Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis
Abstract Background Identifying leading dietary determinants for cardiometabolic risk (CMR) factors is urgent for prioritizing interventions in children. We aimed to identify leading dietary determinants for the change in CMR and create a healthy diet score (HDS) to predict CMR in children. Methods...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-09-01
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| Series: | Nutrition Journal |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12937-020-00611-2 |
| _version_ | 1819049069900726272 |
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| author | Xianwen Shang Yanping Li Haiquan Xu Qian Zhang Ailing Liu Songming Du Hongwei Guo Guansheng Ma |
| author_facet | Xianwen Shang Yanping Li Haiquan Xu Qian Zhang Ailing Liu Songming Du Hongwei Guo Guansheng Ma |
| author_sort | Xianwen Shang |
| collection | DOAJ |
| description | Abstract Background Identifying leading dietary determinants for cardiometabolic risk (CMR) factors is urgent for prioritizing interventions in children. We aimed to identify leading dietary determinants for the change in CMR and create a healthy diet score (HDS) to predict CMR in children. Methods We included 5676 children aged 6–13 years in the final analysis with physical examinations, blood tests, and diets assessed at baseline and one year later. CMR score (CMRS) was computed by summing Z-scores of waist circumference, an average of systolic and diastolic blood pressure (SBP and DBP), fasting glucose, high-density lipoprotein cholesterol (HDL-C, multiplying by − 1), and triglycerides. Machine learning was used to identify leading dietary determinants for CMR and an HDS was then computed. Results The nine leading predictors for CMRS were refined grains, seafood, fried foods, sugar-sweetened beverages, wheat, red meat other than pork, rice, fungi and algae, and roots and tubers with the contribution ranging from 3.9 to 19.6% of the total variance. Diets high in seafood, rice, and red meat other than pork but low in other six food groups were associated with a favorable change in CMRS. The HDS was computed based on these nine dietary factors. Children with HDS ≥8 had a higher decrease in CMRS (β (95% CI): − 1.02 (− 1.31, − 0.73)), BMI (− 0.08 (− 0.16, − 0.00)), SBP (− 0.46 (− 0.58, − 0.34)), DBP (− 0.46 (− 0.58, − 0.34)), mean arterial pressure (− 0.50 (− 0.62, − 0.38)), fasting glucose (− 0.22 (− 0.32, − 0.11)), insulin (− 0.52 (− 0.71, − 0.32)), and HOMA-IR (− 0.55 (− 0.73, − 0.36)) compared to those with HDS ≦3. Improved HDS during follow-up was associated with favorable changes in CMRS, BMI, percent body fat, SBP, DBP, mean arterial pressure, HDL-C, fasting glucose, insulin, and HOMA-IR. Conclusion Diets high in seafood, rice, and red meat other than pork and low in refined grains, fried foods, sugar-sweetened beverages, and wheat are leading healthy dietary factors for metabolic health in children. HDS is strongly predictive of CMR factors. |
| first_indexed | 2024-12-21T11:26:18Z |
| format | Article |
| id | doaj.art-60bb1ec7bf884474ac5d43ee48be8d07 |
| institution | Directory Open Access Journal |
| issn | 1475-2891 |
| language | English |
| last_indexed | 2024-12-21T11:26:18Z |
| publishDate | 2020-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Nutrition Journal |
| spelling | doaj.art-60bb1ec7bf884474ac5d43ee48be8d072022-12-21T19:05:40ZengBMCNutrition Journal1475-28912020-09-0119111610.1186/s12937-020-00611-2Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysisXianwen Shang0Yanping Li1Haiquan Xu2Qian Zhang3Ailing Liu4Songming Du5Hongwei Guo6Guansheng Ma7National Institute for Nutrition and Health, Chinese Center for Disease Control and PreventionDepartment of Nutrition, Harvard T. H. Chan School of Public HealthInstitute of food and nutrition development, Ministry of Agriculture and Rural AffairsNational Institute for Nutrition and Health, Chinese Center for Disease Control and PreventionNational Institute for Nutrition and Health, Chinese Center for Disease Control and PreventionChinese Nutrition SocietySchool of Public Health, Fudan UniversityDepartment of Nutrition and Food Hygiene, School of Public Health, Peking UniversityAbstract Background Identifying leading dietary determinants for cardiometabolic risk (CMR) factors is urgent for prioritizing interventions in children. We aimed to identify leading dietary determinants for the change in CMR and create a healthy diet score (HDS) to predict CMR in children. Methods We included 5676 children aged 6–13 years in the final analysis with physical examinations, blood tests, and diets assessed at baseline and one year later. CMR score (CMRS) was computed by summing Z-scores of waist circumference, an average of systolic and diastolic blood pressure (SBP and DBP), fasting glucose, high-density lipoprotein cholesterol (HDL-C, multiplying by − 1), and triglycerides. Machine learning was used to identify leading dietary determinants for CMR and an HDS was then computed. Results The nine leading predictors for CMRS were refined grains, seafood, fried foods, sugar-sweetened beverages, wheat, red meat other than pork, rice, fungi and algae, and roots and tubers with the contribution ranging from 3.9 to 19.6% of the total variance. Diets high in seafood, rice, and red meat other than pork but low in other six food groups were associated with a favorable change in CMRS. The HDS was computed based on these nine dietary factors. Children with HDS ≥8 had a higher decrease in CMRS (β (95% CI): − 1.02 (− 1.31, − 0.73)), BMI (− 0.08 (− 0.16, − 0.00)), SBP (− 0.46 (− 0.58, − 0.34)), DBP (− 0.46 (− 0.58, − 0.34)), mean arterial pressure (− 0.50 (− 0.62, − 0.38)), fasting glucose (− 0.22 (− 0.32, − 0.11)), insulin (− 0.52 (− 0.71, − 0.32)), and HOMA-IR (− 0.55 (− 0.73, − 0.36)) compared to those with HDS ≦3. Improved HDS during follow-up was associated with favorable changes in CMRS, BMI, percent body fat, SBP, DBP, mean arterial pressure, HDL-C, fasting glucose, insulin, and HOMA-IR. Conclusion Diets high in seafood, rice, and red meat other than pork and low in refined grains, fried foods, sugar-sweetened beverages, and wheat are leading healthy dietary factors for metabolic health in children. HDS is strongly predictive of CMR factors.http://link.springer.com/article/10.1186/s12937-020-00611-2Cardiometabolic risk factorsLeading dietary determinantsHealthy diet scoreMachine learningChildren |
| spellingShingle | Xianwen Shang Yanping Li Haiquan Xu Qian Zhang Ailing Liu Songming Du Hongwei Guo Guansheng Ma Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis Nutrition Journal Cardiometabolic risk factors Leading dietary determinants Healthy diet score Machine learning Children |
| title | Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis |
| title_full | Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis |
| title_fullStr | Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis |
| title_full_unstemmed | Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis |
| title_short | Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis |
| title_sort | leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children a longitudinal analysis |
| topic | Cardiometabolic risk factors Leading dietary determinants Healthy diet score Machine learning Children |
| url | http://link.springer.com/article/10.1186/s12937-020-00611-2 |
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