Table 3. Linear regression analysis stratified by body weight evaluating the association of pasta consumption (grams per day) with BMI in Moli-sani and INHES participantsa.
|
Moli-sani population (N=14 402) |
|||||
|---|---|---|---|---|---|
| Q1 (<62 kg) | Q2 (62–70 kg) | Q3 (70–77 kg) | Q4 (77–86 kg) | Q5 (>86 kg) | |
| Unadjusted models | |||||
| β-coef for 35 g per day increase in pasta intake | −0.11 (0.02) | −0.34 (<0.001) | −0.37 (0.001) | −0.44 (<0.001) | −0.37 (<0.001) |
| Multi-adjusted modelsb | |||||
| β-coef for 35 g per day increase in pasta intake | −0.01 (0.84) | −0.17 (0.001) | −0.19 (0.002) | −0.25 (<0.001) | −0.23 (0.01) |
|
INHES population (N=8964) |
|||||
| Q1 (<60 kg) | Q2 (60–67 kg) | Q3 (67–75 kg) | Q4 (75–83 kg) | Q5 (>83 kg) | |
| Unadjusted models | |||||
| β-coef 48 g per day increase in pasta intake | 0.07 (0.16) | −0.001 (0.99) | −0.18 (0.001) | −0.01 (0.82) | −0.43 (0.03) |
| Multi-adjusted modelsc | |||||
| β-coef for 48 g per day increase in pasta intake | 0.06 (0.19) | 0.03 (0.57) | −0.18 (<0.001) | 0.02 (0.76) | −0.20 (0.04) |
Abbreviations: BMI, body mass index.
a
Results derived from linear regression analysis with main outcome the BMI (kg m−2) and independent variable pasta intake (grams per day) and are presented as β-coefficients (P-value).
b
Models have been adjusted for age, socio-economic status, physical activity level, energy intake and Mediterranean pattern adherence.
c
Models have been adjusted for age, profession type, marital status and physical activity, energy intake and Mediterranean pattern adherence.