Bajunaid Rania, Niu Chaoqun, Hambly Catherine, Liu Zongfang, Yamada Yosuke, Aleman-Mateo Heliodoro, Anderson Liam J, Arab Lenore, Baddou Issad, Bandini Linda, Bedu-Addo Kweku, Blaak Ellen E, Bouten Carlijn V C, Brage Soren, Buchowski Maciej S, Butte Nancy F, Camps Stefan G J A, Casper Regina, Close Graeme L, Cooper Jamie A, Cooper Richard, Das Sai Krupa, Davies Peter S W, Dabare Prasangi, Dugas Lara R, Eaton Simon, Ekelund Ulf, Entringer Sonja, Forrester Terrence, Fudge Barry W, Gillingham Melanie, Goris Annelies H, Gurven Michael, El Hamdouchi Asmaa, Haisma Hinke H, Hoffman Daniel, Hoos Marije B, Hu Sumei, Joonas Noorjehan, Joosen Annemiek M, Katzmarzyk Peter, Kimura Misaka, Kraus William E, Kriengsinyos Wantanee, Kuriyan Rebecca, Kushner Robert F, Lambert Estelle V, Lanerolle Pulani, Larsson Christel L, Leonard William R, Lessan Nader, Löf Marie, Martin Corby K, Matsiko Eric, Medin Anine C, Morehen James C, Morton James P, Must Aviva, Neuhouser Marian L, Nicklas Theresa A, Nyström Christine D, Ojiambo Robert M, Pietiläinen Kirsi H, Pitsiladis Yannis P, Plange-Rhule Jacob, Plasqui Guy, Prentice Ross L, Racette Susan B, Raichlen David A, Ravussin Eric, Redman Leanne M, Reilly John J, Reynolds Rebecca, Roberts Susan B, Samaranayakem Dulani, Sardinha Luis B, Silva Analiza M, Sjödin Anders M, Stamatiou Marina, Stice Eric, Urlacher Samuel S, Van Etten Ludo M, van Mil Edgar G A H, Wilson George, Yanovski Jack A, Yoshida Tsukasa, Zhang Xueying, Murphy-Alford Alexia J, Sinha Srishti, Loechl Cornelia U, Luke Amy H, Pontzer Herman, Rood Jennifer, Sagayama Hiroyuki, Schoeller Dale A, Westerterp Klaas R, Wong William W, Speakman John R
School of Biological Sciences, University of Aberdeen, Aberdeen, UK.
Food and Nutrition Department, King Abdulaziz University, Jeddah, Saudi Arabia.
Nat Food. 2025 Jan;6(1):58-71. doi: 10.1038/s43016-024-01089-5. Epub 2025 Jan 13.
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.
营养流行病学旨在将饮食摄入与慢性病联系起来，但评估饮食摄入量的工具并不准确。识别不可靠数据和误差来源的一种方法是将估计摄入量与总能量消耗（TEE）进行比较。在本研究中，我们使用国际原子能机构双标记水数据库，利用6497名4至96岁个体的TEE测量值，推导出一个TEE预测方程。所得回归方程可根据体重、年龄和性别等易于获取的变量预测预期的TEE，并给出95%的预测范围，可用于筛查饮食研究参与者的误报情况。我们将该方程应用于两个大型数据集（英国国家饮食与营养调查和美国国家健康与营养检查调查），发现误报率超过50%。随着误报率的增加，这些研究中饮食报告的宏量营养素组成出现系统性偏差，导致饮食成分与体重指数之间可能存在虚假关联。
