Combating the obesity epidemic is one of the greatest challenges of modern times. Although diets for weight loss have been around for more than 2,500 years, they have obviously not been particularly successful, as rates of overweight and obesity have continued to increase across the world over the past several decades. According to the World Health Organization (2016), Europe falls in the second place globally with more than 80% of the adult population struggling with excess body weight. Poor diet, both in quantity and quality, and a physically inactive lifestyle are characteristics of modern obesogenic environments and can trigger weight gain in susceptible individuals and thereby increase risk for noncommunicable diseases such as type 2 diabetes, metabolic syndrome, and coronary heart disease.
Personalised nutrition is the customisation of nutritional recommendations in order to prevent or to treat nutrition and metabolism-related disorders. The quantification of circulating biomarkers, either nutritional or clinical, have shown to play a vital role in implementing personalised nutritional strategies. Nutritional biomarkers are an objective reflection of an individual’s dietary intake, whereas clinical biomarkers are an objective reflection of an individual’s health and disease status1. Clinical biomarkers may or may not reflect dietary intake.
The quantification of circulating biomarkers, either nutritional or clinical, have shown to play a vital role in implementing personalised nutritional strategies.
Many association studies conducted previously showed a link between a particular biomarker and a specific nutrient. However, a biomarker may not always reflect a single nutrient. It may reflect a dietary pattern as a whole, in order words, an interaction of different nutrients. The same holds true for clinical biomarkers wherein a single biomarker may indicate a combination of health parameters or metabolic processes. For instance, homocysteine, an important intermediate of the one-carbon metabolism pathway, reflects uptake of various micronutrients, especially the B-vitamins2 and is a marker of several disorders such as cardiovascular diseases, cognitive decline, neural tube defects and different types of cancers3, and is therefore an indicator of several metabolic processes as well.
On the contrary, a combination of biomarkers may better reflect a single nutrient, a specific food category, or a single metabolic process. For example, vitamin C and carotenoids are commonly used biomarkers to assess fruit and vegetable intake. However, because of the variability in vitamin C and carotenoids content within different fruits and vegetables, McGrath et al. suggested to use their combination that might be a better predictor of food and vegetable intake, as compared to using them separately as single biomarkers4,5. Furthermore, combination of biomarkers such as C-reactive protein/albumin, neutrophil-lymphocyte ratio and N-terminal pro-brain natriuretic peptide could provide a higher accuracy for predicting mortality in acute exacerbation of chronic obstructive pulmonary disease patients with heart failure, as compared to what each of these single biomarkers would do6.
Based on the combinations of different biomarkers that reflect a particular nutrient, food category, or process, biomarkers are usually combined in different categories such as related to lipid metabolism, carbohydrate metabolism, inflammation, oxidative stress, etc. In addition, understanding the associations of biomarkers with their specific nutrients or food categories can help in a better categorisation of individuals with respect to their dietary intakes. Unravelling the role of circulating biomarkers as a whole we can therefore provide an overall image of an individual’s dietary pattern, health and lifestyle.
Biomarkers are usually combined in different categories such as related to lipid metabolism, carbohydrate metabolism, inflammation, oxidative stress
Having reference ranges and cut-off values of these biomarkers that are regarded normal physiological measurements in healthy individuals is also essential. This helps facilitate early identification and personalised prevention of nutrition-related disorders. If a value deviates from a normal reference range, it could mean that there is a relative deficiency or an excess of the assessed biomarker. That value does not always indicate a health disorder, but it could be to some extent closer to a pathological value. For many common and already established biomarkers, reference values are easily available. However, this is not the case for all novel biomarkers and therefore, reference values for these biomarkers are currently being generated. Similarly, biomarkers may differ between males and females and may be affected by lifestyle factors other than diet, such as physical activity, and by life trajectory parameters, such as age. For many biomarkers this is not, or poorly, investigated. We also aim to better understand differences due to sex, physical activity and age.
Biomarkers represent individual differences not only through dietary requirements, anthropometrics and environment, but also through genetics, epigenetics, drug response, and the gut microbiota. Although we do not understand all relations yet, biomarkers can already effectively be used to evaluate interventions. For example, biomarkers are used in randomised controlled trials in order to evaluate differences in improvements between personalised and conventional dietary recommendations. Serum, plasma, or urine samples are often taken at time points before and after intervention. Nutritionists and health care providers can therefore modify dietary recommendations at an individual level based on biomarker specificity.
Lastly, investigating the role of circulating biomarkers can improve food and nutrition policies, thereby preventing nutrition-related diseases and improving health in a sustainable manner. PREVENTOMICS aims to use these nutritional and clinical biomarkers for consumers in dietary advice applications.
References
- Picó, C., Serra, F., Rodríguez, A.M., Keijer, J. & Palou, A. Biomarkers of Nutrition and Health: New Tools for New Approaches. Nutrients 11(2019).
- Selhub, J. Homocysteine metabolism. Annu Rev Nutr 19, 217-46 (1999).
- Stover, P.J. One-carbon metabolism-genome interactions in folate-associated pathologies. J Nutr 139, 2402-5 (2009).
- McGrath, A.J. et al. Combining vitamin C and carotenoid biomarkers better predicts fruit and vegetable intake than individual biomarkers in dietary intervention studies. Eur J Nutr 55, 1377-88 (2016).
- Woodside, J.V., Draper, J., Lloyd, A. & McKinley, M.C. Use of biomarkers to assess fruit and vegetable intake. Proc Nutr Soc 76, 308-315 (2017).
- Yao, C. et al. Optimized combination of circulating biomarkers as predictors of prognosis in AECOPD patients complicated with Heart Failure. Int J Med Sci 18, 1592-1599 (2021).
About the authors
Pooja Mandaviya
Pooja Mandaviya is a postdoctoral researcher in molecular epidemiology at the Maastricht Centre for Systems Biology (MaCSBio). She holds a PhD in epigenetics (2018) from the Erasmus University Medical Centre, Rotterdam, a Masters’ degree in bioinformatics (2011) from Manipal University, India, and a Bachelors’ degree in Biotechnology (2009) from Bangalore University, India. Her expertises are analysis of omics datasets, teaching and student mentoring. Her current research aims to generate and evaluate percentile-based reference Values for biomarkers of health status among participants of the Maastricht Study.
Jaap Keijer
Jaap Keijer is a full professor Human and Animal Physiology in 2008. He is a molecular/ biochemical physiologist with a strong interest in in-vivo metabolic pathways; how these are altered with age and in chronic metabolic diseases and how they are affected by food components. By focusing on mitochondria, substrate metabolism and redox metabolism in humans, model animals and cells, he tries to decipher the molecular and biochemical processes underlying physiological functioning, health and disease.
