Novel inflammatory biomarkers may reflect subclinical inflammation in young healthy adults with obesity
Abstract
Introduction: Obesity is often accompanied by low-grade inflammation. In recent years a few blood-based inflammatory markers — neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), lymphocyteto-monocyte ratio (LMR), and monocyte-to-high-density lipoprotein ratio (MHR) — have been identified. They have been proven to correlate well with established inflammatory markers such as hsCRP and have a prognostic value among others in patients with coronary artery disease, heart failure, and malignancies. The aim of the study was to find markers associated with obesity in young heathy adults.
Material and methods: The study group included 321 young healthy adults aged 18-35 years (210 males and 111 females). Partial least squares regression analysis was used to find variables associated with body mass index (BMI). Analysed variables included complete blood count, lipid profile, sex hormone levels, acute-phase protein levels, and blood-based inflammatory markers.
Results: Variables with the strongest association with BMI in the group of men were HDL% and apolipoprotein B, and in the group of women, HDL, HDL%, triglycerides, and MHR. Novel inflammatory markers were not associated with BMI, except MHR. We found significant (p < 0.001) correlations between novel biomarkers (NLR, dNLR) and hsCRP and fibrinogen levels in the group of subjects with obesity.
Conclusions: Blood-based inflammatory markers significantly correlate with hsCRP and fibrinogen in young healthy adults with obesity, which may reflect the subclinical inflammation in this group of individuals.
Keywords: obesityinflammationcomplete blood count
References
- Rizzuto D, Fratiglioni L. Lifestyle factors related to mortality and survival: a mini-review. Gerontology. 2014; 60(4): 327–335.
- Arnlöv J, Sundström J, Ingelsson E. Impact of BMI and the Metabolic Syndrome on the Risk of Diabetes in Middle-Aged Men. Diabetes Care. 2011; 34(1): 61–65.
- Clarke PJ, O'Malley PM, Schulenberg JE, et al. Midlife health and socioeconomic consequences of persistent overweight across early adulthood: findings from a national survey of American adults (1986–2008). Am J Epidemiol. 2010; 172(5): 540–548.
- Gokulakrishnan K, Ranjani H, Weber MB, et al. Effect of lifestyle improvement program on the biomarkers of adiposity, inflammation and gut hormones in overweight/obese Asian Indians with prediabetes. Acta Diabetol. 2017; 54(9): 843–852.
- Klop B, Elte JW, Cabezas MC. Dyslipidemia in obesity: mechanisms and potential targets. Nutrients. 2013; 5(4): 1218–1240.
- Poddar M, Chetty Y, Chetty VT. How does obesity affect the endocrine system? A narrative review. Clin Obes. 2017; 7(3): 136–144.
- Karelis AD. Metabolically healthy but obese individuals. Lancet. 2008; 372(9646): 1281–1283.
- Dyrbuś K, Osadnik T, Desperak P, et al. Evaluation of dyslipidaemia and the impact of hypolipidemic therapy on prognosis in high and very high risk patients through the Hyperlipidaemia Therapy in tERtiary Cardiological cEnTer (TERCET) Registry. Pharmacol Res. 2018; 132: 204–210.
- Phillips CM, Perry IJ. Does inflammation determine metabolic health status in obese and nonobese adults? J Clin Endocrinol Metab. 2013; 98(10): E1610–E1619.
- Song S, Li C, Li S, et al. Derived neutrophil to lymphocyte ratio and monocyte to lymphocyte ratio may be better biomarkers for predicting overall survival of patients with advanced gastric cancer. Onco Targets Ther. 2017; 10: 3145–3154.
- Wasilewski J, Desperak P, Hawranek M, et al. Prognostic implications of mean platelet volume on short- and long-term outcomes among patients with non-ST-segment elevation myocardial infarction treated with percutaneous coronary intervention: A single-center large observational study. Platelets. 2016; 27(5): 452–458.
- Takeuchi H, Kawanaka H, Fukuyama S, et al. Comparison of the prognostic values of preoperative inflammation-based parameters in patients with breast cancer. PLoS One. 2017; 12(5): e0177137.
- Nakamura T, Matsumine A, Matsubara T, et al. Infiltrative tumor growth patterns on magnetic resonance imaging associated with systemic inflammation and oncological outcome in patients with high-grade soft-tissue sarcoma. PLoS One. 2017; 12(7): e0181787.
