Tong et al.(2020) analyzed serum metabolites against adherence to the Mediterranean Diet and against cardiometabolic risk factors, in participants of the Fenland cohort study, to see which metabolites might be involved in the ability of the Mediterranean Diet to prevent cardiometabolic disease, citing Sofi et al. (2014) as an example of this preventative effect. They looked at a FFQ, a targeted metabolomics assay, and labs including insulin, glucose, and cholesterol. Over 10,000 English people between the ages of 30 and 65 who did not have diabetes were included. They found a set of metabolites that was associated with adherence to the Mediterranean diet as a whole rather than to any single component of the diet, and a subset of these was found to significantly diminish the association between Mediterranean Diet and cholesterol, triglycerides, and homeostasis model assessment of insulin resistance (HOMA-IR), when they adjusted for these as for confounders.
A limitation of the FFQ noted was that it did not differentiate between red and white wine, which would have improved content validity since red wine is considered to be a component of the Mediterranean Diet while white wine is not. A content validity limitation noted of the metabolomics assay was that it did not include flavonoids, and that it could not differentiate between palmitic acid and DHA, or between oleic acid and EPA. The authors mention that a previous study of theirs (Tong et al., 2016) had shown that people with higher Mediterranean Diet scores were less likely to have cardiovascular disease, establishing content validity for this measurement.
They also mention that the FFQ had previously been validated. I looked at one of the studies they cited for this (Day et al., 2001), and it raised concerns about both construct and criterion validity. In this study, there were discrepancies between the FFQ and both a 7-day diet recall (construct validity) and biomarkers for nitrogen, potassium and sodium (criterion validity). On the other hand, HOMA-IR has been shown to predict cardiovascular disease (Bonora et al., 2002), which gives HOMA-IR criterion validity as a measurement of cardiometabolic risk.
The FFQ data was turned into Mediterranean Diet scores using software (Mulligan et al., 2014) that codes the raw data into food groups. Internal consistency of the metabolite score is supported by the fact that its correlation with the Mediterranean Diet score was 0.43, compared to 0.26 for vitamin C, which is considered to be a biomarker for a healthy diet.
The metabolomics assay kit that they used includes a calibration procedure utilizing plasma samples to which different specific amounts of certain metabolites have been added; this method helps to improve precision and accuracy, which were found to be within 20% (Siskos et al., 2017). Overall they had good standardization procedures, including using the same FFQ for all the participants, utilizing established quality controls when running the metabolomics assays, and using the same calculation to determine HOMA-IR for all the participants. They also adjusted for potential confounders.
The idea of investigating the role of metabolites that are associated with different diets in the progression of different disease states is relatively new on the scene, but I found another study on this topic from ten years ago (Floegel et al., 2013). Guasch-Ferré et al. (2018) found 16 different studies looking at the metabolites associated with different dietary patterns. Being able to reliably say that eating certain foods leads to certain metabolites appearing in the bloodstream requires modern metabolomics assay technology, and the work done by Siskos et al. (2017) in establishing validity and reliability of this new technology is crucial for advancement of the field. Standardization has been challenging in metabolomics, because of the different methods used by the different labs offering the assays (Tzoulaki et al., 2014). It will be important to look for results that are reproduced across studies with different methodologies, and further standardization would help with being able to compare them.
References
Bonora, E., Formentini, G., Calcaterra, F., Lombardi, S., Marini, F., Zenari, L., Saggiani, F., Poli, M., Perbellini, S., Raffaelli, A., Cacciatori, V., Santi, L., Targher, G., Bonadonna, R., & Muggeo, M. (2002). HOMA-estimated insulin resistance is an independent predictor of cardiovascular disease in type 2 diabetic subjects: prospective data from the Verona Diabetes Complications Study. Diabetes Care, 25(7), 1135–1141.
Day, N., McKeown, N., Wong, M., Welch, A., & Bingham, S. (2001). Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. International Journal of Epidemiology, 30(2), 309–317.
Floegel, A., von Ruesten, A., Drogan, D., Schulze, M. B., Prehn, C., Adamski, J., Pischon, T., & Boeing, H. (2013). Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam. European Journal of Clinical Nutrition, 67(10), 1100–1108.
Guasch-Ferré, M., Bhupathiraju, S. N., & Hu, F. B. (2018). Use of Metabolomics in Improving Assessment of Dietary Intake. Clinical Chemistry, 64(1), 82–98.
Mulligan, A. A., Luben, R. N., Bhaniani, A., Parry-Smith, D. J., O’Connor, L., Khawaja, A. P., Forouhi, N. G., & Khaw, K.-T. (2014). A new tool for converting food frequency questionnaire data into nutrient and food group values: FETA research methods and availability. BMJ Open, 4(3), e004503.
Siskos, A. P., Jain, P., Ro, misch-M. W., Bennett, M., Achaintre, D., Asad, Y., Marney, L., Richardson, L., Koulman, A., Griffin, J. L., Raynaud, F., Scalbert, A., Adamski, J., Prehn, C., & Keun, H. C. (2017). Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma. Analytical Chemistry, 89(1), 656–665.
Sofi, F., Macchi, C., Abbate, R., Gensini, G. F., & Casini, A. (2014). Mediterranean diet and health status: an updated meta-analysis and a proposal for a literature-based adherence score. Public Health Nutrition, 17(12), 2769–2782.
Tong, T. Y. N., Wareham, N. J., Khaw, K.-T., Imamura, F., & Forouhi, N. G. (2016). Prospective association of the Mediterranean diet with cardiovascular disease incidence and mortality and its population impact in a non-Mediterranean population: the EPIC-Norfolk study. BMC Medicine, 14(1), 135.
Tong, T. Y. N., Koulman, A., Griffin, J. L., Wareham, N. J., Forouhi, N. G., & Imamura, F. (2020). A Combination of Metabolites Predicts Adherence to the Mediterranean Diet Pattern and Its Associations with Insulin Sensitivity and Lipid Homeostasis in the General Population: The Fenland Study, United Kingdom. The Journal of Nutrition, 150(3), 568–578.
Tzoulaki, I., Ebbels, T. M. D., Valdes, A., Elliott, P., & Ioannidis, J. P. A. (2014). Design and Analysis of Metabolomics Studies in Epidemiologic Research: A Primer on -Omic Technologies. American Journal of Epidemiology, 180(2), 129–139.
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