Sex difference in the association between pathological albuminuria and subclinical atherosclerosis: insights from the I-Lan longitudinal aging study

Results

A total of 1228 participants (46.0% women; mean age 62.56 ± 8.77 years) without coronary artery disease (CAD), DM, or chronic kidney disease (CKD) were investigated. In women, there were no differences of age, body mass index (BMI), waist circumference, waist-to-height ratio, or prevalence of smoking between subjects with or without PAU. In contrast, men with PAU were older and exhibited elevated waist circumference and an increased prevalence of waist circumference >90 cm (a metabolic syndrome criterion) compared to those with normal AU. Significantly higher prevalence rates of HTN and anti-HTN therapy were observed in both women and men with PAU. However, women had more elevated total body fat than men (37.1% vs. 24.8% respectively; p < 0.001). Men with PAU had significantly higher total body fat than those with normal AU (26.48 ± 7.42 vs. 24.52 ± 6.46; p = 0.027).

Total serum cholesterol, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), hemoglobin A1c, uric acid, and creatinine values were similar between men and women. PAU was associated with higher fasting glucose in women but not in men. Men with PAU exhibited higher triglyceride levels and lower estimated glomerular filtration rate (eGFR). PAU was associated with substantially increased mean CIMT and higher prevalence of SA (CIMT ≥0.75 mm) in men, but not in women (Table 1).

Table 1. Baseline characteristics according to UACR ≥ 30 mg/g or not stratified by gender.

	Female (n = 655)			Male (573)
	UACR <30 mg/g n = 565	UACR ≥30 mg/g n = 90	p value	UACR <30 mg/g n = 485	UACR ≥30 mg/g n = 88	p value
Age (years)	59.9 (54.95–67.75)	60.7 (55.30–69.45)	0.501	61.1 (55.3–70.0)	65.1 (58.28–74.78)	0.001
BMI	23.92 (21.90–26.3)	24.25 (22.18–27.24)	0.303	24.42 (22.34–26.48)	25.33 (22.47–27.34)	0.087
Waist circumference	80.0 (74.0–86.5)	81.5 (75.0–87.13)	0.496	86 (80.5–91.75)	88.75 (82.88–94.0)	0.007
Waist-to-height ratio	0.53 ± 0.067	0.53 ± 0.063	0.351	0.52 ± 0.050	0.54 ± 0.054	<0.001
Smoking (%)	17 (3.0)	3 (3.3)	0.747	160 (33.0)	34 (38.6)	0.328
Underlying disease
Hypertension (%)	161 (28.5)	46 (51.1)	<0.001	147 (30.3)	42 (47.7)	0.002
Anti-Hypertensive agents (%)	73 (12.9)	25 (27.8)	0.001	77 (15.9)	23 (26.1)	0.031
Dyslipidemia	29 (5.1)	9 (10.0)	0.085	16 (3.3)	4 (4.5)	0.529
Statin	26 (4.6)	9 (10.0)	0.043	14 (2.9)	3 (3.4)	0.734
Metabolic syndrome (%)	136 (24.1)	32 (35.6)	0.027	101 (20.8)	30 (34.1)	0.009
Metabolic syndrome_BS	131 (23.2)	27 (30.0)	0.184	133 (27.4)	33 (37.5)	0.073
Metabolic syndrome_BP	301 (53.3)	66 (73.3)	<0.001	281 (57.9)	68 (77.3)	0.001
Metabolic syndrome_HDL	117 (20.7)	26 (28.9)	0.098	67 (13.8)	15 (17.0)	0.411
Metabolic syndrome_TG	86 (15.2)	17 (18.9)	0.354	118 (24.3)	23 (26.1)	0.689
Metabolic syndrome_WC	297 (52.6)	52 (57.8)	0.366	164 (33.8)	41 (46.6)	0.029
Obesity (%)	116 (20.5)	23 (25.6)	0.270	94 (19.4)	24 (27.3)	0.114
Body total fat % (DXA)	37.17 ± 6.01	36.61 ± 7.58	0.505	24.52 ± 6.46	26.48 ± 7.42	0.027
Laboratory data
Total Cholesterol (mg/dl)	205 (182–225.5)	199 (170.75–227.25)	0.181	192 (170.5–215.0)	191.5 (168.8–212.3)	0.979
HDL (mg/dl)	58 (51–69)	56 (47–67.25)	0.141	50 (43–57)	49.5 (42–57)	0.873
LDL (mg/dl)	122 (101–142.5)	118.5 (98–145.75)	0.484	119 (99–142)	122 (105.3–134)	0.550
Fasting glucose (mg/dl)	93 (87–99)	94.5 (89–101.25)	0.028	94 (89–100)	96 (89–104)	0.136
HbA1c (%)	5.8 (5.6–6.0)	5.8 (5.58–6.10)	0.197	5.7 (5.4–5.9)	5.8 (5.5–6.0)	0.068
Uric acid	5.0 (4.4–5.8)	5.1 (4.3–5.9)	0.836	6.4 (5.5–7.2)	6.4 (5.4–7.4)	0.809
Triglyceride (mg/dl)	94 (70–131)	108.5 (79–137.75)	0.056	102 (74–148)	118 (90–151.5)	0.020
Creatinine (mg/dl)	0.67 (0.60–0.75)	0.65 (0.58–0.74)	0.223	0.89 (0.80–1.00)	0.90 (0.77–1.01)	0.677
eGFR (ml/min/1.73 m2)	94.34 (85.81–100.33)	94.98 (87.13–101.23)	0.525	89.49 (79.97–95.98)	86.66 (75.10–96.19)	0.030
UACR	8.09 (5.54–12.97)	55.01 (36.78–97.44)	<0.001	6.06 (3.76–10.40)	47.89 (36.15–72.67)	<0.001
Mean cIMT (mm)	0.6 (0.6–0.7)	0.65 (0.6–0.7)	0.445	0.7 (0.6–0.75)	0.75 (0.66–0.90)	<0.001
cIMT ≥ 0.75 mm	129 (22.8)	16 (17.8)	0.339	155 (32.0)	45 (51.1)	0.001
Values are median (25th–75th percentile) or n (%). Abbreviations: BMI: body mass index; DM: diabetes mellitus; HDL: high density lipoprotein; LDL: low density lipoprotein; HbA1c: hemoglobin A1c; eGFR: estimated glomerular filtration rate; UACR: urine albumin creatinine ratio; cIMT: carotid intima-media thickness.

