Association of Variants of the NFE2L2 Gene with Metabolic and Kidney Function Parameters in Patients with Diabetes and/or Hypertension

Abstract

Background:

Type 2 diabetes mellitus (T2DM) and high blood pressure (HBP) are the main risk factors for chronic kidney disease (CKD). Relationships between variants within the NFE2L2 gene and the presence of environmental risk factors for CKD, such as HBP and hyperglycemia have been suggested; however, their interactions remains unclear.

Aim:

To analyze the association of NFE2L2 variants with metabolic and kidney parameters.

Materials and Methods:

Six-hundred and fifty-one patients grouped according to the diagnosis of T2DM (n =166), T2DM+HBP (n =348) and HBP (n =137) were included. Metabolic characteristics were evaluated to identify risk factors and presence of CKD. Genotyping was performed by polymerase chain reaction (PCR) using two pairs primers for rs35652124 and rs6721961 and by real-time PCR for rs2364723. Logistic regression analyses, adjusted for confounding factors and correction for multiple tests were performed.

Results:

Significant associations between decreased risk for presenting with CKD and the rs35652124 (A allele) and the rs2364723 (G allele) variants were detected. Other variables consistently associated with these alleles were HBP, BMI, waist circumference, uric acid and triglycerides. Haplotypes AAC and GCG (loci order: rs35652124-rs6721961-rs2364723) showed similar trends. After adjustment for age and sex and correction for multiple tests, only rs35652124 (Odds Ratio [OR] = 0.5; Confidence Interval at 95% (CI95%), 0.3-0.9; p = 0.04) and rs2364723 (OR = 0.3; CI95%, 0.1-0.8; p = 0.009) variants remained associated with deceased risk for CKD in T2DM patients.

Conclusion:

This study showed for the first time that NFE2L2 variants are associated with decreased risk for CKD in the presence of environmental/metabolic risk factors related to kidney damage, including HBP, hyperuricemia and albuminuria in Mexican patients with diabetes.

Background

The causes by which chronic kidney disease (CKD) develops are multifactorial and include the interaction between genetic, metabolic, and environmental factors (Xu et al., 2016). Hyperglycemia, high blood pressure (HBP), and albuminuria are the major known risk factors contributing to the decline of kidney function (Choi et al., 2014; López Leal et al., 2017). It is known that, in addition to a state of persistent low-grade inflammation caused by poor metabolic control, patients at risk of CKD, such as those with type 2 diabetes mellitus (T2DM) and hypertension, are exposed to oxidative stress resulting from over generation of reactive oxygen species (ROS) or insufficient endogenous antioxidant defense mechanisms, or both (Lee et al., 2005; Xu et al., 2016).

Such pro-oxidative milieu has been suggested to play a critical role in the onset and progression of CKD and its complications, mainly cardiovascular (Lee et al., 2005; Chen et al., 2017; Esgalhado et al., 2017). Thus, strategies aimed to improve antioxidant or anti-inflammatory responses might help to reduce the risk of cardiovascular complications (Chen et al., 2017).

The nuclear-related factor 2 (Nrf2) is recognized as a master redox regulator since it can activate the transcription of genes coding for proteins that are responsible for eliminating ROS. In its active form, Nrf2 binds to the antioxidant response elements within promoter region of genes such as superoxide dismutase (SOD), myeloperoxidase-1 (MPO-1), and catalase (CAT), among others (Masuko et al., 2011; Ishikawa, 2014). Activation of NRF2 has been associated with anti-inflammatory and cytoprotective actions by regulating oxidative stress occurring in several diseases, including diabetes and cancer (Wang et al., 2015; Testa et al., 2016). Lower levels of activated NRF2 have also been associated with other risk factors for CKD such as obesity, HBP, hyperglycemia, or advanced age (Pi et al., 2010; Choi et al., 2014; Jiménez Osorio et al., 2014, 2016; Wang et al., 2015; Shen et al., 2017).

In humans, NRF2 is encoded by the NFE2L2 gene at chromosome 2 (2q31) and some single nucleotide variants (SNV) influencing the NRF2 expression/activity, which could play a role in health and disease, have been identified (Marzec et al., 2007). Of special interest are rs35652124 (−653 G>A) and rs6721961 (−617 C>A), both located in the promoter region of the gene and associated with reduced transcription rates of NFE2L2 (Marzec et al., 2007).

Rs35652124 has been associated with HBP and risk of cardiovascular death in patients with hemodialysis (Shimoyama et al., 2014b; Testa et al., 2016), whereas rs6721961 has been associated with high levels of oxidative stress, low antioxidant capacity, insulin resistance, and increased risk for developing diabetes (Wang et al., 2015) and HBP (Shimoyama et al., 2014a). By its part, rs2364723 (A/G), located at the first intron of the NFE2L2 gene, has been associated with reduced risk of cardiovascular mortality (Figarska et al., 2014) and common complications in patients with T2DM (Xu et al., 2016). Although there no functional studies of this variation, it has been suggested that the strong linkage disequilibrium with other variants, such as rs35652124, could explain the associations observed (Figarska et al., 2014), so studies considering haplotype analysis are recommended.

