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Correlation between triglyceride-glucose index and diabetic kidney disease risk in adults with type 1 diabetes mellitus

Diabetology & Metabolic Syndrome volume 16, Article number: 239 (2024) Cite this article

Abstract

Background

The triglyceride-glucose (TyG) index is identified as an alternative indicator of insulin resistance (IR) and is associated with macro- and micro-vascular diseases among patients with type 2 diabetes mellitus. The relationship between the TyG index and IR and its impact on diabetic kidney disease (DKD) remains unclear among adults with type 1 diabetes mellitus(T1DM).

Methods

This study comprised a cross-sectional analysis using data from the Guangdong T1DM Translational Medicine Study (GTT) and a longitudinal analysis using data from the type 1 diabetes (T1D) Exchange registry study. Correlation analysis was used to investigate the association between the TyG index and IR. Logistic regression and Cox proportional hazards regression were performed to explore the impact of the TyG index on DKD risk.

Results

The GTT Study included 836 adults (216 with DKD and 620 without DKD). A significant correlation existed between the TyG index and the estimated glucose disposal rate (r=-0.64, p < 0.01). The TyG index was a risk factor for DKD after confounder adjustment (OR = 1.34, 95% CI:1.03–1.74). The T1D Exchange registry study included 8,771 adults (2,050 with DKD and 6,721 without DKD). After adjusting confounding factors, the TyG index was identified as an independent risk factor for DKD at enrollment, with the highest risk of DKD incidence observed in the highest TyG tertile group (OR = 1.92, 95%CI:1.67–2.20). During a median follow-up of 44.58(21.84, 67.09) months, the risk of developing DKD was increased by 32% at every 1 SD increase of the TyG index over time among participants without DKD at enrollment.

Conclusions

The TyG index could be used to assess IR and was identified as an independent risk factor of DKD among adults with T1DM.

Background

Insulin resistance (IR) is a well-known risk factor for macrovascular disease in patients with type 2 diabetes mellitus (T2DM) [1]. The gold standard for evaluating IR remains to measure glucose disposal rate (GDR) using a hyperinsulinemia-euglycemic clamp (HIEC), yet its invasive and complex procedures render it impractical in clinical settings. Therefore, the emerging triglyceride-glucose (TyG) index, calculated by In [fasting triglyceride (TG, mg/dL) *fasting blood glucose (FBG, mg/dL)/2], is more accessible and cost-effective, which has been suggested as one of the reliable surrogate markers of IR [2]. Meta-analyses have shown that the TyG index is associated with an increased risk of both macrovascular and microvascular diseases among patients with T2DM [3,4,5,6,7,8,9].

Meanwhile, IR has also been proven to contribute to the development of diabetic kidney disease (DKD) of type 1 diabetes mellitus (T1DM). (10–11) However, the current homeostasis model assessment is inapplicable for patients with T1DM due to a lack of endogenous insulin. The TyG index might be a potential alternative to IR among participants with T1DM, but this is yet to be confirmed. Additionally, the association between TyG and DKD risk in patients with T1DM also remains unknown.

To investigate the relationship of TyG with IR and its impact on DKD risk among adults with T1DM, we conducted cross-sectional and longitudinal analyses using the data from the Guangdong T1DM translational study (GTT study) [12] and the type 1 diabetes(T1D) Exchange clinic registry [13].

Methods

Study design and data source

This study used the baseline data from the GTT study and then used baseline along with follow-up data from the T1D Exchange clinic registry for external verification.

The GTT study recruited patients with T1DM who were enrolled between June 2010 and June 2018 from 16 tertiary hospitals throughout 12 cities in Guangdong, China. The protocol details have been published previously [12]. It was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University (IRB No. [2014] 2–55). Each participant provided written informed consent.

The T1D Exchange clinic registry encompasses 34,013 participants who received routine clinical care at 83 clinic sites across the United States between July 2007 and April 2018. The crucial data were updated annually. Informed consent, criteria for inclusion, and methods for data collection have been previously reported [13]. The present study used the published data from the T1D Exchange clinic registry, which was permitted.

