Abstract
Aims/Introduction
Diabetic nephropathy (DN) is a complication of diabetes mellitus that is characterized by the gradual loss of kidney function, which results in increased levels of albumin in the urine. The Pro12Ala polymorphism in the peroxisome proliferator‐activated receptor‐γ2 gene has been confirmed to improve insulin sensitivity, but its association with susceptibility to DN in patients with type 2 diabetes remains inconclusive.
Materials and Methods
To examine whether the Pro12Ala polymorphism leads to the development of DN, a case‐control study was carried out in 554 patients with type 2 diabetes. The genotypes of Pro12Ala polymorphism of the peroxisome proliferator‐activated receptor gamma 2 gene were analyzed by real‐time polymerase chain reaction with TaqMan® probe genotyping assay in all patients.
Results
The mean age of the study population was 57.7 ± 8.8 years, with average diabetes duration of 12.8 ± 6.9 years. The prevalence of albuminuria was 43.5%. The frequency of genotype Pro12Pro, Pro12Ala and Ala12Ala genotype were 92.6%, 7.0%, 0.4% in our study population, and 90.4%, 8.9% and 0.7% in normal urinary albumin‐to‐creatinine ratio group, respectively. The Ala carriers (Pro12Ala + Ala12Ala) had significantly lower urinary albumin‐to‐creatinine ratio (15.0 vs 20.5 mg/g, P = 0.001) and better renal function (estimated glomerular filtration rate 81.8 [69.8–97.6] vs 78.7 mL/min/1.73 m2 [61.6–96.2]; P = 0.05) compared with those with the genotype Pro12Pro. After adjustment for age, sex and other confounders, the odds ratio of albuminuria for the Ala12 allele was 0.428 (95% confidence interval 0.195–0.940, P = 0.034]).
Conclusions
Our results suggest that the peroxisome proliferator‐activated receptor gamma 2 Ala12 variant has significant protective effects against albuminuria and DN.
Keywords: Diabetic nephropathies, Peroxisome proliferator‐activated receptor gamma, Polymorphism
The peroxisome proliferator‐activated receptor‐γ2 Ala12 variant has significant protective effects against albuminuria and diabetic nephropathy. These findings suggest that genetic screening can help in the development of personalized therapies for diabetes.
Introduction
Diabetic nephropathy (DN) is a well‐known microvascular complication of diabetes mellitus, and currently the primary cause of chronic kidney disease and end‐stage renal disease (ESRD) globally1. DN is a syndrome characterized by the gradual loss of kidney function with pathological quantities of urine albumin excretion. For the timely intervention against DN, it is quite important to detect the increased urine albumin excretion as soon as possible. Epidemiological studies have shown that the prevalence rates of albuminuria range 19.5–49% among patients with type 2 diabetes mellitus2, 3, 4. Aging, sex, hypertension, hyperglycemia, abnormal lipid profile, smoking, insulin resistance and metabolic syndrome have been reported to be the risk factors for albuminuria2. In addition, increasing evidence shows that genetic factors play an important role in the development of DN5.
Peroxisome proliferator‐activated receptor gamma (PPAR‐γ), a ligand‐activated transcription factor, is a response for the regulation of numerous biological processes, such as cell proliferation, adipocyte cell differentiation and inflammation. Thiazolidinediones serve as PPAR‐γ agonists, and improve blood sugar control in type 2 diabetes mellitus patients through the enhancement of insulin sensitivity6. Furthermore, PPAR‐γ agonists ameliorate metabolic abnormalities of diabetes and consecutive DN7. The Pro12Ala polymorphism is the most frequent variation in the PPARγ gene and accounts for 4–14% of the population8, 9, 10, 11, 12, 13, 14. The Pro12Ala polymorphism is related to reducing the deoxyribonucleic acid (DNA) binding affinity and diminishing the transcriptional activity in vitro 15. Furthermore, individuals who carried the Ala allele had significant elevation in insulin sensitivity15, which might be a protective factor for DN9, 10, 13, 14, 16, 17, 18, 19.
