Abstract
-
Purpose
This study aimed to identify sex-specific predictors of microalbuminuria in patients with type 2 diabetes mellitus. Recognizing sex-based differences in risk factors may facilitate the early detection and prevention of diabetic kidney disease.
-
Methods
A cross-sectional analysis was performed using data from the Korea National Health and Nutrition Examination Survey. Microalbuminuria was defined as a urinary albumin-to-creatinine ratio ≥30 mg/g. Multivariable complex sample logistic regression analyses were conducted separately for male and female. Independent variables included age, duration of diabetes, glycated hemoglobin (HbA1c), fasting blood sugar (FBS), triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C), TG, HDL-C, waist circumference, and systolic blood pressure (SBP).
-
Results
The prevalence of microalbuminuria was higher in male than in female. In both sexes, longer diabetes duration and elevated SBP were associated with microalbuminuria. Among male, FBS, TG/HDL-C ratio, TG, and low HDL-C were significant predictors. In female, HbA1c showed the strongest association, followed by age and diabetes duration.
-
Conclusion
Sex-specific differences were identified in the predictors of microalbuminuria among patients with type 2 diabetes. Incorporating these differences into early screening and individualized care strategies may help improve the prevention of diabetic kidney complications.
-
Key Words: Diabetes mellitus, type 2; Albuminuria; Glycated hemoglobin; Dyslipidemias
INTRODUCTION
Type 2 diabetes mellitus (T2DM) is one of the most prevalent chronic diseases worldwide and remains a major contributor to serious complications, including cardiovascular disease and chronic kidney disease [
1]. In recent years, the management of T2DM has shifted from a sole emphasis on glycemic control to a broader approach that prioritizes patient safety and organ protection. This paradigm shift is intended to prevent life-threatening complications—such as hypoglycemia, cardiovascular events, and diabetic kidney disease—that substantially increase mortality [
2].
Among these complications, diabetic nephropathy is one of the most frequent microvascular outcomes of T2DM, with microalbuminuria widely recognized as its earliest clinical marker [
3]. The presence of microalbuminuria not only signifies the onset of diabetic kidney injury but also serves as an independent risk factor for cardiovascular morbidity and mortality [
4]. As a result, early identification of individuals at risk for microalbuminuria is crucial for timely intervention and improved long-term clinical outcomes [
5].
Microalbuminuria is highly prevalent among individuals with T2DM and has strong associations with both microvascular and macrovascular complications, including diabetic nephropathy, retinopathy, and cardiovascular disease [
6]. The pathogenesis of albuminuria—characterized by tubulointerstitial endothelial dysfunction and small-vessel damage—has been shown to predict both renal failure and cardiovascular mortality [
7,
8]. For patients in the early stages of diabetes, annual screening for microalbuminuria is recommended, as early detection of renal abnormalities can significantly delay the progression of diabetic kidney disease [
9]. Furthermore, prior studies have identified several clinical and biochemical risk factors for microalbuminuria, including poor glycemic control, hypertension, dyslipidemia, and obesity [
10,
11].
A growing body of evidence suggests that sex-based differences exist in the development and progression of diabetic complications. Specifically, male are more likely to develop microalbuminuria and are at greater risk for diabetic retinopathy and neuropathy, whereas female—especially older female—have a higher prevalence of reduced estimated glomerular filtration rate and diabetic kidney disease [
12-
14]. Moreover, during the microalbuminuria stage, male are at increased risk for progression to end-stage kidney disease and exhibit higher mortality rates than female [
15]. These differences may arise from both biological (e.g., hormonal and genetic) and behavioral influences. Nevertheless, many studies have not sufficiently explored sex-specific differences in the risk factors associated with microalbuminuria.
Therefore, this study aimed to identify sex-specific predictors of microalbuminuria in patients with T2DM using data from the Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative dataset well-suited for examining chronic disease patterns in Korean adults. With the growing emphasis on personalized care in diabetes management, identifying sex-specific risk factors for early-stage diabetic kidney disease is essential for advancing nursing assessment and intervention strategies. A deeper understanding of these sex-based differences can guide the development of tailored screening, education, and prevention plans, ultimately improving early detection and improving long-term outcomes for patients with T2DM.