Table 2 shows the correlation coefficients of CIMT and UACR with other variables in women and men. Age, HTN, waist circumference, and waist-to-height ratio were positively correlated with CIMT and UACR in both groups. Obesity and higher BMI were associated with CIMT and UACR only in women, while higher total body fat percentage was positively related to CIMT and UACR in men. Smoking, total serum cholesterol, and low-density cholesterol were unassociated with CIMT or UACR in men and women. Simple linear correlations between UACR and other CV risk factors, including total body fat percentage, BMI, and waist to height ratio by sex are displayed by scatter plots in Figure 1A–1C.

Table 2. Correlation coefficients of UACR and the carotid intima-media thickness with other cardiovascular risk factors in the whole population.

Women	cIMT	UACR
Age	0.348**	0.151**
Smoking	0.044	0.015
Hypertension	0.193**	0.181**
Dyslipidemia	0.033	0.098*
Metabolic syndrome	0.147**	0.157**
Waist	0.164**	0.104**
BMI	0.120**	0.112**
Obesity	0.109**	0.135**
Total body fat percentage	0.046	−0.017
Waist to height ratio	0.188**	0.146**
eGFR	−0.260**	−0.017
Total Cholesterol	−0.029	−0.045
TG	0.091*	0.092*
LDL	−0.002	−0.014
HDL	−0.090*	−0.060
HbA1c	0.081*	0.080*
Uric acid	0.134**	0.033
UACR	0.093*	–
Men	cIMT	UACR
Age	0.390**	0.259**
Smoking	0.025	0.061
Hypertension	0.160**	0.177**
Dyslipidemia	0.001	0.083*
Metabolic syndrome	0.066	0.114**
Waist	0.149**	0.164**
BMI	0.078	0.065
Obesity	0.004	0.045
Total body fat percentage	0.118**	0.154**
Waist to height ratio	0.212**	0.225**
eGFR	−0.220**	−0.124**
Total Cholesterol	−0.024	0.044
TG	0.015	0.111**
LDL	0.041	0.052
HDL	−0.092*	−0.015
HbA1c	0.070	0.128**
Uric acid	0.067	0.030
UACR	0.187**	–
*Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2 tailed). Abbreviations: BMI: body mass index; HDL: high density lipoprotein; LDL: low density lipoprotein; HbA1c: hemoglobin A1c; eGFR: estimated glomerular filtration rate; UACR: urine albumin creatinine ratio; cIMT: carotid intima-media thickness.

Figure 1. (A) Gender disparity between UACR and total body fat percentage. (B) Gender disparity between UACR and BMI. (C) Gender disparity between UACR and Waist to height ratio. (D) Pathological albuminuria and the adjusted odds ratio of subclinical atherosclerosis between sex.

