy. After removing duplicates, 634 studies remained, and 13 were selected after screening the titles and abstracts. Based on the criteria, 13 studies were further evaluated in full-text analyses. Eight studies did not meet the criteria, including 5 with no TyG index and 3 wrong study designs. Finally, 5 studies were included in the systematic review, and 4 of these were used in the meta-analysis.

Quality assessment

The five included studies were assessed using Newcastle-Ottawa Scale (NOS). One case control study, two cross sectional, and two cohort studies were shown to have good quality (Supplementary tables 3-5).

Confidence in cumulative evidence

Overall, studies were rated as good quality based on the NOS for case-control, cross-sectional, and cohort studies, indicating that potential biases were unlikely to substantially impact the findings. High heterogeneity was observed for the TyG index comparing severe versus mild COVID-19 cases, with an I² of 93%. For the TyG index comparing deceased versus surviving patients, moderate heterogeneity was noted, with an I² of 65%. This statistical heterogeneity, along with wide 95% confidence intervals across individual studies, may reflect some inconsistencies. Additionally, the effect estimates were based on a limited number of studies (five for cMPI and three for tdMPI), suggesting notable imprecision. However, no serious issues of indirectness or publication bias were detected that might influence the overall results. Given the limited number of included studies, publication bias was assessed qualitatively, and no unpublished studies were identified in the literature search, minimizing concerns of publication bias. Consequently, the quality of evidence was rated as moderate, as shown in table 1.

Characteristics of the included studies

All five observational studies meeting the inclusion criteria were incorporated into this review, encompassing a total of 5,412 patients. Of these, two studies were conducted in the Asian region (including countries such as China, Korea, Iran, and Pakistan), and one study was conducted in the Americas (Mexico). The most commonly referenced criteria for COVID-19 severity were the National Health Commission of China’s “Guidelines for Diagnosis and Treatment of COVID-19” (Trial Sixth Edition) and the Berlin criteria for severe acute respiratory distress syndrome (ARDS), though some studies did not specify their source for COVID-19 severity criteria. Only one study by Ren et al. [15] provided an optimal TyG index cutoff for severity at 8.5, while for mortality, Ren et al. [15] and Rohani-Rasaf et al. [6] suggested cutoffs between 8.77 and 9.6. Further details on study characteristics are summarized in table 2.

Meta-analysis results

The quantitative synthesis results for TyG index levels were shown in Figure 2.A and 2.B. Based on the results, there were significant differences of TyG index in severe COVID-19 cases compared to mild cases (p=0.0003) and TyG index in deceased compared to survivors (p=0.03). However, the between group comparison of TyG index levels demonstrated moderate (I²=65%) to high heterogeneity (I²=93%). The pooled odds ratio (OR) of TyG index in predicting severity and mortality were also shown to be statistically significant (p=0.0004 and p<0.04, respectively) with low heterogeneity (I²=36% and I²=41%, respectively) as shown in Figure 3A and 3B.

Discussion

The TyG index has been shown to closely correlate with various cardiometabolic conditions [19]. Numerous studies highlight that individuals with these conditions face a higher risk of severe COVID-19. Cardiometabolic syndrome can intensify the inflammatory response in COVID-19 due to its association with IR, which fosters inflammation and oxidative stress, worsening the clinical course of the disease [20]. The cytokine storm induced by COVID-19 often results in severe inflammation and endothelial dysfunction, particularly harmful to those with pre-existing conditions like IR and cardiovascular disease. This dysfunction can impair organ perfusion, heightening the risk of acute complications such as myocardial injury and thromboembolic events [21].

COVID-19 itself can disrupt lipid metabolism and induce or worsen IR through several mechanisms [18]. The cytokine storm, a powerful inflammatory response triggered by the virus, leads to systemic inflammation, which disrupts insulin signaling pathways. Inflammatory mediators activate serine kinases that phosphorylate insulin receptor substrates, impairing their function and resulting in IR [22]. Other mechanisms, such as direct viral effects on pancreatic β-cells or disruptions in hormonal regulation, may also contribute to this resistance [23].