Although it is expected that activation of NRF2 could prevent the progression of kidney disease by protecting cells from oxidative damage (Nezu et al., 2017), the influence of NRF2 variants in patients at high risk for CKD, such as those with T2DM, HBP, or both, has not been explored yet. In this study, three NFE2L2 gene variants, rs35652124, rs6721961, and rs2364723, were investigated for association with CKD presence and metabolic parameters.

Materials and Methods

Study design and subjects

A cross-sectional study was carried out in three primary health care units from the Instituto Mexicano del Seguro Social (IMSS); all patients were Mexican Mestizos and resident of metropolitan area of Guadalajara, Mexico. The patient recruitment period was between 2017 and 2019 and they all signed an informed letter of consent. Six-hundred and fifty-one patients with T2DM and/or HBP with recent diagnosis of CKD were included in the study. For comparisons, patients were grouped as T2DM without HBP (n = 166), T2DM+HBP (n = 348), and HBP without T2DM (n = 137) (Table 1). Patients with concomitant presence of systemic inflammatory diseases, cancer, liver disease, or mental health disorders, as well as pregnant or breastfeeding women were excluded. All patients were volunteers and signed written informed consent prior their recruitment. The study was approved by the Research and Ethics Review Board of the IMSS (authorization number: R-2017-785-072).

Table 1.

General Characteristics, Metabolic, Cardiovascular, and Kidney Function Parameters According to the Diagnosis

Variable	Total (n = 651)	T2DM (n = 166)	T2DM+HBP (n = 348)	HBP (n = 137)
General characteristics
Age (years)	62 ± 11	59 ± 12	63 ± 10^a	64 ± 11^a
T2DM vintage (years)	12 ± 8	12 ± 8	12 ± 8	—
HBP vintage (years)	11 ± 9	—	11 ± 9	10 ± 9
Metabolic and cardiovascular parameters
BMI (kg/m²)	29.4 ± 5.5	28.2 ± 4.8	29.6 ± 5.5^a	30.2 ± 5.6^a
WC (cm)	100 ± 12	97 ± 12	101 ± 12^a	102 ± 14^a
SBP (mm Hg)	130 ± 19	125 ± 17	134 ± 19^a	128 ± 17^b
DBP (mm Hg)	80 ± 11	77 ± 9	81 ± 11^a	81 ± 10^a
Uric acid (mg/dL)	5.5 ± 1.6	5.2 ± 1.3	5.5 ± 1.7	5.9 ± 1.7^a
Fasting glucose (mg/dL)	115 (90-164)	140 (102-195)	131 (103-180)	87 (79-96)^a,b
HbA1c (%)	8.3 ± 2.3	8.6 ± 2.3	8.3 ± 2.3	—
TC (mg/dL)	191 (166-218)	189 (163-217)	191 (165-219)	192 (168-220)
LDL-C (mg/dL)	113 ± 34	115 ± 34	111 ± 35	115 ± 34
HDL-C (mg/dL)	46 ± 13	43 ± 11	45 ± 12	50 ± 16^a,b
Triglycerides (mg/dL)	161 (112-217)	171 (114-217)	164 (112-227)	149 (111-194)^b
Kidney function
Albuminuria (mg/g creatinine)	17 (9-72)	19 (9-69)	23 (10-121)	11 (6-18)^b
eGFR (mL/min/1.73 m²)	88 (71-99)	94 (79-106)	86 (66-99)^a	83 (70-94)^a
Diagnosis of CKD, n (%)	272 (42)	67 (40)	178 (53)^a	27 (18)^b
Early nephropathy, n (%)	179 (66)	50 (28)	118 (66)^a	11 (6)^a,b
Overt nephropathy, n (%)	93 (34)	17 (18)	61 (66)^a	15 (16)^b

p < 0.05 versus T2DM.

p < 0.05 versus T2DM+HBP.

BMI, body mass index; CKD, chronic kidney disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin; HBP, hypertension; HDL-C, high-density cholesterol; LDL-C, low-density cholesterol; SBP, systolic blood pressure; T2DM, type 2 diabetes mellitus; TC, total cholesterol; WC, waist circumference.

Demographic, metabolic, and cardiovascular parameters

A standard questionnaire for demographic information and clinical examination were applied. Weight, height, and waist circumference were measured by standardized methods. Body mass index (BMI) was calculated by dividing the patient's weight into kilograms by the height in square meters (Kg/m²). Glycosylated hemoglobin, fasting glucose, creatinine, uric acid, total cholesterol, low-density cholesterol (LDL-C), high-density cholesterol (HDL-C), and triglyceride levels were determined by usual methods in the Central Laboratory of the Specialty Hospital, IMSS. Diagnoses of T2DM, according to the American Diabetes Association criteria (Riddle, 2018), and of HBP, according to the 2017 ACC/AHA guidelines (Lloyd-Jones et al., 2017), were previously established by primary physicians.

The estimated glomerular filtration rate (eGFR) was calculated using the CKD epidemiology collaboration (CKD-EPI) creatinine equation (Levey and Stevens, 2010). Albuminuria was evaluated using dipsticks (Micral-test II; Roche Diagnostics GmbH, Mannheim, Germany) in first-void urine samples; positive results were confirmed by immunoturbidimetry (Vitros 5600 Integrated System; Ortho Clinical Diagnostics, Rochester, MN) and adjusted to the urinary creatinine level. We defined normal kidney function as the presence of an eGFR ≥90 mL/min/1.73 m² without albuminuria and CKD as the presence of albuminuria (≥30 mg/g) even with a normal eGFR (≥90 mL/min/1.73 m²) or a decreased eGFR (<90 mL/min/1.73 m²), and early nephropathy was defined as CKD stage 1 and 2, and overt nephropathy as CKD stage 3a, 3b, and 4 according to the Kidney Disease Improving Global Outcomes 2012 guidelines (Isakova et al., 2017).

