Association Between Triglyceride-Glucose Index and Diabetic Retinopathy: A Meta-Analysis

Abstract

The objective of this study was to assess the relationship between the triglyceride-glucose (TyG) index, a recently proposed marker of insulin resistance, and the occurrence of diabetic retinopathy (DR), a complication associated with cardiovascular risk. This systematic review and meta-analysis aimed to evaluate the association between the TyG index and DR. To achieve the objective of the meta-analysis, an extensive search was conducted on databases such as PubMed, Embase, and Web of Science to identify observational studies with longitudinal follow-up. Random-effects models were employed to combine the findings, taking into account the potential influence of heterogeneity. Twelve observational studies from 11 reports were included in the meta-analysis, which involved 16 259 patients with type 2 diabetes (T2D). Among them, 4302 (26.5%) were diagnosed as DR. Pooled results showed that a higher TyG index was associated with a higher risk of DR [odds ratio (OR) for the fourth versus the first quartile of TyG index: 1.91, 95% confidence interval (CI): 1.44 to 2.53, p<0.001; I2=72%]. Meta-analysis of TyG index analyzed in continuous variable showed consistent results (OR for per 1 unit increment of TyG index: 1.41, 95% CI: 1.08 to 1.86, p=0.01; I2=82%). Subgroup analysis showed that adjustment of HbA1c or the duration of diabetes did not significantly affect the results (p for subgroup difference all>0.05). In conclusion, a high TyG index was associated with the risk of DR in T2D patients.

Keywords

triglyceride-glucose index - diabetic retinopathy - type 2 diabetes - microvascular - meta-analysis

Introduction

Individuals who have been diagnosed with diabetes are more prone to develop retinopathy, a condition that can lead to severe visual impairment [1] [2]. Additionally, the presence of diabetic retinopathy (DR) has been associated with an increased probability of experiencing adverse outcomes such as non-fatal myocardial infarction, stroke, and cardiovascular mortality in individuals with diabetes [3] [4] [5]. Considering the growing number of people affected by diabetes, it is crucial to identify clinical factors and indicators that are associated with the likelihood of developing DR [6]. Patients diagnosed with type 2 diabetes (T2D) exhibit insulin resistance (IR), a significant factor implicated in the etiology of various vascular complications associated with diabetes [7] [8]. In terms of pathophysiology, IR has been associated with chronic inflammation and oxidative stress [9], both of which contribute to the progression of DR [10] [11]. Recent research has suggested the triglyceride-glucose (TyG) index, a metric derived from fasting blood glucose and triglyceride concentrations, as a reliable indicator for evaluating IR [12] [13]. Observational studies have also indicated a correlation between the TyG index and an elevated probability of macrovascular complications in both the general population and individuals with diabetes [14] [15]. However, the exact relationship between the TyG index and DR remains incompletely understood. Consequently, the objective of this systematic review and meta-analysis was to assess the connection between the TyG index and DR.

Materials and Methods

The research followed the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) guidelines [16] and the Cochrane Handbook [17] consistently during the phases of planning, execution, and documentation.

Inclusion and exclusion criteria of studies

The development of inclusion criteria adhered to the PICOS recommendations and aligned with the objective of the meta-analysis.

P (patients): Patients with T2D.

I (exposure): TyG index was measured and a high TyG index was considered as the exposure. The TyG index was calculated as Ln [fasting triglyceride (mg/dl) × fasting glucose (mg/dl)]/2 [18]. Cutoff for defining a high TyG index was consistent with the value used among the included studies.

C (control): Patients with a low TyG index.

O (outcomes): The prevalence or the incidence of DR compared between patients with high versus low TyG index if TyG index was analyzed as a categorized variable or per 1-unit increment of TyG index if TyG index was analyzed as a continuous variable.

S (study design): This study included observational studies, such as case-control, cross-sectional, or cohort studies. Excluded from the meta-analysis were literature reviews, editorials, meta-analyses, and studies that did not involve patients diagnosed with T2D, did not assess the TyG index as an exposure variable, or did not report the outcome of DR. In instances where there was a duplication of patient populations, the study with the most extensive sample size was incorporated into the meta-analysis.

Search of databases

Studies relevant to the objective of the meta-analysis was identified by search of electronic databases, namely PubMed, Embase, and Web of Science, encompassing the period from inception to August 15, 2023. The search strategy employed relevant terms pertaining to the subject matter of our investigation, aiming to identify studies published within this timeframe, which included: (1) “triglyceride-glucose index” OR “triglyceride and glucose index” OR “TyG index” OR “triglyceride glucose index” OR “triacylglycerol glucose index” OR “TyGI”; and (2) “retinal” OR “retina” OR “retinopathy”. Only studies that met the criteria of being published as full-length articles in English or Chinese and appearing in peer-reviewed journals were included in our analysis. Additionally, during our manual screening process, we thoroughly examined the references cited in relevant original and review articles to identify any potentially relevant studies.

