Performance of the Global Diet Quality Score (GDQS) App in Predicting Nutrient Adequacy and Metabolic Risk Factors among Thai Adults
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved..
BACKGROUND: The Global Diet Quality Score (GDQS) was developed for monitoring nutrient adequacy and diet-related noncommunicable disease risk in diverse populations. A software application (GDQS app) was recently developed for the standardized collection of GDQS data. The application involves a simplified 24-h dietary recall (24HR) where foods are matched to GDQS-food groups using an onboard database, portion sizes are estimated at the food group level using cubic models, and the GDQS is computed.
OBJECTIVES: The study aimed to estimate associations between GDQS scores collected using the GDQS app and nutrient adequacy and metabolic risks.
METHODS: In this cross-sectional study of 600 Thai males and nonpregnant/nonlactating females (40-60 y), we collected 2 d of GDQS app and paper-based 24HR, food-frequency questionnaires (FFQs), anthropometry, body composition, blood pressure, and biomarkers. Associations between application scores and outcomes were estimated using multiple regression, and application performance was compared with that of metrics scored using 24HR and FFQ data: GDQS, Minimum Dietary Diversity-Women, Alternative Healthy Eating Index-2010, and Global Dietary Recommendations score.
RESULTS: In covariate-adjusted models, application scores were significantly (P < 0.05) associated with higher energy-adjusted mean micronutrient adequacy computed using 24HR (range in estimated mean adequacy between score quintiles 1 and 5: 36.3%-44.5%) and FFQ (Q1-Q5: 40.6%-44.2%), and probability of protein adequacy from 24HR (Q1-Q5: 63%-72.5%). Application scores were inversely associated with BMI kg/m2 (Q1-Q5: 26.3-24.9), body fat percentage (Q1-Q5: 31.7%-29.1%), diastolic blood pressure (Q1-Q5: 84-81 mm Hg), and a locally-developed sodium intake score (Q1-Q5: 27.5-24.0 points out of 100); positively associated with high-density lipoprotein cholesterol (Q1-Q5: 49-53 mg/dL) and 24-h urinary potassium (Q1-Q5: 1385-1646 mg); and inversely associated with high midupper arm circumference (Q5/Q1 odds ratio: 0.52) and abdominal obesity (Q5/Q1 odds ratio: 0.51). Significant associations for the application outnumbered those for metrics computed using 24HR or FFQ.
CONCLUSIONS: The GDQS app effectively assesses nutrient adequacy and metabolic risk in population surveys.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:153 |
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| Enthalten in: |
The Journal of nutrition - 153(2023), 12 vom: 01. Dez., Seite 3576-3594 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bromage, Sabri [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 05.01.2024 Date Revised 23.01.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.tjnut.2023.10.007 |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Performance of the Global Diet Quality Score (GDQS) App in Predicting Nutrient Adequacy and Metabolic Risk Factors among Thai Adults |
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| 500 | |a Date Revised 23.01.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a BACKGROUND: The Global Diet Quality Score (GDQS) was developed for monitoring nutrient adequacy and diet-related noncommunicable disease risk in diverse populations. A software application (GDQS app) was recently developed for the standardized collection of GDQS data. The application involves a simplified 24-h dietary recall (24HR) where foods are matched to GDQS-food groups using an onboard database, portion sizes are estimated at the food group level using cubic models, and the GDQS is computed | ||
| 520 | |a OBJECTIVES: The study aimed to estimate associations between GDQS scores collected using the GDQS app and nutrient adequacy and metabolic risks | ||
| 520 | |a METHODS: In this cross-sectional study of 600 Thai males and nonpregnant/nonlactating females (40-60 y), we collected 2 d of GDQS app and paper-based 24HR, food-frequency questionnaires (FFQs), anthropometry, body composition, blood pressure, and biomarkers. Associations between application scores and outcomes were estimated using multiple regression, and application performance was compared with that of metrics scored using 24HR and FFQ data: GDQS, Minimum Dietary Diversity-Women, Alternative Healthy Eating Index-2010, and Global Dietary Recommendations score | ||
| 520 | |a RESULTS: In covariate-adjusted models, application scores were significantly (P < 0.05) associated with higher energy-adjusted mean micronutrient adequacy computed using 24HR (range in estimated mean adequacy between score quintiles 1 and 5: 36.3%-44.5%) and FFQ (Q1-Q5: 40.6%-44.2%), and probability of protein adequacy from 24HR (Q1-Q5: 63%-72.5%). Application scores were inversely associated with BMI kg/m2 (Q1-Q5: 26.3-24.9), body fat percentage (Q1-Q5: 31.7%-29.1%), diastolic blood pressure (Q1-Q5: 84-81 mm Hg), and a locally-developed sodium intake score (Q1-Q5: 27.5-24.0 points out of 100); positively associated with high-density lipoprotein cholesterol (Q1-Q5: 49-53 mg/dL) and 24-h urinary potassium (Q1-Q5: 1385-1646 mg); and inversely associated with high midupper arm circumference (Q5/Q1 odds ratio: 0.52) and abdominal obesity (Q5/Q1 odds ratio: 0.51). Significant associations for the application outnumbered those for metrics computed using 24HR or FFQ | ||
| 520 | |a CONCLUSIONS: The GDQS app effectively assesses nutrient adequacy and metabolic risk in population surveys | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a GDQS | |
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| 650 | 4 | |a dietary diversity | |
| 650 | 4 | |a metabolic syndrome | |
| 650 | 4 | |a noncommunicable disease | |
| 650 | 4 | |a nutrient adequacy | |
| 650 | 4 | |a nutrition surveillance | |
| 650 | 4 | |a nutritional epidemiology | |
| 650 | 7 | |a Micronutrients |2 NLM | |
| 700 | 1 | |a Pongcharoen, Tippawan |e verfasserin |4 aut | |
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| 700 | 1 | |a Moursi, Mourad |e verfasserin |4 aut | |
| 700 | 1 | |a Arsenault, Joanne |e verfasserin |4 aut | |
| 700 | 1 | |a Ali, Nazia Binte |e verfasserin |4 aut | |
| 700 | 1 | |a Batis, Carolina |e verfasserin |4 aut | |
| 700 | 1 | |a Fawzi, Wafaie W |e verfasserin |4 aut | |
| 700 | 1 | |a Winichagoon, Pattanee |e verfasserin |4 aut | |
| 700 | 1 | |a Willett, Walter C |e verfasserin |4 aut | |
| 700 | 1 | |a Kriengsinyos, Wantanee |e verfasserin |4 aut | |
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