Study participants

The inclusion criteria were as follows: (1) participants with T1DM aged 18 years or older; (2) availability of essential information at enrollment, including lipid profiles, glycated hemoglobin (HbA1c), and DKD-related data [history and laboratory measurements including estimated glomerular filtration rate (eGFR)and urinary albumin-creatinine-ratio (UACR)]; and (3) The lipid profile measurements taken under fasting conditions. The exclusion criteria were: (1) pregnant women; (2) those who had undergone pancreas or islet transplant; and (3) those with a known kidney disease other than DKD.

Data collection

Information at enrollment from both databases was collected from medical records, including social-demographic data (age, gender, education level), anthropometric data[body mass index(BMI, kg/m2), systolic blood pressure(SBP, mmHg), and diastolic blood pressure(DBP, mmHg)], medical information (duration of T1DM, onset age of T1DM, previous history of DKD and other known kidney disease), and biological indicators [lipid profiles(mg/dL), HbA1c(%), eGFR(ml/min/1.73 m2), and UACR(mg/g)].

Additionally, waist-to-hip ratio (WHR) was also collected in the GTT study. Ethnicity, annual household income, current smoking status, method of insulin administration (insulin pump or others), continuous glucose monitoring (CGM) use (yes or no), the record of angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor antagonist (ARB) use (yes or no), and total daily insulin dose (TDD, u/kg/d) were also available in the T1D Exchange clinic registry. Follow-up data on medical history, eGFR, and UACR from the T1D Exchange clinic registry were also collected.

Definitions

DKD was defined as an eGFR less than 60 ml/min/1.73 m2 or UACR more than 30 mg/g after excluding other potential causes of kidney injury [14]. BMI was calculated as weight (kg)/height (m)2. The TyG index was calculated as In[fasting TG (mg/dL) *mean blood glucose (MBG, mg/dL)/2][15]. In this study, HbA1c-derived MBG [MBG (mmol/L) = 1.5944*HbA1c (%)-2.594] [16] rather than FBG was used to calculate the TyG index. The eGFR was calculated according to the MDRD equation in the GTT study [17], while the CKD-Epi equation was used in the T1D Exchange clinic registry [18]. In the GTT study, IR was quantified by the estimated GDR, which was calculated via a previously established formula for adults with T1DM: In GDR = 4.964 − 0.121*HbA1c (%)-0.012*DBP (mmHg)-1.409*WHR [19]. Other potential alternative indicators of IR were also evaluated, including (1) triglyceride glucose-body mass index (TyG-BMI), which was calculated as TyG*BMI (kg/m2); (2) triglyceride to high-density lipoprotein ratio(TG/HDL), which was calculated as TG (mg/dL) / HDL (mg/dL); (3) metabolic score for insulin resistance (METS-IR), which was calculated as Ln [(2*MBG (mg/dL)) + TG (mg/dL)]*BMI (kg/m2) / Ln [HDL (mg/dL)] [20].

Statistical analysis

Continuous variables were summarized as mean (standard deviation) or median (interquartile) where appropriate, and categorical variables as frequencies (percentages). Missing data was handled using multiple imputations. Student’s t-test, Wilcoxon signed-rank test, and the χ2 test were appropriate when comparing between 2 groups, while the One-Way ANOVA test, the χ2 test, or the Kruskal-Wallis test were used in the comparison among multiple groups as appropriate. Spearman’s correlation analysis was used to evaluate the correlation between TyG and IR. Multivariate linear regression was used to explore factors influencing TyG levels. Logistic regression analysis and Cox proportional hazard survival regression were used to investigate the relationship between TyG and the odd ratio (OR) or hazard ratio (HR) of DKD. Kaplan-Meier survival curves were constructed to estimate cumulative incidences of DKD in different TyG tertile groups and compared using log-rank testing. Statistical analysis was performed using SPSS 27.0 (SPSS Inc., Chicago, IL, USA), GraphPad Prism (GraphPad Software, San Diego, CA, USA), and R software version 3.2.5. Statistical significance was defined as two-tailed p < 0.05.

Results

The GTT study

Participants characteristics

A total of 836 adults with T1DM were included in the analysis. The median age was 32.15(25.10, 41.00) years, duration of T1DM was 4.25(1.10, 8.30) years, BMI was 20.56(18.82,22.39) kg/m2, HbA1c was 8.50(7.29,10.62) %, and TG was 84.07(61.95,121.20) mg/dL, respectively. The mean TyG was 9.14 ± 0.67, and the estimated GDR was 6.31 ± 2.05. There were 216 participants with DKD and 620 participants without DKD, baseline characteristics according to different groups were summarized in Table 1, Additional file 1: Table S1.