Genotyping

Genomic DNA was obtained from peripheral leukocytes by a conventional salting-out method (Miller et al., 1988). The variants rs35652124 and rs6721961 were both genotyped using a polymerase chain reaction (PCR) with confronting two-pair primers assay, as previously described (Yin et al., 2012) with minor modifications. Briefly, cycling conditions consisted of 95°C for 1 min, 65°C for 60 s, and 72°C for 60 s, by 35 cycles, and 95°C for 60 s, 58°C for 60 s, and 72°C for 60 s by 30 cycles for rs35652124 and rs6721961, respectively. PCR products were electrophoresed on 6% polyacrylamide gels and visualized after silver staining. The variant rs2364723 was genotyped using a predesigned 5´-exonuclease TaqMan genotyping assay (Applied Biosystems, Foster City, CA) on a LightCycler^® 96 Instrument (Roche, CIY, Germany) real-time PCR. As a quality control, 10% of samples were randomly selected and re-genotyped; no inconsistency in the genotype assignment was found.

Statistical analysis

Demographical, metabolic, and cardiovascular data are shown as mean ± standard deviation, median and interquartile range, or percentage frequency, as appropriate. For comparisons according to the diagnosis (T2DM, T2DM+HBP, or HBP), Chi-squared test or one-way analyses of variance with Bonferroni post hoc correction was used, as appropriate. Allele frequencies were calculated by direct counting from observed genotypes. The maximum-likelihood method was used for the estimation of haplotype frequencies; linkage disequilibrium values and Hardy-Weinberg equilibrium (HWE) test were determined using HaploView 4.2 software (Barrett et al., 2005).

The associations of genotypes and haplotypes with all the parameters were analyzed by Binary Logistic Regression adjusted by age and sex separately for each study group (T2DM, T2DM+HBP, and HBP) and according to the carrier status for the minor allele of each SNV under a dominant genetic model. To reduce the risk of bias, Bonferroni correction for multiple testing was used considering all the three genetic markers and the three more frequent observed haplotypes (p = 0.05/3); so a p-value <0.017 was considered significant. Statistical analyses were performed using the statistical software package SPSS version 21.0 for Windows (SPSS, Chicago, IL).

Results

General, metabolic, and cardiovascular characteristics

A total of 651 patients (251 men and 400 women) were included in the study. Forty-two percent (n = 272) had CKD in the following stages: 179 patients with early nephropathy (66%) and 93 with overt nephropathy (34%). Age, BMI, and waist circumference values of patients with T2DM were significantly lower than those with T2DM+HBP or HBP, while patients with HBP had lower systolic blood pressure values compared with patients with T2DM+HBP. Patients with HBP had the highest levels of uric acid and HDL-C, and although they showed the lowest eGFR values, the prevalence of CKD in this group was the lowest (18%) (Table 1).

Genotype, allele, and haplotype frequencies and their association with CKD

Differences in metabolic, cardiovascular, and kidney parameters according to genotype and haplotype in the whole sample were not observed (Supplementary Table S1); so results are described according to the diagnosis group. Genotype proportions were within HWE in all the three groups, except for variant rs35652124 (p = 0.01; Supplementary Table S2) in patients with T2DM due to heterozygote deficiency. There was no significant difference regarding allele or haplotype frequencies among groups, although considering genotypes, lower homozygous rs35652124 G/G proportions were observed in patients with T2DM+HBP and HBP compared to subjects with only T2DM.

The association analyses of both genotypes and haplotypes are shown in Tables 2-5. Some associations were found significant after adjustments for age and sex as covariates. For rs35652124 variant, allele G was found to be protective for CKD and was marginally associated hyperuricemia in T2DM and with higher fasting glucose in T2DM+HBP group patients (Table 2). Regarding rs6721961, T2DM and HBP patients carrying allele A showed lower waist circumference and BMI values, respectively, than C/C homozygous individuals (Table 3).

Table 2.

Association of the Genetic Variant rs35652124 with Metabolic, Cardiovascular and Kidney Function Parameters According to the Diagnosis

