Type 2 diabetes | OR | SE | z |
pvalue | 95% CI | Model |
|---|
| Â | Â | Â | Â | Â | Lower | Upper | pseudo-R
2
|
|---|
A
| Â | Â | Â | Â | Â | Â | Â | Â |
|  | VAT | 2.17 | 0.18 | 9.5 | <2 × 10-16
| 1.85 | 2.54 | 0.07 |
DXA abdominal fat | 1.86 | 0.13 | 8.6 | <2 × 10-16
| 1.61 | 2.14 | 0.05 |
BMI | 1.66 | 0.12 | 7.2 | 2 × 10-13
| 1.45 | 1.91 | 0.04 |
Age | 1.05 | 0.01 | 4.3 | 8 × 10-6
| 1.03 | 1.07 | 0.02 |
B
| Â | Â | Â | Â | Â | Â | Â | Â |
|  | VAT | 2.08 | 0.18 | 8.5 | <2 × 10-16
| 1.76 | 2.47 | 0.08 |
| Â | Age | 1.02 | 0.01 | 2.0 | 0.05 | 1.00 | 1.05 | Â |
- The study sample prevalence (females > = 40 years) estimate for T2D = 0.05. Logistic regressions (n = 2964) presenting unadjusted odds ratios (OR) (A) and best-fit multiple regression model with adjusted OR for visceral adipose fat (VAT) area and age (B). For evidence of the presented best-fit model and an analysis of residuals to account for co-linearity between adiposity variables, see Additional file 1: Tables S2 and S3, respectively. Explanatory variables VAT, DXA and BMI are all standardised, implying a change in odds ratio per unit SD change. For logistic regression, the pseudo-R
2 model-fit statistic is analogous (but not directly comparable) to the ordinary least squares regression R
2 statistic, known as the coefficient of determination. While R
2 can be interpreted as the proportion of variance explained by the model, pseudo-R
2 is loosely interpreted as the proportion of variation in risk liability explained by the model (StatCorp, Texas). Abbreviation: CI - confidence interval.
