Journal of Diabetes Research

Journal of Diabetes Research / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 6353949 | https://doi.org/10.1155/2020/6353949

Naowanit Nata, Ram Rangsin, Ouppatham Supasyndh, Bancha Satirapoj, "Impaired Glomerular Filtration Rate in Type 2 Diabetes Mellitus Subjects: A Nationwide Cross-Sectional Study in Thailand", Journal of Diabetes Research, vol. 2020, Article ID 6353949, 9 pages, 2020. https://doi.org/10.1155/2020/6353949

Impaired Glomerular Filtration Rate in Type 2 Diabetes Mellitus Subjects: A Nationwide Cross-Sectional Study in Thailand

Academic Editor: Michaelangela Barbieri
Received10 Feb 2020
Revised15 Jul 2020
Accepted31 Jul 2020
Published13 Aug 2020

Abstract

Background. Type 2 diabetic mellitus (T2DM) patients with impaired renal function have a higher risk of mortality, and often progress to end-stage renal disease. The study aims to determine the prevalence of kidney disease and investigate the relationship between various factors and impaired renal function in a large population of patients with T2DM. Methods. We conducted a cross-sectional study among 30,377 patients from a nationwide diabetes study involving 602 Thai hospitals. Impaired glomerular filtration rate (GFR) was defined as <60 mL/min per 1.73 m2. Multivariate logistic regression was used to determine the association between standard risk factors and impaired GFR. Results. The prevalence of impaired GFR in a T2DM population was 39.2%. After adjusting for multiple risk factors, advanced age (adjusted OR 11.69 ()), macroalbuminuria (adjusted OR 3.54 ()), high serum uric acid (adjusted OR 2.06 ()), systolic BP 130-139 mmHg (adjusted OR 3.21 ()), hemoglobinA1C (HA1C) <6% (adjusted OR 3.71 ()), and HA1C >7% (adjusted OR 2.53 ()) were found to be associated with a significantly increased risk of impaired GFR among T2DM patients. Conclusion. Almost 40% of patients with T2DM in a nationwide cross-sectional study in Thailand had impaired GFR. Advanced age, albuminuria, hyperuricemia, hypertension, HA1C <6%, and HA1C >7% were independently associated with increased prevalence of impaired GFR.

1. Background

In 2010, the worldwide adult population with type 2 diabetes mellitus (T2DM) was estimated to be about 285 million and by 2030 an increase of 54% to about 439 million is predicted, reflecting a remarkable increase in renal complications [1]. Patients with T2DM with impaired renal function have become an important public health problem [2], and approximately 40% of patients with diabetes develop kidney disease resulting in albuminuria reduced glomerular filtration rate (GFR) or both [3]. The presence of impaired GFR and high levels of renal injury biomarkers had a more rapid decline in renal function and progression of renal disease [4, 5]. Moreover, patients with T2DM with renal impairment and/or albuminuria have an increased mortality risk, especially a higher risk of cardiovascular death, when compared with other diabetic patients without renal impairment [6].

Prevalence rates of chronic kidney disease (CKD) in the general population and among patients with T2DM in Thailand have been documented at a significantly higher rate than those previously reported in other populations [79]. Significantly impaired renal function is defined as estimated GFR less than 60 mL/min/1.73 m2, while markers of kidney damage include albuminuria and/or proteinuria. The Kidney Disease: Improving Global Outcomes (KDIGO) Guidelines recommend staging of CKD based on the underlying cause, estimated GFR, and level of albuminuria. T2DM with CKD often progresses to end-stage renal disease (ESRD). Thus, risk factors that are identified and treated at an early stage may prevent or slow the progression to ESRD in T2DM. Several studies have been conducted on the prevalence of CKD and forms of renal disease among patients with T2DM in Asian countries [1013]. Limited studies have been conducted in a randomly selected T2DM population sample in Thailand [8, 9]. Moreover, the lack of general hospitalized-based screening programs in Thailand has led to patients with T2DM being detected with advanced CKD stage. Quite possibly, early detection of risk factors of kidney disease might have an impact on this problem through earlier intervention. The authors conducted a cross-sectional study to determine the prevalence of kidney disease among patients with T2DM and investigated the relationship between various factors and impaired renal function in a large population of patients with T2DM in a nationwide survey in Thailand.

2. Methods

2.1. Study Population

The present study comprised a nationwide, multicenter, cross-sectional survey of outpatients with T2DM across Thailand. The analysis was performed using the Diabetes Dataset of Medical Records, an ongoing nationwide project, collected between 2013 and 2014. This project was administered by the Medical Research Network of the Consortium of Thai Medical Schools (MedResNet) Thailand, under the sponsorship of the Thai National Health Security Office. Patients and hospitals were sampled using a proportional to size stratified cluster sampling approach, and then, outpatients with T2DM were proportionally sampled from the 602 participating hospitals across Thailand. The study was approved by the Ethics Review Committee for Research in Human Subjects, Ministry of Public Health, Thailand. Informed consent was obtained from all patients.

