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Association of triglyceride-glucose index with prognosis of COVID-19: A population-based study

Background Triglyceride-glucose (TyG) index is a simple and reliable surrogate marker for insulin resistance. Epidemiology studies have shown that insulin resistance is a risk factor for various infectious diseases. We evaluated the prognostic value of TyG index measured before the COVID-19 infection in COVID-19 infected patients. Methods From a nationwide COVID-19 cohort dataset in Korea, we included COVID-19 patients diagnosed between Jan and Jun 2020. Based on the nationwide health screening data between 2015 and 2018, TyG index was calculated as ln [triglyceride (mg/dL) × fasting glucose level (mg/dL)/2]. Primary outcome is development of severe complications of COVID-19 defined as composite of mechanical ventilation, intensive care unit care, high-flow oxygen therapy, and mortality within two months after the diagnosis of COVID-19. Results This study included 3887 patients with COVID-19 confirmed by reverse transcription polymerase chain reaction. Mean ± standard deviation of TyG index was 8.54 ± 0.61. Severe complications of COVID-19 were noted in 289 (7.44%) patients. In the multivariate logistic regression, TyG index was positively associated with severe complications of COVID-19 (adjusted odds ratio: 1.42, 95% confidence interval [1.12–1.79]). Conclusions In COVID-19 infected patients, high TyG index was associated with increased risk for severe complications. TyG index might be useful predictor for the severity of COVID-19 infection.


Introduction
Coronavirus disease 2019 (COVID- 19) is an infectious disease caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. The global pandemic of COVID-19 is still ongoing and the most serious threat to public health in a century. Although most COVID-19 patients show good recovery, a number of patients, especially with underlying comorbidities, have critical complications including respiratory failure that can eventually lead to death [2]. Epidemiological studies have documented that the presence and number of comorbidities correlated with poorer outcome with COVID-19 infection [3]. Along with other comorbidities, diabetes mellitus was considered as a possible risk factor for COVID-19 infection and related complications [4].
Insulin resistance is the common metabolic disorder often associated with type 2 diabetes, representing a pathological condition in which the body's cells become resistant to the hormone insulin [5]. Insulin involves multiple molecular and pathophysiological pathways, and pathologic condition of insulin resistance is closely linked to not only diabetes mellitus, but also to various medical conditions such as hypertension, dyslipidemia, liver disease, cardiovascular disease, neurodegenerative disease, malignancy, obesity, inflammatory and infectious disease [6,7]. In critically ill patients, insulin resistance has been reported to be correlated with the severity of condition and poor clinical outcome [8][9][10]. The triglyceride-glucose (TyG) index is a simple surrogate marker of insulin resistance, calculated using fasting triglyceride and fasting blood glucose [11]. High TyG index is significant indicator of adverse prognosis in general population as well as in patients with various diseases [12,13]. We investigated whether TyG index measured before the COVID-19 infection has a prognostic value to identify those at high-risk for severe complications in COVID-19 infected patients.

Study design and participants
We performed a retrospective observational study for prognosis of COVID-19 infection according to TyG index with a nationwide population-based COVID-19 dataset in South Korea. South Korea has a single-payer public healthcare system, called by National Health Insurance Service (NHIS). NHIS has an information for hospital visit, diagnoses (recorded based on International Statistical Classification of Diseases and Related Health Problems 10th revision), medical procedure, prescription, and mortality of the whole Korean population [14]. Recently, the Korea Centers for Disease Control and Prevention, and the Ministry of Health and Welfare, South Korea released a nationwide COVID-19-related dataset, which includes the data of Korean citizens who underwent real-time reverse transcription polymerase chain reaction (RT-PCR) assays with nasal and pharyngeal swabs to COVID-19 from January 1 to June 4, 2020 [15]. The COVID-19-related dataset contains data on result of COVID-19 test (positive or negative), national health check-up program (2015-2018), health claims data, and mortality of all people who underwent RT-PCR tests for the COVID-19 [16]. The real-time PT PCR assays kit followed the WHO guideline and was validated by the Korea Centers for Disease Control and Prevention [17].

TyG index and covariates
As a public institution, the NHIS provides free health checkups to all Koreans aged ≥ 40 years every two years. The free health checkups program consisted of physical examination of blood pressure, body mass index (BMI), lifestyle survey (medical history, physical activity, smoking habit, alcohol consumption), and laboratory tests including fasting glucose, triglyceride, cholesterol levels [18]. The TyG index was calculated as ln [triglyceride (mg/dL) × fasting glucose level (mg/dL)/2], based on the blood chemistry data measured in the free nationwide health checkups program before the COVID-19 infection (2015-2018) [19]. In order to avoid errors due to extreme values, when the measured value ≥ the 99 percentile value or ≤ the 1 percentile value, they are replaced with the 99% percentile value or the 1 percentile value, subsequently. If study participants underwent twice or more health check-up during the period, the most recent data was used in this study.