METHODS
1. Study Design
This study was conducted as a cross-sectional analysis using raw data from the second year of the seventh cycle (2019) and the first year of the eighth cycle (2020) of the KNHANES, administered by the Korea Disease Control and Prevention Agency and the Ministry of Health and Welfare. KNHANES employs a stratified, multistage, clustered probability sampling design to ensure the national representativeness of households with individuals aged one year or older.
2. Setting and Samples
In accordance with the findings of Xue et al. [
16], which demonstrated a significant association between dyslipidemia (defined in terms of the triglyceride to high-density lipoprotein cholesterol ratio [TG/HDL-C] ratio) and early kidney injury (assessed using the albumin-to-creatinine ratio [ACR]) among adults over 40 years of age, this study included individuals aged 40 years or older to enhance clinical relevance and data reliability.
Of the 15,469 total participants, 7,618 non-diabetic individuals; 6,173 individuals with comorbid conditions that could influence microalbuminuria levels (including pre-existing renal disease, cardiovascular disease, or hypertension diagnosed prior to diabetes); 18 individuals with type 1 diabetes; and 63 individuals with macroalbuminuria (ACR >300 mg/g Cr) were excluded. The final analytic sample comprised 1,597 individuals.
T2DM was defined by the presence of at least one of the following criteria: (1) fasting blood sugar (FBS) ≥126 mg/dL; (2) glycated hemoglobin (HbA1c) ≥6.5%; (3) a physician’s diagnosis of diabetes; or (4) current use of glucose-lowering medications.
Type 1 diabetes was excluded based on self-reported information in KNHANES, including diabetes onset before age 10, insulin-only treatment, or a documented history of type 1 diabetes mellitus diagnosis. Non-diabetic individuals were defined as those who had never received a diabetes diagnosis from a healthcare provider and had normal fasting blood glucose and HbA1c levels.
3. Measurements
1) Microalbuminuria
Microalbuminuria was defined as an ACR of 30 to 300 mg/g Cr, consistent with the guidelines of the Kidney Disease Outcomes Quality Initiative and Kidney Disease: Improving Global Outcomes [
17]. Spot urine samples were collected, and urinary albumin and creatinine concentrations were measured to calculate the ACR.
2) General characteristics
General characteristics included age, economic status, smoking status, alcohol consumption, duration of diabetes, and family history of diabetes. Economic status was classified into four quartiles (low, middle-low, middle-high, and high) based on average monthly household income. Smoking status was categorized as never, former, or current smoker. Alcohol consumption was dichotomized as yes or no. The duration of diabetes was grouped into three categories: less than 10 years, 10–20 years, and more than 20 years. Family history of diabetes was classified as either present (yes) or absent (no).
3) Clinical characteristics
Clinical characteristics included anthropometric data, blood pressure, and biochemical measurements. Anthropometric data were collected by trained KNHANES staff, with height, weight, and waist circumference measured using a stadiometer (SECA 200, SECA, Hamburg, Germany), digital scale (GL-6000-20, G-TECH, Uijeongbu, Korea), and tape measure (SECA 200, SECA), respectively. Body mass index was calculated as weight in kilograms divided by height in meters squared (kg/m²). Blood pressure was measured using a standard sphygmomanometer (Baumanometer Wall Unit 33; Baum, Copiague, NY, USA) after participants had rested for at least five minutes. Both systolic and diastolic blood pressures were recorded.
Biochemical data were obtained from blood and urine samples to reflect participants’ physiological and metabolic profiles. Blood samples were collected after at least 8 hours of overnight fasting. FBS, HbA1c, and serum lipids—including total cholesterol, high-density lipoprotein cholesterol (HDL-C), TG, and low-density lipoprotein cholesterol (LDL-C)—as well as serum creatinine were analyzed using the Hitachi Automatic Analyzer 7600-210 (Hitachi, Tokyo, Japan).
Urine tests were performed on random spot urine samples according to the KNHANES protocol. Urinary albumin was measured by the turbidimetric immunoassay method using the Hitachi Automatic Analyzer 7600 (Hitachi), while urinary creatinine was analyzed using the Jaffe rate-blanked and compensated method with the Roche COBAS 8000 C702 (Roche Diagnostics, Mannheim, Germany).
4. Data Collection
Data were collected according to standardized KNHANES protocols. Participants provided demographic and medical history information through structured interviews. Trained personnel conducted anthropometric measurements and blood pressure assessments, and collected fasting blood and random spot urine samples. Laboratory analyses were performed in certified laboratories following established procedures.
