Factor analysis of diabetic nephropathy in Chinese patients

Diabetes & Metabolic Syndrome: Clinical Research & Reviews 5 (2011) 130–136

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Factor analysis of diabetic nephropathy in Chinese patients Weiwei Zheng a,*, Ling Chen b a b

Department of Emergency, Peking University People’s Hospital, No. 11 Xizhimen South Street, Beijing 100044, China Department of Endocrinology and Metabolism, Peking University People’s Hospital, No. 11 Xizhimen South Street, Beijing 100044, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Diabetic nephropathy Albuminuria Factor analysis Multicollinearity

Objective: To investigate factors related to albuminuria in diabetic nephropathy (DN). Methods: Clinical data of 873 Chinese patients with type 2 diabetes were gathered retrospectively. Urinary albumin–creatinine-ratio was tested thrice for each patient before calculating its mean value for DN diagnosing and staging. Inter-relationship among variables was studied using factor analysis, and factor scores were compared and used as independent variables in regression models to identify the parameter clusters that predict the development and/or progression of DN. Results: Factor analysis reduced 21 original variables to eight unique factors, representing obesity, glycemic, C peptide, lipids, time, renal, blood pressure and metabolism status. Logistic regression revealed that presence of hyperuricemia/gout (OR = 1.821, 95%CI 1.224–2.707), time factor (OR = 1.404, 95%CI 1.194–1.651) and blood pressure factor (OR = 1.424, 95%CI 1.216–1.668) were positively associated with DN, while C peptide factor (OR = 0.816, 95%CI 0.691–0.963) was negatively associated with DN. Ordinal regression revealed another positively related lipid factor (OR = 1.237, 95%CI 1.060– 1.445) besides those determined in Logistic regression. Conclusion: Hyperuricemia/gout, time, blood pressure and lipid factors are predictors of DN, while C peptide factor is negatively associated with the development and/or progression of DN. ß 2012 Diabetes India. Published by Elsevier Ltd. All rights reserved.

1. Introduction As one of the most serious microvascular complications of diabetes mellitus (DM), diabetic nephropathy (DN) has become the most common single cause of end-stage renal disease (ESRD) in North America and Europe. About 20–30% of patients with type 1 (T1DM) or type 2 diabetes (T2DM) develop evidence of nephropathy [1,2]. The earliest clinical evidence of nephropathy is the low but abnormally elevated urine albumin level (30 mg/mg), referred to as microalbuminuria. Without specific interventions, 80% of subjects with T1DM who develop sustained microalbuminuria progress to the stage of overt nephropathy, say, macroalbuminuria (300 mg/mg) over a period of 10–15 years. ESRD develops in 50% of T1DM individuals with macroalbuminuria within 10 years and in 75% by 20 years [2]. A higher proportion of individuals with T2DM are found to have micro- and macro-albuminuria shortly after the diagnosis of their diabetes [3], because diabetes is actually present for

* Corresponding author at: Department of Emergency, Peking University People’s Hospital, No. 11 Xizhimen South Street, Beijing 100044, China. Tel.: +86 10 88325113; fax: +86 10 68318386. E-mail address: [email protected] (W. Zheng).

many years before the diagnosis is made. Without specific interventions, 20–40% of T2DM patients with microalbuminuria progress to overt nephropathy, but only 20% will have progressed to ESRD by 20 years [2]. However, because of the much greater prevalence of T2DM, such patients constitute over half of those patients currently starting on dialysis, impacting negatively the patients’ quality of life and exerting great financial burden on public health. Hyperglycemia [4–7], hypertension [6,7], dyslipidemia [7–9], and diabetic duration [7,10] have been demonstrated in a number of studies to be related with the development and progression of DN. Most published data identifying the potential predictors of DN were conducted using Multiple Logistic Regression. However, correlations among independent variables, namely, multicollinearity, might render instability to regression model and consequently questionable conclusions. Recently, factor analysis, a statistical technique for exploring the inter-related variables, has been applied to investigate the risk factor clusters and to predict cardiovascular disease mortality [11]. However, little information is currently available on the underlying mechanism that regulates DN development. What’s more, few studies have been conducted using factor analysis in Chinese population. Therefore, we applied factor analysis combined with regression models to investigate how the major clinical parameters inter-relate to one another and their effects on DN.