Variable	^aAdjusted OR (95% CI), p
	T2DM (n = 166)	T2DM+HBP (n = 348)	HBP (n = 137)
	AG+GG	AG+GG	AG+GG
Genotype AA (reference)	1.0	1.0	1.0
BMI (≥25 kg/m²)	0.6 (0.2-1.6), 0.26	1.4 (0.7-2.6), 0.16	0.9 (0.3-2.9), 0.58
WC (M > 90: W > 80 cm)	1.5 (0.5-4.0), 0.28	0.9 (0.3-2.4), 0.58	1.6 (0.3-7.9), 0.38
Blood pressure (≥130/85 mmHg)	0.7 (0.3-1.6), 0.33	0.7 (0.4-1.2), 0.18	1.2 (0.5-2.6), 0.35
Uric acid (≥7 mg/dL)	0.3 (0.1-0.9), 0.05	0.7 (0.4-1.4), 0.26	1.0 (0.4-2.6), 0.52
Fasting glucose (≥130 mg/dL)	1.3 (0.6-3.0), 0.30	1.5 (1.0-2.7), 0.05	1.1 (0.4-3.1), 0.51
HbA1c (≥7%)	0.8 (0.3-1.9), 0.46	0.9 (0.5-1.5), 0.52	—
TC (≥200 mg/dL)	0.8 (0.4-1.6), 0.39	0.9 (0.6-1.5), 0.52	1.2 (0.6-2.6), 0.32
LDL-C (≥100 mg/dL)	1.5 (0.7-2.9), 0.16	1.0 (0.6-1.6), 0.52	1.5 (0.7-3.2), 0.17
HDL-C (≤50 mg/dL)	0.5 (0.2-1.3), 0.14	0.8 (0.5-1.3), 0.29	1.0 (0.5-2.1), 0.52
Triglycerides (≥150 mg/dL)	0.9 (0.4-1.9), 0.50	0.6 (0.4-1.0), 0.06	0.9 (0.4-1.9), 0.50
Albuminuria (≥30 mg/g creatinine)	0.5 (0.2-1.1), 0.07	0.8 (0.5-1.3), 0.30	0.9 (0.3-2.9), 0.57
eGFR (≤60 mL/min/1.73 m²)	0.6 (0.2-1.9), 0.31	1.1 (0.5-2.0), 0.44	0.4 (0.1-1.3), 0.13
Diagnosis of CKD (yes)	0.5 (0.2-0.9), 0.04	0.9 (0.5-1.5), 0.44	0.6 (0.2-1.5), 0.23
Early nephropathy (yes)	0.5 (0.2-1.1), 0.08	0.8 (0.5-1.4), 0.34	1.1 (0.3-4.7), 0.55
Overt nephropathy (yes)	0.6 (0.2-1.9), 0.31	1.1 (0.5-2.0), 0.44	0.4 (0.1-1.3), 0.13

Covariance in the adjusted models include age and sex.

CI, confidence interval; M, men; OR, odds ratio; W, women.

Table 3.

Association of the Genetic Variant rs6721961 with Metabolic, Cardiovascular, and Kidney Function Parameters According to the Diagnosis

Variable	^aAdjusted OR (95% CI), p
	T2DM (n = 166)	T2DM+HBP (n = 348)	HBP (n = 137)
	CA+AA	CA+AA	CA+AA
Genotype CC (reference)	1.0	1.0	1.0
BMI (≥25 kg/m²)	0.6 (0.2-1.4), 0.20	0.8 (0.4-1.5), 0.34	0.2 (0.1-0.6), 0.006
WC (M > 90: W > 80 cm)	0.2 (0.1-0.6), 0.006	0.7 (0.3-1.7), 0.34	0.2 (0.1-1.3), 0.08
Blood pressure (≥130/85 mmHg)	0.9 (0.4-1.9), 0.51	0.7 (0.4-1.2), 0.14	0.6 (0.3-1.3), 0.16
Uric acid (≥7 mg/dL)	0.6 (0.1-2.4), 0.39	1.1 (0.6-2.0), 0.34	1.2 (0.5-2.8), 0.35
Fasting glucose (≥130 mg/dL)	0.8 (0.3-1.7), 0.35	0.8 (0.4-1.3), 0.25	0.6 (0.2-1.7), 0.28
HbA1c (≥7%)	0.9 (0.4-1.9),0.46	0.7 (0.4-1.1), 0.09	—
TC (≥200 mg/dL)	1.0 (0.5-2.1), 0.46	0.8 (0.5-1.2), 0.20	0.7 (0.3-1.4), 0.24
LDL-C (≥100 mg/dL)	0.5 (0.2-1.0), 0.06	1.1 (0.6-1.7), 0.38	0.5 (0.2-1.1), 0.06
HDL-C (≤50 mg/dL)	0.9 (0.4-1.9), 0.48	0.8 (0.5-1.3), 0.30	1.5 (0.7-3.0), 0.14
Triglycerides (≥150 mg/dL)	1.2 (0.6-2.5), 0.31	0.6 (0.4-0.9), 0.05	1.1 (0.5-2.2), 0.41
Albuminuria (≥30 mg/g creatinine)	1.1 (0.5-2.3), 0.40	0.8 (0.5-1.3), 0.32	0.9 (0.3-2.6), 0.54
eGFR (≤60 mL/min/1.73 m²)	1.4 (0.4-4.1), 0.33	0.9 (0.5-1.7), 0.50	1.3 (0.4-3.9), 0.39
Diagnosis of CKD (yes)	1.2 (0.6-2.5), 0.29	0.9 (0.6-1.5), 0.52	1.1 (0.4-2.6), 0.48
Early nephropathy (yes)	1.1 (0.5-2.3), 0.44	1.0 (0.6-1.6), 0.51	0.8 (0.2-3.0), 0.52
Overt nephropathy (yes)	1.4 (0.4-4.1), 0.33	0.9 (0.5-1.7), 0.50	1.3 (0.4-3.9), 0.39

Covariance in the adjusted models include age and sex.

