Table 2. Linear regression analysis evaluating the association of pasta consumption with BMI in Moli-sani and INHES participantsa.
|
Moli-sani population (N=14 402) |
||
|---|---|---|
| Women (N=7216) | Men (N=7186) | |
| Pasta-energy residuals | ||
| Unadjusted models | −0.012 (<0.001) | −0.002 (0.07) |
| Multi-adjusted modelsb | −0.007 (0.003) | −0.001 (0.58) |
| Pasta-body weight residualsc | ||
| Unadjusted models | −0.78 (<0.001) | −0.29 (<0.001) |
| Multi-adjusted modelsb | −0.87 (<0.001) | −0.51 (<0.001) |
|
INHES population (N=8964) |
||
| Women (N=4782) | Men (N=4182) | |
| Pasta-energy residuals | ||
| Unadjusted models | −0.004 (0.01) | 0.005 (0.01) |
| Multi-adjusted modelsd | −0.001 (0.36) | 0.002 (0.05) |
| Pasta-body weight residualse | ||
| Unadjusted models | −0.08 (0.25) | −0.40 (<0.001) |
| Multi-adjusted modelsd | −0.18 (0.01) | −0.30 (<0.001) |
Abbreviation: BMI, body mass index.
Results derived from linear regression analysis with main outcome the BMI (kg m−2) and independent variable the pasta-energy residuals or pasta-body weight residuals. Results are presented as β-coefficients (P-value) (for 1 unit increase in predicted residuals).
Models have been adjusted for age, socioeconomic status, physical activity level, energy intake and Mediterranean pattern adherence.
The β-coefficient for 1 unit increase in pasta-body weight residuals corresponded to 35 g per day increase in pasta intake.
Models have been adjusted for age, profession type, marital status, physical activity, energy intake and Mediterranean pattern adherence.
The β-coefficient for 1 unit increase in pasta-body weight residuals corresponded to 48 g per day increase in pasta intake.