Introduction

Chronic kidney disease (CKD) is a public health problem, which imposes health, social, and human burden on societies worldwide [1]. CKD remains asymptomatic until the stage of disease progresses. Moreover, CKD is reported to increase the risk of cardiovascular disease [2] and mortality [3]. Therefore, both prevention and treatment of CKD are important for prevention of end stage of renal dysfunction, cardiovascular disease, and mortality.

Insulin resistance is known to be a risk factor for the progression of CKD [4]. In addition, some previous studies reported that patients with CKD have insulin resistance from the early stage [5,6,7]. Therefore, detection of insulin resistance at an early stage of kidney disease is important in the prevention of CKD. Triglyceride–glucose index (TyG index), which is calculated with fasting plasma glucose and triglycerides, has been suggested as a marker of moderate insulin resistance [8, 9]. Moreover, TyG index was reported to predict the development of type 2 diabetes [10, 11] and cardiovascular disease [12]. However, no previous studies revealed the association between TyG index and the development of CKD. Thus, we aimed to investigate the association between TyG index and the development of CKD using NAfld in the Gifu Area, Longitudinal Analysis (NAGALA) cohort database.

Materials and methods

Study design and study participants

The NAGALA cohort study is an ongoing prospective cohort study that began in 1994 [13]. For this population-based longitudinal analysis, we extracted the participants from a medical examination program at Asahi University Hospital (Gifu, Japan). This medical examination program, which is called a human dock, is aimed to detect chronic diseases and their risk factors and promote public health. More than 8000 participants annually registered and 60% of them receive 1–2 exams per year [14]. In this study, we investigated the effect of TyG index on incident CKD, using the NAGALA database. We extracted the participants who received the medical examination program at Asahi University Hospital from 1994 to 2016. Some previous studies reported that mildly decreased estimated glomerular filtration rate (eGFR) (60–74 mL/min/1.73 m2) has a significantly higher risk of CKD. Therefore, we excluded the participants who had not only with CKD (eGFR < 60 mL/min/1.73 m2) but also mildly decreased eGFR (60–74 mL/min/1.73 m2) at the baseline examination [15, 16]. Moreover, we excluded the participants with medication at the baseline examination. Approval for the study was obtained from the research ethics committees of the Asahi University Hospital, and written informed consent for their data to be used was obtained from all participants.

Standardized questionnaire for lifestyle factors

To determine the lifestyle factors of participants, a standardized questionnaire was performed to all participants [14]. We divided the participants into nonsmokers, ex-smokers, and current smokers. Next, we asked about the type and amount of alcohol consumption per week during the prior month, then estimating the mean ethanol intake per week. Finally, we defined the participants who performed any kind of sport regularly at least once a week as regular exercisers.

Data collection

Body mass index (BMI) was defined as weight in kilograms divided by height in meters squared. We divided the participants into four groups according to their BMI values: lean, BMI < 18.5 kg/m2; normal, ≥ 18.5 to < 23 kg/m2; overweight, ≥ 23 to < 25 kg/m2; obesity, ≥ 25 kg/m2 [17]. The participants’ levels of several factors including fasting plasma glucose, triglycerides, high-density lipoprotein (HDL) cholesterol, and creatinine were measured using venous blood after an overnight fast. We used the Japanese Society of Nephrology equation for calculating each patient’s eGFR: eGFR (mL/min/1.73 m2) = 194 × serum creatinine−1.094 × age−0.287 (× 0.739 for women) [18]. Spot morning urine sample was measured. Participants with serum uric acid level ≥ 416 mmol/L (7.0 mg/dL) for men and ≥ 386 mmol/L (6.5 mg/dL) for women were defined as having hyperuricemia [19]. Moreover, participants with serum HDL-cholesterol level < 1 mmol/L (40 mg/dL) for men and < 1.3 mmol/L (50 mg/dL) for women were defined as having low HDL concentrations. In addition, TG/HDL ratio was calculated in the following formula: TG (mg/dL)/HDL-cholesterol (mg/dL) [20]. Finally, TyG index was calculated as ln[fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2] [8].

Definition of CKD

CKD was defined as decreased estimated glomerular filtration rate (eGFR). GFR was estimated using the Japanese Society of Nephrology equation. When eGFR was less than 60 mL/min/1.73 m2, it was defined as having CKD [21].

Statistical analysis

Statistical analyses were preformed using JMP ver. 12.0 software (SAS Institute, Cary, NC) and a p value < 0.05 was considered significant. Means or frequencies of potential confounding variables were calculated, and continuous variables are presented as the mean (standard deviation, SD). We divided the participants into men and women, because the distribution of TyG index differed between sexes. The p values were analyzed by one-way analysis of variance for continuous variables and chi-squared test for categorical variables. In addition, we checked the characteristics of participants with mildly decreased eGFR or CKD without medication at baseline examination as a subgroup analysis.

