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Table 2 Single-marker and multi-marker analyses in the linear regression

From: Single-marker and multi-marker approaches to appraise the relationships between biomarkers and microalbuminuria in Chinese middle-aged and elderly from communities: a cross-sectional analysis

Biomarkers

Models

UMA

Standardized β value

P value

Homocysteine (μmol/L)

1st stepa

0.275

< 0.001

2nd stepb

0.236

< 0.001

3rd stepc

0.223

< 0.001

HsCRP (mg/dl)

1st stepa

−0.018

0.590

2nd stepb

−0.005

0.888

3rd stepc

−0.002

0.943

NT-proBNP (pg/mL)

1st stepa

0.152

< 0.001

2nd stepb

0.140

< 0.001

3rd stepc

0.129

< 0.001

UA (μmol/L)

1st stepa

0.001

0.987

2nd stepb

−0.049

0.199

3rd stepc

−0.054

0.148

  1. Note: aFirst step: single biomarker in the linear regression model adjusted by age and gender; bSecond step: single biomarker in the linear regression model adjusted by age, gender, body mass index, cigarette smoking, systolic blood pressure, triglyceride, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, fasting blood glucose and glomerular filtration rate; cThird step: all biomarkers simultaneously in the linear regression model adjusted by age, gender, body mass index, cigarette smoking, systolic blood pressure, triglyceride, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, fasting blood glucose and glomerular filtration rate
  2. Abbreviation: UMA urinary albumin, hsCRP high-sensitivity C-reactive protein, NT-proBNP N-terminal prohormone of brain natriuretic peptide, UA: uric acid