1871-4021/$ – see front matter ß 2012 Diabetes India. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.dsx.2012.02.018

W. Zheng, L. Chen / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 5 (2011) 130–136

2. Subjects and methods 2.1. Subjects 2.1.1. Inclusion criteria Patients with T2DM admitted to Endocrinology Department of Peking University People’s Hospital from February 2006 to July 2009. Diagnosis of diabetes was performed according to 1999 WHO criteria. 2.1.2. Exclusion criteria Patients with type 1 and secondary diabetes, pregnant women, those with poor general condition, severe infection, cardiac function of III–IV degree (NYHA classification), or severely impaired hepatic or renal function. 2.2. Methods 2.2.1. Clinical data gathering: Patients’ general information such as gender, age, diabetic duration, and maximal body weight (MBW) were obtained through history inquiry. Ketosis was diagnosed by urine ketone dip test on the admission day. Comorbidity of hypertension was according to 1993 WHO/ISH definition [12]. Coronary heart disease (CHD) was considered to be present if the patient had an ischemic history or typical EKG abnormality. Cerebral vascular disease (CVD) was based on ischemic or hemorrhagic history with definite imageology evidence. As a method to diagnose peripheral atherosclerosis (PA), ultrasonography of carotid and lower limb arteries was performed to detect the presence of thickness or plaque on the wall of those blood vessels. Hyperuricemia/gout was defined as explicit history or plasma uric acid assay. All patients underwent the measurements of systolic blood pressure (SBP), diastolic blood pressure (DBP), height, present body weight (PBW), waist circumference (WC), and hip circumference (HC) on admission. Body mass index (BMI) and wrist-hip-ratio (WHR) were calculated by the following formula: BMI = PBW (kg)/height2 (m2), WHR = WC (cm)/HC (cm). 2.2.2. Blood sampling and biochemical assay Fasting blood samples were drawn before breakfast on the second day of hospitalization for measurements of fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), creatinine (CR), urea acid (UA), total cholesterol (TC), triglycerides (TG), highdensity lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Plasma glucose, plasma lipids and renal function indices were measured using type 7170 biochemical autoanalyzer (Hitachi, Japan). Fasting C peptide (FCP) and 2-h postprandial C peptide (PCP) were measured using Elecsys 2010 electrochemical fluorescent immunoanalyzer (Cobas, Roche). HbA1c was measured by high performance liquid affinity chromatography using PRIMUS UITRA 2 glycosylated hemoglobin analyzer (Bio-Rad, CA, USA), with 4.0–6.0% (20–42 mmol/mol) as normal range. Glycosylated serum protein (GSP) was measured by biochemical emzyme using GlyPro Analyzer (genzyme, UK). 2.2.3. Detection and staging of DN Detection of DN was carried out by albumin–creatinine-ratio (ACR) measuring. Three spot urine samples were taken during three consecutive mornings, and the values of ACR were automatically calculated with urinary albumin and creatinine measured using INTEGRA400D (Roche, USA). The mean value of three measurements was used to classify DN as normo-, microand macro-albuminuria using the following definitions: ACR < 30 mg/mg, normoalbuminuria (Normo-Alb); 30 mg/ mg  ACR < 299 mg/mg, microalbuminuria (Micro-Alb);

