biostudies-literatureUnknown21(1)Ma C<h4>Background</h4>Multivariable linear regression (MLR) models were previously used to predict serum pyridoxal 5'-phosphate (PLP) concentration, the active coenzyme form of vitamin B6, but with low predictability. We developed a deep learning algorithm (DLA) to predict serum PLP based on dietary intake, dietary supplements, and other potential predictors.<h4>Methods</h4>This cross-sectional analysis included 3778 participants aged ≥20 years in the National Health and Nutrition Examination Survey (NHANES) 2007-2010, with completed information on studied variables. Dietary intake and supplement use were assessed with two 24-hour dietary recalls. We included potential predictors for serum PLP concentration in the models, including dietary intake and supplement use, sociodemographic variablesNutrition journal38https://www.ebi.ac.uk/biostudies/studies/S-EPMC9185886biostudies-literatureApplication of the deep learning algorithm in nutrition research - using serum pyridoxal 5'-phosphate as an example.PMC9185886Chen QMa CMitchell DCNa MTucker KLGao XfalseApplication of the deep learning algorithm in nutrition research - using serum pyridoxal 5'-phosphate as an example.<h4>Background</h4>Multivariable linear regression (MLR) models were previously used to predict serum pyridoxal 5'-phosphate (PLP) concentration, the active coenzyme form of vitamin B6, but with low predictability. We developed a deep learning algorithm (DLA) to predict serum PLP based on dietary intake, dietary supplements, and other potential predictors.<h4>Methods</h4>This cross-sectional analysis included 3778 participants aged ≥20 years in the National Health and Nutrition Examination Survey (NHANES) 2007-2010, with completed information on studied variables. Dietary intake and supplement use were assessed with two 24-hour dietary recalls. We included potential predictors for serum PLP concentration in the models, including dietary intake and supplement use, sociodemographic variables2022-01-01T00:00:00Z2022 Jun2025-04-25T20:28:32.327Z2025-02-19T03:52:04.537ZS-EPMC91858863568926510.1186/s12937-022-00793-x