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Fig. 3. Comparison of the <t>multivariable</t> linear regression between dihydrocaffeic acid and plasma glucose adjusted for (A): age, sex, smoking habit, educational level, BMI, physical activity, total energy intake, and hypercholesterolemia; and (B) further adjusted for the use of antidiabetic drugs.
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Fig. 3. Comparison of the <t>multivariable</t> linear regression between dihydrocaffeic acid and plasma glucose adjusted for (A): age, sex, smoking habit, educational level, BMI, physical activity, total energy intake, and hypercholesterolemia; and (B) further adjusted for the use of antidiabetic drugs.
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Fig. 3. Comparison of the <t>multivariable</t> linear regression between dihydrocaffeic acid and plasma glucose adjusted for (A): age, sex, smoking habit, educational level, BMI, physical activity, total energy intake, and hypercholesterolemia; and (B) further adjusted for the use of antidiabetic drugs.
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Fig. 3. Comparison of the multivariable linear regression between dihydrocaffeic acid and plasma glucose adjusted for (A): age, sex, smoking habit, educational level, BMI, physical activity, total energy intake, and hypercholesterolemia; and (B) further adjusted for the use of antidiabetic drugs.

Journal: Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

Article Title: Urinary metabolomics of phenolic compounds reveals biomarkers of type-2 diabetes within the PREDIMED trial.

doi: 10.1016/j.biopha.2023.114703

Figure Lengend Snippet: Fig. 3. Comparison of the multivariable linear regression between dihydrocaffeic acid and plasma glucose adjusted for (A): age, sex, smoking habit, educational level, BMI, physical activity, total energy intake, and hypercholesterolemia; and (B) further adjusted for the use of antidiabetic drugs.

Article Snippet: Logistic and multivariable adjusted regression models were generated using Stata 16.0.

Techniques: Comparison, Clinical Proteomics, Activity Assay