Some studies supported that the PPAR‐γPro12Ala polymorphism might contribute to reducing the risk of DN in type 2 diabetes mellitus patients9, 10, 13, 14, 16, 17, 18, 19, but some other studies suggested there was no significant association8, 20, thus leaving uncertainty about its role in diabetic renal disease. To further understand this issue, we carried out a case–control study with type 2 diabetes mellitus patients of ethnic Chinese backgrounds in southern Taiwan to test the association between PPAR‐γPro12Ala polymorphism and albuminuria.
Methods
Patients with type 2 diabetes mellitus (n = 581) followed up at least 1 year before enrollment were recruited from the outpatient clinic at Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan, between January 2017 and October 2017. After excluding 27 patients with anuria or oliguria due to ESRD under regular hemodialysis, a total of 554 participants were analyzed, and participants were divided into normoalbuminuria and albuminuria groups in October 2017 according to the following definition. Albuminuria was evaluated on the basis of the urine albumin‐to‐creatinine ratio (UACR) obtained from the initial voiding of urine in the morning. Normoalbuminuria (NA) was defined as a UACR <30 mg/g of the last two of three urine samples. Microalbuminuria (MA) was defined as an UACR of ≥30 and <300 mg/g, and macroalbuminuria (MAA) was defined as an UACR ≥300 mg/g in at least two urine samples in the past 3‐ to 6‐month period21. Albuminuria was defined as the presence of MA or MAA. Diabetic retinopathy was evaluated through annual fundus photography by experienced diabetes doctors, and would be referred to ophthalmologists for abnormal findings.
All of the study participants received a standardized clinical and laboratory evaluation according to the standard clinical practice of our institution and recommendations from the Diabetes Association of Republic of China, Taiwan22. Information about alcohol use and smoking habits was obtained using questionnaires. Smoking habits are defined as positive for current smokers regardless of how much they smoked. Alcohol use was defined as positive for more than one drink per day for women and two drinks per day for men. Information regarding the use of antihypertensive medications, lipid‐lowering agents, oral blood glucose‐lowering agents and insulin treatments was collected from the electronic medical record systems at the hospital. Individuals were considered to have diabetes mellitus if they were taking diabetes medications or showed a hemoglobin A1c of ≥6.5% on repeated testing23. Hypertension was defined as either receiving antihypertensive medications or systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg24. The study protocol was approved by the Human Research Ethics Committee at our hospital, and all participants were given written‐form informed consent.
Genotyping
Each participant’s genomic DNA was extracted from leukocytes in peripheral blood samples. The Pro12Ala gene polymorphism was analyzed by real‐time polymerase chain reaction (PCR). The single‐nucleotide polymorphism (SNP) genotyping assays, also known as TaqMan assay, used to detect specific polymorphisms were purchased from Topgen Biotechnology (Kaohsiung City, Taiwan). SNP genotyping assays use TaqMan 5′‐nuclease chemistry for amplifying and detecting specific polymorphisms in purified genomic DNA samples. All probes for quantitative PCR assays were 5′ labeled with FAM/VIC as a reporter and 3′ labeled with minor groove binder non fluorescent quencher as a quencher. A total of 20 ng of DNA was amplified using the TaqMan SNP assay. Preparation of quantitative PCR reaction followed the manufacturer’s instructions (Topgen Biotechnology). The cycling parameters were as follows: (i) 5 min at 95°C; (ii) 30 s at 60°C; (iii) 40 cycles at 95°C for 3 s and then 60°C for 40 s; and (iv) 30 s at 60°C. Real‐time PCR was carried out using 2X AceQ Probe High ROX qPCR Master Mix (Topgen Biotechnology) on StepOne Plus Real Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA). Allelic discrimination was called by StepOne software v2.3 (Applied Biosystems, Grand Island, NY, USA).
