RELATIONSHIP BETWEEN SERUM METAL AND METALLOID LEVELS WITH METABOLIC RISK MARKERS IN OVERWEIGHT AND OBESE WOMEN

DOI: https://doi.org/10.29296/25877313-2020-05-04
Issue: 
5
Year: 
2020

A.A. Tinkov Ph.D. (Med.), Research Scientist, Yaroslavl State University (Yaroslavl); Leader Research Scientist, Sechenov University; Peoples’ Friendship University of Russia (Moscow) E-mail: tinkov.a.a@gmail.com O.P. Ajsuvakova Ph.D. (Chem.), Research Scientist, Yaroslavl State University (Yaroslavl) M.G. Skalnaya Dr.Sc. (Med.), Professor, Chief Research Scientist, Sechenov University (Moscow) A.V. Skalny Dr.Sc. (Med.), Professor, Head of Laboratory, Yaroslavl State University; Head of Laboratory, Sechenov University (Moscow); Head of Department, Yaroslavl State University (Yaroslavl)

The objective of the present study was to assess serum metal and metalloid levels in patients with overweight and obesity, as well as evaluation of its association with metabolic risk markers. Material and methods. A total of 82 adult women with overweight and obesity (BMI > 25), as well as 88 lean (18.5 < BMI < 25) controls were ex-amined. Assessment of serum metal levels was performed using inductively-coupled plasma mass-spectrometry. Serum was also used for evalua-tion of markers of lipid and carbohydrate metabolism, as well as inflammation and oxidative stress. Results. The obtained data demonstrate that patients with obesity were characterized by at herogenic dyslipidemia, insulin resistance, systemic in-flammation, as well as oxidative stress. A significant increase in alanine aminotransferase, γ-glutamyl transferase, and cholinesterase activity was observed. Serum copper and aluminium levels in overweight and obese subjects exceeded the control values by 12% and 17%, respectively. Copper concentration was also considered as a positive predictor of increased BMI (β=0.391; p=0.008) in regression models, being also negatively interrelated with total antioxidant activity (β=0.322; p=0.032) and high-density lipoprotein cholesterol (β=0.241; p=0.024) after adjustment for anthropometric parameters. At the same time, serum vanadium (β=0.576; p=0.027) and chromium (β=0.682; p=0.036) were inversely associated with fasting glu-cose concentration. Zinc level was considered as the most significant predictor of total antioxidant activity (β=0.643; p=0.003). Conclusion. Therefore, serum metal and metalloid levels in patients with obesity are associated with pathogenetic mechanisms of metabolic syn-drome including insulinresistance, at herogenic dyslipidemia, inflammation, and oxidative stress.

Keywords: 
copper
aluminium
metabolic syndrome
insulin resistance
dyslipidemia

References: 
  1. GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 2017; 377:13-27.
  2. Jung U.J., Choi M.S. Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int. J. Mol. Sci. 2014; 15:6184-6223.
  3. Nikonorov A.A., Skalnaya M.G., Tinkov A.A. et al. Mutual interaction between iron homeostasis and obesity pathogenesis. J. Trace Elem. Med. Biol. 2015; 30:207-214.
  4. de Luis D.A., Pacheco D., Izaola O. et al. Micronutrient status in morbidly obese women before bariatric surgery. Surg. Obes. Relat. Dis. 2013; 9:323-327.
  5. Panchal S.K., Wanyonyi S., Brown L. Selenium, vanadium, and chromium as micronutrients to improve metabolic syndrome. Curr. Hypertens. Rep. 2017; 19:10.
  6. Park S.S., Skaar D.A., Jirtle R.L. et al. Epigenetics, obesity and early-life cadmium or lead exposure. Epigenomics. 2017; 9:57-75.
  7. Robberecht H., De Bruyne T., Hermans N. Biomarkers of the metabolic syndrome: Influence of minerals, oligo-and trace elements. J. Trace Elem. Med. Biol. 2017; 43:23-28.
  8. Kim H.N., Song S.W. Concentrations of chromium, selenium, and copper in the hair of viscerally obese adults are associated with insulin resistance. Biol. Trace Elem. Res. 2014; 158:152-157.
  9. Bo S., Durazzo M., Gambino R. et al. Associations of dietary and serum copper with inflammation, oxidative stress, and metabolic variables in adults. J. Nutr. 2008; 138:305-310.
  10. Yang H., Liu C.N., Wolf R.M. et al. Obesity is associated with copper elevation in serum and tissues. Metallomics. 2019; 11:1363-1371.
  11. Tinkov A.A., Polyakova Vikonorov A.A. Chronic administration of iron and copper potentiates adipogenic effect of high fat diet in Wistar rats. Biometals. 2013; 26:447-463.
  12. Persichini T., Percario Z., Mazzon E. et al. Copper activates the NF-κB pathway in vivo. Antioxid. Redox. Signal. 2006; 8:1897-1904.
  13. Malavolta M., Giacconi R., Piacenza F. Plasma copper/zinc ratio: an inflammatory/nutritional biomarker as predictor of all-cause mortality in elderly population. Biogerontology. 2010; 11:309-319.
  14. Bjørklund G., Dadar M., Pivina L. et al. The Role of Zinc and Copper in Insulin Resistance and Diabetes Mellitus. Curr. Med. Chem. 2020; DOI: https://doi.org/10.2174/0929867326666190902122155.
  15. Tinkov A.A., Skalnaya M.G., Aaseth J. et al. Aluminium levels in hair and urine are associated with overweight and obesity in a non-occupationally exposed population. J. Trace Elem. Med. Biol. 2019; 56:139-145.
  16. Mailloux R.J., Lemire J., Appanna V.D. Hepatic response to aluminum toxicity: dyslipidemia and liver diseases. Exp. Cell. Res. 2011; 317:2231-2238.
  17. Feng W., Liu Y., Fei F. et al. Improvement of high-glucose and insulin resistance of chromium malate in 3T3-L1 adipocytes by glucose uptake and insulin sensitivity signaling pathways and its mechanism. RSC Adv. 2019; 9:114-127.
  18. Treviño S., Díaz A., Sánchez-Lara E. et al. Vanadium in biological action: chemical, pharmacological aspects, and metabolic implications in diabetes mellitus. Biol. Trace Elem. Res. 2019; 188:68-98.
  19. Olechnowicz J., Tinkov A., Skalny A. et al. Zinc status is associated with inflammation, oxidative stress, lipid, and glucose metabolism. J. Physiol. Sci. 2018. 68:19-31.
  20. Maret W. Zinc in pancreatic islet biology, insulin sensitivity, and diabetes. Prev. Nutr. Food Sci. 2017; 22:1.
  21. Shao W., Liu Q., He X. et al. Association between level of urinary trace heavy metals and obesity among children aged 6–19 years: NHANES 1999–2011. Environ. Sci. Poll. Res. 2017; 24:11573-11581.
  22. Kawakami T., Hanao N., Nishiyama K. et al. Differential effects of cobalt and mercury on lipid metabolism in the white adipose tissue of high-fat diet-induced obesity mice. Toxicol. Appl. Pharm. 2012; 258:32-42.