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. 2022 Sep 20:9:1018083.
doi: 10.3389/fmed.2022.1018083. eCollection 2022.

The non-linear relationship between triglyceride-glucose index and risk of chronic kidney disease in hypertensive patients with abnormal glucose metabolism: A cohort study

Qing Zhu  1   2 , Yuan Chen  1   3 , Xintian Cai  1   2 , Li Cai  2 , Jing Hong  2 , Qin Luo  2 , Yingli Ren  2 , Yanying Guo  1   3 , Nanfang Li  2
Affiliations

The non-linear relationship between triglyceride-glucose index and risk of chronic kidney disease in hypertensive patients with abnormal glucose metabolism: A cohort study

Qing Zhu et al. Front Med (Lausanne). .

Abstract

Background: Triglyceride-glucose (TyG) index has been reported to be associated with cardiovascular disease (CVD). However, few studies have focused on TyG index and the risk of chronic kidney disease (CKD). Thus, this study aims to explore the relationship between TyG index and CKD.

Methods: A total of 2,033 participants with hypertension between January 2012 and May 2019 were included in the longitudinal observational study. All patients are grouped according to the TyG index quartile. CKD was defined as estimated glomerular filtration rate (eGFR) < 60 ml/min per 1.73 m2 and/or positive proteinuria. Multivariate Cox proportional hazards models were used to investigate the relationship between TyG index and CKD.

Results: During a median follow-up of 31 months, 302 participants developed CKD, with a mean age of 55.5 years and median TyG of 8.94. Compared with those in the lowest quartile of TyG index, participants in the highest quartile of TyG index exhibited 1.63-fold higher hazard ratio (95% CI: 1.14-2.33, P = 0.007) for presence of CKD. And restricted cubic spline analysis showed the relationship between TyG index and CKD is non-linear (P non-linearity = 0.021). The hazard ratio for CKD first fell and after rising until around 8.94 of TyG index and started to increase rapidly afterward (P for TyG < 0.001).

Conclusion: Higher TyG index is associated with the increased risk for CKD. Early intervention of metabolic factors may prevent the occurrence of CKD, thereby reducing the incidence of CVD and premature death.

Keywords: chronic kidney disease; diabetes; hypertension; insulin resistance; triglyceride-glucose index.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Patient screening flowchart.
FIGURE 2
FIGURE 2
Kaplan–Meier curve of cumulative incidence of CKD based on quartiles of TyG index; P-value was generated based on log-rank test. CKD, chronic kidney disease.
FIGURE 3
FIGURE 3
Restricted cubic splines (RCS) for the shape of the association of TyG with CKD. It was used a multivariate Cox regression model of restricted cubic spline with 4 knots (at the 5th, 35th, 65th, and 95th percentiles) of TyG adjusting for potential covariates (including sex, age, smoke, drink, BMI, duration of hypertension, duration of diabetes, SBP, DBP, LDL-C, HDL-C, Cr, BUN, UA, lipid-lowering drugs, antidiabetic drugs, and antihypertensive drugs). The reference point for TyG index was 8.94 (the median of TyG).
FIGURE 4
FIGURE 4
Restricted cubic splines (RCS) for the shape of the association of TyG with eGFR. It was used a multivariate linear regression model of RCS with 4 knots (at the 5th, 35th, 65th, and 95th percentiles) of TyG adjusting for potential covariates (the same as Figure 3). The curve was centered at the median value of 8.94.
FIGURE 5
FIGURE 5
Stratification analysis on association between TyG with CKD. Results were derived from multivariate Cox regression and presented as hazard ratio (adjusting covariates: sex, age, smoke, drink, BMI, duration of hypertension, duration of diabetes, SBP, DBP, LDL-C, HDL-C, Cr, BUN, UA, lipid-lowering drugs, antidiabetic drugs, and antihypertensive drugs).
See this image and copyright information in PMC

References

    1. Woo KT, Choong HL, Wong KS, Tan HB, Chan CM. The contribution of chronic kidney disease to the global burden of major noncommunicable diseases. Kidney Int. (2012) 81:1044–5. 10.1038/ki.2012.39 - DOI - PubMed
    1. Herzog CA, Asinger RW, Berger AK, Charytan DM, Díez J, Hart RG, et al. Cardiovascular disease in chronic kidney disease. A clinical update from kidney disease: Improving global outcomes (KDIGO). Kidney Int. (2011) 80:572–86. 10.1038/ki.2011.223 - DOI - PubMed
    1. Kalantar-Zadeh K, Jafar TH, Nitsch D, Neuen BL, Perkovic V. Chronic kidney disease. Lancet. (2021) 398:786–802. 10.1016/S0140-6736(21)00519-5 - DOI - PubMed
    1. Lin M, Heizhati M, Wang L, Gan L, Li M, Yang W, et al. Prevalence and associated factors of kidney dysfunction in patients with hypertension and/or diabetes mellitus from a primary care population in northwest China. Int J Gen Med. (2021) 14:7567–78. 10.2147/IJGM.S329604 - DOI - PMC - PubMed
    1. Tannor EK, Sarfo FS, Mobula LM, Sarfo-Kantanka O, Adu-Gyamfi R, Plange-Rhule J. Prevalence and predictors of chronic kidney disease among Ghanaian patients with hypertension and diabetes mellitus: A multicenter cross-sectional study. J Clin Hypertens. (2019) 21:1542–50. 10.1111/jch.13672.PMID - DOI - PMC - PubMed