Do Global Dietary Nutrient Datasets Associate with Human Biomarker Assessments? A Regression Analysis
Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved..
BACKGROUND: To assess the state and trends of global nutrition, our best tools are nationally representative assessments using human biomarker assays, yet these are expensive and logistically challenging. We instead often rely on more easily produced global nutrient datasets-measures of nutrients provided by the diet-as a proxy for nutritional intake and deficiency, due to their greater geographic and temporal coverage. However, the accuracy of global nutrient datasets is questionable.
OBJECTIVE: We aimed to test whether estimates of inadequate dietary intake derived from existing global nutrient datasets reliably associate with biophysical deficiency.
DESIGN: We performed linear regressions of estimates of inadequate dietary nutrient intake derived from three global nutrient datasets-Global Dietary Database, Global Nutrient Database, and Global Expanded Nutrient Supply (GENuS) model-against the existing suite of nationally representative biomarker survey data for three key nutrients of global concern in two vulnerable demographic groups: zinc, folate, and vitamin A in females of childbearing age; and zinc and vitamin A in children younger than 5 y.
RESULTS: We found significant associations (P < 0.1) for only 3 of 22 regressions between global nutrition datasets and biophysical deficiency: zinc for females of childbearing age from GENuS and Global Dietary Database, and zinc for children under 5 y from GENuS. Folate and vitamin A show no reliable relationship between nutrient datasets and independent biomarker surveys. Applying the successful models for zinc to the accompanying full datasets yield estimates of global zinc deficiency of 31%-37% for these demographic groups.
CONCLUSIONS: We found that few estimates of nutritional inadequacy from global dietary datasets are associated with more direct measures of biophysical deficiency from biomarker studies. Researchers and policymakers must be cautious when applying global nutrient datasets to questions of global health and use them for limited applications.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:119 |
|---|---|
| Enthalten in: |
The American journal of clinical nutrition - 119(2024), 1 vom: 01. Jan., Seite 69-75 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Smith, Matthew R [VerfasserIn] |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Completed 08.01.2024 Date Revised 08.01.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.ajcnut.2023.10.020 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM363904492 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM363904492 | ||
| 003 | DE-627 | ||
| 005 | 20240114232006.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.ajcnut.2023.10.020 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1252.xml |
| 035 | |a (DE-627)NLM363904492 | ||
| 035 | |a (NLM)37898436 | ||
| 035 | |a (PII)S0002-9165(23)66224-0 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Smith, Matthew R |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Do Global Dietary Nutrient Datasets Associate with Human Biomarker Assessments? A Regression Analysis |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 08.01.2024 | ||
| 500 | |a Date Revised 08.01.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a BACKGROUND: To assess the state and trends of global nutrition, our best tools are nationally representative assessments using human biomarker assays, yet these are expensive and logistically challenging. We instead often rely on more easily produced global nutrient datasets-measures of nutrients provided by the diet-as a proxy for nutritional intake and deficiency, due to their greater geographic and temporal coverage. However, the accuracy of global nutrient datasets is questionable | ||
| 520 | |a OBJECTIVE: We aimed to test whether estimates of inadequate dietary intake derived from existing global nutrient datasets reliably associate with biophysical deficiency | ||
| 520 | |a DESIGN: We performed linear regressions of estimates of inadequate dietary nutrient intake derived from three global nutrient datasets-Global Dietary Database, Global Nutrient Database, and Global Expanded Nutrient Supply (GENuS) model-against the existing suite of nationally representative biomarker survey data for three key nutrients of global concern in two vulnerable demographic groups: zinc, folate, and vitamin A in females of childbearing age; and zinc and vitamin A in children younger than 5 y | ||
| 520 | |a RESULTS: We found significant associations (P < 0.1) for only 3 of 22 regressions between global nutrition datasets and biophysical deficiency: zinc for females of childbearing age from GENuS and Global Dietary Database, and zinc for children under 5 y from GENuS. Folate and vitamin A show no reliable relationship between nutrient datasets and independent biomarker surveys. Applying the successful models for zinc to the accompanying full datasets yield estimates of global zinc deficiency of 31%-37% for these demographic groups | ||
| 520 | |a CONCLUSIONS: We found that few estimates of nutritional inadequacy from global dietary datasets are associated with more direct measures of biophysical deficiency from biomarker studies. Researchers and policymakers must be cautious when applying global nutrient datasets to questions of global health and use them for limited applications | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a dietary folate | |
| 650 | 4 | |a dietary vitamin A | |
| 650 | 4 | |a dietary zinc | |
| 650 | 4 | |a global health | |
| 650 | 4 | |a global nutrition | |
| 650 | 4 | |a nutritional biomarkers | |
| 650 | 4 | |a nutritional deficiency | |
| 650 | 4 | |a nutritional status | |
| 650 | 7 | |a Vitamin A |2 NLM | |
| 650 | 7 | |a 11103-57-4 |2 NLM | |
| 650 | 7 | |a Folic Acid |2 NLM | |
| 650 | 7 | |a 935E97BOY8 |2 NLM | |
| 650 | 7 | |a Zinc |2 NLM | |
| 650 | 7 | |a J41CSQ7QDS |2 NLM | |
| 650 | 7 | |a Biomarkers |2 NLM | |
| 700 | 1 | |a Myers, Samuel S |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t The American journal of clinical nutrition |d 1954 |g 119(2024), 1 vom: 01. Jan., Seite 69-75 |w (DE-627)NLM000025402 |x 1938-3207 |7 nnns |
| 773 | 1 | 8 | |g volume:119 |g year:2024 |g number:1 |g day:01 |g month:01 |g pages:69-75 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.ajcnut.2023.10.020 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 119 |j 2024 |e 1 |b 01 |c 01 |h 69-75 | ||