5. Ethical Considerations
This study was a secondary analysis of KNHANES data in which no personally identifiable information was included. Approval for use of the raw data was obtained from the Korea Disease Control and Prevention Agency, and the study was exempted from review by the Institutional Review Board of the Public Institution Bioethics Committee (IRB No. P01-202211-01-041). During the original survey, written informed consent was obtained from all participants after they were informed about the purpose of data collection, voluntary participation, and their right to withdraw. Only anonymized data were provided to the researchers.
6. Data Analysis
All analyses accounted for the complex sampling design of KNHANES by incorporating sampling weights, stratification variables (Kstrata), and primary sampling units, as recommended by the Korea Disease Control and Prevention Agency (KDCA). This methodology ensured nationally representative estimates and accurate variance calculations.
Descriptive statistics were used to summarize the general characteristics of the study population. For categorical and binary variables, complex sample chi-square tests were used to compare proportions between groups. For continuous variables, complex sample t-tests were performed to assess mean differences.
To identify risk factors associated with microalbuminuria, complex sample logistic regression analysis was conducted. The dependent variable was the presence of microalbuminuria, defined as a urinary ACR ≥30 mg/g. To assess sex-specific associations, regression analyses were stratified by sex. All independent variables were analyzed as continuous variables without categorization. Odds ratios (ORs) with 95% confidence intervals (CIs), z-statistics, and p-values were reported. A two-sided p-value of <.05 was considered statistically significant. All statistical analyses were performed using R software version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria), utilizing the survey and car packages to address the complex sampling design and assess multicollinearity.
RESULTS
1. Sex Differences in Microalbuminuria According to General Characteristics
The prevalence of microalbuminuria was significantly higher in male than in female (10.5% vs. 8.8%,
p<.001). In both sexes, the highest prevalence was observed among participants aged 70 years or older, those in the low-income group, and individuals with a diabetes duration of less than 10 years. Among male, microalbuminuria was particularly more common in ex-smokers and in those who reported alcohol consumption (
Table 1).
In particular, significant sex differences in microalbuminuria were observed for age (
p<.001), hemoglobin (
p<.001), TG/HDL-C ratio (
p<.001), TG (
p<.001), HDL-C (
p<.001), serum creatinine (
p<.001), waist circumference (
p<.001), and diastolic blood pressure (DBP) (
p<.001). In the total, microalbuminuria was also significantly associated with age (
p=.015), diabetes duration (
p<.001), HbA1c (
p<.001), FBS (
p<.001), TG/HDL-C ratio (
p<.001), TG (
p=.003), HDL-C (
p=.002), LDL-C (
p=.012), serum creatinine (
p=.008), systolic blood pressure (SBP) (
p<.001), and waist circumference (
p=.037) (
Table 2).
Complex sample logistic regression analyses were conducted separately for male and female to examine sex-specific associations between glycemic and lipid markers and the presence of microalbuminuria. The dependent variable was the presence of microalbuminuria, while the independent variables included age, duration of diabetes, HbA1c, fasting blood glucose, TG/HDL-C ratio, TG, HDL-C, waist circumference, and SBP. To evaluate multicollinearity among independent variables, variance inflation factors were computed, and all values were below the threshold of 10, indicating no significant multicollinearity.
The models estimated ORs and corresponding 95% CIs. Z-values and p-values were reported to determine statistical significance. All independent variables were analyzed as continuous measures without categorization. Sex-stratified modeling was utilized to identify potential sex-specific risk profiles and to minimize the dilution of differential effects that may arise in pooled analyses.
Table 3 presents the sex-stratified ORs for significant risk factors associated with microalbuminuria. While diabetes duration and SBP were significant predictors in both sexes, other associated factors differed notably between male and Female.
Among male patients, the strongest predictor was the TG/HDL-C ratio (OR=1.33, 95% CI=0.99–1.79, p=.048), followed by SBP (OR=1.03, 95% CI=1.02–1.05, p<.001), diabetes duration (OR=1.03, 95% CI=1.01–1.05, p=.008), FBS (OR=1.01, 95% CI=1.00–1.01, p=.015), TG (OR=1.01, 95% CI=1.00–1.01, p=.021), and HDL-C (OR=0.96, 95% CI=0.94–0.99, p=.006).