Table 1 The baseline characteristics of participants stratified by DKD occurrence
Full size table

Association between TyG index and IR

Spearman’s correlation analysis revealed a moderate linear correlation between the TyG index and GDR (r=-0.64, p < 0.01) (Additional file 1: Figure S1). In contrast, weaker correlations were observed between TyG-BMI, TG/HDL, and METS_IR with GDR, with correlation coefficients of -0.25 (p < 0.01), -0.14 (p < 0.01), and − 0.27 (p < 0.01), respectively.

The relationship of the TyG index with DKD incidence

In the base unadjusted model, the TyG index was detected as a risk factor for DKD [OR (95%CI): 1.55(1.24,1.96), p < 0.01]. In model 1(after adjusting age, onset age of T1DM, gender, education level, duration of T1DM, BMI, and WHR), the significant associations between TyG index and DKD risk remained [OR (95%CI): 1.53 (1.21, 1.96), p < 0.01]. In model 2(after further adjusting SBP, DBP, TC, LDL-c, and HDL-c), this influence of TyG on DKD incidence was slightly attenuated [OR (95%CI):1.34(1.03,1.74), p = 0.03] (Table 2).

Table 2 Associations between TyG and DKD risk in the logistic regression models in the GTT study
Full size table

In the base unadjusted model, neither TyG-BMI (OR = 1.00) nor METS-IR (OR = 1.02) were significant predictors of DKD (all p > 0.05). While TG/HDL was identified as a predictor of DKD [OR (95%CI):1.20(1.06,1.37), p < 0.01], its contribution was less than that of the TyG index. After adjusting for confounding factors, the impact of TG/HDL on DKD risk remained, albeit less pronounced than that of the TyG index.

T1D Exchange clinic registry

Participants characteristics

A total of 8,771 adults with T1DM were included in the analysis. The screening flowchart is described in Additional file 1: Figure S2. The median age was 36.00(23.00, 51.00) years, with a duration of T1DM of 17.00(9.00, 28.00) years, and a BMI of 26.21 (23.30, 29.81) kg/m2, respectively. At baseline, the median HbA1c was 7.70(7.00, 8.60) %, TG was 75.00(55.00, 110.00) mg/dL, and the TyG index was 8.80 (8.43, 9.25), respectively. The cross-sectional analysis involved 2,050 participants with DKD and 6,721 without DKD. Baseline characteristics according to different groups were summarized in Table 1, Additional file 1: Table S2.

Among the 6,721 participants without DKD initially, 5,439 adults had follow-up data available for longitudinal analysis, with a median follow-up of 44.58(21.84, 67.09) months. The median age was 34.00 (22.00, 49.00) years, duration of T1DM was 15.00(8.00, 25.00) years, HbA1c was 7.60(6.90, 8.40) %, and TG was 72.00(53.00, 104.00) mg/dL, respectively. The mean TyG was 8.80 ± 0.61. As per the tertiles of TyG levels at enrollment, participants were grouped into TyG tertile 1 (< 8.56), TyG tertile 2 (8.56–9.08), and TyG tertile 3 (≥ 9.08). The median duration of T1DM before developing DKD among the three groups was 25.43 (13.85, 37.93) years, 24.72(14.13, 35.28) years, and 20.34(12.86, 30.61) years, respectively. Shorter durations of T1DM at the onset of DKD were observed in the TyG tertile 3 group compared to the TyG tertile 1 group(p = 0.02).

Association between TyG index and IR

Multiple linear regression indicated that Black Non-Hispanic, female, smoking, and higher levels of TC, TDD, BMI, and DBP were associated with higher TyG levels, whereas higher annual household income and education level, insulin pump use, CGM use, higher levels of HDL-c and LDL-c were associated with lower TyG levels (p < 0.05) (Additional file 1: Table S3).

