Details

Autor(en) / Beteiligte

• RUEY LENG LOO
• CHAN, Queenie
• BROWN, Ian J
• ROBERTSON, Claire E
• STAMLER, Jeremiah
• NICHOLSON, Jeremy K
• HOLMES, Elaine
• ELLIOTT, Paul

Titel

A Comparison of Self-Reported Analgesic Use and Detection of Urinary Ibuprofen and Acetaminophen Metabolites by Means of Metabonomics The INTERMAP Study

Ist Teil von

• American journal of epidemiology, 2012-02, Vol.175 (4), p.348-358

Ort / Verlag

Cary, NC: Oxford University Press

Erscheinungsjahr

2012

Link zum Volltext

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• Information on dietary supplements, medications, and other xenobiotics in epidemiologic surveys is usually obtained from questionnaires and is subject to recall and reporting biases. The authors used metabolite data obtained from hydrogen-1 (or proton) nuclear magnetic resonance ((1)H NMR) analysis of human urine specimens from the International Study of Macro-/Micro-Nutrients and Blood Pressure (INTERMAP Study) to validate self-reported analgesic use. Metabolic profiling of two 24-hour urine specimens per individual was carried out for 4,630 participants aged 40-59 years from 17 population samples in Japan, China, the United Kingdom, and the United States (data collection, 1996-1999). (1)H NMR-detected acetaminophen and ibuprofen use was low (∼4%) among East Asian population samples and higher (>16%) in Western population samples. In a comparison of self-reported acetaminophen and ibuprofen use with (1)H NMR-detected acetaminophen and ibuprofen metabolites among 496 participants from Chicago, Illinois, and Belfast, Northern Ireland, the overall rate of concordance was 81%-84%; the rate of underreporting was 15%-17%; and the rate of underdetection was approximately 1%. Comparison of self-reported unspecified analgesic use with (1)H NMR-detected acetaminophen and ibuprofen metabolites among 2,660 Western INTERMAP participants revealed similar levels of concordance and underreporting. Screening for urinary metabolites of acetaminophen and ibuprofen improved the accuracy of exposure information. This approach has the potential to reduce recall bias and other biases in epidemiologic studies for a range of substances, including pharmaceuticals, dietary supplements, and foods.