Evaluation of potential metabolomic-based biomarkers of protein, carbohydrate and fat intakes using a controlled feeding study

• Published in: European journal of nutrition Vol. 60; no. 8; pp. 4207 - 4218
• Main Authors: Zheng, Cheng, Gowda, G. A. Nagana, Raftery, Daniel, Neuhouser, Marian L., Tinker, Lesley F., Prentice, Ross L., Beresford, Shirley A. A., Zhang, Yiwen, Bettcher, Lisa, Pepin, Robert, Djukovic, Danijel, Gu, Haiwei, Barding, Gregory A., Song, Xiaoling, Lampe, Johanna W.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2021; Springer Nature B.V
• Subjects: Biomarkers; Carbohydrates; Chemistry; Chemistry and Materials Science; Chromatography; Diet; Gas chromatography; Liquid chromatography; Mass spectrometry; Mass spectroscopy; Metabolites; Metabolomics; Mimicry; NMR; Nuclear magnetic resonance; Nutrition; Original Contribution; Post-menopause; Prediction models; Proteins; Scientific imaging; Urine; Womens health; Metabolomics; Carbohydrate; Controlled feeding study; Postmenopausal women; Protein; Dietary biomarker
• ISSN: 1436-6207; 1436-6215
• DOI: 10.1007/s00394-021-02577-1

Purpose
Objective biomarkers of dietary exposure are needed to establish reliable diet-disease associations. Unfortunately, robust biomarkers of macronutrient intakes are scarce. We aimed to assess the utility of serum, 24-h urine and spot urine high-dimensional metabolites for the development of biomarkers of daily intake of total energy, protein, carbohydrate and fat, and the percent of energy from these macronutrients (%E).
Methods
A 2-week controlled feeding study mimicking the participants’ habitual diets was conducted among 153 postmenopausal women from the Women’s Health Initiative (WHI). Fasting serum metabolomic profiles were analyzed using a targeted liquid chromatography–tandem mass spectrometry (LC–MS/MS) assay for aqueous metabolites and a direct-injection-based quantitative lipidomics platform. Urinary metabolites were analyzed using
1
H nuclear magnetic resonance (NMR) spectroscopy at 800 MHz and by untargeted gas chromatography–mass spectrometry (GC–MS). Variable selection was performed to build prediction models for each dietary variable.
Results
The highest cross-validated multiple correlation coefficients (CV-R
2
) for protein intake (%E) and carbohydrate intake (%E) using metabolites only were 36.3 and 37.1%, respectively. With the addition of established dietary biomarkers (doubly labeled water for energy and urinary nitrogen for protein), the CV-R
2
reached 55.5% for energy (kcal/d), 52.0 and 45.0% for protein (g/d, %E), 55.9 and 37.0% for carbohydrate (g/d, %E).
Conclusion
Selected panels of serum and urine metabolites, without the inclusion of doubly labeled water and urinary nitrogen biomarkers, give a reliable and robust prediction of daily intake of energy from protein and carbohydrate.