Finally, allele rs2364723G (Table 4) was found protective for hyperuricemia, albuminuria, and CKD (mainly for early nephropathy) in T2DM patients. Conversely, this same allele was observed associated with hyperglycemia in the T2DM+HBP group. However, after correction for multiple testing, only associations of rs6721961 with low BMI/waist circumference in T2DM and HBP groups (p = 0.006) and rs2364723 with low risk for CKD in T2DM group (p = 0.009) remained significant.

Table 4.

Association of the Genetic Variant rs2364723 with Metabolic, Cardiovascular, and Kidney Function Parameters According to the Diagnosis

Variable	^aAdjusted OR (95% CI), p
	T2DM (n = 166)	T2DM+HBP (n = 348)	HBP (n = 137)
	GC+GG	GC+GG	GC+GG
Genotype CC (reference)	1.0	1.0	1.0
BMI (≥25 kg/m²)	0.7 (0.3-1.9), 0.39	1.3 (0.7-2.6), 0.20	1.5 (0.5-4.5), 0.29
WC (M > 90: W > 80 cm)	1.3 (0.4-3.7), 0.37	0.7 (0.2-1.9), 0.34	1.9 (0.4-9.2), 0.31
Blood pressure (≥130/85 mmHg)	0.5 (0.2-1.1), 0.08	0.8 (0.4-1.3), 0.25	1.3 (0.6-2.9), 0.29
Uric acid (≥7 mg/dL)	0.3 (0.1-0.9), 0.03	0.9 (0.5-1.7), 0.51	1.1 (0.4-2.8), 0.46
Fasting glucose (≥130 mg/dL)	1.1 (0.5-2.7), 0.41	1.8 (1.0-3.1), 0.02	0.9 (0.3-2.6), 0.56
HbA1c (≥7%)	0.6 (0.2-1.5), 0.21	1.3 (0.7-2.0), 0.25	—
TC (≥200 mg/dL)	0.9 (0.4-1.9), 0.53	1.3 (0.8-2.1), 0.16	1.3 (0.6-2.8), 0.29
LDL-C (≥100 mg/dL)	1.2 (0.6-2.5), 0.31	1.1 (0.6-1.8), 0.37	1.4 (0.6-3.1), 0.23
HDL-C (≤50 mg/dL)	0.8 (0.3-1.8), 0.40	0.7 (0.4-1.3), 0.21	1.0 (0.4-2.1), 0.56
Triglycerides (≥150 mg/dL)	1.3 (0.6-2.6), 0.28	0.7 (0.4-1.2), 0.17	1.0 (0.5-2.3), 0.48
Albuminuria (≥30 mg/g creatinine)	0.4 (0.2-0.9), 0.02	1.1 (0.7-1.8), 0.33	1.1 (0.3-3.8), 0.53
eGFR (≤60 mL/min/1.73 m²)	0.7 (0.2-2.4), 0.43	1.0 (0.5-1.9), 0.51	0.7 (0.2-2.3), 0.40
Diagnosis of CKD (yes)	0.3 (0.1-0.8), 0.009	1.1 (0.7-1.8), 0.33	0.8 (0.3-2.1), 0.43
Early nephropathy (yes)	0.4 (0.1-0.8), 0.01	1.1 (0.6-1.9), 0.36	1.0 (0.2-4.0), 0.63
Overt nephropathy (yes)	0.7 (0.2-2.4), 0.43	1.0 (0.5-1.9), 0.51	0.7 (0.2-2.3), 0.40

Covariance in the adjusted models include age and sex.

Considering all the three loci, five NRF2 haplotypes were inferred, three of them, ACC (40%), AAC (30%), and GCG (27%) (loci order: rs35652124-rs6721961-rs2364723), represented ≥90% of all estimated haplotypes (Supplementary Table S2). In consequence, these three common haplotypes were considered for further analysis; homozygotes for major alleles, ACC haplotype, were considered the reference for all comparisons. As observed in the genotype analysis, protective effects were observed for some variables in all the three study groups (Table 5), but similar to genotypes, after correction for multiple comparisons, only AAC haplotype remained associated with low risk of increased waist circumference in patients with T2DM and with HBP.

Table 5.

Association of the NFE2L2 Haplotypes with Metabolic, Cardiovascular, and Kidney Function Parameters According to the Diagnosis