Sex-based disparities are shown in Table 3. Multivariate logistic regression analysis of women showed that age (adjusted odds ratio [aOR] 1.076, 95% CI: 1.042–1.110) and HTN (aOR 2.089, 95% CI 1.223–3.570) were independently associated with SA but not PAU after adjusting for confounding factors (aOR 0.571, 95% CI 0.302–1.080). For men, independent factors related to SA were PAU (aOR 1.881, 95% CI 1.093–3.236), age (aOR 1.105, 95% CI 1.075–1.136), and BMI (aOR 1.149, 95% CI 1.039–1.271). After multivariate adjustment, the interaction between PAU and sex was significant (p = 0.005) (Figure 1D).

Table 3. Univariate and multivariate logistic regression analysis of factors associated with the incidence of cIMT ≥0.75 mm (75th percentile) in non-DM patients (n = 1228).

Female (n = 655) Variable	Univariate analysis			Multivariate analysis
	OR	95% CI	p value	OR	95% CI	p value
UACR ≥30 mg/g	0.731	0.411−1.299	0.285	0.571	0.302−1.080	0.085
Age	1.096	1.071−1.123	<0.001	1.076	1.042−1.110	<0.001
BMI	1.061	1.009−1.114	0.020	1.023	0.938−1.115	0.608
Smoking	1.939	0.759−4.955	0.166
Hypertension	2.541	1.738−3.716	<0.001	2.089	1.223−3.570	0.007
Anti-HTN medications	1.905	1.191−3.048	0.007	0.827	0.432−1.583	0.567
Dyslipidemia	1.680	0.825−3.418	0.153
Total Cholesterol	1.001	0.996−1.006	0.695
TG	1.003	1.000−1.007	0.059
HDL	0.992	0.978−1.005	0.227
LDL	1.001	0.996−1.007	0.646
UA	1.359	1.157−1.596	<0.001	1.179	0.961−1.446	0.114
FBS	1.001	0.983−1.019	0.939	0.982	0.960−1.004	0.103
eGFR	0.953	0.938−0.968	<0.001	0.993	0.970−1.016	0.529
Metabolic syndrome	2.061	1.387−3.063	<0.001	1.486	0.824−2.678	0.188
Total body fat percentage	1.005	0.975−1.035	0.766
Male (n = 573)
UACR ≥30 mg/g	2.228	1.407−3.528	0.001	1.881	1.093−3.236	0.023
Age	1.087	1.065−1.109	<0.001	1.105	1.075−1.136	<0.001
BMI	1.055	1.003−1.110	0.039	1.149	1.039−1.271	0.007
Smoking	1.045	0.727−1.501	0.812
Hypertension	2.046	1.426−2.934	<0.001	1.660	0.951−2.898	0.075
Anti-HTN medications	1.518	0.978−2.357	0.063	0.714	0.360−1.416	0.335
Dyslipidemia	1.253	0.504−3.119	0.627
Total Cholesterol	0.998	0.993−1.004	0.521
TG	0.998	0.996−1.000	0.080
HDL	0.992	0.979−1.006	0.272
LDL	1.003	0.997−1.008	0.291
UA	1.094	0.965−1.240	0.160
FBS	0.999	0.982−1.016	0.897
eGFR	0.974	0.960−0.989	0.001	1.007	0.986−1.029	0.508
Metabolic syndrome	1.364	0.913−2.036	0.130
Total body fat percentage	1.037	1.009−1.066	0.009	0.976	0.930−1.025	0.338
Abbreviations: BMI: body mass index; HTN: hypertension; TG: triglyceride; HDL: high density lipoprotein; LDL: low density lipoprotein; UA: uric acid; FBS: fasting blood sugar; eGFR: estimated glomerular filtration rate.

Methods

Study population

This retrospective cohort study was excerpted from the first wave of the I-Lan Longitudinal Aging Study (ILAS), a cohort study of community-dwelling adults aged more than 50 years who were randomly recruited through household registration records. The ILAS design, participant recruitment, and data collection have been reported previously [42]. Exclusion criteria of the present study were: (i) inability to cooperate or communicate with study investigators; (ii) declined to grant consent; (iii) currently institutionalized; comorbidities, such as active cancer, sepsis, heart failure, chronic obstructive pulmonary disease, or functional dependence; (v) life expectancy of less than six months; and (vi) planned to leave I-Lan county. A total of 1,839 community-dwelling older adults were enrolled from August 2011 to August 2013. After in-person face-to-face interviews conducted by well-trained research nurses, all participants provided written informed consent. Three hundred eighty-two subjects were excluded, including 88 with coronary artery disease, 124 with incomplete data, 320 with DM, and 79 with renal dysfunction (Figure 2). The study protocol complied with the Declaration of Helsinki and was approved by the institutional review board of the National Yang-Ming University (YM103008).

Figure 2. Flow chart of patient enrollment.

