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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: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 (? (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.

SUBMITTER: Shang X

PROVIDER: S-EPMC7502204 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis.

Shang Xianwen X Shang Xianwen X Li Yanping Y Xu Haiquan H Zhang Qian Q Liu Ailing A Du Songming S Guo Hongwei H Ma Guansheng G

Nutrition journal 20200919 1

<h4>Background</h4>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.<h4>Methods</h4>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 summi ...[more]

PMID: 32950062

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Project description:Diet quality indices score dietary intakes against recommendations, whereas dietary patterns consider the pattern and combination of dietary intakes. Studies evaluating both methodologies in relation to cardiometabolic health in a nationally representative sample are limited. The aim of the present study was to investigate the relationship between diet quality, dietary patterns and markers of cardiometabolic health in Australian adults.Dietary data, using two 24-h dietary recalls, were collected from adults in the cross-sectional Australian Health Survey 2011-2013 (n?=?2121; 46.4 (SE 0.48) years). Diet quality was estimated using the Dietary Guideline Index (DGI). Dietary patterns (DPs), derived using reduced rank regression, were estimated using fiber density, SFA: PUFA and total sugars intake as intermediate markers. Multi-variable adjusted linear regression analyses were used to examine associations between diet quality and DPs and blood biomarkers, body mass index, waist circumference, diastolic and systolic blood pressure and an overall cardiometabolic risk score.DGI was associated with lower glucose (coef -?0.009, SE 0.004; P-trend?=?0.033), body mass index (coef -?0.017, SE 0.007; P-trend?=?0.019) and waist circumference (coef -?0.014, SE 0.005; P-trend?=?0.008). Two dietary patterns were derived: dietary pattern-1 was characterized by higher intakes of pome fruit and wholegrain bread, while dietary pattern-2 was characterized by higher intakes of added sugars and tropical fruit. Dietary pattern-1 was associated with lower body mass index (coef -?0.028, SE 0.007; P-trend<?0.001) and waist circumference (coef -?0.017, SE 0.005; P-trend?=?0.001). There was a trend towards lower cardiometabolic risk score. Dietary pattern-2 was associated with lower HDL-cholesterol (coef -?0.026, SE 0.012; P-trend?=?0.028). There was a trend towards lower diastolic blood pressure. No associations with other markers were observed.Better diet quality and healthier dietary patterns were primarily associated with favorable anthropometric markers of cardiometabolic health. Findings support the need for comparison of whole-diet based methodologies that take into consideration the interactions between foods and nutrients. Longitudinal studies are warranted to better understand causal relationships between diet and cardiometabolic health.

Project description:BackgroundCardiometabolic diseases are complex traits which are influenced by several single nucleotide polymorphisms (SNPs). Thus, analysing the combined effects of multiple gene variants might provide a better understanding of disease risk than using a single gene variant approach. Furthermore, studies have found that the effect of SNPs on cardiometabolic traits can be influenced by lifestyle factors, highlighting the importance of analysing gene-lifestyle interactions.AimsIn the present study, we investigated the association of 15 gene variants with cardiometabolic traits and examined whether these associations were modified by lifestyle factors such as dietary intake and physical activity.MethodsThe study included 110 Minangkabau women [aged 25-60 years and body mass index (BMI) 25.13 ± 4.2 kg/m2] from Padang, Indonesia. All participants underwent a physical examination followed by anthropometric, biochemical and dietary assessments and genetic tests. A genetic risk score (GRS) was developed based on 15 cardiometabolic disease-related SNPs. The effect of GRS on cardiometabolic traits was analysed using general linear models. GRS-lifestyle interactions on continuous outcomes were tested by including the interaction term (e.g. lifestyle factor*GRS) in the regression model. Models were adjusted for age, BMI and location (rural or urban), wherever appropriate.ResultsThere was a significant association between GRS and BMI, where individuals carrying 6 or more risk alleles had higher BMI compared to those carrying 5 or less risk alleles (P = 0.018). Furthermore, there were significant interactions of GRS with protein intake on waist circumference (WC) and triglyceride concentrations (Pinteraction = 0.002 and 0.003, respectively). Among women who had a lower protein intake (13.51 ± 1.18% of the total daily energy intake), carriers of six or more risk alleles had significantly lower WC and triglyceride concentrations compared with carriers of five or less risk alleles (P = 0.0118 and 0.002, respectively).ConclusionsOur study confirmed the association of GRS with higher BMI and further showed a significant effect of the GRS on WC and triglyceride levels through the influence of a low-protein diet. These findings suggest that following a lower protein diet, particularly in genetically predisposed individuals, might be an effective approach for addressing cardiometabolic diseases among Southeast Asian women.

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Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis.

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Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis.

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