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ACR  300 mg/mg, macroalbuminuria (Macro-Alb) [2]. Microand Macro-Alb were respectively considered as the early and late stage of DN, thus combined as a single group (DN group) in 2group comparisons and Logistic Regression. 3. Statistical analysis Data were stored and processed using SPSS software package. Numerical variables of normal distribution were expressed as mean  SD, while those of nonnormal distribution as median (quartile). One-Way ANOVA and T test were performed to compare means of normally distributed variables. Multiple comparisons were conducted using least significant difference (LSD) method to evaluate between-group difference. Nonparametric test (Mann–Whitney U Test or Kruskal–Wallis Test) were performed in nonnormally distributed variables. x2 test was used to analyze categorical variables. Significant level of the above statistical analyses was set as a = 0.05, with P < 0.05 as statistically significant. Significant level of Kruskal–Wallis Test and R  C division x2 test was set as a0 = 0.017, with P < 0.017 as statistically significant [13]. Factor analysis (FA) was applied to identify specific clusters of DN predictors, on the premise that the inter-relationship among a set of variables can be synthesized and explained by a small number of unique unmeasured and uncorrelated ‘factors’. Conventionally, FA consists of two procedures: (1) factor extraction to estimate the number of factors, and (2) factor rotation to determine constituents of each factor in terms of the original variables. The following 21 diabetes-related parameters were included in the present FA: age, DM duration, MBW, PBW, WC, WHR, BMI, SBP, DBP, FPG, HbA1c, GSP, CR, UA, ACR, TC, TG, HDL-C, LDL-C, FCP, and PCP. Kaiser–Meyer–Olkin measure (KMO)’s Measure of Sampling Adequacy (MSA) was conducted to determine if a common factor model was appropriate for each variable and for all variables together. Bartlett’s test of sphericity was applied to test the significance of the correlations among the variables in the correlation matrix. KMO’s MSA value <0.5 or Bartlett’s P value >0.05 implied FA might be impertinent for the data matrix [14,15]. Method of principal components analysis was performed to extract common factors and orthogonal (varimax) rotation to facilitate factors’ interpretation. The number of factors was determined considering those components with eigenvalues >1; and original variables that had a factor loading j0.35j with a particular factor were considered to be its major constituents [15]. Finally, factor scores computed for each subject of identified factors were used as independent variables (continuous variables) in regression models to determine clusters pertaining DN. In addition, some clinical comorbidities and risk factors (dichotomous variables, defined as ‘0’ if absent, and ‘1’ if present) were also included as independents. The selection of independents followed such criteria that (1) variables which differences were of statistical significance, or (2) those of no significant difference but considered to be related with DN according to previous researches. In this study, Binary Logistic Regression (Forward method, likelihood ratio test) was primarily used to estimate the association of independents and DN by Wald and P value, which less than 0.05 was considered as statistically significant. The ability of each independent to relate DN was determined by partial regression coefficient (B) and odds ratio (OR = EXP(B)) along with its 95% confidence interval (95%CI), with OR above 1 representing positive correlation and below 1 negative. Stages or severity gradients of DN could be considered as ordinal categorical variables with more than two orderly levels. Therefore we conducted Ordinal Regression where dependent variable was defined on a tier basis: Normo-Alb was defined as ‘0’, Micro-Alb as ‘1’, and Macro-Alb as ‘2’. Here OR indicated the odds ratio for dependent variable to elevate one or more levels, in

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prevalence of diabetes-related macrovascular complications and metabolic disorders. (Table 1)

response to the independent variable changing for every one unit [16].

4.2. Comparisons of clinical parameters 4. Results Both 2-group and 3-group comparisons have observed significant differences in age, diabetic duration, height, SBP, CR, UA, TC, and TG (P < 0.05). Macro-Alb group showed apparently advanced age and prolonged duration, as well as higher levels of WHR, SBP, CR, UA, TC, and TG. In addition, 3-group comparison revealed significantly lower FPG, HbA1c, and GSP (P < 0.05) in Macro-Alb group. Patients with albuminuria (both Micro- and Macro-Alb) tended to be slightly shorter than those of Normo-Alb group (Table 2).

4.1. Subjects’ general information and clinical comorbidities A total of 873 patients with T2DM were finally included in this analysis. There were 527 (60.4%) males and 346 (39.6%) females, with age median (quartile) of 56.0 (48.0–66.0) years and diabetic duration median (quartile) of 7.0 (1.0–12.0) years. Of all the subjects, 205 presented themselves with DN, of which 148 (17.0%) were with Micro-Alb and 57(6.5%) with Macro-Alb. Two-group comparison (Normo-Alb vs. DN) revealed significantly higher prevalence of hypertension, CVD, PA and hyperuricemia/gout in DN group. Three-group comparison (Normo- /Micro- / Macro-Alb) clarified the Macro-Alb group as to have the highest

4.3. Factor analysis After FA, most of the 21 variables were significantly interrelated with one another and correlations coefficients indicated a

Table 1 Comparison of general conditions and clinic comorbidities among DN groups: N (%).a

N Male Ketosis Hypertension Hyperlipidemia CHD CVD PA Hyperuricemia/Gout

Total

Normo-Alb

Micro-Alb

Macro-Alb

P (2-group)

P (3-group)

873 527 65 471 580 91 166 733 159

668 412 50 328 436 63 115 545 108

148 82 14 93 102 20 34 133 32

57 33 1 50 42 8 17 55 19

0.157 0.169 0.000 0.187 0.083 0.014 0.000 0.005

0.342 0.936 0.000 0.340 0.222 0.027 0.001 0.003

(100) (60.4) (7.4) (54.0) (66.4) (10.4) (19.0) (84.0) (18.2)