Statistical analysis
Patients’ clinical and biochemical characteristics were presented as the mean ± standard deviation or percentages. The values of triglyceride, UACR, creatinine and estimated glomerular filtration rate (eGFR) were natural log‐transformed due to the non‐normal distribution. Data analysis was carried out using the statistical package for social sciences (SPSS) 25 software (IBM Corporation, Armonk, NY, USA). Statistical significance of the differences between the groups was determined by χ2‐tests for categorical variables, and unpaired Student’s t‐test for continuous variables. Binary logistic regression was used to describe the associations of variables with the presence of albuminuria controlling for potential confounders. In all statistical tests, P < 0.05 was considered statistically significant.
Results
Clinical and laboratory characteristics of the study participants are shown in Table 1. The Pro12Pro genotype was significantly higher in the albuminuria group when compared with the group without albuminuria (95.4 vs 90.4%, P = 0.025). Compared with the patients without albuminuria, those patients with albuminuria have a significantly high percentage of hypertension (82.2% vs 56.2%, P = 0.001), poor renal function (eGFR 70.6 vs 86.1 mL/min/1.73 m2, P = 0.001), higher triglyceride levels (130.0 vs 108.0 mg/dL, P = 0.001) and lower high‐density lipoprotein cholesterol levels (45.8 vs 48.5 mg/dL, P = 0.017). In addition, more patients with albuminuria receive insulin injection, take angiotensin‐converting enzyme inhibitor/angiotensin II receptor antagonist as antihypertensive treatment and use statin as lipid‐lowering drugs.
Table 1.
Clinical and laboratory characteristics of study patients with and without albuminuria
| Parameters | Normoalbuminuria | Albuminuria | P * |
|---|---|---|---|
| Sample size (n) | 313 | 241 | – |
| Age (years) | 60.8 ± 9.9 | 62.6 ± 9.5 | 0.033 |
| Sex (female) | 170 (54.3%) | 96 (39.8%) | 0.001 |
| Genotype | |||
| Pro/Pro | 283 (90.4%) | 230 (95.4%) | 0.025 |
| Pro/Ala + Ala/Ala | 30 (9.6%) | 11 (4.6%) | |
| Diabetes duration (years) | 13.2 ± 7.5 | 14.3 ± 8.2 | 0.098 |
| Hypertension (%) | 176 (56.2%) | 198 (82.2%) | 0.001 |
| SBP (mmHg) | 137.5 ± 17.8 | 140.3 ± 20.3 | 0.085 |
| DBP (mmHg) | 77.9 ± 12.3 | 77.9 ± 11.5 | 0.945 |
| Weight (kg) | 70.3 ± 14.0 | 70.3 ± 13.6 | 0.983 |
| BMI (kg/m2) | 26.8 ± 4.5 | 26.7 ± 4.0 | 0.701 |
| Waist (cm) | 90.0 ± 11.1 | 91.7 ± 9.9 | 0.063 |
| Metabolic syndrome (%) | 223 (71.2%) | 194 (80.5%) | 0.012 |
| HbA1c (%) | 7.4 ± 0.9 | 7.4 ± 1.1 | 0.284 |