The TyG index, which correlates with IR and metabolic disorders, is also linked to COVID-19 severity. This index, calculated from fasting triglyceride and glucose levels, is simple, cost-effective, and has shown good sensitivity and specificity [24]. Despite these benefits, it is not yet widely used in clinical practice [25]. The underlying mechanisms associated with COVID-19 also remain poorly understood.

Our meta-analysis has found higher TyG index values in severe and deceased COVID-19 patients, supporting similar findings in other diseases. One study reported a significant correlation between the TyG index and the severity of coronary artery disease (CAD), suggesting the TyG index may serve as an independent diagnostic marker for CAD severity [26]. Another study identified the TyG index as an independent predictor of poor outcomes, including mortality, in critically ill stroke patients, particularly those with ischemic strokes [27]. Severe ARDS in COVID-19 patients were also found to have higher TyG index compared with mild or moderate ARDS [16]. In patients with ARDS and COVID-19, IR is the primary cause of hyperglycemia. This contrasts with ARDS patients without COVID-19, where pancreatic beta-cell failure predominates [28].

This study also found that TyG index is a statistically significant predictor of COVID-19 severity and mortality. IR may exacerbate COVID-19 severity through pathways involving the angiotensin-converting enzyme 2 (ACE2) protein, systemic inflammation, pancreatic β-cell injury, and thrombosis [29]. Elevated pro-inflammatory cytokines could also contribute to the alterations in lipid profile by enhancing the expression of the scavenger receptor class B type 1 during COVID-19 infection [30]. Thus, elevated TyG index levels are associated with worsening COVID-19 outcomes.

In a study by Ren et al., a TyG index cut-off of 8.5 was identified as optimal for predicting COVID-19 mortality, with a sensitivity of 87.1% and a specificity of 38.2%, resulting in an AUROC of 0.687 [15]. Meanwhile, research by Rasaf et al. reported a TyG index cut-off of 8.77 in COVID-19 patients, with a sensitivity of 60.9%, a specificity of 63.5%, and an AUC of 0.596 [6]. In another study, Biter et al. found a different threshold, suggesting a TyG cut-off of 4.97 for nondiabetic COVID-19 patients experiencing myocardial injury, showing a sensitivity of 82% and a specificity of 66% [31]. These studies collectively indicate that TyG index levels offer valuable prognostic insight for assessing COVID-19 mortality risk.

Heterogeneity analysis

Heterogeneity was evaluated using the I² statistics showed very high heterogeneity (93%), indicating substantial variability in effect sizes among studies for comparison between severe vs mild COVID-19. This variation in heterogeneity might be due to differences in the number of participants and different definitions of the severity of COVID-19 criteria. The definition of severe COVID-19 cases differs among studies. Some define it by requiring mechanical ventilation, while others include criteria like respiratory rate or oxygen saturation. These varying definitions can lead to differences in study outcomes and interpretations that impact the heterogeneity. In contrast, the TyG index value of deceased compared to survivor groups showed lower heterogeneity with I²=65%, this might be due to gender composition between males and females being similar.

Publication bias analysis

Since our meta-analysis included fewer than 10 studies, using a funnel plot assessment could result in misinterpretation and unreliable conclusions. Therefore, we conducted a qualitative evaluation using the GRADE assessment, as detailed in table 3. Publication bias often occurs due to a preference among authors or publishers for studies with significant findings. In this review, all of the studies showed no serious risk of publication bias and the overall quality of evidence is moderate reported COVID-19 outcomes.

Strengths and limitations

To the best of our knowledge, this is the first comprehensive systematic review and meta-analysis on TyG index in COVID-19 patients. However, our study had some limitations. The number of studies were relatively small because the TyG index was not a routine assessment in COVID-19. High heterogeneity and the observational nature of data are important limitations of these findings.

Future direction

Future research could explore more the diagnostic accuracy of TyG index and determined the optimal cut-off value to predict the severity and mortality outcome of COVID-19 patients.