(100) (61.7) (7.5) (49.1) (65.3) (9.4) (17.2) (81.6) (16.2)

(100) (55.4) (9.5) (62.8)* (68.9) (13.5) (23.0) (89.9)* (21.6)

(100) (57.9) (1.8) (87.7)*,# (73.7) (14.0) (29.8)b (96.5)* (33.3)*

P values of statistical significance highlighted in bold. a Significant level of pairwise comparisons was calculated as a0 = a/N, N = n(n  1)/2, and ‘n’ was the number of groups which was 3 in this study, so a0 = 0.05/3 = 0.017 [13]. b Compared with Normo-Alb group, difference was significant at p = 0.018. * Compared with Normo-Alb group, P < 0.017. # Compared with Micro-Alb group, P < 0.017.

Table 2 Comparison of original clinical parameters among DN groups: mean  SD or M(quantile).a

Age (years) Duration (years) MBW (kg) PBW (kg) Height (cm) BMI (kg/m2) WC (cm) HC (cm) WHR SBP (mmHg) DBP (mmHg) FPG (mmol/L) HbA1c (%) GSP (mmol/L) CR (mmol/L) UA (mmol/L) ACR (mg/mg) TC (mmol/L) TG (mmol/L) HDL-C (mmol/L) LDL-C (mmol/L) FCP (ng/ml) PCP (ng/ml)

Total

Normo-Alb

Micro-Alb

Macro-Alb

P (2-group)

P (3-group)

56.0 (48.0–66.0) 7.0 (1.0–12.0) 79.0 (70.0–88.0) 70.0 (62.0–78.0) 168.0 (160.0–173.0) 24.97 (22.94–27.31) 91.71  9.59 99.0 (94.0–105.0) 0.9196  0.0611 130.0 (120.0–145.0) 80.0 (70.0–85.0) 7.32 (5.96–9.19) 8.8 (7.2–10.6) 393.0 (286.4–530.9) 78.0 (68.0–88.0) 317.6  88.8 12.49 (7.36–27.64) 4.94  1.00 1.48 (1.04–2.08) 1.05 (0.91–1.23) 3.13  0.83 1.81 (1.23–2.49) 3.52 (2.24–5.57)

55.96  11.92 6.0 (1.0–11.0) 79.0 (70.0–88.38) 70.0 (62.0–78.0) 168.0 (160.0–174.0) 24.94 (23.02–27.13) 91.68  9.54 99.62  7.72 0.9201  0.0617 130.0 (120.0–140.0) 80.0 (70.0–85.0) 7.29 (5.99–8.98) 8.8 (7.1–10.7) 400.2 (288.9–536.5) 77.23  15.35 312.5  85.7 9.95 (6.34–14.70) 4.89  0.95 1.46 (1.02–2.02) 1.04 (0.90–1.21) 3.12  0.82 1.78 (1.26–2.45) 3.59 (2.26–5.81)

59.74  11.85* 8.5 (3.0–14.8)* 78.5  13.7 69.4  11.8 165.0 (158.3–173.0) 25.28  3.66 91.34  10.02 100.03  8.83 0.9127  0.0548 135.0 (121.3–150.0)* 80.0 (70.0–90.0) 8.19  2.76 9.45  2.20 436.0  179.0 81.11  18.44 321.6  94.3 65.17 (40.64–118.00) 4.92  1.00 1.57 (1.05–2.48) 1.10  0.28 3.09  0.83 2.08  1.29 3.23 (1.90–5.31)

64.42  9.62* # 13.4  7.2*,# 78.1  12.3 69.4  9.7 164.6  8.3 25.59  2.97 93.04  9.18 99.83  6.75 0.9321  0.0682# 150.5  22.3*,# 82.2  13.7 7.21  2.39# 8.38  1.88# 319.8  149.2*,# 101.0 (84.0–133.0)*,# 367.7  94.4*,# 1645.51  1309.43*,# 5.50  1.33*,# 2.04  1.25b 1.16  0.30 3.30  0.92 2.04  1.12 3.71  2.09

0.000 0.000 0.759 0.261 0.017 0.549 0.858 0.578 0.689 0.000 0.142 0.592 0.514 0.107 0.000 0.002 0.000 0.033 0.017 0.167 0.638 0.408 0.083

