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Development of a diet pattern assessment tool for coronary heart disease risk reduction

OBJECTIVE: Existing diet indices have gaps including neglect of the patterns of intake known to affect the final metabolic impact and use of measurement units prone to reporting error, and have applicability that is limited to specific populations. This study sought to develop a tool for diet-patter...

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Detalles Bibliográficos
Autores principales: Kohli, Aparna, Pandey, Ravindra M., Siddhu, Anupa, Reddy, K. Srinath
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9526230/
https://www.ncbi.nlm.nih.gov/pubmed/36193539
http://dx.doi.org/10.1016/j.puhip.2022.100317
Descripción
Sumario:OBJECTIVE: Existing diet indices have gaps including neglect of the patterns of intake known to affect the final metabolic impact and use of measurement units prone to reporting error, and have applicability that is limited to specific populations. This study sought to develop a tool for diet-pattern assessment (Prudent Approach to Cardiovascular Epidemic, for Indians – Diet Quality Index (iPACE-DQI)) to reduce diet-related coronary-heart-disease (CHD) risk. STUDY DESIGN: The iPACE-DQI was developed on a 0–100 points scale (higher numeric value healthier). A proof-of-concept analysis was done to examine its construct validity and relation with risk-markers. METHODS: Development of iPACE-DQI was partly guided by ‘prudent diet’ principles, with assessment focus on quality, quantity, and the pattern of intake. In the second part of the study, construct validity was evaluated by association of iPACE-DQI score with nutrients. Further, relationship of the score with risk-markers high-sensitivity C-reactive protein(hs-CRP), body-mass-index(BMI) and body-fat-percent was examined at single-point-in-time (baseline), and predictive ability of score change on hs-CRP change was evaluated in a proof-of-concept 12-weeks pre-post intervention, among free-living Indians (25–44years,n = 55) in an urban setting. RESULTS: The iPACE-DQI consists of eight main components. Associations of iPACE-DQI score with mean daily intake of key nutrients were robust and in expected direction [total-dietary-fiber (r = 0.5, p < 0.001), crude-fiber (r = 0.6, p < 0.001), protein (r = 0.5, p < 0.001), total-fat (r = −0.4, p = 0.002), vitamin-C (r = 0.5, p < 0.001), total-carbohydrate (r = 0.3, p = 0.017)]. Trends of hs-CRP, BMI and body-fat-percent across increasing diet-pattern score showed highest degree of abnormality in lowest tertile (≤35). Logistic regression model indicated higher likelihood for hs-CRP reduction (OR: 1.6, 95% CI 0.5–4.9) among those with ≥20% increase in iPACE-DQI score as compared with <20% increase or no-increase over 12-weeks CONCLUSION: The iPACE-DQI is a 100-point scale that assesses diet-pattern with respect to CHD-risk. The proposed tool could be useful for researchers/health practitioners to track diet-pattern change and concomitant CHD-risk reduction.