Patients with T2DM, receiving medical care in participating hospitals for at least 12 months, were included in the present study. T2DM was reviewed using retrieved medical and personal data, including baseline demographic characteristics, hypertension, use of antihypertensive or lipid-lowering medications, comorbidities, available electrocardiogram (ECG) data and results, and biochemical variables including urine albumin, serum creatinine, uric acid, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, fasting plasma glucose levels, and hemoglobinA1C (HA1C). Only patients with serum creatinine available were included in the study. Exclusion criteria included acute kidney injury episode, pregnancy, unspecified type of DM, and patient life expectancy <1 year.

Trained research nurses and technicians administered a structured questionnaire and performed blood pressure (BP) and anthropometric measurements. BP was measured using a mercury sphygmomanometer and included three measurements. The mean of the second and third systolic and diastolic BP measurements was used in the analysis. Body weight, height, waist circumference, and hip circumference were measured according to standard protocol. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. All participants received treatment based on the standard strategies for diabetes, hypertension, and hyperlipidemia.

The presence of impaired GFR or CKD was assessed by measuring serum creatinine. An estimate of the GFR was obtained by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [14]. Patients were assigned to one of the following categories of eGFR: 1 (≥90), 2 (60–89), 3 (30–59), 4 [15–29], and 5 (<15 mL/min/1.73 m2). Finally, subjects were classified as having no CKD or CKD on the basis of the value of GFR <60 mL/min per 1.73 m2, according to the National Kidney Foundation’s Kidney Disease Outcomes Quality Initiative.

2.2. Statistical Analysis

Continuous data were described as mean and standard deviation (SD). Categorical variables were described in percentage. Unpaired Student’s -test was used to compare continuous variables and the chi-square test was used to evaluate proportions between groups. To determine associations with impaired GFR and various factors, we first examined the unadjusted relationships (odds ratio (OR) with 95% confidence intervals (CI) and then adjusted the models for age, sex, BMI, waist circumference, smoking, systolic BP, diastolic BP, HA1C, hemoglobin, serum uric acid, triglycerides, HDL-cholesterol, and albuminuria using multivariate logistic regression analysis. All results were considered significant when value was <0.05.

3. Results

A total of 30,377 patients were included in the study of whom 11,909 (39.2%) had GFR <60 mL/min per 1.73 m2. The majority with impaired GFR were at stage 3 (,729, 32%) with a few patients at stage 4 CKD (, 5.2%) or stage 5 CKD (, 2%) (Figure 1). One single random UACR was obtained among 3,243 patients (10.6%). The average duration of diabetes was years. The mean HbA1C was , and 31.3% of patients had an HbA1C <7%. The mean systolic and diastolic BP were and  mmHg, respectively, while 44.9% of patients had a systolic BP <130 mmHg and 70.7% of patients had a diastolic BP <80 mmHg.

The clinical characteristics of patients with and without impaired GFR are shown in Table 1. In the entire population, 76.8% had hypertension, 69.9% had dyslipidemia, 7.1% had diabetic neuropathy, 6.6% had coronary heart disease, 4% had gout, 3.1% had cerebrovascular disease, and 0.6% had peripheral arterial disease as comorbid diseases. All comorbid diseases except dyslipidemia were significantly higher among patients with T2DM with impaired GFR. Age, percentage of females, and systolic BP were higher () among patients with impaired GFR. Percentage of factors related to smoking, body weight, BMI, diastolic BP, and waist circumference was lower () among patients with impaired GFR (Table 1).


Baseline profilesTotal
Impaired GFR
<60 mL/min/1.73 m2
Nonimpaired GFR
≥60 mL/min/1.73 m2
value

Age (years)<0.001
Female (n, %)20955 (69%)8424 (70.7%)12531 (67.9%)<0.001
Body weight (kg)<0.001
BMI (kg/m2)<0.001
Waist circumference (cm)<0.001
Smoking (n, %)1346 (4.4%)376 (3.2%)970 (5.3%)<0.001
Systolic BP (mmHg)<0.001
Diastolic BP (mmHg)<0.001
Comorbid diseases
Hypertension (n, %)23,342 (76.8%)10,102 (84.8%)13,240 (71.7%)<0.001
Dyslipidemia (n, %)21,238 (69.9%)8,395 (70.5%)12,843 (69.5%)0.078
Gout (n, %)1,204 (4%)755 (6.3%)449 (2.4%)<0.001
Cerebrovascular disease (n, %)946 (3.1%)467 (3.9%)479 (2.6%)<0.001
Coronary heart disease (n, %)2010 (6.6%)1072 (9%)938 (5.1%)<0.001
Peripheral arterial disease (n, %)181 (0.6%)110 (0.9%)71 (0.4%)<0.001
Diabetic neuropathy (n, %)2166 (7.1%)1048 (8.8%)1118 (6.1%)<0.001

All values are expressed as and percentage.
Abbreviation: BMI: body mass index; BP: blood pressure; GFR: glomerular filtration rate.