The relationship of the TyG index with DKD incidence

Cross-sectional analysis

Logistic regression was performed to investigate the impact of baseline TyG levels on the risk of DKD. Models 1 and 2 included covariates consistent with the GTT study except for WHR, which was only available in the GTT study. Model 3 was further adjusted for additional factors, including ethnicity, current smoking status, CGM use, method of insulin administration, annual household income, ACEI/ARB use, and TDD (Additional file 1: Table S4, Fig. 1a). In model 3, the risk of developing DKD was increased by 44% at every 1 SD increase of TyG index, with the highest risk of DKD incidence observed in the highest TyG tertile group [OR (95%CI): 1.30(1.13, 1.50) in TyG tertile 2, and 1.92(1.67, 2.20) in TyG tertile 3, respectively].

Fig. 1
figure 1

The relationship between the TyG index and the incidence of DKD. (a) cross-sectional analysis using enrollment data from the T1D Exchange clinic registry; (b) longitudinal analysis using follow-up data from the T1D Exchange clinic registry. Model 1 adjusted for age at consent, onset age of T1DM, gender, education level, duration of T1DM, and BMI. Model 2 was further adjusted for TC, HDL-c, LDL-c, SBP, and DBP. Model 3 was further adjusted for ethnicity, current smoking status, CGM use, method of insulin administration, TDD, annual household income, and ACEI/ARB use. TyG tertile 1:< 8.56; TyG tertile 2: 8.56–9.08; TyG tertile 3: ≥9.08. TyG, triglyceride glucose index; DKD, diabetic kidney disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; T1DM, type 1 diabetes mellitus; TC, total cholesterol; LDL-c, low-density lipoprotein cholesterol; HDL-c, high-density lipoprotein cholesterol; CGM, continuous glucose monitoring; TDD, total daily insulin dose; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor antagonist. OR, odd ratio; HR, hazard ratios; CI, confidence interval

Full size image

Longitudinal analysis

Kaplan–Meier survival curves for the hazard of developing DKD, stratified by the TyG tertile groups, are presented in Fig. 2. Significant differences existed in the hazard of developing DKD among three TyG tertile groups (log-rank test, p < 0.01). The DKD risk gradually increased with higher TyG, and the group with the highest TyG demonstrated the highest incidence of DKD development.

Fig. 2
figure 2

Kaplan–Meier survival curve-cumulative incidences of DKD in different TyG tertile groups. TyG tertile 1:< 8.56; TyG tertile 2: 8.56–9.08; TyG tertile 3: ≥9.08. TyG index, triglyceride-glucose index; DKD, diabetic kidney disease

Full size image

Cox proportional-hazards regression models were built to explore the relationship between TyG and the incidence of DKD ​(Additional file 1: Table S5, Fig. 1b). The trend of DKD development remained consistent across TyG as categorical values or continuous variables. TyG emerged as a significant independent risk factor for DKD among adults with T1DM, and the HR of a per-SD increase in TyG for DKD was 1.32(1.20,1.45) in the final model 3 after confounder adjustment. This suggested that the risk of developing DKD was increased by 32% at every 1 SD increase of the TyG index. Besides, when comparing against TyG tertile 1 as the reference, the highest tertile of TyG exhibited a hazard ratio ranging from 0.84 to 1.07-fold higher for incidence of DKD [HR (95%CI): 2.02(1.64, 2.47) in the base model, 2.07(1.67,2.55) in model 1, 1.99(1.58, 2.49) in model 2, and 1.84(1.47, 2.32) in model 3, respectively].

Discussion

To our knowledge, this is the first study to investigate the relationship between the TyG index and IR and its impact on DKD risk in adults with T1DM. In the GTT study, there was a moderate relationship between the TyG index and IR, and increased TyG level was closely associated with a higher risk of DKD among Chinese adults with T1DM. Our findings were supported by cross-sectional and longitudinal data from the US T1D Exchange clinic registry, which included a large sample.

The TyG index, initially proposed by Simental-Mendía, L. E. et al., has been widely accepted as a novel, cost-effective, and reliable marker of IR [2]. Previous studies have confirmed TyG levels as an important risk factor for macrovascular complications in patients with or without T2DM [3,4,5,6,7,8,9]. While the core pathogenesis of T1DM is insulin deficiency, studies have observed a growing prevalence of IR in patients with T1DM, attributed to longstanding insulin therapy and obesity [19, 21,22,23,24,25]. HIEC is still the prevailing method for assessing IR in patients with T1DM, but various formulas estimating GDR using multiple clinical parameters have been developed as a sensible proxy (19, 24, 26, 27).