Cox proportional hazard model was used to calculate unadjusted and adjusted hazard ratio (HR) and 95% confidence interval (CI) for incident CKD. We adjusted for age, BMI categories, waist circumference, smoking status, exercise, logarithm of alcohol consumption, systolic blood pressure, serum albumin, hemoglobin A1c, hyperuricemia, low HDL-cholesterol concentration, high LDL-cholesterol concentration, CRP, creatinine, and gamma-glutamyltransferase.

In addition, the area under the curve (AUC) of several factors, including TyG index, for the incident CKD was calculated by the receiver-operating characteristic (ROC) curve.

Results

We enrolled 27,944 participants without any medication. At first, we divided them into two groups according to sex, and there were 16,454 men and 11,490 women. Among them, we excluded 3617 participants (2565 men and 1052 women) with CKD, 9744 participants with mildly decreased eGFR (6010 men and 3734 women) and 2501 participants with medication (1811 men and 690 women) at baseline examination. In addition, we also excluded 372 participants whose data were missed. Finally, 6026 men and 5686 women were analyzed in this study (Fig. 1).

Fig. 1
figure 1

Study flow diagram for the registration of participants

Full size image

The baseline characteristics of the participants are shown in Table 1. TyG index in men was significantly higher than that in women (8.25 (0.66) vs 7.66 (0.57), p　<0.001). In addition, the characteristics of the participants with CKD or mildly decreased eGFR at baseline examination are shown in Supplementary Table 1. TyG index of participants with CKD or mildly decreased eGFR at baseline examination was significantly higher than that without both in men and women [men: 8.54 (0.63) vs 8.25 (0.66), p < 0.001; women: 8.05 (0.57) vs 7.66 (0.57), p < 0.001]. Next, we investigated unadjusted HRs and 95% CIs for the incidence of CKD (Table 2). In univariate analyses, TyG index was significant risk for incident CKD (men, HR 1.59, 95% CI 1.21–2.07, p = 0.001; women, HR 2.25, 95% CI 1.70–2.96, p < 0.001). In multivariate analyses, TyG index presented the significant risk for incident CKD in both men and women (men, HR 1.32, 95% CI 1.02–1.70, p = 0.036; women, HR 1.50, 95% CI 1.05–2.13, p = 0.024) (Table 3).

Table 1 Characteristics of study participants of cohort study at the baseline examination according to sex
Full size table
Table 2 Unadjusted HRs and 95% CIs for the incidence of CKD
Full size table
Table 3 Cox proportional hazards for incident CKD
Full size table

In addition, in ROC analyses, AUC of TyG index was 0.593, and that of BMI, serum creatinine, HbA1c, TG, TG/HDL ratio, or waist circumference was 0.512 (p = 0.007 vs. TyG index), 0.468 (p < 0.001 vs. TyG index), 0.564 (p = 0.494 vs. TyG index), 0.598 (p = 0.179 vs. TyG index), 0.587 (p = 0.573 vs. TyG index), or 0.528 (p = 0.021 vs. TyG index), in men, respectively. AUC of TyG index was 0.634, and that of BMI, serum creatinine, HbA1c, TG, TG/HDL ratio or waist circumference was 0.594 (p = 0.156 vs. TyG index), 0.455 (p < 0.001 vs. TyG index), 0.474 (p < 0.001 vs. TyG index), 0.633 (p = 0.886 vs. TyG index), 0.632 (p = 0.815 vs. TyG index), and 0.552 (p = 0.006 vs. TyG index), in women, respectively (Fig. 2).

Fig. 2
figure 2

Area under the receiver-operating characteristic (ROC)curve (AUC) [95% confidence interval (CI)] of several factors for incident CKD. a TyG index, b BMI, c serum creatinine, d HbA1c, e TG, f TG/HDL ratio, and g waist circumference. AUC of TyG index was 0.593, and that of BMI, serum creatinine, HbA1c, TG, TG/HDL ratio or waist circumference was 0.512 (p = 0.007 vs. TyG index), 0.468 (p < 0.001 vs. TyG index), 0.564 (p = 0.494 vs. TyG index), 0.598 (p = 0.179 vs. TyG index), 0.587 (p = 0.573 vs. TyG index), or 0.528 (p = 0.021 vs. TyG index), in men, respectively. AUC of TyG index was 0.634, and that of BMI, serum creatinine, HbA1c, TG, TG/HDL ratio or waist circumference was 0.594 (p = 0.156 vs. TyG index), 0.455 (p < 0.001 vs. TyG index), 0.474 (p < 0.001 vs. TyG index), 0.633 (p = 0.886 vs. TyG index), 0.632 (p = 0.815 vs. TyG index), and 0.552 (p = 0.006 vs. TyG index), in women, respectively