| UACR (mg/g) | 9.4 (5.5–15.6) | 105.7 (50.0–242.2) | 0.001 |
| Serum creatinine (mg/dL) | 0.8 (0.7–1.0) | 1.0 (0.8–1.3) | 0.001 |
| eGFR (mL/min/1.73 m2) | 86.1 (71.8–99.1) | 70.6 (54.4–88.5) | 0.001 |
| Retinopathy (%) | 61 (19.5%) | 65 (27.0%) | 0.037 |
| Total cholesterol (mg/dL) | 167.8 ± 29 | 168.2 ± 33.4 | 0.893 |
| HDL (mg/dL) | 48.5 ± 12.4 | 45.8 ± 13.7 | 0.017 |
| LDL (mg/dL) | 89.3 ± 21.9 | 90.3 ± 25.7 | 0.653 |
| Triglyceride (mg/dL) | 108.0 (75.0–156.0) | 130.0 (86.3–188.0) | 0.001 |
| Smoking (%) | 28 (8.9%) | 26 (10.8%) | 0.468 |
| Alcohol consumption (%) | 20 (6.4%) | 15 (6.2%) | 0.937 |
| TZD (%) | 59 (18.8%) | 52 (21.6%) | 0.427 |
| Hypoglycemic treatment | |||
| OADs (%) | 263 (84.0%) | 176 (73.0%) | 0.007† |
| Insulin (%) | 6 (1.9%) | 6 (2.5%) | |
| Insulin + OADs (%) | 42 (13.4%) | 58 (24.1%) | |
| Diet control (%) | 2 (0.6%) | 1 (0.4%) | |
| Antihypertensive treatment | |||
| ACEI/ARB (%) | 139 (44.4%) | 179 (74.3%) | 0.001 |
| Lipid‐lowering drugs | |||
| Statin (%) | 228 (72.8%) | 194 (80.5%) | 0.036 |
| Fibrate (%) | 24 (7.7%) | 23 (9.5%) | 0.432 |
Data are expressed as the mean ± standard deviation, median (interquartile range) or n (%).
ACEI, angiotensin‐converting enzyme inhibitor; ARB, angiotensin II receptor antagonist; BMI, body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin A1c; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; OADs, oral antidiabetic drugs; SBP, systolic blood pressure; TZD, thiazolidinedione; UCAR, urine albumin‐to‐creatinine ratio.
P‐values were calculated by an unpaired Student’s t‐test or χ2 analysis, or Fisher’s exact test (†), sequentially.
As shown in Table 2, 513 (92.6%) patients were found to have the Pro12Pro (PP) genotype, 39 (7.0%) had the Pro12Ala (PA) genotype and two (0.4%) had the Ala12Ala (AA) genotype. The Ala12 allele frequency was 4%. The Pro12Ala genotyping call rate was 100%. The distribution of Pro12Ala polymorphism in our study participants followed the Hardy–Weinberg equilibrium (P = 0.184). There was no significant difference in age, sex, bodyweight, diabetes duration, glycated hemoglobin A1c value, oral antidiabetic drugs, antihypertensive treatment and lipid‐lowering drugs between individuals with and without the Ala12 allele (Table 2). The Ala carriers (PA and AA) had significantly lower UACR (15.0 vs 20.5 mg/g, P = 0.001) and better renal function (eGFR 81.8 [69.8–97.6] vs 78.7 mL/min/1.73 m2 [61.6–96.2], P = 0.05) compared with those with the Pro12Pro genotype. The PP genotype had clearly a higher percentage of DN (44.8% vs 26.8%, P = 0.025), as compared with PA and AA genotypes (Figure 1).
Table 2.