The equal applicability of TyG, which has been a well-established IR marker for T2DM [28], remains uncertain among patients with T1DM. The data from the GTT study observed a moderate correlation between TyG and GDR. Due to the absence of WHR to calculate GDR, we failed to verify this relationship in the T1D Exchange clinic registry. Nonetheless, established determinants of IR such as TDD, smoking, BMI, and DBP were associated with elevated TyG levels in the longitudinal cohort from the T1D Exchange clinic registry. These findings confirmed the potential of TyG as a surrogate marker of IR in patients with T1DM. Moreover, the correlation between the TyG index and IR was found to be more robust than that observed for TyG-BMI, TG/HDL, and METS-IR, which have been utilized as alternative indicators of IR in previous studies.

Previous studies found that patients with MetS were more likely to have microvascular complications in T1DM [10, 21, 28,29,30,31]. To determine the relationship between TyG and DKD in T1DM, we conducted a cross-sectional analysis using baseline data from the GTT study, further followed by external validation using the T1D Exchange clinic registry. The follow-up data of T1D Exchange also revealed a gradual increase in DKD risk with a higher TyG level after confounder adjustment, and DKD risk was increased by 32% at every 1 SD increase of the TyG index. These findings indicated that TyG was an independent risk factor for DKD among adults with T1DM. Given its accessibility and cost-effectiveness, the TyG index could serve as a valuable tool for primary care physicians in evaluating IR. Besides, the duration of T1DM at the onset of DKD was significantly shorter in the group with the highest TyG tertile compared to the group with the lowest. This suggested that patients with higher TyG levels might experience a shorter DKD-free duration after diagnosis of T1DM. Among alternative markers of IR assessed, the TyG index proved to be the superior predictor of DKD in adults with T1DM.

Our findings underscored the importance of IR in preventing DKD in T1DM. As a crucial component of TyG, managing TG levels deserves attention. Hypertriglyceridemia can trigger the secretion of inflammatory mediators and exert a proapoptotic effect on kidney endothelium both in vitro and in vivo [32]. Animal experiments suggest that fenofibrate can mitigate DKD by enhancing impaired renal tubular fatty acid oxidation, a pathogenic factor of tubulointerstitial fibrosis [33, 34]. The efficacy of fenofibrate intervention in preventing or ameliorating DKD among patients with T2DM remains controversial.[35 – 36] It remains uncertain whether lowering TG while optimizing glycemic control can effectively improve DKD among patients with T1DM.

The strengths of our study were as follows: (1) the large sample size; 2) we conducted a cross-sectional analysis among Chinese adults with T1DM and further verified it using a longitudinal cohort from the United States, and the consistency of the outcomes reinforced the reliability of our conclusions; 3) GDR that was used to evaluate IR in the GTT study was calculated via a previously established model using clinical parameters. GDR from the clinical model, though not directly measured, showed a high correlation with the direct method. As a result, it remains still more reliable than other assessment methods.

Of course, there were some limitations in our study: (1) Due to the lack of WHR data, we were unable to use data from the T1D Exchange Clinic Registry to verify the relationship between TyG and GDR further externally; (2) this study only included adults with T1DM, which limits the generalizability of our findings to other subpopulations. Nevertheless, our study revealed that a higher TyG level was associated with an increased risk of DKD; the impact of lowering TyG levels and alleviating IR on DKD still warrants further investigation.

Conclusions

The TyG index could also be used to assess IR and was identified as an independent risk factor of DKD among adults with T1DM. Clinicians should pay attention to IR in this population. Future studies are needed to further investigate the impact of lowering TyG levels and alleviating IR on the prevention of DKD.

Data availability

The datasets from the GTT study are available upon reasonable request; the T1D Exchange clinic registry datasets are available on the T1D ExHomepage (cdc.gov).