The current medications of patients with and without impaired GFR are shown in Table 2. For BP-lowering agents, the percentage of those using diuretics, antiadrenergic drugs, calcium channel blockers, and vasodilators and females was higher () among patients with impaired GFR. For glycemic and lipid-lowering agents, the percentage of those using biguanides, sulfonylurea, and thiazolidinedione was lower () among patients with impaired GFR, but the percentage of those using insulin and fibrates was higher () among patients with impaired GFR.


MedicationsTotal
Impaired GFR
<60 mL/min/1.73 m2
Non-impaired GFR
≥60 mL/min/1.73 m2
value

Blood pressure lowering agents
Using any blood pressure lowering agents22148 (72.9%)9668 (81.2%)12480 (67.6%)<0.001
(i) Diuretics6360 (20.9%)3168 (26.6%)3192 (17.3%)<0.001
(ii) Antiadrenergic drugs6139 (20.2%)3007 (25.2%)3132 (17%)<0.001
(iii) Calcium channel blockers11418 (37.6%)5525 (46.4%)5893 (31.9%)<0.001
(iv) ACEI/ARB16447 (54.1%)6369 (53.5%)10078 (54.6%)0.063
(v) Vasodilators1009 (3.3%)739 (6.2%)270 (1.5%)<0.001
Glycemic lowering agents
Using any glycemic lowering agents29317 (96.5%)11328 (95.1%)17989 (97.4%)<0.001
(i) Biguanides21591 (71.1%)6143 (51.6%)15448 (83.6%)<0.001
(ii) Sulfonylurea19524 (64.3%)6716 (56.4%)12808 (69.4%)<0.001
(iii) Thiazolidinedione2527 (8.3%)906 (7.6%)1621 (8.8%)<0.001
(iv) Alpha-glucosidase inhibitor189 (0.6%)65 (0.5%)124 (0.7%)0.174
(v) DPP-4 inhibitors209 (0.7%)89 (0.7%)120 (0.6%)0.315
(vi) GLP-1 agonists18 (0.1%)8 (0.1%)10 (0.1%)0.649
(vii) Insulin6893 (22.7%)3875 (32.5%)3018 (16.3%)<0.001
Lipid lowering agents
Using any lipid lowering agents21835 (71.9%)8723 (73.2%)13112 (71%)<0.001
(i) Statin18205 (59.9%)7176 (60.3%)11029 (59.7%)0.351
(ii) Fibrate4242 (14%)1810 (15.2%)2432 (13.2%)<0.001

All values are expressed as percentage.
Abbreviation: ACEI: angiotensin converting enzyme inhibitor; ARB: angiotensin receptor blocker; DPP-4 inhibitors: dipeptidyl peptidase-4 inhibitors; GLP-1 agonist: glucagon-like peptide 1 receptor agonists.

Table 3 displays the patient data according to impaired GFR status. The mean values of estimated GFR among patients with and without impaired GFR were  mL/min/1.73 m2 and  mL/min/1.73 m2, respectively. The impaired GFR patient group had significantly lower fasting plasma glucose, HA1C, hemoglobin, and HDL-cholesterol and had higher serum uric acid, serum potassium, total cholesterol, triglycerides, and albuminuria.


Laboratory profilesTotal
Impaired GFR
<60 mL/min/1.73 m2
Non-impaired GFR
≥60 mL/min/1.73 m2
value

BUN (mg/dL)<0.001
Serum creatinine (mg/dL)<0.001
Estimated GFR (mL/min/1.73 m2)<0.001
Fasting plasma glucose (mg/dL)<0.001
HemoglobinA1C (%)0.001
Hemoglobin (g/dL)<0.001
Serum potassium (mEq/L)<0.001
Serum uric acid (mg/dL)<0.001
Total cholesterol (mg/dL)<0.001
Triglycerides (mg/dL)<0.001
HDL-cholesterol (mg/dL)<0.001
LDL-cholesterol (mg/dL)0.399
Median UACR (mg/gCr)30.1 (17, 118)50 (30, 201.4)30 (12.6, 82)<0.001

All values are expressed as and percentage.
Abbreviation: BUN: blood urea nitrogen; GFR: glomerular filtration rate; HDL: high density lipoprotein; LDL: low density lipoprotein; UACR: urine albumin creatinine ratio.

To identify putative risk factors associated with impaired GFR, we performed multivariate logistic regression analysis in the model for all variables. A significant association was found regarding various clinical and laboratory factors with impaired GFR in the entire population as an unadjusted risk ratio as shown in Table 4. After adjusting for multiple factors, advanced age, high systolic BP, HA1C <6%, HA1C >7%, high serum uric acid, and high albuminuria were associated with a significantly increased risk of impaired GFR. Diastolic BP differed significantly among individuals with and without impairment but was not independently associated with impaired GFR.