Full size image

Discussion

In this cohort study of over 10,000 Japanese individuals, for the first time, we investigated the association between TyG index and incident CKD. CKD, which has become a public health problem [1], is the risk of cardiovascular disease [2] and mortality [3]. Hence, intervention at an early stage of the disease is desirable. Some previous reports revealed the association between TyG index and incident type 2 diabetes [10] and cardiovascular disease [12]. However, there is no report of the association between TyG index and incident CKD. We showed that high TyG index is associated with risk of incident CKD. In fact, we compared area under the curve (AUC) of TyG index with that of BMI, serum creatinine, HbA1c, TG, TG/HDL ratio [20], and waist circumference. Then, TyG index was equal or superior to other markers of insulin resistance, including TG, TG/HDL ratio, waist circumference, and HbA1c for predicting CKD. Among them, AUCs of TG both in men and women were almost the same as those of TyG index. Therefore, TG itself might also be a risk marker of incident CKD.

Several groups demonstrated the association between insulin resistance and CKD [5, 22, 23]. Some animal and human experimental studies demonstrated that hyperinsulinemia induces renal vasodilatation, increases sodium reabsorption, enhances the renin–angiotensin system, and induces glomerular hyperfiltration, which increases GFR [24,25,26]. Increased filtration per nephron causes nephron loss and results in glomerular hypertension, which induces glomerular sclerosis and subsequent renal dysfunction [27]. Moreover, in some clinical studies, insulin resistance is already present in patients with mild degrees of renal dysfunction [6, 7, 28]. The association between insulin resistance and CKD could be explained by some biological mechanisms such as inflammation, oxidative stress, and metabolic acidosis. First, inflammation and CKD are known to be related each other [29]. Recently, Shimobayashi et al. [30] demonstrated that insulin resistance induces inflammation in adipose tissue by inhibiting insulin-signaling pathway and increasing monocyte chemoattractant protein 1 production. M2 macrophage activated by inflammation in adipose tissue produces and releases proinflammatory cytokines such as interleukin (IL)-6 and tumor necrosis factor (TNF)-α [31]. IL-6 induces endothelial dysfunction and vascular hypertrophy in response to angiotensin II [32]. In addition, TNF-α was also reported to induce endothelial dysfunction [33]. Endothelial dysfunction is associated with incident CKD [34]. Therefore, Both IL-6 [32] and TNF-α [33] induces endothelial dysfunction, which has close association with incident CKD [34]. Second, insulin resistance induces oxidative stress [34]. Oxidative stress and inflammation impair the activation of nuclear factor erythroid-2-related factor-2 which protects against tissue injury of kidney [35]. In fact, TyG index showed a positive association with CRP in this study (men, r = 0.055, p < 0.001, women, r = 0.051, p = 0.045, by Pearson’s correlation coefficient). Third, metabolic acidosis, which is induced by hyperglycemia [36], also causes insulin resistance [37]. Metabolic acidosis causes change in function of kidney, including an increase in renal plasma flow and GFR, to excrete the excess acid load [38]. Glomerular hyperfiltration may facilitate the progression of CKD [27]. A previous study reported the association between metabolic acidosis and urine pH [39]. In this study, TyG index showed a negative association with urine pH (men, r = − 0.113, p < 0.001, women, r = − 0.086, p < 0.001, by Pearson’s correlation coefficient). Taken together, inflammation, oxidative stress, and metabolic acidosis induced by insulin resistance play key roles in pathogenesis of CKD.

The strengths of our study include using the standardized questionnaire for lifestyle factors, and the relatively large population-based longitudinal research. Our study has also several limitations. First, we assessed proteinuria with urine dipstick test and did not quantify proteinuria. A dipstick test is often used in general practice and < 1+ or less than trace has a high negative predictive value in the general community setting [40]. In addition, we confirmed urine experiments once. If we performed urine experiments multiple times, we could evaluate CKD more accurately. Second, the follow-up was medium term; therefore, the statistical power might be limited. Third, sodium and protein intake are associated with pathogenesis of CKD [41, 42]. However, we did not have the data of them. If we had the data, we could more accurately investigate the association between TyG index and incident CKD. Fourth, participants in this study received a health examination; therefore, part of them might have changed and improved their lifestyles, which prevent incident CKD. Finally, almost all participants were Japanese; therefore, it is uncertain whether our findings can be generalized to other ethnic groups.

In conclusion, we demonstrated, for the first time, that TyG index can be a predictor of incident CKD.