Abbreviations

IR:

Insulin Resistance

MetS:

Metabolic Syndrome

T2DM:

Type 2 Diabetes Mellitus

GDR:

Glucose Disposal Rate

HIEC:

Hyperinsulinemia-Euglycemic Clamp

TyG:

Triglyceride Glucose Index

TG:

Triglyceride

FBG:

Fasting Blood Glucose

T1DM:

Type 1 Diabetes Mellitus

DKD:

Diabetic Kidney Disease

GTT:

Study Guangdong Type 1 Diabetes Mellitus Translational Study

HbA1c:

Hemoglobin

GFR:

Glomerular Filtration Rate

UACR:

Urinary Albumin-Creatinine-Ratio

WHR:

Waist-To-Hip Ratio

BMI:

Body Mass Index

SBP:

Systolic Blood Pressure

DBP:

Diastolic Blood Pressure

CGM:

Continuous Glucose Monitoring

ACEI:

Angiotensin-Converting Enzyme Inhibitor

ARB:

Angiotensin Receptor Antagonist

TDD:

Total Daily Insulin Dose

MBG:

Mean Blood Glucose

OR:

Odds of Ratio

HR:

Hazard Ratio

HDL-c:

High-Density Lipoprotein Cholesterol

TC:

Total Cholesterol

LDL-c:

Low-Density Lipoprotein Cholesterol

CI:

Confidence Interval

TyG-BMI:

Triglyceride Glucose-Body Mass Index

TG/HDL:

Triglyceride to High-Density Lipoprotein Ratio

METS-IR:

Metabolic Score for Insulin Resistance

References

  1. Muzurović E, Mikhailidis DP, Mantzoros C. Non-alcoholic fatty liver disease, insulin resistance, metabolic syndrome and their association with vascular risk. Metabolism. 2021;119:154770.

    Article  PubMed  Google Scholar 

  2. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304.

    Article  PubMed  Google Scholar 

  3. Yang Y, Huang X, Wang Y, et al. The impact of triglyceride-glucose index on ischemic stroke: a systematic review and meta-analysis. Cardiovasc Diabetol. 2023;22(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Khalaji A, Behnoush AH, Khanmohammadi S, et al. Triglyceride-glucose index and heart failure: a systematic review and meta-analysis. Cardiovasc Diabetol. 2023;22(1):244.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Liang S, Wang C, Zhang J, et al. Triglyceride-glucose index and coronary artery disease: a systematic review and meta-analysis of risk, severity, and prognosis. Cardiovasc Diabetol. 2023;22(1):170.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ren X, Jiang M, Han L, Zheng X. Association between triglyceride-glucose index and chronic kidney disease: a cohort study and meta-analysis. Nutr Metab Cardiovasc Dis. 2023;33(6):1121–8.

    Article  CAS  PubMed  Google Scholar 

  7. Behnoush AH, Khalaji A, Ghondaghsaz E, et al. Triglyceride-glucose index and obstructive sleep apnea: a systematic review and meta-analysis. Lipids Health Dis. 2024;23(1):4.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Azarboo A, Behnoush AH, Vaziri Z, et al. Assessing the association between triglyceride-glucose index and atrial fibrillation: a systematic review and meta-analysis. Eur J Med Res. 2024;29(1):118.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kassab HS, Osman NA, Elrahmany SM. Assessment of triglyceride-glucose index and ratio in patients with type 2 diabetes and their relation to Microvascular complications. Endocr Res. 2023;48(4):94–100.

    Article  CAS  PubMed  Google Scholar 

  10. Girgis CM, Scalley BD, Park KE. Utility of the estimated glucose disposal rate as a marker of microvascular complications in young adults with type 1 diabetes. Diabetes Res Clin Pract. 2012;96(3):e70–2.

    Article  PubMed  Google Scholar 

  11. Kilpatrick ES, Rigby AS, Atkin SL. Insulin resistance, the metabolic syndrome, and complication risk in type 1 diabetes: double diabetes in the Diabetes Control and complications Trial. Diabetes Care. 2007;30(3):707–12.

    Article  CAS  PubMed  Google Scholar 

  12. Liu L, Yang D, Zhang Y, et al. Glycaemic control and its associated factors in Chinese adults with type 1 diabetes mellitus. Diabetes Metab Res Rev. 2015;31(8):803–10.

    Article  CAS  PubMed  Google Scholar 

  13. Beck RW, Tamborlane WV, Bergenstal RM, et al. The T1D Exchange clinic registry. J Clin Endocrinol Metab. 2012;97(12):4383–9.

    Article  CAS  PubMed  Google Scholar 

  14. Cosentino F, Grant PJ, Aboyans V et al. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD [published correction appears in Eur Heart J. 2020;41(45):4317]. Eur Heart J. 2020;41(2):255–323.

  15. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, 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.