FactorsCrude OR (95% CI) valueAdjusted OR (95% CI) value

Female1.15 (1.09, 1.2)<0.0011.01 (0.54, 1.91)0.964
Age (years)
 <50Reference1Reference1
 50-592.2 (2.00, 2.43)<0.0012.97 (0.77, 11.52)0.114
 60-695.39 (4.90, 5.93)<0.00111.68 (3.13, 43.61)<0.001
 >7012.55 (11.36, 13.87)<0.00121.85 (5.64, 84.69)<0.001
BMI (kg/m2)
 BMI ≤22.9Reference1Reference1
 BMI 23-24.90.75 (0.70, 0.80)<0.0010.98 (0.46, 2.09)0.950
 Obese I, BMI 25-29.90.66 (0.63, 0.70)<0.0010.71 (0.33, 1.52)0.375
 Obese II, BMI ≥300.52 (0.48, 0.56)<0.0010.86 (0.29, 2.57)0.780
Waist circumference (cm)0.99 (0.99, 0.99)<0.0010.99 (0.95, 1.02)0.423
Smoking0.90 (0.84, 0.96)0.0021.35 (0.60, 3.01)0.464
Systolic BP (mmHg)
 <120Reference1Reference1
 120-1291.15 (1.07, 1.24)<0.0012.72 (1.05, 7.00)0.038
 130-1391.32 (1.23, 1.42)<0.0013.21 (1.30, 7.96)0.012
 >1401.81 (1.68, 1.95)<0.0012.34 (0.92, 5.98)0.075
Diastolic BP (mmHg)
 <80Reference1Reference1
 80-890.64 (0.61, 0.68)<0.0010.52 (0.28, 1.03)0.052
 90-990.54 (0.47, 0.61)<0.0010.66 (0.15, 2.93)0.581
 ≥1000.94 (0.62, 1.45)0.7921.00 (0.00, 1.00)0.999
HemoglobinA1C (%)
 <61.14 (1.05, 1.24)0.0023.71 (1.65, 8.32)0.001
 6-7Reference1Reference1
 >70.87 (0.81, 0.92)<0.0012.53 (1.38, 4.63)0.003
Hemoglobin (g/dL)0.69 (0.68, 0.71)<0.0010.86 (0.73, 1.01)0.071
Serum uric acid (mg/dL)1.42 (1.38, 1.46)<0.0012.06 (1.73, 2.46)<0.001
HDL-cholesterol (mg/dL)0.99 (0.99, 0.99)<0.0011.00 (0.99, 1.02)0.864
UACR (mg/gCr)
 <30Reference1Reference1
 30-3002.10 (1.77, 2.50)<0.0012.47 (1.39, 4.40)0.002
 >3005.15 (3.77, 7.03)<0.0013.54 (1.50, 8.40)0.004

All associations significant at P<0.05.
Abbreviation: BP: blood pressure; BMI: body mass index; UACR: urine albumin creatinine ratio.
aAdjusted by age, sex, BMI, waist circumference, smoking, systolic BP, diastolic BP, HA1C, hemoglobin, serum uric acid, triglycerides, HDL-cholesterol, and albuminuria.

Compared with age <50 years, the multivariate-adjust odds for impaired GFR of 60-69 years, and >70 years were 11.69 () and 21.85 (), respectively. The multivariate-adjust odds for impaired GFR of systolic BP 120-129 mmHg and 130-139 mmHg were 2.72 () and 3.21 (), respectively, compared with systolic BP <120 mmHg. The multivariate-adjust odds for impaired GFR of albuminuria 30-300 mg/gCr and > 300 mg/gCr were 2.47 () and 3.54 (), respectively, compared with albuminuria <30 mg/gCr (Table 4).

The multivariate-adjusted odds for impaired GFR of HA1C <6%, and >7% were 3.71 () and 2.53 (), respectively, compared with HA1C 6-7% (Table 4). Finally, a clear J-shaped relationship was observed between HA1C levels and impaired GFR in adjusted models (Figure 2).

Univariate analysis showed a significant association of impaired GFR with female, aging, low BMI, smoking, high systolic BP, low diastolic BP, low HA1C, low hemoglobin, high serum uric acid, hypercholesterolemia, hypertriglyceridemia, low HDL, and high albuminuria (Table 5). Multiple logistic regression analysis after adjusting potential factors revealed aging [adjusted OR 95% CI 1.09 (1.07 to 1.12)], systolic BP [adjusted OR 95% CI 1.02 (1.00 to 1.04)], HA1C [adjusted OR 95% CI 1.16 (1.04 to 1.31], hemoglobin [adjusted OR 95% CI 0.85 (0.74 to 0.98)], serum uric acid [adjusted OR 95% CI 1.90 (1.64 to 2.20)], and albuminuria [adjusted OR 95% CI 1.01 (1.01 to 1.02)] were still significantly associated with impaired GFR in patients with T2DM (Table 5).