Conclusion

In this study, TyG index levels in COVID-19 were observed to be higher in severe and deceased cases. Significant pooled odds ratio of TyG index in severity and mortality COVID-19 cases were also demonstrated. Therefore, this index might exhibit potential as a valuable marker in determining the severity and mortality of COVID-19 patients.

Supplementary

Литература/References

1. Sharma A., Ahmad Farouk I., Lal S.K. COVID-19: a review on the novel coronavirus disease evolution, transmission, detection, control and prevention. Viruses. 2021; 13 (2): 202.

2. Zhu Y., Sharma L., Chang D. Pathophysiology and clinical management of coronavirus disease (COVID-19): a mini-review. Front Immunol. 2023; 14: 1116131.

3. Silaghi-Dumitrescu R., Patrascu I., Lehene M., Bercea I. Comorbidities of COVID-19 patients. Medicina (Kaunas). 2023; 59 (8): 1393.

4. Chen M., Zhu B., Chen D., Hu X., Xu X., Shen W.J., et al. COVID-19 may increase the risk of insulin resistance in adult patients without diabetes: a 6-month prospective study. Endocr Pract. 2021; 27 (8): 834-41.

5. Guerrero-Romero F., Simental-Mendía L.E., González-Ortiz M., Martínez-Abundis E., Ramos-Zavala M.G., Hernández-González S.O., 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): 3347-51.

6. Rohani-Rasaf M., Mirjalili K., Vatannejad A., Teimouri M. Are lipid ratios and triglyceride-glucose index associated with critical care outcomes in COVID-19 patients? PLoS One. 2022; 17 (8): e0272000.

7. Minh H.V., Tien H.A., Sinh C.T., Thang D.C., Chen C.H., Tay J.C., et al. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens (Greenwich). 2021; 23 (3): 529-37.

8. Ilias I., Diamantopoulos A., Pratikaki M., Botoula E., Jahaj E., Athanasiou N., et al. Glycemia, beta-cell function and sensitivity to insulin in mildly to critically ill Covid-19 patients. Medicina (Kaunas). 2021; 57 (1): 68.

9. Liu 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.

10. Zhang Y., Ding X., Hua B., Liu Q., Gao H., Chen H., et al. Predictive effect of triglyceride‑glucose index on clinical events in patients with type 2 diabetes mellitus and acute myocardial infarction: results from an observational cohort study in China. Cardiovasc Diabetol. 2021; 20 (1): 43.

11. Ling Q., Chen J., Liu X., Xu Y., Ma J., Yu P., et al. The triglyceride and glucose index and risk of nonalcoholic fatty liver disease: a dose-response meta-analysis. Front Endocrinol (Lausanne). 2022; 13: 1043169.

12. Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372: n71.

13. Wells G., Wells G., Shea B., Shea B., O’Connell D., Peterson J., et al. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. 2014. URL: https://www.semanticscholar.org/paper/The-Newcastle-Ottawa-Scale-(NOS)-for-Assessing-the-Wells-Wells/c293fb316b6176154c3fdbb8340a107d9c8c82bf (date of access November 27, 2024).

14. Guyatt G., Oxman A.D., Akl E.A., Kunz R., Vist G., Brozek J., et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. 2011; 64 (4): 383-94.

15. Ren H., Yang Y., Wang F., Yan Y., Shi X., Dong K., et al. Association of the insulin resistance marker TyG index with the severity and mortality of COVID-19. Cardiovasc Diabetol. 2020; 19 (1): 58.

16. Alvarado M., Campos-Campos L., Guerrero-Romero F., Simental-Mendía L.E. The triglycerides and glucose index is an independent risk factor for acute respiratory distress syndrome in patients with COVID-19. Metab Syndr Relat Disord. 2024; 22 (4): 276-80.