Statistical analyses
For comparison of continuous and categorical variables between groups, independent t-tests and chi-square tests were performed, respectively. To evaluate the relationship between TyG index and severe complications of COVID-19 infection, we used univariable and multivariable logistic regression analyses. To detect and measure the collinearity in a multivariable regression model, we calculated variance inflation factor (VIF) for each variable. Small value of VIF indicates low correlation among variables and it is believed acceptable if it is less than 5 [26]. The subgroup analyses were conducted according to the sex, age (dichotomized more than 60 years old), history of hypertension, and history of diabetes mellitus. The results were presented as odds ratio (OR) and 95% confidence interval (CI). To help the understanding of the association between TyG index and COVID-19 outcomes, we draw a spline curve representing OR for primary outcome according to the continuous level of TyG index. All statistics were executed using R software, version 3.3.3 (R Foundation for Statistical Computing, Vienna, Austria), and SAS 9.4 version (SAS Inc., Cary, NC, USA). Two-sided P-values less than 0.05 were considered significant.

Participants and demographics
The Korean nationwide COVID-19 cohort consisted of 212,678 participants aged ≥ 20 years who received RT-PCR test for COVID-19. Among them, 7713 patients who tested positive in the COVID-19 PCR test were included in this study, excluding those who tested negative in the COVID-19 PCR test (n = 204,965). Among these 7713 patients, there were 3934 patients who had an available data for national health screening program in 2015-2018. We additionally excluded 47 patients with incomplete data for analysis. Finally, 3887 patients with confirmed COVID-19 diagnosis were included in this study (Fig. 1).
Distribution of TyG index according to the presence of severe complications of COVID-19 are demonstrated in Fig. 2. A spline curve demonstrated the positive association between TyG index and severe complications of COVID-19 (Fig. 3). In subgroup analysis by sex, age, hypertension, and diabetes mellitus, the positive association of TyG index with severe complications of COVID-19 was consistently noted (all p for interaction > 0.05, Fig. 4).