In contrast, for female patients, HbA1c was the most influential predictor (OR=1.41, 95% CI=1.15–1.72, p=.001), followed by age (OR=1.03, 95% CI=1.01–1.06, p=.011), diabetes duration (OR=1.02, 95% CI=1.00–1.05, p=.048), and SBP (OR=1.02, 95% CI=1.01–1.04, p<.001).
DISCUSSION
This study aimed to identify sex-specific predictors of microalbuminuria in patients with type 2 diabetes using data from KNHANES. The findings demonstrated distinct risk profiles for male and female, underscoring the need for sex-specific strategies in the early detection and prevention of diabetic nephropathy.
Consistent with previous research [
10,
11], the prevalence of microalbuminuria was higher in male than in female. In both sexes, the highest prevalence was seen among older adults, individuals in the low-income group, and those with a diabetes duration of less than 10 years. Notably, in male, microalbuminuria was significantly more common among ex-smokers and individuals who reported alcohol consumption, highlighting the impact of modifiable behavioral risk factors.
Longer diabetes duration and elevated SBP were significantly associated with microalbuminuria in both male and Female. These findings suggest that chronic hyperglycemia and persistent hypertension contribute to early renal impairment regardless of sex. Sustained high glucose levels can cause endothelial dysfunction, oxidative stress, and glomerular hyperfiltration, all of which promote microvascular injury within the kidneys. Similarly, elevated SBP increases intraglomerular pressure, which accelerates albumin leakage into the urine.
These pathophysiological mechanisms are consistent with previous findings. Kundu et al. [
18] reported that microalbuminuria was positively correlated with both the duration of diabetes and HbA1c levels among patients with T2DM, reflecting the cumulative renal burden of prolonged poor glycemic control. Likewise, Maiti et al. [
19] found that the prevalence of microalbuminuria increased with longer diabetes duration, underscoring the progressive nature of diabetic nephropathy.
However, sex differences emerged in other associated clinical variables. Among males, dyslipidemia-related indicators—such as the TG/HDL-C ratio, TG, and low HDL-C—were significant. The TG/HDL-C ratio is a well-established marker of insulin resistance and is strongly linked to both cardiovascular and renal complications in diabetes [
20,
21]. Prior studies have shown that a higher TG/HDL-C ratio is associated with the presence and progression of microalbuminuria, as well as with declines in glomerular filtration rate among individuals with T2DM [
16,
22]. In line with previous research [
23], this study found that lower HDL-C levels were related to a higher prevalence of microalbuminuria in male, possibly due to HDL-C’s protective effects on endothelial integrity and renal function [
24]. Thus, dyslipidemia may play a significant role in the early pathogenesis of diabetic kidney disease in male.
In contrast, among females, HbA1c was the most influential predictor, followed by age, diabetes duration, and SBP. This finding is consistent with earlier reports suggesting that glycemic burden has a greater impact on renal complications in female [
25], potentially due to hormonal changes and metabolic alterations after menopause [
26]. HbA1c, as a marker of long-term glycemic control, is closely associated with the development of diabetic complications [
27]. Chronic hyperglycemia promotes vascular damage through tissue hypoxia, which is a key mechanism in diabetic nephropathy [
28]. Multiple studies have demonstrated a significant association between elevated HbA1c levels and microalbuminuria in T2DM [
18,
19,
28,
29].
Taken together, these findings underscore the importance of integrating sex-stratified risk assessments into nursing care for patients with T2DM. The predictors of microalbuminuria differed by sex, highlighting the need for tailored preventive strategies. Among males, dyslipidemia-related variables were significantly associated with microalbuminuria, suggesting that lipid management may play a key role in reducing early renal risk. Prior studies have shown that dyslipidemia contributes to glomerular injury via mechanisms such as endothelial dysfunction and insulin resistance, with the TG/HDL-C ratio serving as a reliable indicator of these processes [
20-
22]. Among females, poor glycemic control—reflected by elevated HbA1c—was the most prominent factor associated with microalbuminuria, followed by age. This sex-specific pattern may be attributable to hormonal and metabolic changes after menopause, which may amplify the renal impact of hyperglycemia [
25-
28]. These findings suggest that male patients may benefit from more aggressive lipid management, while improved glycemic control and age-specific interventions may be particularly effective for female patients.
This study has several strengths, including the use of a large, nationally representative sample and analytic methods that accounted for the complex sampling design of KNHANES. Nonetheless, certain limitations should be acknowledged. The cross-sectional design precludes causal inference. In addition, potential confounders such as medication use, dietary intake, physical activity, and hormonal status were not included and may have influenced the observed associations.
