Examining Dietary Patterns in Relation to Chronic Disease

Krebs‐Smith, Susan M.; Subar, Amy F.; Reedy, Jill

doi:10.1161/circulationaha.115.018010

Cited by 48 publications

(33 citation statements)

References 12 publications

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“…While the results of our CTA are exploratory they identify certain characteristics previously suggested as risk factors or risk indicators for both obesity and dental caries [ 14 , 26 , 36 ]. The study supports recent views that data driven-outcome dependant methods such as CTA are potentially useful for investigating dietary components or patterns most associated with a health outcome and are a valid, non-parametric, alternative to logistic regression analysis [ 19 ]. The results of this analysis must be cautiously interpreted by gauging the model performance ( Table 2 ) and understanding the limitations of both the data structure and classification tree algorithms.…”

Section: Discussionsupporting

confidence: 85%

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Weight Status and Dental Problems in Early Childhood: Classification Tree Analysis of a National Cohort

et al. 2017

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A poor quality diet may be a common risk factor for both obesity and dental problems such as caries. The aim of this paper is to use classification tree analysis (CTA) to identify predictors of dental problems in a nationally representative cohort of Irish pre-school children. CTA was used to classify variables and describe interactions between multiple variables including socio-demographics, dietary intake, health-related behaviour, body mass index (BMI) and a dental problem. Data were derived from the second (2010/2011) wave of the ‘Growing Up in Ireland’ study (GUI) infant cohort at 3 years, n = 9793. The prevalence of dental problems was 5.0% (n = 493). The CTA model showed a sensitivity of 67% and specificity of 58.5% and overall correctly classified 59% of children. Ethnicity was the most significant predictor of dental problems followed by longstanding illness or disability, mother’s BMI and household income. The highest prevalence of dental problems was among children who were obese or underweight with a longstanding illness and an overweight mother. Frequency of intake of some foods showed interactions with the target variable. Results from this research highlight the interconnectedness of weight status, dental problems and general health and reinforce the importance of adopting a common risk factor approach when dealing with prevention of these diseases.

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Section: Discussionsupporting

confidence: 85%

“…Data-driven methods are being increasingly proposed to empirically derive dietary patterns associated with chronic disease [ 19 ]. Methods that aim to uncover the relationship between independent variables and a dependent variable are described as supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Weight Status and Dental Problems in Early Childhood: Classification Tree Analysis of a National Cohort

et al. 2017

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“…In addition to screening and individualizing nutrition intervention plans, the DST can provide a systematic way to evaluate diets for comparison across research studies and to identify factors associated with ‘at-risk’ nutritional status. Scores and indices are preferred ways to measure diet across different research studies and cohorts [ 9 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Validation of a Dietary Screening Tool in a Middle-Aged Appalachian Population

et al. 2018

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Proactive nutrition screening is an effective public health strategy for identifying and targeting individuals who could benefit from making dietary improvements for primary and secondary prevention of disease. The Dietary Screening Tool (DST) was developed and validated to assess nutritional risk among rural older adults. The purpose of this study was to evaluate the utility and validity of the DST to identify nutritional risk in middle-aged adults. This cross-sectional study in middle-aged adults (45–64 year olds, n = 87) who reside in Appalachia, examined nutritional status using an online health survey, biochemical measures, anthropometry, and three representative 24-h dietary recalls. The Healthy Eating Index (HEI) was calculated to describe overall diet quality. Adults identified by the DST with a nutrition risk had lower HEI scores (50 vs. 64, p < 0.001) and were much more likely to also be considered at dietary risk by the HEI (OR 11.6; 3.2–42.6) when compared to those not at risk. Those at risk had higher energy-adjusted total fat, saturated fat, and added sugar intakes and lower intakes of dietary fiber, and several micronutrients than those classified as not at risk by the DST. Similarly, the at-risk group had significantly lower serum levels of α-carotene, β-carotene, cryptoxanthin, lutein, and zeaxanthin but did not differ in retinol or methylmalonic acid compared with those not at risk. The DST is a valid tool to identify middle-aged adults with nutritional risk.

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“…6 From a surveillance perspective, some dietary pattern indices are useful in determining how well populations meet national dietary recom mendations. 7,8 The Healthy Eating Index (HEI) 9 is a diet quality score originally developed by the United States Department of Agriculture. Similarities between the dietary recommendations for Canada and the United States facilitate the adaptation of the American HEI for Canada.…”

Section: Introductionmentioning

confidence: 99%

Addition of food group equivalents to the Canadian Diet History Questionnaire II for the estimation of the Canadian Healthy Eating Index-2005

McInerney

Koushik

et al. 2018

Health Promot Chronic Dis Prev Can

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Introduction Poor diet quality has been shown to increase the risk of common chronic diseases that can negatively impact quality of life and burden the healthcare system. Canada’s Food Guide evidence-based recommendations provide dietary guidance aimed at increasing diet quality. Compliance with Canada’s Food Guide can be assessed with the Canadian Healthy Eating Index (C-HEI), a diet quality score. The recently designed Canadian Diet History Questionnaire II (C-DHQ II), a comprehensive food frequency questionnaire could be used to estimate the C-HEI in Canadian populations with the addition of food group equivalents (representing Canada’s Food Guide servings) to the C-DHQ II nutrient database. We describe methods developed to augment the C-DHQ II nutrient database to estimate the C-HEI. Methods Food group equivalents were created using food and nutrient data from existing published food and nutrient databases (e.g. the Canadian Community Health Survey — Cycle 2.2 Nutrition [2004]). The variables were then added to the C-DHQ II companion nutrient database. C-HEI scores were determined and descriptive analyses conducted for participants who completed the C-DHQ II in a cross-sectional Canadian study. Results The mean (standard deviation) C-HEI score in this sample of 446 adults aged 20 to 83 was 64.4 (10.8). Women, non-smokers, and those with more than high school education had statistically significant higher C-HEI scores than men, smokers and those with high school diplomas or less. Conclusion The ability to assess C-HEI using the C-DHQ II facilitates the study of diet quality and health outcomes in Canada.

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Examining Dietary Patterns in Relation to Chronic Disease

Cited by 48 publications

References 12 publications

Weight Status and Dental Problems in Early Childhood: Classification Tree Analysis of a National Cohort

Weight Status and Dental Problems in Early Childhood: Classification Tree Analysis of a National Cohort

Validation of a Dietary Screening Tool in a Middle-Aged Appalachian Population

Addition of food group equivalents to the Canadian Diet History Questionnaire II for the estimation of the Canadian Healthy Eating Index-2005

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