Discussion
The key finding of our study based on a nationwide populationbased COVID-database in South Korea is that high TyG index, which represents insulin resistance, was associated with the increased risk for severe complications of COVID-19 patients.
In a previous hospital-based study with 151 COVID-19 positive patients, the TyG index levels were significantly higher in the patients with severe complication or mortality [27]. Among patients hospitalized for COVID-19, those with critical COVID-19 presented higher levels of fasting glucose, triglycerides, both of which are components of the TyG index [28]. Another hospital-based casecontrol study with 43 patients revealed that biochemical markers of insulin resistance may be related with severe complications of COVID-19 [29]. Our result is in lines with these previous studies for the potential role of TyG index and markers of insulin resistance as an indicator for the severe complications of COVID-19 [27]. Unlike the prior studies, we calculated TyG index based on a nationwide health screening data measured before the COVID-19 infection. It has been known that COVID-19 infection itself can induce insulin resistance and lipid metabolic dysregulation [30,31]. The previous case series demonstrated that COVID-19 infection triggered uncontrolled hyperglycemia and insulin resistance in acute phase [32][33][34]. Therefore, worse prognosis of COVID-19 patients with high TyG index measured in acute phase might be due to that critical illness with COVID-19 infection itself cause further insulin resistance [35]. We found that the TyG index measured before the COVID-19 infection also had a significant association with COVID-19 prognosis. This result added evidence that underlying insulin resistance in health condition is one of determinant for the prognosis of COVID-19 infection. Regarding the interplay between inflammation and insulin resistance, baseline insulin resistance may hold key to improving immune responses against COVID-19 [36]. In addition, our study showed that TyG index easily calculated from a pre-existing blood test can serve as a simple tool to predict the risk of severe complications from COVID-19 without additional test. It will be applicable to screening those at high-risk in general population who need to pay more attention to COVID-19 infection and determining priority group for COVID-19 vaccination.
Although our study is not a study for the mechanism, we may allow proposing hypotheses that may explain the association between the TyG index and severe COVID-19 outcome. TyG index was associated with metabolic syndrome, dyslipidemia, future development of diabetes mellitus, subclinical coronary artery disease, arterial stiffness, and occurrence of major cardiovascular events [11,37,38]. Epidemiologic data have documented that individuals with high TyG index are at increased risk for nonalcoholic fatty liver disease, which is also closely related to obesity and insulin resistance [39,40]. It has been established that the presence and number of these comorbidities in COVID-19 patients, which closely related with Data are represented as number of patients (%) or mean ± standard deviation. Q, quartile; HDL, High-density lipoprotein; LDL, Low-density lipoprotein; TyG index is calculated by the following formula: ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. a Primary outcome is composite of mechanical ventilation, intensive care unit care, high-flow oxygen therapy, and mortality within two months after COVID-19 diagnosis b P-value is derived from independent t-test or Chi's square test between patient groups according to primary outcome.   insulin resistance and TyG index, are associated with poor prognosis of COVID-19 [38,41,42]. Because TyG index is considered a good surrogate marker of the comorbidities, those with high TyG index may have more risk factors and worse clinical outcomes in COVID-19 patients [27].
In COVID-19 infection, immunologic response plays a key role to maintain health and pathogenesis [43]. Current evidence suggested that dysregulated immune response may be responsible for COVID-19 pathogenesis and related critical illness, including lymphopenia, neutrophilia, dysregulation of monocytes/macrophages, impaired type I interferon response, antibody-dependent enhancement, and cytokine storm [44,45]. Studies in insulin resistance have revealed a clear association between the chronic activation of pro-inflammatory signaling pathways/impaired immune response and decreased insulin sensitivity [46,47]. Macrophages chronically exposed to high levels of insulin result in sustained inflammatory cytokine production and altered response to pathogen [48]. It has become apparent that the impaired immune response through multiple pathways in insulin resistance can lead to systemic alterations contributing to excessive production of pro-inflammatory cytokines, also known as a cytokine storm [36,49]. Because hyperactivated inflammatory responses with cytokine storm occurring in COVID-19 are regarded major pathogenesis of critical illness with COVID-19 [50], chronic activation of pro-inflammatory signaling and altered immunologic response with insulin resistance, presented as high TG index, could be a predisposing factor for severe complications of COVID-19 [27]. Accordingly, viremia may be worsened by insulin resistance, and this increased risk of viremia may cause poor prognosis or morality after COVID-19 infection [51,52].
Our study has limitations. First, because our study had a retrospective cohort design, the causal relationship could not be proven. Second, in the health screening cohort, young people under the age of 40 could not be included, so there would be a selection bias. Third, it is difficult to generalize our results for overall ethnicity because our dataset consists of only the Korean general population. Fourth, because our dataset checked the lipid profile before the COVID-19 infection, there was a lack of the serial laboratory finding or the change of the lipid profile after COVID-19. Fifth, concurrent medications before and after COVID-19 infection might be a possible confounding factor on the prognosis, which were not evaluated in current study. Since the TyG index is the calculated product of fasting triglyceride level and fasting blood glucose level, it could be affected by the anti-diabetic or lipid-lowering agents taken at the time of health screening. Finally, since insulin levels were not measured in our nationwide screening dataset, we could not directly check the insulin resistance.
In conclusion, our study demonstrated that TyG index measured before the COVID-19 infection was significantly associated with increased risk for severe complications of COVID-19. TyG index, which is simply calculated from fasting triglyceride and fasting blood glucose, may be a useful prognostic biomarker in COVID-19 infection. Further study is need for the pathophysiologic linking between insulin resistance and COVID-19 adverse outcomes.

Funding
This study was supported by a new faculty research seed money grant of Yonsei University College of Medicine for 2022 (2022-32-0058). This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1I1A1A01060447 to JK, 2021R1F1A1048113 to T-JS, 2021R1I1A1A01059868 to YC). The funders had no role in study design, data collection and analysis, the decision to publish, or preparation of the manuscript.

Ethics approval
The NHIS data was fully anonymized for privacy protection. Due to the retrospective analysis based on the fully anonymized data from our dataset, this study was approved by the Institutional Review Board of our institution (Seoul Hospital Ewha Womans University College of Medicine 2020-10-021), and the requirement for informed consent was waived.

Data availability
The COVID-19 database used in the present study is available from the National Health Insurance Sharing Service [http://nhiss. nhis.or.kr/bd/ab/bdaba021eng.do], but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. To access the data, a complete application form, research proposal, and an approval document for research purposes from an institutional review board should be submitted to the NHIS for review by the inquiry committee of research support.

Declaration of Competing Interest
The authors declare no conflicts of interest.

Author's contributions
Tae-Jin Song and Yoonkyung Chang designed the research and drafted the manuscript. Jinkwon Kim and Jimin Jeon performed the statistical analysis and the interpretation of data. Tae-Jin Song and Jinkwon Kim participated in the interpretation of all data and the critical revision of the manuscript for intellectual content. All authors read and approved the manuscript.