Variable	^aAdjusted OR (95% CI), p
	T2DM (n = 166)		T2DM+HBP (n = 348)		HBP (n = 137)
	AAC	GCG	AAC	GCG	AAC	GCG
Haplotype ACC (reference)	1.0	1.0	1.0	1.0	1.0	1.0
BMI (≥25 kg/m²)	0.7 (0.2-1.9), 0.36	1.0 (0.4-2.6), 0.54	0.8 (0.4-1.6), 0.33	0.7 (0.3-1.5), 0.28	0.1 (0.03-0.7), 0.009	0.3 (0.1-1.9), 0.19
WC (M > 90: W > 80 cm)	0.2 (0.1-0.8), 0.02	0.8 (0.2-3.1), 0.54	0.6 (0.2-1.6), 0.23	0.7 (0.2-2.1), 0.40	0.4 (0.3-0.5), 0.02	0.6 (0.1-4.5), 0.15
Blood pressure (≥130/85 mmHg)	0.8 (0.3-1.8), 0.41	0.5 (0.2-1.1), 0.07	0.8 (0.4-1.3), 0.24	1.3 (0.7-2.3), 0.18	0.8 (0.3-1.8), 0.39	1.2 (0.5-3.1), 0.37
Uric acid (≥7 mg/dL)	0.4 (0.1-1.8), 0.20	0.2 (0.1-0.9), 0.05	0.9 (0.4-1.7), 0.46	0.6 (0.3-1.2), 0.12	0.9 (0.3-2.3), 0.54	0.8 (0.2-2.3), 0.46
Fasting glucose (≥130 mg/dL	0.6 (0.2-1.5), 0.22	0.6 (0.2-1.4), 0.19	0.6 (0.3-1.1), 0.11	0.6 (0.3-1.1), 0.10	0.6 (0.2-1.8), 0.29	0.5 (0.1-1.7), 0.22
HbA1c (≥7%)	0.8 (0.3-1.9), 0.39	1.0 (0.4-2.3), 0.55	0.7 (0.4-1.3), 0.20	1.0 (0.6-1.9), 0.45	—	—
TC (≥200 mg/dL)	1.0 (0.4-2.2), 0.56	0.9 (0.4-1.9), 0.52	0.8 (0.4-1.3), 0.25	1.1 (0.6-1.9), 0.37	0.4 (0.1-0.9), 0.03	0.8 (0.3-1.9), 0.42
LDL-C (≥100 mg/dL)	0.5 (0.2-1.1), 0.06	0.9 (0.4-1.9), 0.48	1.2 (0.7-2.1), 0.23	1.0 (0.6-1.7), 0.51	0.4 (0.1-0.9), 0.04	1.4 (0.5-3.7), 0.33
HDL-C (≤50 mg/dL)	0.8 (0.3-2.0), 0.41	0.6 (0.2-1.4), 0.18	0.6 (0.3-1.1), 0.08	0.6 (0.3-1.1), 0.06	2.0 (0.9-4.6), 0.06	1.5 (0.6-3.5), 0.24
Triglycerides (≥150 mg/dL)	1.2 (0.5-2.8), 0.37	0.9 (0.4-1.8), 0.46	0.6 (0.3-0.9), 0.03	0.7 (0.4-1.3), 0.23	1.2 (0.5-2.6), 0.38	1.0 (0.4-2.5), 0.53
Albuminuria (≥30 mg/g creatinine)	1.0 (0.4-2.3), 0.51	0.5 (0.2-1.2), 0.09	0.8 (0.5-1.4), 0.34	1.7 (1.0-3.0), 0.03	0.6 (0.1-2.6), 0.42	1.2 (0.3-4.3), 0.50
eGFR (≤60 mL/min/1.73 m²)	1.5 (0.4-5.2), 0.33	0.9 (0.2-3.3), 0.60	0.9 (0.5-1.8), 0.54	0.9 (0.4-1.8), 0.50	0.7 (0.2-2.4), 0.42	0.3 (0.1-1.9), 0.20
Diagnosis of CKD (yes)	1.1 (0.5-2.5), 0.40	0.5 (0.2-1.2), 0.09	0.9 (0.5-1.5), 0.45	1.3 (0.7-2.2), 0.19	0.5 (0.3-2.2), 0.46	0.6 (0.1-1.9), 0.28
Early nephropathy (yes)	0.9 (0.4-2.2), 0.57	0.5 (0.2-1.1), 0.08	0.9 (0.5-1.6), 0.47	1.3 (0.8-2.4), 0.15	1.0 (0.2-4.5), 0.60	1.1 (0.2-5.2), 0.60
Overt nephropathy (yes)	1.5 (0.4-5.2), 0.33	0.9 (0.2-3.3), 0.60	0.9 (0.5-1.8), 0.54	0.9 (0.4-1.8), 0.50	0.7 (0.2-2.4), 0.42	0.3 (0.1-1.9), 0.20

Covariance in the adjusted models include age and sex. Haplotype frequency: ACC (40%); AAC (30%); GCG (27%); others (3%).

Finally, the relationship between CKD with metabolic, cardiovascular, and genetic variables according to the diagnosis group is shown in Table 6. HBP, T2DM vintage, and hyperuricemia were the strongest risk factors for the presence of CKD in T2DM and T2DM+HBP groups; in contrast, variants rs35652124 and rs2364723 were associated with low odds for CKD in patients with T2DM (odds ratio [OR] = 0.5, confidence interval at 95% [CI95%], 0.3-0.9; p = 0.04 and OR = 0.3, CI95%, 0.1-0.8; p = 0.009; respectively).

Table 6.

Association of Chronic Kidney Disease with Variants of the NFE2L2 Gene, Metabolic and Cardiovascular Parameters According to the Diagnosis