Data extraction and quality evaluation

Two authors conducted literature searches, collected data, and assessed the quality of the studies separately. In instances where inconsistencies arose, the authors engaged in discussions to reach a consensus. The analysis of the studies involved gathering data pertaining to study details, design attributes, sample size, patient demographics, approaches to TyG index analysis (categorized or continuous variable), duration of follow-up in cohort studies, methods for diagnosing DR, number of patients with DR in each study, and potential confounding factors adjusted when the association between TyG index and DR were analyzed. The quality of the study was evaluated using the Newcastle-Ottawa Scale (NOS) [19]. This scale assesses the quality of cohort studies based on three dimensions: the selection of study groups, the comparability of these groups, and the ascertainment of the outcome of interest. For case-control and cross-sectional studies, this scale assess the quality of the studies also via three aspects: selection and definitions of cases and controls, comparability between cases and controls, and ascertainment of expose between cases and controls. The NOS varied between one to nine stars, with a higher star indicating a better study quality.

Statistics

Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were utilized as the variables to assess the relationship between TyG index and DR in patients with T2D. In order to stabilize and standardize the variance, a logarithmic transformation was implemented on the OR and its corresponding standard error in each study [20]. The Cochrane Q test and the I² statistic [21] were utilized to assess between-study heterogeneity. A value of I² exceeding 50% signifies the existence of substantial heterogeneity among the studies. The random-effects model was employed for synthesizing the results, as it is acknowledged for its ability to accommodate potential heterogeneity [17]. Sensitivity analysis excluding one dataset at a time was performed to evaluate the robustness of the finding. Additionally, predefined subgroup analysis was conducted to explore whether the results were consistent between studies with and without the adjustment of glycolated hemoglobin (HbA1c) or duration of diabetes. Publication bias was estimated using a funnel plot, which involved visual assessments of symmetry, as well as Egger’s regression asymmetry test [22]. The statistical analyses were conducted using RevMan (Version 5.1; Cochrane Collaboration, Oxford, UK) and Stata software (version 12.0; Stata Corporation, College Station, TX, USA).

Results

Database search and study retrieval

[Fig. 1] illustrates the procedure employed for conducting the literature search and study retrieval. Initially, a total of 186 records were acquired from the designated database, and subsequently, 35 duplicate entries were eliminated. Upon scrutinizing the titles and abstracts, an additional 130 studies were excluded due to their incompatibility with the objectives of the meta-analysis. Following comprehensive evaluations of the full texts of 21 studies, ten were excluded based on the rationales outlined in [Fig. 1]. Consequently, eleven records were deemed suitable for the subsequent meta-analysis [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33].

Fig. 1 Flowchart of database search and study inclusion.

Study characteristics

Since one reports included a cross-sectional study (Neelam 2023a) and a prospective cohort study (Neelam 2023b), these studies were included independently in the meta-analysis [32]. Overall, 12 studies from 11 reports were available, which included one case control study [27], nine cross-sectional studies [23] [24] [25] [26] [29] [30] [31] [32] [33], one retrospective [28] and one prospective cohort study [32]. The characteristic of the studies are summarized in [Table 1]. These studies were conducted in India, China, Singapore, Egypt, and the United States, and were published within the timeframe of 2019 to 2023. Overall, 16 259 patients with T2D were included. The mean ages of the patients were 51 to 64 years, and the proportion of men was 41 to 87%. The TyG index was analyzed as categorized variables [23] [24] [26] [27] [28] [30] [33], or continuous variables [25] [28] [29] [31] [32] [33] among the included studies. The diagnosis of DR was achieved with fundus photograph, and 4302 (26.5%) patients were diagnosed as DR. The included studies demonstrated varying degrees of adjustment for age, gender, body mass index, comorbidities, duration of diabetes, and HbA1c when examining the relationship between the TyG index and DR. The NOS of these studies ranged from seven to nine, indicating their high quality ([Table 2]).

Table 1 Characteristics of the included studies.