17. Ashraf F., Mehmood M., Aurang Zeb M., Rehman J., Siddiqui F.M. Evaluation of triglyceride-glucose index as a marker for severity in new onset diabetes in patients of COVID-19 pneumonia - a single center study. Access Microbiol [Electronic resource]. 2022. URL: https://www.microbiologyresearch.org/content/journal/acmi/10.1099/acmi.0.000548.v1 (date of access November 27, 2024).

18. Chang Y., Jeon J., Song T.J., Kim J. Association of triglyceride-glucose index with prognosis of COVID-19: a population-based study. J Infect Public Health. 2022; 15 (8): 837-44.

19. Araújo S.P., Juvanhol L.L., Bressan J., Hermsdorff H.H.M. Triglyceride glucose index: a new biomarker in predicting cardiovascular risk. Prev Med Rep. 2022; 29: 101941.

20. Frere J.J., tenOever B.R. Cardiometabolic syndrome - an emergent feature of long COVID? Nat Rev Immunol. 2022; 22 (7): 399-400.

21. Al Mahmeed W., Al-Rasadi K., Banerjee Y., Ceriello A., Cosentino F., Galia M., et al. Promoting a syndemic approach for cardiometabolic disease management during COVID-19: The CAPISCO International Expert Panel. Front Cardiovasc Med. 2021; 8: 787761.

22. Santos A., Magro D.O., Evangelista-Poderoso R., Saad M.J.A. Diabetes, obesity, and insulin resistance in COVID-19: molecular interrelationship and therapeutic implications. Diabetol Metab Syndr. 2021; 13 (1): 23.

23. Grubišić B., Švitek L., Ormanac K., Sabo D., Mihaljević I., Bilić-Ćurčić I., et al. Molecular mechanisms responsible for diabetogenic effects of COVID-19 infection-induction of autoimmune dysregulation and metabolic disturbances. Int J Mol Sci. 2023; 24 (14): 11576.

24. Lopez-Jaramillo P., Gomez-Arbelaez D., Martinez-Bello D., Abat M.E.M., Alhabib K.F., Avezum Á., 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): e23-33.

25. Toh E.M.S., Lim A.Y.L., Ming C., Yeo L.L.L., Sia C.H., Tan B.W.Q., et al. Association of triglyceride-glucose index with clinical outcomes in patients with acute ischemic stroke receiving intravenous thrombolysis. Sci Rep. 2022; 12 (1): 1596.

26. Tang L., Xu X., Chen M., Li J., Pu X. Association of triglyceride-glucose index with severity of coronary artery disease among male patients. Sci Rep. 2024; 14 (1): 20342.

27. Zhang B., Liu L., Ruan H., Zhu Q., Yu D., Yang Y., et al. Triglyceride-glucose index linked to hospital mortality in critically ill stroke: an observational multicentre study on eICU database. Front Med [Electronic resource]. 2020; 7: 591036. URL: https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2020.591036/full (date of access November 27, 2024).

28. Reiterer M., Rajan M., Gómez-Banoy N., Lau J.D., Gomez-Escobar L.G., Ma L., et al. Hyperglycemia in acute COVID-19 is characterized by insulin resistance and adipose tissue infectivity by SARS-CoV-2. Cell Metab. 2021; 33 (11): 2174-88.e5.

29. Gangadharan C., Ahluwalia R., Sigamani A. Diabetes and COVID-19: role of insulin resistance as a risk factor for COVID-19 severity. World J Diabetes. 2021; 12 (9): 1550-62.

30. Zinellu A., Paliogiannis P., Fois A.G., Solidoro P., Carru C., Mangoni A.A. Cholesterol and triglyceride concentrations, COVID-19 severity, and mortality: a systematic review and meta-analysis with meta-regression. Front Public Health. 2021; 9: 705916.

31. Biter H.İ., Kalyoncuoğlu M., Tosu A.R., Çakal S., Apaydın Z., Gümüşdağ A., et al. Prognostic value of the TyG index for in-hospital mortality in nondiabetic COVID-19 patients with myocardial injury. Rev Assoc Med Bras (1992). 2022; 68 (9): 1297-302.

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