Logistic LASSO Regression for Dietary Intakes and Obesity: NHANES (2007-2016)

Abstract

Abstract Background: Many investigations have explored the relationship between dietary intake and obesity risk/incidence, but they have only assessed obesity-related dietary patterns and/or single nutrients, not taking into account the diversity of dietary variables or multicollinearity among multiple nutrients. Therefore, this study aimed to use logistic LASSO regression combined with logistic regression to overcome multicollinearity, and overall investigate the possible association between dietary factors and obesity by the National Health and Nutrition Examination Survey (NHANES) (2007-2016). Method: Logistic LASSO regression was performed to examine the relationship between 59 dietary variables, and subsequently identify the most relevant variables associated with obesity. Then we used logistic regression to test the relevant variables, and finally used the receiver operating characteristic curve (ROC) to test the effect of logistic regression. Using the methods described above, we explored the relationship between dietary intake and obesity in 12135 NHANES participants (2007-2016). Result: The set of factors screened by LASSO regression, obesity risk factors including cholesterol and PFUA 20:4 (β>0); protective factors including vitamin E, caffeine, folate, vitamin C and copper (β<0). Ultimately, after multivariate unadjusted and adjusted logistic regression tests as well as ROC tests, four in the factor set associated with obesity were selected. Statistically significant dietary factors only folate (OR=0.80), vitamin C (OR=0.95), copper (OR=0.60) and PFUA 20:4 (OR=2.57) in adjusted logistic regression. Conclusion: Dietary intake of folate, vitamin C and copper negatively correlated with obesity, but PFUA 20:4 positively inversely. Necessary to assess the effective levels of folate, vitamin C and copper supplementation in obese subjects.