Study [Ref]	Location	Design	No. of participants	Mean age (years)	Male (%)	TyG index analysis	Follow-up duration (years)	Methods for DR diagnosis	No. of patients with DR	Variables adjusted
Hameed 2019 [23]	India	CS	416	55.3	41.6	Q4:Q1	NA	Fundus photograph	70	Age, sex, duration of diabetes, HbA1C, SBP, DBP, TC, HDL-c, WC and BMI
Chiu 2020 [24]	China	CS	1990	64.1	43	Q4:Q1	NA	Ophthalmic examinations including fundoscopy	694	Age, sex, PP, BMI, WC, HbA1c, TC, eGFR, and statin or fibrate use
Yao 2021 [27]	China	CC	2112	56.1	57.9	Q4:Q1	NA	Fundus photograph	596	Age, sex, duration of diabetes, use of antidiabetic agents, HR, SBP, PP, height, weight, BMI, HbA1c, and TC
Srinivasan 2021 [26]	India	CS	1413	56.3	53	Q4:Q1	NA	Fundus photograph	255	Age, sex, smoking, and BP
Pan 2021 [25]	China	CS	4721	59.6	53.6	Continuous	NA	Fundus photograph	1095	Age, sex, HbA1c, smoking habit, and BMI
Li 2022 [28]	China	RC	1153	58.9	86.5	Q4:Q1 and continuous	6.6	Fundus photograph	140	Age, sex, duration of diabetes, smoking, alcohol drinking, exercise, SBP, HDL-c, BMI, and HbA1c
Wang 2022 [30]	China	CS	1061	59.4	81.2	Q4:Q1	NA	Fundus photograph	275	Age, sex, HbA1c, smoking habit, duration of diabetes, SBP, UA, and BMI
Shan 2022 [29]	China	CS	456	53.5	64	Continuous	NA	Fundus photograph	217	Age, sex, duration of diabetes, smoking, alcohol drinking, exercise, SBP, TC, HDL-c, LDL-c, eGFR, BMI, HbA1c, and concurrent medications
Neelam 2023a [32]	Singapore	CS	1339	59.2	55.9	Continuous	NA	Fundus photograph	404	Age, sex, ethnicity, duration of diabetes, BMI, eGFR, SBP, and WHR, uACR
Neelam 2023b [32]	Singapore	PC	210	51.1	57.2	Continuous	3.2	Fundus photograph	30	Age, sex, ethnicity, duration of diabetes, BMI, eGFR, and uACR
Kassab 2023 [31]	Egypt	CS	500	54	45	Continuous	NA	Fundus photograph	263	Age, sex, duration of diabetes, HbA1c, and uACR
Zhou 2023 [33]	USA	CS	888	62.2	50	Q4:Q1 and continuous	NA	Fundus photograph	263	Age, sex, race, education, income, HDL-c, LDL-c, TC, and HTN

TyG: Triglyceride-glucose; DR: Diabetic retinopathy; CS: Cross-sectional; CC: Case-control; RC: Retrospective cohort; PC: Prospective cohort; Q4:Q1: The fourth versus the first quartile; NA: Not applicable; HbA1c: Glycolated hemoglobin; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; TC: Total cholesterol; HDL-c: High-density lipoprotein cholesterol; LDL-c: Low-density lipoprotein cholesterol; WC: Waist circumference; BMI: Body mass index; eGFR: Estimated glomerular filtrating rate; PP: Pulse rate; HR: Heart rate; WHR: Waist-hip ratio; UA: Uric acid; uACR: Urine albumin creatinine ratio; HTN: Hypertension.

Table 2 Study quality evaluation via the Newcastle-Ottawa scale.

Cohort study [Ref]	Representativeness of the exposed cohort	Selection of the non-exposed cohort	Ascertainment of exposure	Outcome not present at baseline	Control for age and sex	Control for other confounding factors	Assessment of outcome	Enough long follow-up duration	Adequacy of follow-up of cohorts	Total
Li 2022 [28]	0	1	1	1	1	1	1	1	1	8
Neelam 2023b [32]	1	1	1	1	1	1	1	0	1	8
Cross-sectional study [Ref]	Adequate definition of cases	Representativeness of cases	Selection of controls	Definition of controls	Control for age and sex	Control for other confounders	Exposure ascertainment	Same methods for events ascertainment	Non-response rates	Total
Hameed 2019 [23]	1	0	1	1	1	1	1	1	1	8
Chiu 2020 [24]	1	0	1	1	1	1	1	1	1	8
Yao 2021 [27]	1	1	1	1	1	1	1	1	1	9
Srinivasan 2021 [26]	1	0	1	1	1	0	1	1	1	7
Pan 2021 [25]	1	0	1	1	1	1	1	1	1	8
Wang 2022 [30]	1	1	1	1	1	1	1	1	1	9
Shan 2022 [29]	1	1	1	1	1	1	1	1	1	9
Neelam 2023a [32]	1	1	1	1	1	1	1	1	1	9
Kassab 2023 [31]	1	0	1	1	1	1	1	1	1	8
Zhou 2023 [33]	1	0	1	1	1	0	1	1	1	7