Clinical and laboratory characteristics of patients classified according to peroxisome proliferator‐activated receptor gamma Pro12Ala genotypes
| Parameters | Pro/Pro (513) | Pro/Ala (39) + Ala/Ala (2) | P * |
|---|---|---|---|
| Sample size (n) | 513 | 41 | – |
| Age (years) | 61.6 ± 9.9 | 61.5 ± 7.8 | 0.984 |
| Sex (female) | 244 (47.6%) | 22 (53.7%) | 0.452 |
| Diabetes duration (years) | 13.6 ± 7.7 | 14.7 ± 8.7 | 0.374 |
| Hypertension (%) | 346 (67.4%) | 28 (68.3%) | 0.911 |
| SBP (mmHg) | 138.8 ± 18.9 | 137.3 ± 19.3 | 0.614 |
| DBP (mmHg) | 78.0 ± 12.0 | 76.0 ± 11.0 | 0.299 |
| Weight (kg) | 70.2 ± 13.9 | 71.3 ± 12.2 | 0.631 |
| BMI (kg/m2) | 26.7 ± 4.3 | 27.3 ± 4.1 | 0.380 |
| Waist (cm) | 90.7 ± 10.6 | 91.4 ± 11.1 | 0.671 |
| Metabolic syndrome (%) | 388 (75.6%) | 29 (70.7%) | 0.484 |
| HbA1c (%) | 7.4 ± 1.0 | 7.4 ± 0.8 | 0.977 |
| UACR (mg/g) | 20.5 (8.5–87.4) | 15.0 (4.7–54.9) | 0.001 |
| Serum creatinine (mg/dL) | 0.9 (0.7–1.1) | 0.8 (0.7–1.0) | 0.011 |
| eGFR (mL/min/1.73 m2) | 78.7 (61.6–96.2) | 81.8 (69.8–97.6) | 0.05 |
| Retinopathy (%) | 117 (22.8%) | 9 (22.0%) | 0.992 |
| Total cholesterol (mg/dL) | 168.3 ± 31.1 | 163.5 ± 30.0 | 0.342 |
| HDL (mg/dL) | 47.4 ± 13.4 | 45.7 ± 7.6 | 0.212 |
| LDL (mg/dL) | 90.0 ± 23.5 | 86.9 ± 25.4 | 0.427 |
| Triglyceride (mg/dL) | 117.0 (80.0–174.0) | 109.0 (86.5–146.8) | 0.404 |
| Smoking (%) | 53 (10.3%) | 1 (2.4%) | 0.165 |
| Alcohol use (%) | 33 (6.4%) | 2 (4.9%) | 1.0 |
| TZD (%) | 105 (20.5%) | 6 (14.6%) | 0.369 |
| Hypoglycemic treatment | |||
| OADs (%) | 407 (79.3%) | 32 (78.0%) | 0.329† |
| Insulin (%) | 11 (2.1%) | 1 (2.4%) | |
| Insulin + OADs (%) | 93 (18.1%) | 7 (17.1%) | |
| Diet control (%) | 2 (0.4%) | 1 (2.4%) | |
| Antihypertensive treatment | |||
| ACEI/ARB (%) | 291 (56.7%) | 27 (65.9%) | 0.255 |
| Lipid‐lowering drugs | |||
| Statin (%) | 389 (75.8%) | 33 (80.5%) | 0.500 |
| Fibrate (%) | 46 (9.0%) | 1 (2.4%) | 0.239 |
Data are expressed as the mean ± standard deviation, median (interquartile range) or n (%).
ACEI, angiotensin‐converting enzyme inhibitor; ARB, angiotensin II receptor antagonist; BMI, body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin A1c; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; OADs, oral antidiabetic drugs; SBP, systolic blood pressure; TZD, thiazolidinedione; UCAR, urine albumin‐to‐creatinine ratio.
P‐values were calculated by an unpaired Student’s t‐test, chi‐squared test mark analysis or Fisher’s exact test (†), sequentially.
Figure 1.
Albuminuria prevalence of the peroxisome proliferator‐activated receptor gamma Pro12Ala polymorphism in type 2 diabetes patients. The peroxisome proliferator‐activated receptor gamma 2 Ala12 variant has significant protective effects against albuminuria and diabetic nephropathy. These findings suggest that genetic screening can help in the development of personalized therapies for diabetes. DN, diabetic nephropathy; No‐DN, no diabetic nephropathy.
The distribution of PPAR‐γ genotypes and their relationship with DN is shown in the Table 3. The frequency of genotype PP, PA and AA were 90.4%, 8.9% and 0.7% in the NA group; 94.7%, 5.3% and 0.0% in the MA group; and 98.0%, 2.0% and 0% in the MAA group, respectively. There is a linear‐by‐linear decrease in the association between (P = 0.012) NA, MA and MAA with Ala allele (Table 3), suggesting that the Ala allele might be a protective factor that shields an individual from the presence of albuminuria.
