Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake

Rania Bajunaid; Chaoqun Niu; Catherine Hambly; Zongfang Liu; Yosuke Yamada; Heliodoro Aleman-Mateo; Liam J Anderson; Lenore Arab; Issad Baddou; Linda Bandini; Kweku Bedu-Addo; Ellen E Blaak; Carlijn V C Bouten; Soren Brage; Maciej S Buchowski; Nancy F Butte; Stefan G J A Camps; Regina Casper; Graeme L Close; Jamie A Cooper; Richard Cooper; Sai Krupa Das; Peter S W Davies; Prasangi Dabare; Lara R Dugas; Simon Eaton; Ulf Ekelund; Sonja Entringer; Terrence Forrester; Barry W Fudge; Melanie Gillingham; Annelies H Goris; Michael Gurven; Asmaa El Hamdouchi; Hinke H Haisma; Daniel Hoffman; Marije B Hoos; Sumei Hu; Noorjehan Joonas; Annemiek M Joosen; Peter Katzmarzyk; Misaka Kimura; William E Kraus; Wantanee Kriengsinyos; Rebecca Kuriyan; Robert F Kushner; Estelle V Lambert; Pulani Lanerolle; Christel L Larsson; Klaas R Westerterp; Et al.

doi:10.1038/s43016-024-01089-5

Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake

Rania Bajunaid, Chaoqun Niu, Catherine Hambly, Zongfang Liu, Yosuke Yamada^*, Heliodoro Aleman-Mateo, Liam J Anderson, Lenore Arab, Issad Baddou, Linda Bandini, Kweku Bedu-Addo, Ellen E Blaak, Carlijn V C Bouten, Soren Brage, Maciej S Buchowski, Nancy F Butte, Stefan G J A Camps, Regina Casper, Graeme L Close, Jamie A CooperRichard Cooper, Sai Krupa Das, Peter S W Davies, Prasangi Dabare, Lara R Dugas, Simon Eaton, Ulf Ekelund, Sonja Entringer, Terrence Forrester, Barry W Fudge, Melanie Gillingham, Annelies H Goris, Michael Gurven, Asmaa El Hamdouchi, Hinke H Haisma, Daniel Hoffman, Marije B Hoos, Sumei Hu, Noorjehan Joonas, Annemiek M Joosen, Peter Katzmarzyk, Misaka Kimura, William E Kraus, Wantanee Kriengsinyos, Rebecca Kuriyan, Robert F Kushner, Estelle V Lambert, Pulani Lanerolle, Christel L Larsson, Klaas R Westerterp^*, Et al.

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.

Original language	English
Article number	100203
Pages (from-to)	58-71
Number of pages	14
Journal	Nature food
Volume	6
Issue number	1
DOIs	https://doi.org/10.1038/s43016-024-01089-5
Publication status	Published - 13 Jan 2025

Keywords

Humans
Energy Intake
Aged
Adolescent
Adult
Female
Child
Middle Aged
Male
Child, Preschool
Aged, 80 and over
Young Adult
Self Report
Nutrition Surveys
Energy Metabolism/physiology
Diet
Body Mass Index
Water

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Bajunaid, R., Niu, C., Hambly, C., Liu, Z., Yamada, Y., Aleman-Mateo, H., Anderson, L. J., Arab, L., Baddou, I., Bandini, L., Bedu-Addo, K., Blaak, E. E., Bouten, C. V. C., Brage, S., Buchowski, M. S., Butte, N. F., Camps, S. G. J. A., Casper, R., Close, G. L., ... Et al. (2025). Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake. Nature food, 6(1), 58-71. Article 100203. https://doi.org/10.1038/s43016-024-01089-5

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title = "Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake",

abstract = "Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.",

keywords = "Humans, Energy Intake, Aged, Adolescent, Adult, Female, Child, Middle Aged, Male, Child, Preschool, Aged, 80 and over, Young Adult, Self Report, Nutrition Surveys, Energy Metabolism/physiology, Diet, Body Mass Index, Water",