In conclusion, this study highlights the importance of recognizing sex-specific patterns in the predictors of microalbuminuria among patients with T2DM. Incorporating these differences into individualized prevention and treatment strategies may improve early detection and improve patient outcomes.
From a nursing perspective, these findings reinforce the need to consider sex-specific risk factors in routine assessments and patient education. Nurses are ideally positioned to detect early signs of renal complications and deliver tailored care plans that include lifestyle counseling and support for self-management. Sex-based nursing interventions have the potential to increase treatment adherence and improve long-term outcomes.
CONCLUSION
This study demonstrated that the predictors of microalbuminuria in patients with type 2 diabetes differ by sex. In both males and females, longer diabetes duration and elevated SBP were commonly associated with microalbuminuria. However, additional risk factors varied by sex. In males, FBS and dyslipidemia-related markers—including TG/HDL-C ratio, TG, and HDL-C—were significantly associated with microalbuminuria. In contrast, among females, HbA1c showed a stronger association, along with age and diabetes duration.
These findings suggest that the pathophysiology of diabetic kidney complications may differ by sex, requiring differentiated prevention and management strategies. Incorporating sex-specific risk factors into clinical practice may enhance early detection and support more personalized interventions. Future research should focus on developing and validating sex-stratified care models to improve long-term renal outcomes in individuals with type 2 diabetes.
-
CONFLICTS OF INTEREST
The authors declared that there is no conflict of interest.
-
AUTHORSHIP
Study conception and/or design acquisition - ESB and HSK; analysis - interpretation of the data- ESB and HSK; and drafting or critical revision of the manuscript for important intellectual content - ESB and HSK.
-
FUNDING
This research was supported by "Regional Innovation Strategy (RIS)" Through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2022RIS-005).
-
ACKNOWLEDGEMENT
None.
-
DATA AVAILABILITY STATEMENT
The data can be obtained from the corresponding authors.
Table 1.Sex Differences in Microalbuminuria According to General Characteristics (N=1,597)
|
Variables |
Categories |
Microalbuminuria (30–300 mg/g Cr) |
Normoalbuminuria (<30 mg/g Cr) |
χ² (p) |
|
n (%) |
χ² (p) |
n (%) |
χ² (p) |
|
Total |
Male |
Female |
Total |
Male |
Female |
|
Sex |
Total |
309 (19.3) |
168 (10.5) |
141 (8.8) |
30.90 (<.001) |
1,288 (80.6) |
644 (40.3) |
644 (40.3) |
12.88 (<.001) |
0.90 (.168) |
|
Male |
- |
168 (10.5) |
- |
|
- |
644 (40.3) |
- |
|
|
|
Female |
- |
- |
141 (8.8) |
|
- |
- |