FactorsUnivariate analysisMultivariate logistic regression analysis
Odd ratio (95% CI) valueOdd ratio (95% CI) value

Female1.15 (1.09, 1.20)<0.0010.86 (0.51, 1.45)0.564
Age (years)1.09 (1.08, 1.09)<0.0011.09 (1.07, 1.12)<0.001
BMI (kg/m2)0.95 (0.94, 0.95)<0.0010.98 (0.93, 1.02)0.326
Smoking (yes)0.90 (0.84, 0.96)0.0021.01 (0.51, 1.99)0.987
Systolic BP (mmHg)1.02 (1.02, 1.02)<0.0011.02 (1.00, 1.04)0.030
Diastolic BP (mmHg)0.97 (0.96, 0.97)<0.0010.98 (0.96, 1.01)0.311
HemoglobinA1C (%)0.98 (0.97, 0.99)0.0011.16 (1.04, 1.31)0.009
Hemoglobin (g/dL)0.69 (0.68, 0.71)<0.0010.85 (0.74, 0.98)0.029
Serum uric acid (mg/dL)1.42 (1.38, 1.46)<0.0011.90 (1.64, 2.20)<0.001
Total cholesterol (mg/dL)1.01 (1.01, 1.02)<0.0011.00 (0.99, 1.01)0.966
Triglycerides (mg/dL)1.01 (1.01, 1.02)<0.0011.01 (0.99, 1.02)0.144
HDL-cholesterol (mg/dL)0.99 (0.98, 0.99)<0.0011.00 (0.99, 1.01)1.000
UACR (mg/gCr)1.01 (1.01, 1.02)<0.0011.01 (1.01, 1.02)0.045

Abbreviation: BP: blood pressure; BMI: body mass index; UACR: urine albumin creatinine ratio.
aAdjusted by age, sex, BMI, waist circumference, smoking, systolic BP, diastolic BP, HA1C, hemoglobin, serum uric acid, triglycerides, HDL-cholesterol, and albuminuria.

4. Discussion

To the best of our knowledge, this comprised a large nationwide study to assess the prevalence of kidney disease and associated factors among patients with T2DM in Thailand. In this study, we found that almost 40% of 30,377 studied patients with T2DM had GFR less than 60 mL/min/1.73 m2. Advanced age, albuminuria, high serum uric acid, high systolic BP, HA1C <6%, and HA1C >7% were the major factors associated with impaired GFR in multivariable models.

Data from the United Kingdom Prospective Diabetes Study (UKPDS) demonstrated that after 15 years, approximately 28% of patients had significant albuminuria [3], and another study from the UK showed a prevalence of clinically and significantly impaired GFR less than 60 mL/min/1.73 m2 of 31% among patients with T2DM [15]. In addition, data from Asian populations also showed the prevalence of albuminuria and CKD among patients with T2DM was approximately 25-40% and 30%, respectively [1013]. Therefore, the prevalence of impaired GFR reported in our study was similar to that seen in previous reports.

Microvascular complications among patients with T2DM are a consequence of prolonged hyperglycemia. Many studies have reported the impact of poor glycemic control and albuminuria on the development of diabetic complications [1618]. Similarly, a prospective study of patients with T2DM confirmed that baseline albuminuria and poor glycemic control were important initiators as well as accelerators for the progression of albuminuria and development of diabetic nephropathy [19, 20]. Our findings confirmed that the optimal glycemic control with HA1C at 6-7% revealed a significantly low risk of impaired GFR. However, our study indicated that low HA1C, <6%, was associated with impaired renal function. Probably, this was because glucose homeostasis is extremely altered among patients with T2DM with CKD, who are exposed to a high risk of both hyperglycemia and hypoglycemia [21]. Glycemic control in advanced CKD improves spontaneously with the progression of declining GFR especially at time of initiating dialysis therapy, leading to low hemoglobinA1c levels (<6%), and some patients required cessation of hypoglycemic agents and insulin [22]. The main reason for improving glycemic control in advanced CKD is postulated to be impaired renal insulin degradation and clearance, reduced renal gluconeogenesis, and uremic malnutrition [23]. Moreover, these factors can contribute to a lower than usual threshold for clinical hypoglycemia, which is a common complication among patients with advanced CKD and undergoing dialysis [24].

Our study documented a strong positive correlation between advanced age and increased risk of decreased GFR among patients with T2DM. The findings in the present study are consistent with related studies, indicating age was an independent risk factor in the development of CKD in the general population [25] and among individuals with T2DM [19, 20]. Among hypertensive patients with CKD, BP control is essential to minimize the progression of CKD and reduce CKD-related complications. The most common predictor of the progression to diabetic nephropathy and albuminuria among patients with T2DM is uncontrolled systolic BP [12]. Our findings confirmed that optimal systolic BP control, <120 mmHg, was associated with significantly low risk of impaired GFR similar to previous findings. However, in our study, diastolic BP control was lower among patients with decreased GFR, and high levels of diastolic BP were not independently related to impaired GFR. Our results are also similar to those reported in the Reduction in Endpoints in NIDDM in the Angiotensin II Antagonist Losartan (RENAAL) study. The RENAAL study showed that increases in systolic BP increased the risk for ESRD, but this relationship was not seen for diastolic BP [26]. In addition, 70% of diastolic BP control obtained in our population was much higher than those published in a related study [27], so this improvement should be interpreted with caution, because individuals with no BP data over the study period were not included in our analysis.