    Article  CAS  PubMed  Google Scholar 

  16. Greci LS, Kailasam M, Malkani S, et al. Utility of HbA(1c) levels for diabetes case finding in hospitalized patients with hyperglycemia. Diabetes Care. 2003;26(4):1064–8.

    Article  PubMed  Google Scholar 

  17. Ma YC, Zuo L, Chen JH, et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol. 2006;17(10):2937–44.

    Article  PubMed  Google Scholar 

  18. Levey AS, Stevens LA, Schmid CH et al. A new equation to estimate glomerular filtration rate [published correction appears in Ann Intern Med. 2011;155(6):408]. Ann Intern Med. 2009;150(9):604–612.

  19. Zheng X, Huang B, Luo S, Yang D, Bao W, Li J, et al. A new model to estimate insulin resistance via clinical parameters in adults with type 1 diabetes. Diabetes Metab Res Rev. 2017;33(4).

  20. Mu X, Wu A, Hu H, Yang M, Zhou H. Correlation between alternative insulin resistance indexes and diabetic kidney disease: a retrospective study. Endocrine. 2024;84(1):136–47.

    Article  CAS  PubMed  Google Scholar 

  21. Huang Q, Yang D, Deng H, et al. Association between metabolic syndrome and microvascular complications in Chinese adults with type 1 diabetes Mellitus. Diabetes Metab J. 2022;46(1):93–103.

    Article  PubMed  Google Scholar 

  22. Merger SR, Kerner W, Stadler M, et al. Prevalence and comorbidities of double diabetes. Diabetes Res Clin Pract. 2016;119:48–56.

    Article  CAS  PubMed  Google Scholar 

  23. Davis TM, Bruce DG, Davis WA. Prevalence and prognostic implications of the metabolic syndrome in community-based patients with type 1 diabetes: the Fremantle Diabetes Study. Diabetes Res Clin Pract. 2007;78(3):412–7.

    Article  PubMed  Google Scholar 

  24. Pathak V, Mishra I, Baliarsinha AK, Choudhury AK. Prevalence of Insulin Resistance in type 1 diabetes Mellitus and its correlation with metabolic parameters: the double trouble. Eurasian J Med. 2022;54(2):107–12.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Donga E, Dekkers OM, Corssmit EP, Romijn JA. Insulin resistance in patients with type 1 diabetes assessed by glucose clamp studies: systematic review and meta-analysis. Eur J Endocrinol. 2015;173(1):101–9.

    Article  CAS  PubMed  Google Scholar 

  26. Williams KV, Erbey JR, Becker D, Arslanian S, Orchard TJ. Can clinical factors estimate insulin resistance in type 1 diabetes? Diabetes. 2000;49(4):626–32.

    Article  CAS  PubMed  Google Scholar 

  27. Szadkowska A, Pietrzak I, Mianowska B, Markuszewski L, Bodalska-Lipińska J, Bodalski J. Wskaźnik insulinooporności u dzieci i młodziezy chorej na cukrzyce typu 1 -- uproszczony sposób oceny [Insulin resistance in type 1 diabetic children and adolescents -- a simplified method of estimation]. Endokrynol Diabetol Chor Przemiany Materii Wieku Rozw. 2006;12(2):109–15.

    PubMed  Google Scholar 

  28. Thorn LM, Forsblom C, Fagerudd J, et al. Metabolic syndrome in type 1 diabetes: association with diabetic nephropathy and glycemic control (the FinnDiane study). Diabetes Care. 2005;28(8):2019–24.

    Article  PubMed  Google Scholar 

  29. Committee MW, Bonadonna R, Cucinotta D, Fedele D, Riccardi G, Tiengo A. The metabolic syndrome is a risk indicator of microvascular and macrovascular complications in diabetes: results from Metascreen, a multicenter diabetes clinic-based survey. Diabetes Care. 2006;29(12):2701–7.

    Article  Google Scholar 

  30. Bhagadurshah RR, Eagappan S, Kasthuri Santharam R, Subbiah S. The impact of body Mass Index, residual Beta cell function and estimated glucose disposal rate on the development of double diabetes and microvascular complications in patients with type 1 diabetes Mellitus. Cureus. 2023;15(11):e48979.