644 (40.3) |
|
|
|
Age (year) |
|
|
|
|
18.78 (<.001) |
|
|
|
39.26 (<.001) |
6.63 (.044) |
|
40–49 |
25 (1.6) |
21 (1.3) |
4 (0.3) |
|
118 (7.4) |
83 (5.2) |
35 (2.2) |
|
|
|
50–59 |
60 (3.8) |
38 (2.4) |
22 (1.4) |
|
274 (17.2) |
156 (9.8) |
118 (7.4) |
|
|
|
60–69 |
88 (5.5) |
51 (3.2) |
37 (2.3) |
|
430 (26.9) |
213 (13.3) |
217 (13.6) |
|
|
|
≥70 |
136 (8.5) |
58 (3.6) |
78 (4.9) |
|
466 (29.2) |
192 (12.0) |
274 (17.2) |
|
|
|
Economic status |
|
|
|
|
2.20 (.533) |
|
|
|
6.81 (.078) |
6.16 (.104) |
|
High |
70 (4.4) |
33 (2.1) |
37 (2.3) |
|
305 (19.1) |
166 (10.4) |
139 (8.7) |
|
|
|
Middle-high |
59 (3.7) |
32 (2.0) |
27 (1.7) |
|
296 (18.5) |
155 (9.7) |
141 (8.8) |
|
|
|
Middle-low |
79 (4.9) |
44 (2.8) |
35 (2.2) |
|
352 (22.0) |
173 (10.8) |
179 (11.2) |
|
|
|
Low |
101 (6.3) |
59 (3.7) |
42 (2.6) |
|
335 (21.0) |
150 (9.4) |
185 (11.6) |
|
|
|
Smoking status |
|
|
|
|
157.77 (<.001) |
|
|
|
771.42 (<.001) |
1.17 (.556) |
|
Never smoker |
167 (10.5) |
36 (2.3) |
131 (8.2) |
|
696 (43.6) |
100 (6.3) |
596 (37.3) |
|
|
|
Ex-smoker |
82 (5.1) |
77 (4.8) |
5 (0.3) |
|
371 (23.2) |
350 (21.9) |
21 (1.31) |
|
|
|
Current smoker |
60 (3.8) |
55 (3.4) |
5 (0.3) |
|
221 (13.8) |
194 (12.2) |
27 (1.7) |
|
|
|
Alcohol consumption |
|
|
|
|
81.37 (<.001) |
|
|
|
244.58 (<.001) |
0.62 (.433) |
|
Yes |
141 (8.8) |
116 (7.3) |
25 (1.6) |
|
556 (34.8) |
417 (26.1) |
139 (8.7) |
|
|
|
No |
168 (10.5) |
52 (3.3) |
116 (7.3) |
|
732 (45.8) |
227 (14.2) |
505 (31.6) |
|
|
|
Duration of DM (year) |
|
|
|
|
1.62 (.445) |
|
|
|
1.26 (.533) |
23.44 (<.001) |
|
<10 |
189 (11.8) |
106 (6.6) |
83 (5.2) |
|
960 (60.1) |
483 (30.2) |
477 (29.9) |
|
|
|
10–20 |
73 (4.6) |
35 (2.2) |
38 (2.4) |
|
217 (13.6) |
102 (6.4) |
115 (7.2) |
|
|
|
>20 |
47 (2.9) |
27 (1.7) |
20 (1.3) |
|
111 (6.9) |
59 (3.7) |
52 (3.3) |
|
|
|
Family history of DM |
|
|
|
|
0.24 (.626) |
|
|
|
2.54 (.111) |
0.58 (.445) |
|
Yes |
116 (7.3) |
61 (3.8) |
55 (3.4) |
|
514 (32.2) |
243 (15.2) |
271 (17.0) |
|
|
|
No |
193 (12.0) |
107 (6.7) |
86 (5.4) |
|
774 (48.4) |
401 (25.1) |
373 (23.4) |
|
|
Table 2.Differences in Microalbuminuria According to Clinical Characteristics
|
Characteristics |
Microalbuminuria (30–300 mg/g Cr) |
Normoalbuminuria (<30 mg/g Cr) |
Total t (p) |
|
M±SD |
t (p) |
M±SD |
t (p) |
|
Total |
Male |
Female |
Total |
Male |
Female |
|
Age (year) |
66.24±10.98 |
63.46±11.14 |
69.55±9.84 |
–5.11 (<.001) |
64.56±10.14 |
62.80±10.42 |
66.32±9.54 |
–6.31 (<.001) |
2.45 (.015) |
|
Duration of DM (year) |
8.68±9.70 |
8.06±9.49 |
9.43±9.94 |
–1.23 (.220) |
5.95±7.94 |
5.81±8.04 |
6.09±7.85 |
–0.64 (.521) |
4.60 (<.001) |
|
Hemoglobin (g/dL) |
13.92±1.76 |
14.75±1.64 |
12.93±1.35 |
10.69 (<.001) |
13.91±1.56 |
14.73±1.42 |
13.09±1.24 |
22.20 (<.001) |
0.11 (.915) |
|
HbA1c (%) |
7.65±1.52 |
7.62±1.49 |