author = "Rania Bajunaid and Chaoqun Niu and Catherine Hambly and Zongfang Liu and Yosuke Yamada and Heliodoro Aleman-Mateo and Anderson, {Liam J} and Lenore Arab and Issad Baddou and Linda Bandini and Kweku Bedu-Addo and Blaak, {Ellen E} and Bouten, {Carlijn V C} and Soren Brage and Buchowski, {Maciej S} and Butte, {Nancy F} and Camps, {Stefan G J A} and Regina Casper and Close, {Graeme L} and Cooper, {Jamie A} and Richard Cooper and Das, {Sai Krupa} and Davies, {Peter S W} and Prasangi Dabare and Dugas, {Lara R} and Simon Eaton and Ulf Ekelund and Sonja Entringer and Terrence Forrester and Fudge, {Barry W} and Melanie Gillingham and Goris, {Annelies H} and Michael Gurven and {El Hamdouchi}, Asmaa and Haisma, {Hinke H} and Daniel Hoffman and Hoos, {Marije B} and Sumei Hu and Noorjehan Joonas and Joosen, {Annemiek M} and Peter Katzmarzyk and Misaka Kimura and Kraus, {William E} and Wantanee Kriengsinyos and Rebecca Kuriyan and Kushner, {Robert F} and Lambert, {Estelle V} and Pulani Lanerolle and Larsson, {Christel L} and Westerterp, {Klaas R} and {Et al.}",

year = "2025",

month = jan,

day = "13",

doi = "10.1038/s43016-024-01089-5",

language = "English",

volume = "6",

pages = "58--71",

journal = "Nature food",

issn = "2662-1355",

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Bajunaid, R, Niu, C, Hambly, C, Liu, Z, Yamada, Y, Aleman-Mateo, H, Anderson, LJ, Arab, L, Baddou, I, Bandini, L, Bedu-Addo, K, Blaak, EE, Bouten, CVC, Brage, S, Buchowski, MS, Butte, NF, Camps, SGJA, Casper, R, Close, GL, Cooper, JA, Cooper, R, Das, SK, Davies, PSW, Dabare, P, Dugas, LR, Eaton, S, Ekelund, U, Entringer, S, Forrester, T, Fudge, BW, Gillingham, M, Goris, AH, Gurven, M, El Hamdouchi, A, Haisma, HH, Hoffman, D, Hoos, MB, Hu, S, Joonas, N, Joosen, AM, Katzmarzyk, P, Kimura, M, Kraus, WE, Kriengsinyos, W, Kuriyan, R, Kushner, RF, Lambert, EV, Lanerolle, P, Larsson, CL, Westerterp, KR & Et al. 2025, 'Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake', Nature food, vol. 6, no. 1, 100203, pp. 58-71. https://doi.org/10.1038/s43016-024-01089-5

TY - JOUR

T1 - Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake

AU - Bajunaid, Rania

AU - Niu, Chaoqun

AU - Hambly, Catherine

AU - Liu, Zongfang

AU - Yamada, Yosuke

AU - Aleman-Mateo, Heliodoro

AU - Anderson, Liam J

AU - Arab, Lenore

AU - Baddou, Issad

AU - Bandini, Linda

AU - Bedu-Addo, Kweku

AU - Blaak, Ellen E

AU - Bouten, Carlijn V C

AU - Brage, Soren

AU - Buchowski, Maciej S

AU - Butte, Nancy F

AU - Camps, Stefan G J A

AU - Casper, Regina

AU - Close, Graeme L

AU - Cooper, Jamie A

AU - Cooper, Richard

AU - Das, Sai Krupa

AU - Davies, Peter S W

AU - Dabare, Prasangi

AU - Dugas, Lara R

AU - Eaton, Simon

AU - Ekelund, Ulf

AU - Entringer, Sonja

AU - Forrester, Terrence

AU - Fudge, Barry W

AU - Gillingham, Melanie

AU - Goris, Annelies H

AU - Gurven, Michael

AU - El Hamdouchi, Asmaa

AU - Haisma, Hinke H

AU - Hoffman, Daniel

AU - Hoos, Marije B

AU - Hu, Sumei

AU - Joonas, Noorjehan

AU - Joosen, Annemiek M

AU - Katzmarzyk, Peter

AU - Kimura, Misaka

AU - Kraus, William E

AU - Kriengsinyos, Wantanee

AU - Kuriyan, Rebecca

AU - Kushner, Robert F

AU - Lambert, Estelle V

AU - Lanerolle, Pulani

AU - Larsson, Christel L

AU - Westerterp, Klaas R

AU - Et al.

PY - 2025/1/13

Y1 - 2025/1/13

N2 - Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.

AB - Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.

KW - Humans

KW - Energy Intake

KW - Aged

KW - Adolescent

KW - Adult

KW - Female

KW - Child

KW - Middle Aged

KW - Male

KW - Child, Preschool

KW - Aged, 80 and over

KW - Young Adult

KW - Self Report

KW - Nutrition Surveys

KW - Energy Metabolism/physiology

KW - Diet

KW - Body Mass Index

KW - Water

U2 - 10.1038/s43016-024-01089-5

DO - 10.1038/s43016-024-01089-5

M3 - Article

SN - 2662-1355

VL - 6

SP - 58

EP - 71

JO - Nature food

JF - Nature food

IS - 1

M1 - 100203

ER -

Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake

Abstract

Keywords

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