Dietary patterns at 7 year-old and their association with cardiometabolic health at 10 year-old

Pinto, Andreia; Santos, Ana Cristina; Lopes, Carla; Oliveira, Andréia

doi:10.1016/j.clnu.2019.05.007

Cited by 17 publications

(22 citation statements)

References 27 publications

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“…Our findings agree with these studies showing that high consumption of refined grains, fried foods, SSBs, roots and tubers, or wheat was associated with a higher increase in CMRS. The positive association between SSBs intake and CMR factors has been reported in many studies [16,17,30]. Although refined grains and wheat were not linked to CMR factors in children, their harmful effect on CMR has been reported in adults [31].…”

Section: Discussionmentioning

confidence: 88%

“…Dietary patterns derived by posterior methods including principal component analysis, cluster analysis, and latent class analysis have been linked to CMR factors in children [15][16][17]40]. These studies showed that Western dietary pattern, high energy-dense pattern, or sweet dietary pattern were associated with high CMR [15][16][17], whereas vegetable and the wholemeal pattern was associated with favorable changes in CMR factors [40]. Although these findings may imply which food groups are associated with CMR factors, these dietary patterns can hardly be obtained in other studies.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies are also limited by small sample sizes, cross-sectional design, or failure to adjust for important confounders. Although dietary patterns have been well linked to CMR factors in children in some countries [15][16][17], they cannot be applied to other populations given that a healthy diet pattern in one study can be hardly derived from other studies. For example, a healthy diet pattern in one study was high in vegetables, fruits, and dairy [15], while a healthy pattern in another study was high in vegetables, fruits, fish, crackers, and bread [17].…”

Section: Introductionmentioning

confidence: 99%

“…Although dietary patterns have been well linked to CMR factors in children in some countries [15][16][17], they cannot be applied to other populations given that a healthy diet pattern in one study can be hardly derived from other studies. For example, a healthy diet pattern in one study was high in vegetables, fruits, and dairy [15], while a healthy pattern in another study was high in vegetables, fruits, fish, crackers, and bread [17]. Diet indices have also been developed for diet quality assessment, however, these indices are shown to be weak predictors of CMR factors [18,19].…”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2020

Nutr J

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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.

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Section: Discussionmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

et al. 2020

Nutr J

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“…Increasing evidence has highlighted the importance of diet, physical activity, and sleep on metabolic health in children [1,2,8,9]. Studies have shown that Western dietary patterns, high energy-dense patterns, or sweet dietary patterns were associated with a higher risk of cardiometabolic risk (CMR) factors [10][11][12], whereas vegetable and wholemeal patterns were associated with favorable changes in CMR factors [13]. Cardiorespiratory fitness (CRF) represents physical activity habits [14] and is an indicator of accumulated physical activity in the long-term [15].…”

Section: Introductionmentioning

confidence: 99%

The Clustering of Low Diet Quality, Low Physical Fitness, and Unhealthy Sleep Pattern and Its Association with Changes in Cardiometabolic Risk Factors in Children

Shang

et al. 2020

Nutrients

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The clustering of diet quality, physical activity, and sleep and its association with cardiometabolic risk (CMR) factors remains to be explored. We included 5315 children aged 6–13 years in the analysis. CMR score (CMRS) was computed by summing Z-scores of waist circumference, an average of systolic and diastolic blood pressure, fasting glucose, high-density lipoprotein cholesterol (multiplying by −1), and triglycerides. Low diet quality and low cardiorespiratory fitness (CRF) were more likely to be seen in a pair, but low diet quality was less likely to be clustered with unhealthy sleep patterns. Low diet quality, low CRF, and unhealthy sleep pattern was associated with a 0.63, 0.53, and 0.25 standard deviation (SD) higher increase in CMRS, respectively. Compared to children with no unhealthy factor (−0.79 SD), those with ≥1 unhealthy factor had a higher increase (−0.20 to 0.59 SD) in CMRS. A low diet quality-unhealthy sleep pattern resulted in the highest increase in CMRS, blood pressure, and triglycerides. A low diet quality–low CRF-unhealthy sleep pattern resulted in the highest increase in fatness and fasting glucose. Unhealthy factor cluster patterns are complex; however, their positive associations with changes in CMR factors are consistently significant in children. Some specific patterns are more harmful than others for cardiometabolic health.

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Toddler dietary patterns from the INSIGHT randomized clinical trial comparing responsive parenting versus control: A latent class analysis

Hernandez,

Hohman,

Ferrante

et al. 2023

Obesity

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ObjectiveThe aim of this study was to examine the effect of a responsive parenting (RP) intervention on toddler diet and explore associations with mothers' infant feeding practices and child weight status.MethodsINSIGHT tested an RP intervention designed for the prevention of obesity against a safety control among primiparous mothers and their infants. Mothers reported on feeding practices, as well as toddler diet with a Food Frequency Questionnaire (n = 229). Trained research staff obtained child anthropometrics at age 2 years.ResultsLatent class analysis identified three dietary patterns: high fruits and vegetables (HFV, 31%); meat, potatoes, and added sugars (MPAS, 24%); and high juice, low fruits and vegetables (JLFV, 45%). Toddler dietary pattern was not related to study group (RP, control) or child weight status at age 2 years. Mothers who reported more structure‐based feeding had toddlers that were more likely to have the healthier, HFV dietary pattern than MPAS and JLFV. Findings for control‐based feeding practices were mixed; maternal restriction was associated with the HFV dietary pattern, whereas the use of food as a reward was associated with MPAS and JLFV.ConclusionsMothers' structure‐based feeding practices in infancy, as well as some control‐based feeding practices, were associated with later healthier toddler dietary patterns.

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Dietary patterns at 7 year-old and their association with cardiometabolic health at 10 year-old

Cited by 17 publications

References 27 publications

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

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

The Clustering of Low Diet Quality, Low Physical Fitness, and Unhealthy Sleep Pattern and Its Association with Changes in Cardiometabolic Risk Factors in Children

Toddler dietary patterns from the INSIGHT randomized clinical trial comparing responsive parenting versus control: A latent class analysis

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