Variable	^aAdjusted OR (95% CI), p
	T2DM (n = 166)	T2DM+HBP (n = 348)	HBP (n = 137)
	CKD	CKD	CKD
Normal kidney function (reference)	1.0	1.0	1.0
BMI (≥25 kg/m²)	0.7 (0.3-1.5), 0.24	0.5 (0.2-0.9), 0.01	1.8 (0.4-8.7),0.33
WC (M > 90: W > 80 cm)	0.8 (0.3-2.0),0.40	0.4 (0.1-0.9), 0.04	0.5 (0.1-2.8),0.36
Blood pressure (≥130/85 mmHg)	5.7 (2.8-11.8), 0.0001	2.8 (1.8-4.4), 0.0001	0.6 (0.2-1.6), 0.26
Uric acid (≥7 mg/dL)	10.7 (2.3-49.4), 0.0001	1.7 (1.0-2.9), 0.03	3.5 (1.4-8.9), 0.008
Fasting glucose (≥130 mg/dL	1.0 (0.4-2.3), 0.51	1.2 (0.7-2.1), 0.19	1.8 (0.6-5.3), 0.19
HbA1c (≥7%)	1.0 (0.5-2.1), 0.52	1.9 (1.2-3.0), 0.002	—
TC (≥200 mg/dL)	1.8 (1.0-3.4), 0.04	1.4 (0.9-2.1), 0.06	1.3 (0.5-3.0), 0.34
LDL-C (≥100 mg/dL)	0.6 (0.3-1.2), 0.11	0.9 (0.6-1.5), 0.50	1.4 (0.5-3.8), 0.28
HDL-C (≤50 mg/dL)	1.8 (0.9-3.9), 0.06	1.1 (0.7-1.8), 0.27	0.9 (0.3-2.1), 0.51
Triglycerides (≥150 mg/dL)	1.2 (0.6-2.4), 0.26	0.9 (0.6-1.4), 0.41	1.2 (0.5-3.0), 0.36
rs35652124 (AG+GG)	0.5 (0.3-0.9), 0.04	0.9 (0.5-1.5), 0.44	0.6 (0.2-1.5), 0.23
rs6721961 (CA+AA)	1.2 (0.6-2.5), 0.29	0.9 (0.6-1.5), 0.52	1.1 (0.4-2.6), 0.48
rs2364723 (GC+GG)	0.3 (0.1-0.8), 0.009	1.1 (0.7-1.8), 0.33	0.8 (0.3-2.1), 0.43
Haplotype AAC	1.1 (0.5-2.5), 0.40	0.9 (0.5-1.5), 0.45	0.8 (0.3-2.2), 0.46
Haplotype GCG	0.5 (0.2-1.2), 0.09	1.3 (0.7-2.2), 0.19	0.6 (0.1-1.9), 0.28

Covariance in the adjusted models include age and sex.

Discussion

In this study, the association of genotypes and haplotypes of three variants of NFE2L2 gene with metabolic, cardiovascular, and kidney parameters, including the presence of CKD in patients with T2DM, HBP, or both, was investigated. As it was previously reported in Mexican patients (Cueto Manzano et al., 2014; López Leal et al., 2017), multiple risk factors for the onset and progression of renal impairment were common in our sample and were the main predictors of CKD such as uncontrolled glycemic and HBP, the presence of cardiovascular disease, and hyperuricemia. Therefore, clinical management aimed to achieve adequate control of these parameters should be paramount to prevent CKD in persons with T2DM and HBP.

It is also noteworthy that patients with only HBP were older and had lower eGFR values than patients with T2DM (with/without HBP), but the prevalence of CKD in the former was the lowest. This is important since given that a low GFR value not necessarily means presence of CKD, some have suggested that diagnosis of CKD based on GFR estimation should be adjusted by age, race, and other demographical variables, although this remains controversial (Cueto Manzano et al., 2014; Levey et al., 2015, 2020). Notwithstanding, all these factors are modifiable, and the goal of all therapeutic management is to avoid further complications; however, it is well recognized that, despite having proper therapeutic control, some patients will develop complications such as kidney disease (Covic et al., 2012).

This has led to hypothesize that other factors, such as genetic variations, could play a role in relevant cellular or physiological process related to kidney function in patients with diabetes or hypertension (Covic et al., 2012). In consequence, identification of nephropathy susceptibility genes could help to identify new therapeutic targets. Accordingly, in this study, we reported that three common variants in the promoter region of NFE2L2 gene, which codes for the transcription factor NRF2, a master redox regulator, were associated with some variables considered important in the risk for CKD assessment, such as blood pressure, hyperglycemia, albuminuria, hyperuricemia, and presence of CKD itself. The observed NFE2L2 allele frequencies were similar to those reported for other populations, including Mexicans (Córdova et al., 2010; Figarska et al., 2014; Shimoyama et al., 2014a; Jiménez Osorio et al., 2016).

Genetic association analysis revealed that T2DM patients carrying allele rs35652124G had a lower risk of CKD and rs2364723G had a lower risk of CKD and albuminuria. Similar results were observed for the haplotype, including these two alleles (rs356521224G-rs6721961C-rs2364723G), although without statistical significance. A study conducted in Mexican patients reported higher risk of lupus nephritis in heterozygous patients for rs35652124 variant compared to homozygous patients for the G allele (Córdova et al., 2010), which is according to the “protective” effect of the G allele observed in our study. On the other hand, it should be noted that other NFE2L2 gene variants have been associated with the presence of diabetes complications such as nephropathy and retinopathy, among others; however, there is not enough evidence to support our findings (Xu et al., 2016).

It has been suggested that these complications could be related to increased oxidative stress due to low NRF2 expression (Yamamoto et al., 2004; Marzec et al., 2007), as it was observed in diabetic rs35652124 AA subjects who presented greater decline in NRF2 expression compared with homozygous GG and showed strong association with risk of diabetic foot ulcers (Teena et al., 2020). However, more research about the influence of specific genotypes on the oxidative stress status will provide better understanding of the role of genetic variation in the susceptibility to kidney complications in diabetic or hypertensive patients.