Results of meta-analysis

Pooled results of seven studies [23] [24] [26] [27] [28] [30] [33] showed that a higher TyG index was associated with a higher risk of DR (OR for the fourth versus the first quartile of TyG index: 1.91, 95% CI: 1.44 to 2.53, p<0.001; I²=72%; [Fig. 2a]). Sensitivity analysis by excluding one dataset at a time showed consistent result (OR: 1.73 to 2.12, p<0.05). In addition, subgroup analyses showed similar results in studies with and without adjustment of HbA1c (p for subgroup difference=0.43, [Fig. 2b]) and in studies with and without adjustment of the duration of diabetes (p for subgroup difference=0.08, [Fig. 2c]).

Fig. 2 Forest plots for the meta-analyses regarding the association between TyG index analyzed as categorized variables and the risk of DR. a: Overall meta-analysis; b: Subgroup analysis according to the adjustment of HbA1c; and c: Subgroup analysis according to the adjustment of diabetes duration.

Seven studies analyzed TyG index as continuous variables [25] [28] [29] [31] [32] [33]. Since two studies reported datasets independently according to sex [28] and HbA1c of the patients [32], these datasets were included into the meta-analysis separately. Meta-analysis of TyG index analyzed in continuous variable showed consistent results (OR for per 1 unit increment of TyG index: 1.41, 95% CI: 1.08 to 1.86, p=0.01; I²=82%; [Fig. 3a]). Sensitivity analysis by excluding one dataset at a time did not significantly affect the results (OR: 1.32 to 1.54, p<0.05). Subgroup analysis suggested that adjustment of HbA1c (p for subgroup difference=0.42, [Fig. 3b]), or duration of diabetes (p for subgroup difference=0.81, [Fig. 3c]) did not significantly affect the results.

Fig. 3 Forest plots for the meta-analyses regarding the association between TyG index analyzed as continuous variables and the risk of DR. a: Overall meta-analysis; b: Subgroup analysis according to the adjustment of HbA1c; and c: Subgroup analysis according to the adjustment of diabetes duration.

Publication bias

The funnel plots depicting the meta-analyses of the association between TyG index DR with TyG index presented as categorized and continuous variables are presented in [Fig. 4a, b]. Upon visual inspection, the plots exhibit symmetrical patterns, indicating a minimal presence of publication bias. Furthermore, the application of Egger’s regression tests yielded p-values of 0.22 and 0.81, further supporting the notion of a low probability of publication bias.

Fig. 4 Funnel plots for the publication bias underlying the meta-analyses of TyG index and the risk of DR. a: Funnel plots for the meta-analysis with TyG index analyzed as categorized variable; and b: Funnel plots for the meta-analysis with TyG index analyzed as continuous variable.

Discussion

This meta-analysis included data from 11 observational studies and demonstrated a significant positive correlation between a high TyG index and the risk of DR in individuals with T2D. The consistency of these results was observed across studies that categorized the TyG index as both continuous and categorical variables, as well as in sensitivity and subgroup analyses that were pre-defined. In summary, these findings suggest that a high TyG index is associated with an increased risk of DR, and measuring the TyG index may be crucial in evaluating the risk of DR in T2D patients.