Table 3.
Distribution of peroxisome proliferator‐activated receptor gamma genotypes in different categories of diabetic nephropathy
| Diabetic nephropathy | n | Genotype | Allele | |||||
|---|---|---|---|---|---|---|---|---|
| Pro12Pro (PP) | Pro12Ala (PA) | Ala12Ala (AA) | P * | Pro (P) | Ala (A) | P * | ||
| Normoalbuminuria | 313 | 283 (90.4) | 28 (8.9) | 2 (0.7) | 0.181 | 594 (94.9) | 32 (5.1) | 0.012† |
| Microalbuminuria | 189 | 179 (94.7) | 10 (5.3) | 0 (0) | 368 (97.4) | 10 (2.6) | ||
| Macroalbuminuria | 52 | 51 (98.0) | 1 (2.0) | 0 (0) | 103 (99.0) | 1 (1.0) | ||
Data are expressed as percentages. *P‐values were calculated by χ2 analysis or the Mantel–Haenszel test for trends (†).
To clarify the contributions of the PPARγ Pro12Ala gene polymorphism to the risk of albuminuria, multivariate logistic regression analyses were carried out with the possible confounders. After adjustment for univariate parameters, including age, sex, genotype, diabetes duration, hypertension, systolic blood pressure, metabolic syndrome, eGFR, retinopathy, high‐density lipoprotein, triglyceride, hypoglycemic treatment, the use of angiotensin‐converting enzyme inhibitor/angiotensin II receptor antagonist and statin, the Ala12 variant of Pro12Ala polymorphism is a significantly protective factor for albuminuria (odds ratio 0.428, 95% confidence interval 0.195–0.940, P = 0.034; Table 4).
Table 4.
Logistic regression analysis for the risk of albuminuria
| Parameters | Univariate analysis | Multivariate analysis* | ||
|---|---|---|---|---|
| OR (95% CI) | P‐value | OR (95% CI) | P‐value | |
| PPAR‐γ (Ala12) | 0.451 (0.221–0.920) | 0.025 | 0.428 (0.195－0.940) | 0.034 |
| Age | 1.019 (1.001–1.037) | 0.033 | – | – |
| Male sex | 1.796 (1.277–2.524) | 0.001 | 1.795 (1.169－2.757) | 0.008 |
| Hypertension | 3.584 (2.407–5.338) | 0.001 | – | – |
| Metabolic syndrome | 1.666 (1.115–2.490) | 0.012 | – | – |
| eGFR | 0.974 (0.967–0.982) | 0.001 | 0.985 (0.976－0.994) | 0.001 |
| Retinopathy | 1.526 (1.024–2.274) | 0.037 | 1.725 (1.087－2.739) | 0.021 |
| HDL | 0.983 (0.969–0.997) | 0.017 | – | – |
| Triglyceride | 1.004 (1.002–1.007) | <0.001 | 1.003 (1.000－1.006) | 0.040 |
| Hypoglycemic treatment | 1.395 (1.128–1.724) | 0.007 | 1.348 (1.049－1.732) | 0.020 |
| ACEI/ARB use | 3.614 (2.509–5.205) | <0.001 | 2.554 (1.331－4.900) | 0.005 |
| Statin use | 0.650 (0.434–0.974) | 0.036 | – | – |
Adjusted for age, sex, genotype, diabetes duration, hypertension, systolic blood pressure (SBP), waist, metabolic syndrome, estimated glomerular filtration rate (eGFR), retinopathy, high‐density lipoprotein (HDL), triglyceride, hypoglycemic treatment, the use of angiotensin‐converting enzyme inhibitor (ACEI)/angiotensin II receptor antagonist (ARB) and statin.