- Rajwa P, Życzkowski M, Paradysz A, et al. Novel hematological biomarkers predict survival in renal cell carcinoma patients treated with nephrectomy. Arch Med Sci. 2017: 1–10.
- Wasilewski J, Pyka Ł, Hawranek M, et al. Prognostic value of neutrophil‑to‑lymphocyte ratio in predicting long-term mortality in patients with ischemic and nonischemic heart failure. Pol Arch Med Wewn. 2016; 126(3): 166–173.
- Osadnik T, Wasilewski J, Lekston A, et al. The platelet-to-lymphocyte ratio as a predictor of all-cause mortality in patients with coronary artery disease undergoing elective percutaneous coronary intervention and stent implantation. J Saudi Heart Assoc. 2015; 27(3): 144–151.
- Rajwa P, Życzkowski M, Paradysz A, et al. Evaluation of the prognostic value of LMR, PLR, NLR, and dNLR in urothelial bladder cancer patients treated with radical cystectomy. Eur Rev Med Pharmacol Sci. 2018; 22(10): 3027–3037.
- Karataş MB, Çanga Y, Özcan KS, et al. Monocyte to high-density lipoprotein ratio as a new prognostic marker in patients with STEMI undergoing primary percutaneous coronary intervention. Am J Emerg Med. 2016; 34(2): 240–244.
- Akboga MK, Balci KG, Maden O, et al. Usefulness of monocyte to HDL-cholesterol ratio to predict high SYNTAX score in patients with stable coronary artery disease. Biomark Med. 2016; 10(4): 375–383.
- Osadnik T, Osadnik K, Pawlas N, et al. Metabolic and genetic profiling of young adults with and without family history of premature coronary heart disease (MAGNETIC). Study design and methodology. Arch Med Sc. 2018; [Epub ahead of print].
- Osadnik T, Pawlas N, Lonnie M, et al. Family history of premature coronary artery disease (P-CAD) — non-modifiable risk factor? Dietary patterns of young healthy offspring of P-CAD patients: a case-control study (MAGNETIC Project). Nutrients. 2018; 10(10): E1488.
- Gromski PS, Muhamadali H, Ellis DI, et al. A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. Anal Chim Acta. 2015; 879: 10–23.
- Dahlén EM, Tengblad A, Länne T, et al. Abdominal obesity and low-grade systemic inflammation as markers of subclinical organ damage in type 2 diabetes. Diabetes Metab. 2014; 40(1): 76–81.
- Schmidt M, Saad M, Duncan B. Subclinical inflammation and obesity, diabetes and related disorders. Drug Discov Today: Dis Mech. 2005; 2(3): 307–312.
- Duncan BB, Schmidt MI. The epidemiology of low-grade chronic systemic inflammation and type 2 diabetes. Diabetes Technol Ther. 2006; 8(1): 7–17.
- de Heredia FP, Gómez-Martínez S, Marcos A. Obesity, inflammation and the immune system. Proc Nutr Soc. 2012; 71(2): 332–338.
- Gregor MF, Hotamisligil GS. Inflammatory mechanisms in obesity. Annu Rev Immunol. 2011; 29: 415–445.
- Rodríguez-Hernández H, Simental-Mendía LE, Rodríguez-Ramírez G, et al. Obesity and inflammation: epidemiology, risk factors, and markers of inflammation. Int J Endocrinol. 2013; 2013: 678159.
- Ellulu MS, Patimah I, Khaza'ai H, et al. Obesity and inflammation: the linking mechanism and the complications. Arch Med Sci. 2017; 13(4): 851–863.
- Cinkajzlová A, Mráz M, Haluzík M. Lymphocytes and macrophages in adipose tissue in obesity: markers or makers of subclinical inflammation? Protoplasma. 2017; 254(3): 1219–1232.
- Ghigliotti G, Barisione C, Garibaldi S, et al. Adipose tissue immune response: novel triggers and consequences for chronic inflammatory conditions. Inflammation. 2014; 37(4): 1337–1353.
- Vieira-Potter VJ. Inflammation and macrophage modulation in adipose tissues. Cell Microbiol. 2014; 16(10): 1484–1492.