Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2022 Dec 15;14(24):5321. doi: 10.3390/nu14245321

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© 2022 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

PMC Copyright notice

(A) Receiver operating characteristic curve analysis for predictive models. (B) Variables selection by least absolute shrinkage and selection operator (LASSO) logistic regression. The vertical dotted line points to the optimal lambda value and the number of optimal predictors. (C) Receiver operating characteristic curve validation of risk prediction model in the training and validation set. (D) Calibration curves of MCI, SA, and MCI and SA in the training and validation set. (E) Multivariate logistic regression analyses and nomogram for predicting the MCI, SA, and MCI and SA probability. (1) MCI–Ctrl group; (2) SA–Ctrl group; (3) MCI and SA–Ctrl group. (a) Selection of the parameters in the LASSO model by 10-fold cross-validation based on minimum criteria; (b) the pathway of coefficients among all variables; (c) the training set; (d) the validation set. AUC, area under the curve; BMR, basal metabolic rate; PCho, phosphorylcholine; OR, odds ratio. Adjusted for age, sex, education, BMI, TC, TAG, LDL-C, HDL-C, hypertension, diabetes, dyslipidemia, arthritis. * p < 0.05, ** p < 0.01. Q1,1st quartile; Q2, 2nd quartile; Q3, 3rd quartile; Q4, 4th quartile.

(A) Receiver operating characteristic curve analysis for predictive models. (B) Variables selection by least absolute shrinkage and selection operator (LASSO) logistic regression. The vertical dotted line points to the optimal lambda value and the number of optimal predictors. (C) Receiver operating characteristic curve validation of risk prediction model in the training and validation set. (D) Calibration curves of MCI, SA, and MCI and SA in the training and validation set. (E) Multivariate logistic regression analyses and nomogram for predicting the MCI, SA, and MCI and SA probability. (1) MCI–Ctrl group; (2) SA–Ctrl group; (3) MCI and SA–Ctrl group. (a) Selection of the parameters in the LASSO model by 10-fold cross-validation based on minimum criteria; (b) the pathway of coefficients among all variables; (c) the training set; (d) the validation set. AUC, area under the curve; BMR, basal metabolic rate; PCho, phosphorylcholine; OR, odds ratio. Adjusted for age, sex, education, BMI, TC, TAG, LDL-C, HDL-C, hypertension, diabetes, dyslipidemia, arthritis. * p < 0.05, ** p < 0.01. Q1,1st quartile; Q2, 2nd quartile; Q3, 3rd quartile; Q4, 4th quartile.