Discussion
The present study demonstrated that the Ala12 variant of the Pro12Ala polymorphism of the PPARγ2 gene showed significant risk reduction with albuminuria after adjustments were made for the related risk factors. The Pro12 allele is significantly associated with higher serum creatinine and UACR levels. In Caucasian populations9, 16, 17, the Ala12 allele had shown a protective effect against worsening albuminuria and DN. However, the protective role of the Pro12Ala polymorphism is not consistent in Asian studies, and Mori et al.8 reported no significant difference in nephropathy prevalence between PP and PA + AA genotypes among the Japanese population. The present results are in agreement with the results from Li et al.10 and Liu et al.13, which reported the Ala12 variant significantly protects against DN in a central Han cluster of Chinese patients25. To the best of our knowledge, this is the first report of Pro12Ala polymorphism significantly related to a reduced risk of albuminuria for type 2 diabetes mellitus patients in Taiwan, mostly of southern Han Chinese ancestry26.
The first evidence for the connection between the Pro12Ala polymorphism in the PPARγ gene and improving insulin sensitivity was reported by Deeb et al. 15 Furthermore, gene knockout mouse models confirmed that PPARγ plays an important role in regulating insulin sensitivity27, which might be connected to the pathogenesis of albuminuria28. Currently, it is recognized that the Pro12Ala polymorphism plays an important role in the risk reduction of albuminuria in patients with type 2 diabetes mellitus 9, 10, 13, 14, 16, 17, 18, 19. In addition, the Ala12 allele showed enhanced resistance of oxidative stress29. Oxidative stress is associated with hyperglycemia, insulin resistance and DN30. Thus, the Pro12Ala polymorphism might alleviate DN through the mechanism of improving insulin sensitivity and increasing resistance of oxidative stress.
The United Kingdom Prospective Diabetes Study revealed that independent risk factors for the development of albuminuria were male sex, increased waist circumference, triglyceride, low‐density lipoprotein, glycated hemoglobin A1c, smoking and previous retinopathy31. Some studies have shown an association between albuminuria and hypertension32, 33. In the present study, individuals with albuminuria are significantly associated with male sex, triglyceride and retinopathy, but not with higher waist circumference, low‐density lipoprotein, glycated hemoglobin A1c and hypertension after adjustment for confounders. Furthermore, the Pro12 allele is an additional risk factor.
A meta‐analysis showed the Ala carriers have a lower chance of developing diabetic retinopathy among Caucasian type 2 diabetes mellitus patients, but not among Asian patients18. The ethnic differences might be related to the fact that the Ala allele is detected more often in Caucasians (14%), but is relatively lower in Asians (4% of Japanese and 4% of Chinese)8, 9, 10, 11, 12, 13, 14. The present study shows that the Ala carriers also are less likely to develop diabetic retinopathy compared with the Pro12Pro genotype, but is not statistically significant, partly due to the low frequency of Ala carriers in the present series. As a complex disease, diabetic retinopathy is a complication involving polygenic and environmental factors, and the contributing factor of the Pro12Ala polymorphism in the PPARγ2 gene should require further studies to elucidate its role.
However, there were several limitations to the present study. First, this study was a case–control study; therefore, it could not explore causal relationships between the risk factors and the development of albuminuria and DM nephropathy. Second, those patients with ESRD were excluded from the study for anuria. Therefore, we could not survey the relationship between the polymorphism and ESRD risk.
In conclusion, the present study shows that the PPAR‐γ2 Ala12 variant is related to risk reduction of albuminuria among patients with type 2 diabetes mellitus. Further studies are still required to elucidate the role of this polymorphism for predicting and treating kidney dysfunction in type 2 diabetes mellitus.
Disclosure
The authors declare no conflict of interest.
Acknowledgments
This work was supported by the Chang Gung Memorial Hospital under grant [CORPG8F1001]. We thank Hsin‐Yi Chien, Chih‐Yun Lin and the Biostatistics Center, Kaohsiung Chang Gung Memorial Hospital, for statistics work.
J Diabetes Investig 2020; 11: 923–929
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