Anthropometry, demography, and laboratory examinations

The research nurse collected the participants’ demographic and medical data, including weight, height, BMI, and waist circumference. Brachial blood pressure was measured with a mercury sphygmomanometer after the subjects rested for 15 min. Medical histories of underlying diseases, medications, and smoking were obtained from personal interviews and medical records. Peripheral blood samples were collected at 7–9 AM, after fasting for at least ten hours, to determine the concentrations of hemoglobin A1c, fasting blood glucose, total cholesterol, HDL, LDL, triglyceride, uric acid, and high-sensitivity C-reactive protein by using an automatic analyzer (ADVIA 1800; Siemens, Malvern, PA, USA).

Metabolic syndrome was defined according to the criteria proposed by Taiwan’s Ministry of Health and Welfare, with more than three of the following risk determinants: (i) waist circumference >90 cm for men or >80 cm for women; (ii) systolic blood pressure ≥130 mmHg, diastolic blood pressure ≥85 mmHg, or taking antihypertensive agents; (iii) HDL <40 mg/dL for men or <50 mg/dL for women; triglyceride ≥150 mg/dL; (iv) fasting blood glucose ≥100 mg/dL or antihyperglycemic therapy. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [43]. Renal dysfunction was defined as a glomerular filtration rate of <60 (mL × min^–1 per 1.73 m² body surface area) according to the 2012 KDIGO CKD classification [44].

All participants underwent whole-body dual-energy X-ray absorptiometry to measure total fat weight with a Lunar Prodigy instrument (GE Healthcare, Madison, WI, USA). Total fat percentage was defined as total fat weight divided by total body mass.

Definition of albuminuria

A single voided morning urine sample was used to measure UACR (mg/g). UACR measured in a spot urine sample is highly correlated with 24-h urine albumin excretion [45]. UACR ≥30 mg/g was considered pathological [46]. Patients were separated into two groups whether UACR ≥30 mg/g. The flowchart of patient enrollment and classification is illustrated in Figure 2.

Assessment of CIMT and SA

CIMT was measured using a high-resolution, broad-width, linear array transducer (LOGIQ 400 PRO; GE, Cleveland, OH, USA) at the level of the common carotid artery. All examinations were performed by the same trained technician who obtained bilateral longitudinal views of the proximal to distal sections of the common carotid arteries. The mean CIMT was defined as the average of right-and left-side CIMT values. The 75th percentile of CIMT was defined as the upper limit of normal, the threshold indicative of increased cardiovascular risk and early phase of SA [47, 48]. In the present study, the 75th percentile of CIMT was 0.75 mm.

Statistical analysis

Data were expressed as frequencies (percentages) for categorical variables and as means ± standard deviations for continuous variables with normal distribution and median with the interquartile range due to non-normal distribution. The chi-square test was used for comparisons between two groups of categorical variables. The independent t-test was employed for continuous variables with normal distribution. The Kruskal-Wallis test was used for non-normally distributed continuous variables. Pearson and Spearman’s tests were used to assess the correlation between the UACR and CIMT with other variables. Associations between factors and SA were expressed as odds ratios (ORs). Multivariate logistic regression evaluated the SA by dividing both sexes after using the enter methods to explore the association between microalbuminuria and subclinical atherosclerosis and other variables. ORs with 95% confidence intervals (95% CI) for the risk of CIMT ≥0.75 mm were reported. Statistical analyses were performed using SPSS (version 22.0; IBM Corporation, Armonk, New York, NY, USA). Two-tailed p values <0.05 were regarded as statistically significant.

Consent for publication

No individual participant data were reported that would require consent from the participant to publish.

Availability of data and materials

The dataset used and analyzed during the current study is available from the corresponding author upon request.

Author Contributions

Data were interpreted and analyzed by YWL and CCC with assistance from RHC and YLT. YWL drafted the manuscript. CCC and PHH revised the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Ethical Statement and Consent

The study protocol complied with the Declaration of Helsinki and was approved by the institutional review board of the National Yang-Ming University (YM103008). All participants provided written informed consent.

Funding

This study was supported in part by research grants from the Novel Bioengineering and Technological Approaches to Solve Two Major Health Problems in Taiwan program, sponsored by the Taiwan Ministry of Science and Technology Academic Excellence Program (MOST 108-2633-B-009-001); Taipei Veterans General Hospital (VGH-V100E2-002 and VGHUST103-G7-2-1); the National Taiwan University Hospital, Hsinchu Branch (107-HCH002 and 108-HCH004); and the Ministry of Science and Technology (MOST-105-2314-B-002-119, 106-2314-B-002-173-MY3, MOHW 106-TDU-B-211-113001). The funding institutions took no part in the study design, data collection or analysis, publication intent, or manuscript preparation.

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