0.000 0.000 0.948 0.531 0.046 0.426 0.515 0.845 0.118 0.000 0.292 0.026 0.007 0.000 0.000 0.000 0.000 0.000 0.035 0.173 0.224 0.691 0.223

*

MBW: maximal body weight; PBW: present body weight; BMI: body mass index; WC: waist circumference; HC: hip circumference; WHR: wrist hip ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; FPG: fasting plasma glucose; HbA1c: glycosylated hemoglobin; GSP: glycosylated serum protein; CR: creatinine; UA: urea acid; ACR: albumin creatinine ratio; TC: total cholesterol; TG: triglyceride; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; FCP: fasting C peptide; PCP: 2 h postprandial C peptide. P values of statistical significance highlighted in bold. a Significant level of pairwise comparisons was set as: for LSD, a = 0.05 [17], for Kruskal–Wallis Test, a0 = 0.017 [13]. b Compared with Normo-Alb group, difference was significant at P = 0.025 * Compared with Normo-Alb group, difference had statistical significance. # Compared with Micro-Alb group, difference had statistical significance.

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Table 3 Factor loadings after varimax rotation.

Age Duration MBW PBW WC WHR BMI SBP DBP FPG HbA1c GSP CR UA ACR TC TG HDL-C LDL-C FCP PCP Eigenvalues Variance% Cumulative Variance % Clinical Significance

Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

Factor 6

Factor 7

Factor 8

0.131 0.039 0.773 0.869 0.906 0.630 0.807 0.038 0.165 0.072 0.023 0.134 0.092 0.225 0.041 0.006 0.125 0.254 0.066 0.191 0.129 3.491 16.623 16.623 Obesity

0.063 0.284 0.047 .049 .039 .109 .139 .054 0.055 0.698 0.900 0.851 0.009 0.182 0.111 0.070 0.009 0.097 0.105 0.150 0.306 2.336 11.126 27.749 Glycemic

0.015 0.442 0.019 0.100 0.177 0.125 0.155 0.015 0.084 0.023 0.138 0.181 0.111 0.255 0.148 0.042 0.143 0.444 0.064 0.815 0.826 2.006 9.551 37.299 C Peptide

0.025 0.048 0.121 .068 0.045 0.075 0.077 0.050 0.022 0.098 0.059 0.008 0.011 0.149 0.216 0.917 0.072 0.378 0.899 0.003 0.053 1.921 9.146 46.446 Lipid

0.852 0.558 0.414 0.349 0.191 0.402 0.079 0.318 0.196 0.044 0.110 0.093 0.170 0.045 0.049 0.004 0.060 0.128 0.035 0.056 0.061 1.758 8.370 54.815 Time

0.115 0.211 0.126 0.109 0.025 0.029 0.001 0.134 0.054 0.111 0.043 0.062 0.852 0.537 0.697 0.059 0.039 0.184 0.070 0.098 0.015 1.681 8.004 62.820 Renal

0.079 0.027 0.032 0.071 0.061 0.000 0.111 0.849 0.879 0.008 0.007 0.012 0.020 0.021 0.160 0.061 0.062 0.097 0.003 0.027 0.080 1.573 7.491 70.311 BP

0.113 0.139 0.026 0.052 0.065 0.013 0.088 0.037 0.034 0.314 0.083 0.165 0.056 0.227 0.006 0.306 0.920 0.204 0.131 0.148 0.003 1.259 5.997 76.308 Metabolic

MBW: maximal body weight; PBW: present body weight; WC: waist circumference; WHR: wrist hip ratio; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; FPG: fasting plasma glucose; HbA1c: glycosylated hemoglobin; GSP: glycosylated serum protein; CR: creatinine; UA: urea acid; ACR: albumin creatinine ratio; TC: total cholesterol; TG: triglyceride; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; FCP: fasting C peptide; PCP: 2 h postprandial C peptide. Factor loadings  j0.35j highlighted in bold.