To the best of our knowledge, this study potentially represents the inaugural meta-analysis investigating the correlation between the TyG index and DR in individuals with T2D. Several methodological strengths are evident in this meta-analysis. We conducted a comprehensive search across three widely utilized literature databases, retrieving the most recent publications that examined the relationship between the TyG index and DR. Furthermore, we conducted two separate meta-analyses, analyzing the TyG index as both a categorical and continuous variable, and the consistent outcomes further substantiated the reliability of our findings. All of the studies included in the analysis utilized data from multivariate analyses, suggesting a potential independent association between a high TyG index and DR. Additional sensitivity analyses, in which one dataset was excluded at a time, indicated that the results were not primarily influenced by any individual study, thus demonstrating the robustness of the findings. Furthermore, subgroup analysis based on HbA1c levels and duration of diabetes yielded similar results, indicating that the relationship between the TyG index and DR is likely to be unaffected by these two factors. This is important as it has been confirmed that both HbA1c [34] [35] and duration of diabetes [36] are important risk factors of DR in patients with T2D. These findings collectively provide evidence of a correlation between IR and DR in individuals diagnosed with T2D. The hyperinsulinemic-euglycemic clamp test, traditionally considered the most accurate method for assessing IR, is deemed impractical for routine clinical use due to its complexity and high cost [37]. In contrast, the TyG index, a recently proposed surrogate index for IR, offers a simple and convenient alternative that can be calculated using routine biochemical analysis of triglyceride and fasting blood glucose levels upon admission. When compared to the hyperinsulinemic-euglycemic clamp test, the TyG index provides a cost-effective and efficient approach to measuring IR. Moreover, prior research has effectively confirmed the capacity of the TyG index to precisely indicate the extent of IR. A preliminary investigation has showcased the effectiveness of the TyG index in identifying individuals with IR across a varied population encompassing healthy volunteers, obese individuals, and patients with diabetes [38]. The TyG index demonstrated a notable sensitivity (96.5%) and specificity (85.0%) when compared to the hyperinsulinemic-euglycemic clamp test [38]. Furthermore, a proposition has been made suggesting that the TyG index may exhibit superior performance compared to the homeostatic model assessment in evaluating insulin resistance (HOMA-IR) [39], thereby substantiating its practical applicability as a prognostic indicator for individuals experiencing acute ischemic stroke within real-world clinical environments. In clinical settings, both HOMA-IR and TyG index are alternative indices of IR. Among them, HOMA-IR is frequently utilized in clinical contexts [12], while the TyG index has been suggested as a reliable surrogate marker for IR [40]. Despite the lack of consensus on the optimal index for indicating IR, a previous study has indicated that the TyG index exhibits a stronger correlation with the results of the hyperglycemic clamp test compared to HOMA-IR [39]. The TyG index, a recently proposed surrogate index for IR [41], can be readily and conveniently computed using routine biochemical analysis of triglyceride and fasting blood glucose levels upon admission, without requiring the use of insulin assays. In comparison to the gold-standard hyperinsulinemic-euglycemic clamp test, the TyG index offers a cost-effective and efficient means of measuring IR. The findings of the meta-analysis additionally broaden the scope of the TyG index as an indicator for assessing the risk of DR in patients diagnosed with T2D.

The mechanisms underlying the association between TyG index and DR may be multifactorial. By definition, IR is primarily characterized by diminished insulin sensitivity, a common feature in numerous metabolic disorders. It is widely observed in diabetes, and extensive research has substantiated that chronic low-grade inflammation resulting from obesity exacerbates IR, thereby contributing to the onset and progression of diabetic complications [42]. Multiple investigations conducted on both diabetic patients and animal models have demonstrated that the diabetic environment induces heightened local production of inflammatory molecules, including cytokines, chemokines, and growth factors [43]. Moreover, IR has also been related to the enhanced degree of oxidative stress [44]. Oxidative stress in DR can be attributed to and caused by metabolic abnormalities resulting from hyperglycemia. These abnormalities involve the heightened flow of the polyol pathway and hexosamine pathway, the over-activation of protein kinase C isoforms, and the buildup of advanced glycation end products [45]. As a consequence, the excessive accumulation of reactive oxygen species leads to mitochondrial impairment, cellular apoptosis, inflammation, lipid peroxidation, and structural and functional changes in the retina [45]. These mechanisms have been widely recognized as crucial pathological factors underlying the development of DR [45]. Studies are warranted to determine the key molecular pathways underlying the association between IR and DR.

This study possesses several limitations that warrant acknowledgement. First, a significant proportion of the studies incorporated in this analysis were cross-sectional and retrospective in nature, thereby introducing the potential for selection and recall biases. In order to authenticate the findings, it is imperative to undertake extensive prospective studies on a large scale. Second, the cutoffs of TyG index varied among the included studies, efforts are still needed to clarify the optimal cutoff of TyG index for predicting the risk of DR. Indeed, determining the optimal cutoff of TyG index for indicating IR has been recognized as a common challenge of studies on this topic, which deserve further investigations [12]. Additionally, despite the utilization of multivariate regression analyses in the included studies to ascertain the association, it is crucial to recognize the impact of other confounding factors. Ultimately, due to the nature of this meta-analysis being based on observational studies, it is not possible to establish a causal relationship between a high TyG index and the heightened risk DR in patients with T2D.

Conclusions

The results of the meta-analysis suggest an association between a high TyG index and the increased risk of DR in patients with T2D. However, further prospective studies are necessary to confirm these findings. Due to the fact that measuring TyG index in real-world clinical practice is convenient and inexpensive, these results of the meta-analysis support the potential use of measuring TyG index for assessing the risk of DR.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Publication History

Received: 17 November 2023

Accepted after revision: 26 February 2024

Article published online:
26 April 2024