Table 1.
Predictor variable |
BMI
|
Fat mass
|
Waist circumference
|
Obesity
|
Metabolic syndrome
|
|||||
---|---|---|---|---|---|---|---|---|---|---|
β (s.e.) | P-value | β (s.e.) | P-value | β (s.e.) | P-value | OR (95% CI) | P-value | OR (95% CI) | P-value | |
Sex | 0.07 (0.4) | 0.056 | −0.27 (0.8) | 0.000 | 0.39 (8.4) | 0.000 | 1.0 (0.7–1.3) | 0.806 | 2.1 (1.4–3.2) | 0.000 |
Chronotype | −0.06 (0.2) | 0.166 | −0.03 (0.4) | 0.528 | −0.04 (4.3) | 0.289 | 0.9 (0.8–1.1) | 0.353 | 1.2 (1.0–1.4) | 0.121 |
Sleep duration | −0.04 (0.2) | 0.333 | −0.02 (0.4) | 0.432 | −0.05 (4.3) | 0.123 | 0.9 (0.8–1.1) | 0.192 | 1.0 (0.8–1.2) | 0.679 |
Social jetlag | 0.10 (0.2) | 0.012 | 0.08 (0.5) | 0.031 | 0.07 (5.1) | 0.052 | 1.2 (1.0–1.5) | 0.045 | 1.3 (1.0–1.6) | 0.031 |
Abbreviations: BMI, body mass index; CI, confidence interval; OR, odds ratio. We used linear regression models to test associations with continuous outcome measures of BMI, fat mass and waist circumference. The table shows the standardized coefficient (β), s.e. and P-value associated with each predictor variable. We used logistic regressions to test associations with binary outcome measures of obesity and the metabolic syndrome. The odds ratio for chronotype, sleep duration and social jetlag reflect the effect of a one-unit (s.d.) increase in the predictor variables. Significant P-values (P < 0.05) are shown in bold. Sex was coded as: female = 1, male = 2.