On the other hand, it is well recognized that uric acid levels increase as kidney damage progresses due to its lower excretion (Mallat et al., 2016; Tseng et al., 2019); recent studies recommend consideration of this increment as causative of kidney injury by promoting oxidative stress, endothelial dysfunction, kidney fibrosis, and inflammation (Su et al., 2020). Our results suggest that minor alleles of the NFE2L2 variants studied here could be protective against negative effects of uric acid by preventing nitric oxide-mediated kidney vascular endothelium injury, as seen in dopaminergic cells SH-SY5Y and MES235 (Zhang et al., 2014). However, further experimental and clinical studies are needed to support this suggestion.

Both rs2364723 (allele G) and rs35652124 (allele G) represent a higher risk for hyperglycemia, there was no association with HbA1c >7% in patients with T2DM+HBP. Previously, the association of rs6721961 (allele C) variant with increased glucose concentrations in Mexican patients with diabetes and obesity was reported (Jiménez Osorio et al., 2016). In our sample, these three alleles were identified forming the haplotype GCG (rs35652124-rs6721961-rs2364723); however, no association with increased glucose of this haplotype was detected and no significant LD value was observed between these two loci (Supplementary Fig. S1). To date, few studies have evaluated the association of these variants with glucose levels, given that glucose homeostasis depends on multiple factors; more studies about the role of NFE2L2 genotypes (or haplotypes) in this regard are required.

Our study also revealed the association of rs6721961A allele with lower risk of increased waist circumference and high BMI in patients with T2DM and patients with HBP, respectively. Similar behavior of the haplotype AAC was observed (Table 5). Although the influence on overweight or obesity of this variant has been scarcely evaluated, a 10-year follow-up study reported that the rs2364723C allele was associated with a stronger decrease in BMI (Adam et al., 2017). To our knowledge, there is no association study of NFE2L2 haplotypes with overweight/obesity; our observations lead us to suggest that AAC haplotype could play a role in adiposity control.

In addition, the variant rs6721961 was slightly associated with low risk of high serum lipid concentrations in patients with T2DM with/without HBP, specifically for hypertriglyceridemia. These trends for association were also observed in the haplotype analysis, in which carrier patients of AAC haplotype showed lower risk of hypercholesterolemia and elevated levels of LDL-C.

A study reported association of rs2364723 variant with lower triglyceride levels (Figarska et al., 2014); this was not observed in our research; as mentioned above, it is plausible that the observed effect of rs2364723C allele could be explained, in part, by its proximity to rs6721961 locus, although no haplotype analysis was performed in such a study. In this sense, an experimental study revealed that NFE2L2 gene expression regulates the expression of lypogenic genes and affects the accumulation and deposition of lipids in aortic lesions (Huang et al., 2010; Barajas et al., 2011), suggesting that this genotype may have direct impact on lipid homeostasis (Huang et al., 2010).

Our study has both strengths and weaknesses. First, the patients were all Mexican-Mestizos residents of one geographical area and all the measurements were done by only one trained team, which reduce the risk of bias due to genetic substructure or differences in how evaluations were done. On the other side, the observed genetic associations could be of limited statistical power, given the small sample in groups, which drives us to be cautious in the interpretation of results. Also, the cross-sectional design allowed us to evaluate the associations at a given time, raising the need for longitudinal or intervention studies aimed at investigating the role of NFE2L2 genotypes in the onset and progression of CKD and to determine the relationship of risk factors with NRF2 expression.

The three analyzed variants and haplotypes do not exclude association with other loci, inside or outside the NFE2L2 gene and even more, with other proteins involved in NRF2 pathway. Finally, other possible confounding factors such as diet, exercise and/or drug treatment were not taken into account, which could increase the risk of misinterpretation due to analysis bias. Despite these limitations, we are confident that our study is relevant in the search for genetic markers in Mexican patients with diabetes/hypertension.

Conclusions

Our study revealed for the first time that variants of the NFE2L2 gene were associated with low risk for CKD and some unwanted traits related to kidney damage, such as HBP, hyperuricemia, and albuminuria in Mexican diabetic patients. Even though CKD is multifactorial in origin, analysis of NFE2L2 gene variants and its impact of kidney function and metabolic parameters open a new research area. This study could contribute to the design of specific interventions adapted to the genetic profile and as a reference in the search for prognostic markers that could help early detection of susceptible individuals to develop CKD as a complication of two of the most common diseases affecting the population, such as diabetes and hypertension.

Footnotes

Acknowledgment

We thank the personnel and patients of the Familiar Medical Units nos. 34, 78, and 51 by their support and collaboration to this study.

Authors' Contributions

E.F.G.G.: conceptualization, formal analysis, original draft, methodology, and review and editing. L.C.S.: conceptualization, original draft, methodology, and review and editing. A.M.C.M.: original draft, methodology, and review and editing. M.A.V.M.: formal analysis, original draft, and review and editing. R.S.M.Z.: formal analysis, original draft, and review and editing. J.L.L.: formal analysis, original draft, and review and editing. J.R.P.: formal analysis, original draft, and review and editing. F.M.C.: conceptualization, original draft, methodology, and review and editing.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This study was partially funded by the Jalisco Delegation of the IMSS. E.F.G.G. had fellowships by the National Council of Science and Technology (CONACyT) and IMSS (590174 and 991443591, respectively).

Supplementary Material

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