Multiple regression analysis with SerpinB1 as the dependent variable
| Model 1 | β | p Value | (R2: 0.3762) |
|---|---|---|---|
| LDL-C (mg/dL) | −0.5256 | 0.0072* | |
| eGFR (ml/min/1.73 m2) | 0.0780 | 0.7099 | |
| HbA1c (%) | 0.3100 | 0.0741 | |
| Age (years) | 0.4053 | 0.0498* | |
| Model 2 | β | p Value | (R2: 0.4840) |
| LDL-C (mg/dL) | −0.8298 | 0.0009* | |
| HbA1c (%) | 0.4802 | 0.0163* | |
| Age (years) | 0.5174 | 0.0081* | |
| BMI (kg/m2) | 0.6678 | 0.0605 | |
| Insulin (μU/mL) | −0.6826 | 0.0400* | |
| Model 3 | β | p Value | (R2: 0.4526) |
| LDL-C (mg/dL) | −0.6667 | 0.0021* | |
| HbA1c (%) | 0.4336 | 0.0314* | |
| Age (years) | 0.3706 | 0.0517 | |
| BMI (kg/m2) | 0.2450 | 0.2753 | |
| log10 hsCRP (mg/L) | −0.1295 | 0.4619 | |
| log10 GGT (U/L) | −0.2931 | 0.1499 | |
| Model 4 | β | p Value | (R2: 0.2903) |
| LDL-C (mg/dL) | −0.4845 | 0.0108* | |
| TG (mg/dL) | −0.0174 | 0.9234 | |
| UA (mg/dL) | −0.1451 | 0.4355 | |
| SBP (mm Hg) | 0.2400 | 0.1690 | |
| Model 5 | β | p Value | (R2: 0.4501) |
| LDL-C (mg/dL) | −0.5594 | 0.0096* | |
| HbA1c (%) | 0.2885 | 0.1541 | |
| Age (years) | 0.3353 | 0.1033 | |
| Insulin (μU/mL) | −0.3370 | 0.1366 | |
| CAVI index | −0.3377 | 0.1421 |
All variables followed the normal distribution confirmed by a χ2 goodness of fit test.
The number of patients for various markers were 30 except for Insulin (n=29), CAVI index (n=24).
Multiple regression analysis with SerpinB1 as the dependent variable was performed using 5 models.
β: standard partial regression coefficient, *p<0.05 is defined as statistical significance.
BMI, body mass index; CAVI, cardio-ankle vascular index; eGFR, estimated glomerular filtration rate; GGT, gamma-glutamyl transpeptidase; HbA1c, hemoglobin A1c; hsCRP, high-sensitivity C reactive protein, LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TG, triglyceride; UA, uric acid.