metabolic diseases

 

Who does not want to live happy and healthy to the end of their days? Soon, there could be help at hand in the form of metabotyping, which categorizes individuals based on metabolic profiles to deliver personalized nutrition plans to manage metabolic diseases or, even prevent their onset.

Nutritional requirements vary not only according to age, gender, and pregnancy status but also from individual to individual. Since genetic, lifestyle, and environmental factors, including gut microbes, could influence the metabolic makeup (metabolome) of an individual and their nutritional requirements, a systems approach is increasingly being adopted towards nutrition to help manage metabolic diseases.

Metabotyping

Metabotyping is the characterization of an individual’s metabolic makeup by measuring the levels of various metabolites in the urine, blood, or feces and the subsequent categorization of individuals into groups based on their metabolic profiles.

Previous studies have shown the benefits of metabotyping to customizing drug treatments and nutritional plans. However, there are inconsistencies in terms of the metabolites that are used to group individuals for targeted drug treatment or nutritional intervention. A review published in the June issue of the British Journal of Nutrition documents the wide variety of metabotyping studies that have been carried out in humans thus far,1and some of its insights are summarized here.

Surveying Previous Studies

The review surveyed all data in the scientific literature until May 2016. One part of the literature searches involved examining data from healthy individuals or populations. A second part involved examining data from patients with chronic diet-related metabolic diseases, which included obesity, metabolic syndrome, diabetes, dyslipidemia, hyperlipidemia, hyperuricemia, gout, and hypertension.

The authors cited several studies including one wherein healthy individuals were classified into five groups based on their serum levels of certain metabolites associated with metabolic syndrome and vitamin-D levels. Of these groups, one group with lower levels of vitamin-D but higher levels of adipokines was subsequently found to respond positively to vitamin D supplementation with improved metabolite levels after supplementation.2

As the metabolites screened varied from study to study, it was hard to compare metabotypes across studies. For example, in one study patients with metabolic syndrome were classified into subgroups using measurements of waist circumference, systolic and diastolic blood pressure, high-density lipoprotein (HDL), triacylglycerides (TAG), fasting glucose levels, and medication.3However, in another study, patients with metabolic syndrome were grouped based on the levels of fatty acids in their serum.4 Therefore, the authors recommend developing standards for defining the metabotypes associated with specific metabolic diseases, for instance, diabetes or dyslipidemia.

Improved Health Outcomes

Overall, the paper presents a comprehensive overview of all metabotyping studies carried out in healthy humans or humans with metabolic diseases. The recommendations based on the findings in this article include a refinement in the definition of generally valid metabotypes in large populations, the development of a stricter definition of metabotypes, and widescale implementation of the results of metabolic research in the treatment of metabolic disease.

Customized nutritional intervention was found to improved health outcomes in many studies and implementing these methods in public healthcare plans may translate into substantial cost savings.

Written by Usha B. Nair, Ph.D.

References

  1. Riedl A, Gieger C, Hauner H, Daniel H, Linseisen J. Metabotyping and its application in targeted nutrition: an overview. Br J Nutr. 2017 Jul 19:1-14. doi: 10.1017/S0007114517001611. [Epub ahead of print] PubMed PMID: 28720150.
  2. O’Sullivan A, Gibney MJ, Connor AO, Mion B, Kaluskar S, Cashman KD, Flynn A, Shanahan F, Brennan L. Biochemical and metabolomic phenotyping in the identification of a vitamin D responsive metabotype for markers of the metabolic syndrome. Mol Nutr Food Res. 2011 May;55(5):679-90. doi: 10.1002/mnfr.201000458. Epub 2011 Jan 14. PubMed PMID: 21240901.
  3. Arguelles W, Llabre MM, Sacco RL, Penedo FJ, Carnethon M, Gallo LC, Lee DJ, Catellier DJ, González HM, Holub C, Loehr LR, Soliman EZ, Schneiderman N. Characterization of metabolic syndrome among diverse Hispanics/Latinos living in the United States: Latent class analysis from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Int J Cardiol. 2015 Apr 1;184:373-9. doi: 10.1016/j.ijcard.2015.02.100. Epub 2015 Feb 27. PubMed PMID: 25745986; PubMed Central PMCID: PMC4417385.
  4. Žák A, Burda M, Vecka M, Zeman M, Tvrzická E, Staňková B. Fatty acid composition indicates two types of metabolic syndrome independent of clinical and laboratory parameters. Physiol Res. 2014;63 Suppl 3:S375-85. PubMed PMID: 25428743.
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