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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

Feature

Coefficient

Std. error

p-value

Hypertension

α (intercept)

0.3241

0.050

<0.001

Carbs

0.7652

0.075

<0.001

Fats

0.3225

0.066

<0.001

Sugar

0.2402

0.072

0.001

Proteins

−0.2868

0.050

<0.001

Fibre

−0.0627

0.051

0.216

Income

0.0477

0.042

0.259

%Females

−0.2124

0.038

<0.001

Average age

0.1664

0.037

<0.001

Education

0.2451

0.042

<0.001

Durbin–Watson stat. = 2.048

Adj \(R^{2} = 0.388\)

Cholesterol

α (intercept)

0.2645

0.047

<0.001

Carbs

0.5877

0.070

<0.001

Fats

0.3382

0.062

<0.001

Sugar

0.2441

0.067

<0.001

Proteins

−0.2745

0.046

<0.001

Fibre

−0.0268

0.047

0.569

Income

−0.0184

0.039

0.640

%Females

−0.2322

0.036

<0.001

Average age

0.1272

0.035

<0.001

Education

0.1751

0.039

<0.001

Durbin–Watson stat. = 2.001

Adj \(R^{2} = 0.345\)

Diabetes

α (intercept)

0.5073

0.041

<0.001

Carbs

0.57659

0.061

<0.001

Fats

0.5002

0.054

<0.001

Sugar

0.4992

0.059

<0.001

Proteins

−0.5137

0.041

<0.001

Fibre

−0.1312

0.041

0.002

Income

−0.1222

0.034

<0.001

%Females

−0.3536

0.031

<0.001

Average age

−0.0290

0.030

0.342

Education

−0.0947

0.035

<0.006

Durbin–Watson stat. = 2.000

Adj \(R^{2} = 0.598\)