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Table 1 Linear regressions that predict the three chronic diseases from individual nutrients and socio-demographic control variables (income, gender, age, education level)

From: Large-scale and high-resolution analysis of food purchases and health outcomes

FeatureCoefficientStd. errorp-value
Hypertension
α (intercept)0.32410.050<0.001
Carbs0.76520.075<0.001
Fats0.32250.066<0.001
Sugar0.24020.0720.001
Proteins−0.28680.050<0.001
Fibre−0.06270.0510.216
Income0.04770.0420.259
%Females−0.21240.038<0.001
Average age0.16640.037<0.001
Education0.24510.042<0.001
Durbin–Watson stat. = 2.048Adj \(R^{2} = 0.388\)
Cholesterol
α (intercept)0.26450.047<0.001
Carbs0.58770.070<0.001
Fats0.33820.062<0.001
Sugar0.24410.067<0.001
Proteins−0.27450.046<0.001
Fibre−0.02680.0470.569
Income−0.01840.0390.640
%Females−0.23220.036<0.001
Average age0.12720.035<0.001
Education0.17510.039<0.001
Durbin–Watson stat. = 2.001Adj \(R^{2} = 0.345\)
Diabetes
α (intercept)0.50730.041<0.001
Carbs0.576590.061<0.001
Fats0.50020.054<0.001
Sugar0.49920.059<0.001
Proteins−0.51370.041<0.001
Fibre−0.13120.0410.002
Income−0.12220.034<0.001
%Females−0.35360.031<0.001
Average age−0.02900.0300.342
Education−0.09470.035<0.006
Durbin–Watson stat. = 2.000Adj \(R^{2} = 0.598\)