pattern of moderate significant correlation. Major strong correlations were observed for age and diabetic duration; for MBW, PBW, BMI, WC, and WHR; for SBP and DBP; for FPG, HbA1c and GSP; for CR, UA, and ACR; for TC, HDL-C, and LDL-C; for FCP and PCP (correlation matrix not shown). KMO’s MSA was 0.626, and Bartlett’s test of sphericity resulted in P = 0.000, supporting a tenable FA assumption for this matrix. Principal components analysis reduced 21 variables into eight factors with eigenvalues greater than 1, and the cumulative variance was 76.308%. Factor loadings after varimax rotation were presented in Table 3. Eight factors respectively represented various clinical information: the first factor (obesity factor) bore the greatest loadings on MBW, PBW, BMI, WC, and WHR, implicating the status of obesity. The second factor (glycemic factor) bore the greatest loadings on FPG, HbA1c, and GSP, implicating the status of blood glucose control. The third factor bore the greatest loadings on FCP and PCP, and relatively higher negative loadings on diabetic duration and HDLC. On the one hand, that suggests the declining C peptide level with diabetic duration. On the other hand, higher C peptide level in T2DM subjects often indicates endogenous hyperinsulinemia and insulin resistance, which usually coexist with low HDL-C level. Therefore, the third factor defined as C peptide factor was in concordance with its clinical implication. The fourth factor (lipid factor) bore the greatest loadings on TC and LDL-C and relatively high loading on HDL-C, in agreement with the linear relation of the three. The fifth factor (time factor) bore the greatest loadings on age and diabetic duration, meanwhile slightly higher loadings on MBW and WHR. The sixth factor (renal factor) bore the greatest loadings on CR, UA, and ACR, implicating the renal status. The seventh factor (BP factor) bore the greatest loadings on SBP and DBP, implicating the condition of blood pressure control. The last factor (metabolic factor) bore the greatest loading on TG, meanwhile relatively higher loadings on TC and FPG. The reason might be that hyperglycemia is frequently accompanied by hypertriglyceridemia, so the last factor bore the information of

both lipids and glucose metabolism and could be termed as metabolic factor. 4.4. Comparison of mean factor scores among three DN groups We calculated the mean values of scores of the eight factors in every group. Statistical analyses showed that the differences on the mean scores of BP, time, lipid, glycemic, and C peptide factors were of statistical significance (P < 0.05). Mean scores of BP and time factors in Normo-Alb group were less than 0, whereas those in Micro-Alb group were more than 0 and those in Macro-Alb group were even higher. Value of mean score of C peptide factor in Macro-Alb group was far below 0, whereas that in Micro-Alb group above 0 and that in Normo-Alb

Fig. 1. Tendency of mean factor scores among various DN groups (* indicates the factor which mean score has statistically significant difference).

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group the highest. Normo- and Micro-Alb group had similar mean scores of lipid and glycemic factors, but Macro-Alb group had the highest and lowest values regarding the two factors. (Fig. 1)

gout (OR = 0.509, 95%CI 0.340–0.762) were negatively related to its development and/or progression (Table 4B). 5. Discussion

4.5. Regression analyses of DN 4.5.1. Logistic regression of DN Results of Logistic regression were displayed in Table 4A: Statistic test built a significant model with x2 = 49.691 (P = 0.000). Hyperuricemia/gout, C peptide factor, time factor and BP factor finally entered the regression equation. Presence of hyperuricemia/gout (OR = 1.821, 95%CI 1.214–2.707), time factor (OR = 1.404, 95%CI 1.194–1.651) and BP factor (OR = 1.424, 95%CI 1.216–1.668) were significantly associated with the presence of DN. C peptide factor (OR = 0.816, 95%CI 0.691–0.963) was negatively related to DN. (Table 4A) 4.5.2. Ordinal regression of DN Results of ordinal regression were displayed in Table 4B: Likelihood ratio of total model test x2 = 72.223, P = 0.000, 2LL = 1121.765. Goodness of fit test revealed a satisfactory model (Pearson test P = 0.952, Deviance test P = 1.000). Besides an additional positively related lipid factor (OR = 1.237, 95%CI 1.060–1.445), Wald test revealed a result generally resembling that of Logistic Regression in the following aspects: time factor (OR = 1.315, 95%CI 1.099–1.575) and BP factor (OR = 1.471, 95%CI 1.260–1.719) were positively associated with DN development and/or progression, while C peptide factor (OR = 0.775, 95%CI 0.656–0.915) and absence of hyperuricemia/

5.1. Factor analysis Firstly nominated by R Frisch in 1934, the term multicollinearity means that some or all of the independent variables in a logistic regression model have complete or partial correlations. The existence of multicollinearity inflates the variances of the parameter estimates, then underestimates the statistical significance of individual independent variables while the overall model might be strongly significant. Multicollinearity may also result in changes in the signs and magnitudes of the partial regression coefficient estimates from one sample to another, and consequently in incorrect conclusions about relationships between independent and dependent variables. Based on this point, it is essential to examine the correlations (for continuous and ordinal variables) and associations (for nominal variables) among independent variables. The fewer unique uncorrelated synthesized parameter clusters, namely, ‘factors’, are the linear combinations of specific clusters of original variables. In this study, principal component analysis was used to determine the multicollinearity among 21 original variables and FA applied to reduce the number of variables by extracting the ‘common factors’. Supplanting the numerous original variables by fewer factors could satisfactorily meet the demand of the regression model in investigating relationship between DN and

Table 4A Positive results of logistic regression on DN. Items

B

S.E.