    PubMed  PubMed Central  Google Scholar 

  31. Šimonienė D, Platūkiene A, Prakapienė E, Radzevičienė L, Veličkiene D. Insulin Resistance in type 1 diabetes Mellitus and its Association with Patient’s Micro- and macrovascular complications, sex hormones, and other Clinical Data. Diabetes Ther. 2020;11(1):161–74.

    Article  PubMed  Google Scholar 

  32. Hukportie DN, Li FR, Zhou R, et al. Lipid variability and risk of microvascular complications in action to Control Cardiovascular Risk in Diabetes (ACCORD) trial: a post hoc analysis. J Diabetes. 2022;14(6):365–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kang HM, Ahn SH, Choi P, et al. Defective fatty acid oxidation in renal tubular epithelial cells has a key role in kidney fibrosis development. Nat Med. 2015;21(1):37–46.

    Article  CAS  PubMed  Google Scholar 

  34. Martin WP, Nair M, Chuah YHD, et al. Dietary restriction and medical therapy drives PPARα-regulated improvements in early diabetic kidney disease in male rats. Clin Sci (Lond). 2022;136(21):1485–511.

    Article  CAS  PubMed  Google Scholar 

  35. Fegan PG, Shore AC, Mawson D, Tooke JE, MacLeod KM. Microvascular endothelial function in subjects with type 2 diabetes and the effect of lipid-lowering therapy. Diabet Med. 2005;22(12):1670–6.

    Article  CAS  PubMed  Google Scholar 

  36. Sacks FM. After the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study: implications for fenofibrate. Am J Cardiol. 2008;102(12A):34L-40L. 37. Ansquer JC, Foucher C, Rattier S, Taskinen MR, Steiner G; DAIS Investigators. Fenofibrate reduces progression to microalbuminuria over 3 years in a placebo-controlled study in type 2 diabetes: results from the Diabetes Atherosclerosis Intervention Study (DAIS). Am J Kidney Dis. 2005;45(3):485–493.

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Acknowledgements

We thank the team of the T1D Exchange clinic registry for providing researchers with open access to their data. The analyses presented in this paper are the author’s own and have not undergone review by the registry. This analysis project received no specific public, commercial, or not-for-profit sector support.

Funding

This study was supported by the National Key R&D Program of China 2023(2023ZD508203) and the Diabetes Mellitus Research Fund Program from Shanghai Medical and Health Development Foundation (DMRFP_II_14 from SHMHDF).

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Author notes

  1. Mengyun Lei, Ping Ling and Yongwen Zhou contributed equally to this work.

Authors and Affiliations

  1. Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China

    Mengyun Lei, Ping Ling, Yongwen Zhou, Jing Lv, Ying Ni, Hongrong Deng, Chaofan Wang, Daizhi Yang, Xubin Yang, Wen Xu & Jinhua Yan

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  1. Mengyun Lei
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Contributions

MYL, PL, and YWZ: Conduct/data collection and analysis; Write the manuscript. MYL, PL, and YWZ contribute equally to this paper. JL and YN: Data collection; Writing manuscript. HRD, CFW, and DZY: Writing manuscript. JHY, WX, and XBY: Design; Writing manuscript. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Xubin Yang, Wen Xu or Jinhua Yan.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (IRB No. [2014] 2–55) and has been performed under the guidelines of the Helsinki Declaration. Informed consent was obtained from all individual participants included in the study.

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The authors declare no competing interests.

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13098_2024_1468_MOESM1_ESM.docx

Supplementary Material 1: Additional file 1: Figure S1. The relationship between the TyG index and GDR in the GTT study. Figure S2. The screening flowchart from the T1D Exchange clinic registry. Table S1. The additional characteristics of participants stratified by DKD from the GTT study. Table S2. The additional characteristics of participants stratified by DKD at baseline from the T1D Exchange clinic registry. Table S3. The significant factors associated with TyG in the multiple linear regression model. Table S4. Associations between TyG and the prevalence of DKD at enrollment using the logistic regression models. Table S5. Associations between TyG and the prevalence of DKD during the follow-up period using Cox proportional-hazards regression models. (575KB, docx)

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Lei, M., Ling, P., Zhou, Y. et al. Correlation between triglyceride-glucose index and diabetic kidney disease risk in adults with type 1 diabetes mellitus. Diabetol Metab Syndr 16, 239 (2024). https://doi.org/10.1186/s13098-024-01468-9

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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