7.67±1.57 |
–0.31 (.759) |
6.99±1.08 |
6.98±1.09 |
7.01±1.08 |
–0.45 (.656) |
7.12 (<.001) |
|
FBS (g/dL) |
150.80±50.74 |
153.49±48.43 |
147.59±53.36 |
1.01 (.313) |
131.48±33.25 |
133.58±33.38 |
129.37±33.02 |
2.27 (.023) |
6.37 (<.001) |
|
BMI (kg/m2) |
25.56±3.61 |
25.82±3.43 |
25.24±3.81 |
1.39 (.165) |
25.39±3.54 |
25.37±3.43 |
25.40±3.67 |
-0.15 (.880) |
0.75 (.452) |
|
TG/HDL-C ratio |
4.44±4.51 |
5.35±5.56 |
3.33±2.39 |
4.25 (<.001) |
3.46±3.04 |
3.86±3.69 |
3.06±2.14 |
4.75 (<.001) |
3.60 (<.001) |
|
TG (mg/dL) |
183.24±193.27 |
218.46±247.68 |
141.26±75.74 |
3.83 (<.001) |
148.68±101.90 |
160.40±121.46 |
136.95±75.86 |
4.16 (<.001) |
3.04 (.003) |
|
TC (mg/dL) |
172.23±46.25 |
174.80±48.41 |
169.60±43.53 |
0.99 (.322) |
174.28±42.02 |
171.83±41.79 |
176.73±42.13 |
–2.09 (.036) |
-0.64 (.521) |
|
HDL-C (mg/dL) |
47.66±11.17 |
43.25±10.35 |
47.84±12.77 |
–3.43 (.001) |
45.35±11.73 |
46.04±11.00 |
49.29±11.11 |
–5.28 (<.001) |
-3.15 (.002) |
|
LDL-C (mg/dL) |
96.88±37.60 |
87.86±44.53 |
93.51±36.75 |
–1.22 (.223) |
90.43±41.19 |
93.71±37.82 |
100.05±37.14 |
–3.03 (.002) |
-2.51 (.012) |
|
BUN (mg/dL) |
17.48±5.83 |
17.18±5.35 |
17.83±6.36 |
–0.96 (.336) |
17.14±5.07 |
17.32±4.91 |
16.96±5.22 |
1.28 (.200) |
0.94 (.346) |
|
Serum Cr (mg/dL) |
0.88±0.29 |
0.98±0.28 |
0.77±0.26 |
6.68 (<.001) |
0.83±0.21 |
0.95±0.18 |
0.72±0.18 |
22.41 (<.001) |
2.68 (.008) |
|
Waist circumference (cm) |
91.45±9.39 |
93.75±8.79 |
88.71±9.36 |
4.85 (<.001) |
90.21±9.30 |
92.13±8.98 |
88.28±9.22 |
7.59 (<.001) |
2.09 (.037) |
|
SBP (mmHg) |
131.88±16.85 |
131.89±17.48 |
131.88±16.13 |
0.00 (.997) |
124.73±15.02 |
123.38±14.43 |
126.08±15.48 |
–3.24 (<.001) |
6.84 (<.001) |
|
DBP (mmHg) |
75.22±12.01 |
77.42±12.74 |
72.60±10.53 |
3.65 (<.001) |
74.37±9.72 |
75.60±9.97 |
73.15±9.32 |
4.54 (<.001) |
1.15 (.252) |
Table 3.Sex-Specific Adjusted ORs for Microalbuminuria
|
Variables |
Male |
Female |
|
OR (95% CI) |
Z-value |
p-value |
OR (95% CI) |
Z-value |
p-value |
|
Age (year) |
1.01 (0.99–1.03) |
1.08 |
.282 |
1.03 (1.01–1.06) |
2.53 |
.011*
|
|
Duration of DM (year) |
1.03 (1.01–1.05) |
2.67 |
.008*
|
1.02 (1.00–1.05) |
1.97 |
.048*
|
|
HbA1c (%) |
1.20 (0.98–1.47) |
1.73 |
.083 |
1.41 (1.15–1.72) |
3.34 |
.001*
|
|
FBS (g/dL) |
1.01 (1.00–1.01) |
2.43 |
.015*
|
1.00 (1.00–1.01) |
1.26 |
.206 |
|
TG/HDL-C ratio |
1.33 (0.99–1.79) |
1.93 |
.048*
|
0.80 (0.62–0.98) |
–1.98 |
.054 |
|
TG (mg/dL) |
1.01 (1.00–1.01) |
2.30 |
.021*
|
0.99 (0.99–1.00) |
–1.78 |
.075 |
|
HDL-C (mg/dL) |
0.96 (0.94–0.99) |
–2.75 |
.006*
|
1.01 (0.99–1.03) |
0.75 |
.455 |
|
Waist circumference (cm) |
1.02 (0.98–1.04) |
1.67 |
.096 |
1.00 (0.98–1.02) |
–0.01 |
.995 |
|
SBP (mmHg) |
1.03 (1.02–1.05) |
5.52 |
<.001*
|
1.02 (1.01–1.04) |
3.61 |
<.001*
|
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