Wald

P value

OR

Hyperuricemia/Gout C Peptide Factor Time Factor BP Factor Constant

0.599 0.203 0.339 0.354 1.372

0.202 0.085 0.083 0.081 0.096

8.764 5.773 16.794 19.200 202.694

0.003 0.016 0.000 0.000 0.000

1.821 0.816 1.404 1.424 0.254

95% CI for OR Lower bound

Upper bound

1.224 0.691 1.194 1.216

2.707 0.963 1.651 1.668

Table 4B Results of Ordinal Regression on DN. Items

B

S.E.

Wald

P value

OR

95% CI for OR Lower bound

Upper bound

Constant DN = 0 Constant DN = 1 Obesity factor Glycemic factor C peptide factor Lipid factor Time factor BP factor Metabolic factor Ketosis = 0 Ketosis = 1 CHD = 0 CHD = 1 CVD = 0 CVD = 1 Hyperuricemia/gout = 0 Hyperuricemia/gout = 1 PA = 0 PA = 1

0.244 1.821 0.107 0.000 0.255 0.213 0.274 0.386 0.077 0.147 0a 0.188 0a 0.129 0a 0.676 0a 0.485 0a

0.446 0.456 0.084 0.088 0.085 0.079 0.092 0.080 0.081 0.340

0.299 15.938 1.613 0.000 9.013 7.266 8.883 23.601 0.919 0.186

0.584 0.000 0.204 0.996 0.003 0.007 0.003 0.000 0.338 0.666

1.276 6.178 0.899 1.000 0.775 1.237 1.315 1.471 1.080 0.863

0.533 2.527 0.763 0.842 0.656 1.060 1.099 1.260 0.922 0.443

3.059 15.120 1.060 1.188 0.915 1.445 1.575 1.719 1.265 1.682

0.261

0.517

0.472

0.829

0.497

1.383

0.203

0.402

0.526

0.879

0.590

1.310

0.206

10.752

0.001

0.509

0.340

0.762

0.294

2.713

0.100

0.616

0.346

1.096

Independent variables significantly related to DN highlighted in bold. a For dichotomous independent variables, this level was set as control with the default value as 0. Estimate value (B) of the other level was obtained by comparing with the control.

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explainable variables. We have achieved satisfactory outcomes of eight common factors representing the severity of obesity, status of blood glucose control, residual secretion function of islet b-cell (C peptide level), plasma lipids level, age and diabetic duration, renal condition, blood pressure level and metabolism of lipids and glucose. Therefore we presume that there are at least eight common factors contributing to DN. 5.2. Regression analyses of DN The diagnosis and staging of DN were made based on mean ACR, one of the contributors to renal factor, so definitely ACR had lineal correlation with the dependent variable itself. Therefore we substituted renal factor with hyperuricemia/gout as an independent and found it a predictor of DN. Hyperuricemia could contribute to albuminuria by exacerbating renal impairment, which in return elevates the uric acid level. Furthermore, hyperuricemia usually combines with other metabolic disorders and is significantly associated with later development of persistent macroalbuminuria [18]. Time factor, represented by age and diabetic duration in this study, was identified as a risk factor of DN. The effect of advanced age and prolonged duration on increased DN risk has been well addressed before [7,8,10], because diabetic duration determined the exposure time of the other risk factors. Hypertension has also been verified as the risk factor of microalbuminuria by a number of studies [6,7,19]. DN is closely related with the predisposition of essential hypertension in diabetic patients, in that it affects multiple mechanisms in DN development probably through genetic factors [20,21]. T1DM patients with persistent albuminuria showed a significant correlation between the rate of decline in glomerular filtration rate (GFR) and diastolic blood pressure [22]. In T2DM, SBP was one of the significant independent factors associated with the presence of albuminuria [23]. It is not clear, however, if hypertension worsens renal disease or is simply a marker for more severe renal involvement [21]. Observational studies have demonstrated dyslipidemia might increase the risk of DN [7–9], and evidence has been established to prove the effectiveness of lipid-lowering therapy in ameliorating albuminuria progression [24]. In this study, lipid factor was mainly represented by TC, LDL-C and HDL-C, especially the first two. Ordinal but not Logistic Regression analysis did find positive correlation with lipid factor and DN. Such differences in results between two models could be partially contributed by the one more categorical level in the dependent variable of Ordinal Regression. From Table 2 and Fig. 1 we could find higher values of original lipid parameters and lipid factor score in Macro-Alb group, although some of them did not reach statistical significance. This might indicate that dyslipidemia plays a crucial role when microalbuminuria progresses into macroalbuminuria. However, since patients tend to get poorer lipid profile with diabetes progression, this cross-sectional study just theoretically explained this correlation by mathemetical technique. Applying this result to explain practical problems in clinical setting would need to be further verified. Negative correlation between C peptide factor and DN was highlighted once again in our study. On the one hand, serum C peptide level decreases as diabetes progresses, so regression models might purely reveal mathematical but not causal relationship. On the other hand, however, the residual secretion function of pancreatic islet could probably reduce DN, and this protective role of C peptide has been corroborated by prospective studies. Observations from DCCT revealed that in those subjects receiving intensive treatment, participants with minimal or greater stimulated C-peptide secretion uniformly experienced