Hyperuricemia is highly prevalent among patients with CKD, and it independently predicted the development of T2DM [28]. Hyperuricemia was also associated with an increased risk of CKD and ESRD in cross-sectional and long-term cohort studies [2931]. The results of the present study also suggested that in the Thai T2DM population, high serum uric acid levels are correlated with increased prevalence of impaired GFR. Experimental studies have confirmed that uric acid can accelerate renal injury in animal models via a mechanism linked to increased blood pressure, COX-2-mediated vascular injury, tubulointerstitial fibrosis, and cell infiltration as well as arteriolopathy of the preglomerular vessels [32, 33]. Thus, these studies, including our study, raised the possibility that high uric acid levels may mediate renal disease and progression.

The duration of diabetes is a very important factor in the development of diabetic kidney disease. Several studies have indicated that the duration of diabetes was related to the severity of nephropathy, especially when it first appears 10-15 years after the onset of T1DM and after 5-10 years among patients with T2DM [34, 35]. In contrast, the average duration of diabetes was 6.9 years with 40% of impaired GFR in our study. However, differences in race and in various other risk factors for developing CKD in T2DM probably have an important role as well. The majority of patients in our study were at high risk for CKD progression including advanced age, poor glycemic control (70% of patients had an HbA1C ≤7%), and uncontrolled hypertension (55% of patients had a systolic BP >130 mmHg).

The prevalence of coronary heart disease was relatively low in our study, because coronary heart disease was defined as myocardial infarction or history of coronary revascularization in only medical records and data on ECG testing was available only 6,531 patients (21.5%) in the study. Moreover, the prevalence of coronary heart disease was 1% in the Thai general population [36]. In contrast, Caucasian patients with T2DM at age 45-75 years showed a higher prevalence of coronary heart disease [37]. However, our study included patients with T2DM aged from 35 to 75 years.

This study has several strengths. The data were carefully collected. The study population included a large sample of patients with T2DM in an Asian population and all serum creatinine assays were made in a standard laboratory using the enzymatic method assay. However, the study had several limitations. First, data on albuminuria was unavailable among all patients with T2DM. Therefore, our results indicated such effects only for advanced CKD with GFR less than 60 mL/min/1.73 m2. Second, only a single serum creatinine value and single UACR was used to assess CKD. Therefore, distinguishing between patients with transient impairment of GFR and albuminuria and those with a persistent alteration was not possible. Additionally, serum creatinine was not measured in a single centralized laboratory, and this may have led to some variability in GFR estimation. Finally, because this was a hospital-based study, it could have introduced a referral bias and generalizability of results; therefore, it might be limited.

5. Conclusion

The patients with T2DM in the Thai national survey had significantly impaired GFR (40%). This suggested that CKD was a major concern for patients with T2DM in Thailand. Aging, systolic hypertension, albuminuria, poor glycemic control, and hyperuricemia were independently associated with increased prevalence of impaired GFR. Our data supports that the early detection of these factors should be a routine strategy to prevent CKD in Thailand.

Data Availability

The excel of individual clinical data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that no potential conflict of interest exists.

Acknowledgments

We gratefully acknowledge the contributions of the general practitioners. We would like to thank the Dollapas Punpanich, Research Unit, Department of Medicine, Phramongkutklao Hospital and College of Medicine for statistical analysis. The authors thank the Medical Research Network of the Consortium of Thai Medical Schools (MedResNet) Thailand which granted the use of the Diabetes and Hypertension Dataset in the DAMUS website.