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significantly lower rates of albuminuria events than did the group with undetectable C-peptide (P < 0.05) [25]. Furthermore, intensive insulin therapy markedly slowed the decline of endogeneous insulin secretion and consequently risk for microvascular complications [26]. Recently, prospective controlled studies discovered that islet cell transplantation yielded improved HbA1c and less decline of GFR, compared with intensive medical therapy. Besides lower HbA1c level, the presence of C peptide was another crucial factor that could potentially arrest the progression of DN [27]. Findings of these studies imply that even a minimal residual b-cell function in patients of T1DM could be beneficial in risk reduction of DN. Unlike T1DM patients, higher C peptide level in T2DM patients usually indicates higher endogeneous insulin level and components of metabolic syndrome, therefore increases the incidence of macrovascular complications [28–30]; but its effect on microvascular complications is still protective [28]. However, different from the results of previous studies [4,5,7,31–33], glycemic factor failed to be found related with DN in this study. But we cannot conclude that blood glucose level exerts no influence on the development of DN. Because most of the patients on inclusion to the study had successfully controlled their blood glucose within a satisfactory range during long time treatment, whereas glycemic control in the early years after diabetes onset and ‘fluctuations’ in HbA1c might predict the incidence of DN [34]. Besides, the measurements of FPG, HbA1c and GSP were based on spot samples, therefore could not exactly represent the long-term blood glucose level, rendering vulnerability to the assessment of results. In a word, even though the OR value of continuous variables usually lack practical meaning in interpreting results of regression analyses, we might generally hold that longer duration, older age, hypertension, hyperuricemia/gout, and dyslipidemia could suggest coexisting DN; while higher level of C peptide, say, having residual function of b-cell, is associated with decreased likelihood of DN. 5.3. Relationship with macrovascular complications Vascular endomembrane damage and endothelial dysfunction are considered to be the common initial factors and pathological mechanisms of both macro- and microvascular complications [35,36], therefore there is justification that we had identified close association between macro- and microvascular diseases in diabetic patients. Table 1 showed that Macro-Alb group had the highest prevalence of macrovascular complications (CHD, CVD, and PA), although some of those were of no (CHD) or marginal significance (for CVD, P = 0.018). 5.4. Study limitations This cross-sectional study cannot prevent itself from being with methodological problems. The study design is incapable of estimating causal relation directly. Therefore, it just figured out some predictors but not risk factors of DN, considering that two clinical manifestations might be caused by one underlying etiology. Bias of selection, information, and confounding existed in several steps of the study. For instance, duration is the confounding bias of the relationship between glycemic factor and DN. Therefore, the conclusion that glycemic control does not affect DN should be drawn with circumspection. In addition, information of angiotensin-converting enzyme inhibitors usage was limited and obscure, regarding its well-ascertained effectiveness in reducing urinary albumin excretion [37]. Conflict of interest Nothing to declare.

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