References

  1. G. Tramonti and Y. S. Kanwar, “Review and discussion of tubular biomarkers in the diagnosis and management of diabetic nephropathy,” Endocrine, vol. 43, no. 3, pp. 494–503, 2013. View at: Publisher Site | Google Scholar
  2. B. Satirapoj and S. G. Adler, “Comprehensive approach to diabetic nephropathy,” Kidney Research and Clinical Practice, vol. 33, no. 3, pp. 121–131, 2014. View at: Publisher Site | Google Scholar
  3. A. I. Adler, R. J. Stevens, S. E. Manley et al., “Development and progression of nephropathy in type 2 diabetes: the United Kingdom prospective diabetes study (UKPDS 64),” Kidney International, vol. 63, no. 1, pp. 225–232, 2003. View at: Publisher Site | Google Scholar
  4. B. Satirapoj, K. Aramsaowapak, T. Tangwonglert, and O. Supasyndh, “Novel tubular biomarkers predict renal progression in type 2 diabetes mellitus: a prospective cohort study,” Journal Diabetes Research, vol. 2016, article 3102962, pp. 1–9, 2016. View at: Publisher Site | Google Scholar
  5. T. Ninomiya, V. Perkovic, B. E. de Galan et al., “Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes,” Journal of the American Society of Nephrology, vol. 20, no. 8, pp. 1813–1821, 2009. View at: Publisher Site | Google Scholar
  6. M. Afkarian, M. C. Sachs, B. Kestenbaum et al., “Kidney disease and increased mortality risk in type 2 diabetes,” Journal of the American Society of Nephrology, vol. 24, no. 2, pp. 302–308, 2013. View at: Publisher Site | Google Scholar
  7. V. Perkovic, A. Cass, A. A. Patel et al., “High prevalence of chronic kidney disease in Thailand,” Kidney International, vol. 73, no. 4, pp. 473–479, 2008. View at: Publisher Site | Google Scholar
  8. S. Narenpitak and A. Narenpitak, “Prevalence of chronic kidney disease in type 2 diabetes in primary health care unit of Udon Thani province, Thailand,” Journal of the Medical Association of Thailand, vol. 91, no. 10, pp. 1505–1513, 2008. View at: Google Scholar
  9. C. Ngarmukos, P. Bunnag, N. Kosachunhanun et al., “Thailand diabetes registry project: prevalence, characteristics and treatment of patients with diabetic nephropathy,” Journal of the Medical Association of Thailand, vol. 89, Supplement 1, pp. S37–S42, 2006. View at: Google Scholar
  10. W. Jia, X. Gao, C. Pang et al., “Prevalence and risk factors of albuminuria and chronic kidney disease in Chinese population with type 2 diabetes and impaired glucose regulation: Shanghai diabetic complications study (SHDCS),” Nephrology, Dialysis, Transplantation, vol. 24, no. 12, pp. 3724–3731, 2009. View at: Publisher Site | Google Scholar
  11. B. Lu, X. Song, X. Dong et al., “High prevalence of chronic kidney disease in population-based patients diagnosed with type 2 diabetes in downtown Shanghai,” Journal of Diabetes and its Complications, vol. 22, no. 2, pp. 96–103, 2008. View at: Publisher Site | Google Scholar
  12. H.-H. Parving, J. B. Lewis, M. Ravid, G. Remuzzi, and L. G. Hunsicker, “Prevalence and risk factors for microalbuminuria in a referred cohort of type II diabetic patients: a global perspective,” Kidney International, vol. 69, no. 11, pp. 2057–2063, 2006. View at: Publisher Site | Google Scholar
  13. A. Y. T. Wu, for the MAPS Investigators, N. C. T. Kong et al., “An alarmingly high prevalence of diabetic nephropathy in Asian type 2 diabetic patients: the MicroAlbuminuria prevalence (MAP) study,” Diabetologia, vol. 48, no. 1, pp. 17–26, 2005. View at: Publisher Site | Google Scholar
  14. A. S. Levey, L. A. Stevens, C. H. Schmid et al., “A new equation to estimate glomerular filtration rate,” Annals of Internal Medicine, vol. 150, no. 9, pp. 604–612, 2009. View at: Publisher Site | Google Scholar
  15. J. P. New, R. J. Middleton, B. Klebe et al., “Assessing the prevalence, monitoring and management of chronic kidney disease in patients with diabetes compared with those without diabetes in general practice,” Diabetic Medicine, vol. 24, no. 4, pp. 364–369, 2007. View at: Publisher Site | Google Scholar
  16. The Diabetes Control and Complications (DCCT) Research Group, “Effect of intensive therapy on the development and progression of diabetic nephropathy in the Diabetes Control and Complications Trial,” Kidney International, vol. 47, no. 6, pp. 1703–1720, 1995. View at: Publisher Site | Google Scholar
  17. B. Satirapoj, “Nephropathy in diabetes,” Advances in Experimental Medicine and Biology, vol. 771, pp. 107–122, 2012. View at: Publisher Site | Google Scholar
  18. P. Rossing, P. Hougaard, and H. H. Parving, “Risk factors for development of incipient and overt diabetic nephropathy in type 1 diabetic patients: a 10-year prospective observational study,” Diabetes Care, vol. 25, no. 5, pp. 859–864, 2002. View at: Publisher Site | Google Scholar
  19. V. Viswanathan, P. Tilak, and S. Kumpatla, “Risk factors associated with the development of overt nephropathy in type 2 diabetes patients: a 12 years observational study,” The Indian Journal of Medical Research, vol. 136, no. 1, pp. 46–53, 2012. View at: Google Scholar
  20. M. A. Gall, P. Hougaard, K. Borch-Johnsen, and H. H. Parving, “Risk factors for development of incipient and overt diabetic nephropathy in patients with non-insulin dependent diabetes mellitus: prospective, observational study,” BMJ, vol. 314, no. 7083, pp. 783–788, 1997. View at: Publisher Site | Google Scholar
  21. R. Pecoits-Filho, H. Abensur, C. C. R. Betônico et al., “Interactions between kidney disease and diabetes: dangerous liaisons,” Diabetology and Metabolic Syndrome, vol. 8, no. 1, p. 50, 2016. View at: Publisher Site | Google Scholar
  22. K. Kalantar-Zadeh, S. F. Derose, S. Nicholas, D. Benner, K. Sharma, and C. P. Kovesdy, “Burnt-out diabetes: impact of chronic kidney disease progression on the natural course of diabetes mellitus,” Journal of Renal Nutrition, vol. 19, no. 1, pp. 33–37, 2009. View at: Publisher Site | Google Scholar
  23. R. H. Mak, “Impact of end-stage renal disease and dialysis on glycemic control,” Seminars in Dialysis, vol. 13, no. 1, pp. 4–8, 2001. View at: Google Scholar
  24. R. Arem, “Hypoglycemia associated with renal failure,” Endocrinology and Metabolism Clinics of North America, vol. 18, no. 1, pp. 103–121, 1989. View at: Publisher Site | Google Scholar
  25. Q.-L. Zhang and D. Rothenbacher, “Prevalence of chronic kidney disease in population-based studies: systematic review,” BMC Public Health, vol. 8, no. 1, p. 117, 2008. View at: Publisher Site | Google Scholar
  26. G. L. Bakris, M. R. Weir, S. Shanifar et al., “Effects of blood pressure level on progression of diabetic nephropathy: results from the RENAAL study,” Archives of Internal Medicine, vol. 163, no. 13, pp. 1555–1565, 2003. View at: Publisher Site | Google Scholar
  27. G. Leoncini, F. Viazzi, E. A. Rosei et al., “Chronic kidney disease in hypertension under specialist care: the I-DEMAND study,” Journal of Hypertension, vol. 28, no. 1, pp. 156–162, 2010. View at: Publisher Site | Google Scholar
  28. Q. Lv, X. F. Meng, F. F. He et al., “High serum uric acid and increased risk of type 2 diabetes: a systemic review and meta-analysis of prospective cohort studies,” PLoS One, vol. 8, no. 2, article e56864, 2013. View at: Publisher Site | Google Scholar
  29. B. Satirapoj, O. Supasyndh, A. Chaiprasert et al., “Relationship between serum uric acid levels with chronic kidney disease in a Southeast Asian population,” Nephrology, vol. 15, no. 2, pp. 253–258, 2010. View at: Publisher Site | Google Scholar
  30. R. P. Obermayr, C. Temml, M. Knechtelsdorfer et al., “Predictors of new-onset decline in kidney function in a general middle-european population,” Nephrology, Dialysis, Transplantation, vol. 23, no. 4, pp. 1265–1273, 2007. View at: Publisher Site | Google Scholar
  31. P. Zhu, Y. Liu, L. Han, G. Xu, and J. M. Ran, “Serum uric acid is associated with incident chronic kidney disease in middle-aged populations: a meta-analysis of 15 cohort studies,” PLoS One, vol. 9, no. 6, article e100801, 2014. View at: Publisher Site | Google Scholar
  32. D. H. Kang, T. Nakagawa, L. Feng et al., “A role for uric acid in the progression of renal disease,” Journal of the American Society of Nephrology, vol. 13, no. 12, pp. 2888–2897, 2002. View at: Publisher Site | Google Scholar
  33. M. Mazzali, J. Kanellis, L. Han et al., “Hyperuricemia induces a primary renal arteriolopathy in rats by a blood pressure-independent mechanism,” American Journal of Physiology. Renal Physiology, vol. 282, no. 6, pp. F991–F997, 2002. View at: Publisher Site | Google Scholar
  34. S. Rudberg, R. Osterby, G. Dahlquist, G. Nyberg, and B. Persson, “Predictors of renal morphological changes in the early stage of microalbuminuria in adolescents with IDDM,” Diabetes Care, vol. 20, no. 3, pp. 265–271, 1997. View at: Publisher Site | Google Scholar
  35. C. E. Mogensen, “Microalbuminuria predicts clinical proteinuria and early mortality in maturity-onset diabetes,” The New England Journal of Medicine, vol. 310, no. 6, pp. 356–360, 1984. View at: Publisher Site | Google Scholar
  36. P. Tatsanavivat, V. Klungboonkrong, A. Chirawatkul et al., “Prevalence of coronary heart disease and major cardiovascular risk factors in Thailand,” International Journal of Epidemiology, vol. 27, no. 3, pp. 405–409, 1998. View at: Publisher Site | Google Scholar
  37. A.-B. E. Wiréhn, C. J. Östgren, and J. M. Carstensen, “Age and gender differences in the impact of diabetes on the prevalence of ischemic heart disease: a population-based register study,” Diabetes Research and Clinical Practice, vol. 79, no. 3, pp. 497–502, 2008. View at: Publisher Site | Google Scholar

Copyright © 2020 Naowanit Nata et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views433
Downloads764
Citations

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.