Biomarkers and Data Visualization of Insulin Resistance and Metabolic Syndrome: An Applicable Approach

Overview

Journal Life (Basel)

Specialty Biology

Date 2024 Sep 28

PMID 39337979

Authors

Christos Sotiropoulos

Nikolaos Giormezis

Vayianos Pertsas

Theodoros Tsirkas

Affiliations

Soon will be listed here.

Abstract

Type 2 diabetes, prediabetes, and insulin resistance (IR) are widespread yet often undetected in their early stages, contributing to a silent epidemic. Metabolic Syndrome (MetS) is also highly prevalent, increasing the chronic disease burden. Annual check-ups are inadequate for early detection due to conventional result formats that lack specific markers and comprehensive visualization. The aim of this study was to evaluate low-budget biochemical and hematological parameters, with data visualization, for identifying IR and MetS in a community-based laboratory. In a cross-sectional study with 1870 participants in Patras, Greece, blood samples were analyzed for key cardiovascular and inflammatory markers. IR diagnostic markers (TyG-Index, TyG-BMI, Triglycerides/HDL ratio, NLR) were compared with HOMA-IR. Innovative data visualization techniques were used to present metabolic profiles. Notable differences in parameters of cardiovascular risk and inflammation were observed between normal-weight and obese people, highlighting BMI as a significant risk factor. Also, the inflammation marker NHR (Neutrophils to HDL-Cholesterol Ratio) Index was successful at distinguishing the obese individuals and those with MetS from normal individuals. Additionally, a new diagnostic index of IR, combining BMI (Body Mass Index) and NHR Index, demonstrated better performance than other well-known indices. Lastly, data visualization significantly helped individuals understand their metabolic health patterns more clearly. BMI and NHR Index could play an essential role in assessing metabolic health patterns. Integrating specific markers and data visualization in routine check-ups enhances the early detection of IR and MetS, aiding in better patient awareness and adherence.

Citing Articles

Kidney Damage in Pediatric Obesity: Insights from an Emerging Perspective.

Forcina G, Luciano M, Frattolillo V, Mori S, Monaco N, Guarino S J Clin Med. 2024; 13(23).

PMID: 39685484 PMC: 11642363. DOI: 10.3390/jcm13237025.

Chronically Increased Levels of Circulating Insulin Secondary to Insulin Resistance: A Silent Killer.

Fazio S, Bellavite P, Affuso F Biomedicines. 2024; 12(10).

PMID: 39457728 PMC: 11505545. DOI: 10.3390/biomedicines12102416.

References

Boyko E . Ruling out or ruling in disease with the most sensitive or specific diagnostic test: short cut or wrong turn?. Med Decis Making. 1994; 14(2):175-9. DOI: 10.1177/0272989X9401400210. View

Rus M, Crisan S, Andronie-Cioara F, Indries M, Marian P, Pobirci O . Prevalence and Risk Factors of Metabolic Syndrome: A Prospective Study on Cardiovascular Health. Medicina (Kaunas). 2023; 59(10). PMC: 10608643. DOI: 10.3390/medicina59101711. View

Zhang Z, Citardi D, Xing A, Luo X, Lu Y, He Z . Patient Challenges and Needs in Comprehending Laboratory Test Results: Mixed Methods Study. J Med Internet Res. 2020; 22(12):e18725. PMC: 7752528. DOI: 10.2196/18725. View

Song B, Zhao X, Yao T, Lu W, Zhang H, Liu T . Triglyceride Glucose-Body Mass Index and Risk of Incident Type 2 Diabetes Mellitus in Japanese People With Normal Glycemic Level: A Population-Based Longitudinal Cohort Study. Front Endocrinol (Lausanne). 2022; 13:907973. PMC: 9336540. DOI: 10.3389/fendo.2022.907973. View

Bennet L, Stenkula K, Cushman S, Brismar K . BMI and waist circumference cut-offs for corresponding levels of insulin sensitivity in a Middle Eastern immigrant versus a native Swedish population - the MEDIM population based study. BMC Public Health. 2016; 16(1):1242. PMC: 5148840. DOI: 10.1186/s12889-016-3892-1. View

Pi-Sunyer X . The medical risks of obesity. Postgrad Med. 2009; 121(6):21-33. PMC: 2879283. DOI: 10.3810/pgm.2009.11.2074. View

Bermudez V, Salazar J, Martinez M, Chavez-Castillo M, Olivar L, Calvo M . Prevalence and Associated Factors of Insulin Resistance in Adults from Maracaibo City, Venezuela. Adv Prev Med. 2016; 2016:9405105. PMC: 4989131. DOI: 10.1155/2016/9405105. View

Marzban S, Najafi M, Agolli A, Ashrafi E . Impact of Patient Engagement on Healthcare Quality: A Scoping Review. J Patient Exp. 2022; 9:23743735221125439. PMC: 9483965. DOI: 10.1177/23743735221125439. View

Sinaiko A, Steinberger J, Moran A, Prineas R, Vessby B, Basu S . Relation of body mass index and insulin resistance to cardiovascular risk factors, inflammatory factors, and oxidative stress during adolescence. Circulation. 2005; 111(15):1985-91. DOI: 10.1161/01.CIR.0000161837.23846.57. View

10.

Sumner A, Cowie C . Ethnic differences in the ability of triglyceride levels to identify insulin resistance. Atherosclerosis. 2007; 196(2):696-703. DOI: 10.1016/j.atherosclerosis.2006.12.018. View

11.

Selvi N, Nandhini S, Sakthivadivel V, Lokesh S, Srinivasan A, Sumathi S . Association of Triglyceride-Glucose Index (TyG index) with HbA1c and Insulin Resistance in Type 2 Diabetes Mellitus. Maedica (Bucur). 2021; 16(3):375-381. PMC: 8643546. DOI: 10.26574/maedica.2021.16.3.375. View

12.

Jamar G, de Almeida F, Gagliardi A, Sobral M, Ping C, Sperandio E . Evaluation of waist-to-height ratio as a predictor of insulin resistance in non-diabetic obese individuals. A cross-sectional study. Sao Paulo Med J. 2017; 135(5):462-468. PMC: 10027251. DOI: 10.1590/1516-3180.2016.0358280417. View

13.

Feng Y, Zhao D, Zhang N, Yu C, Zhang Q, Thijs L . Insulin Resistance in Relation to Lipids and Inflammation in Type-2 Diabetic Patients and Non-Diabetic People. PLoS One. 2016; 11(4):e0153171. PMC: 4830613. DOI: 10.1371/journal.pone.0153171. View

14.

Unger G, Benozzi S, Perruzza F, Pennacchiotti G . Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinol Nutr. 2014; 61(10):533-40. DOI: 10.1016/j.endonu.2014.06.009. View

15.

Kurniawan L . Triglyceride-Glucose Index As A Biomarker Of Insulin Resistance, Diabetes Mellitus, Metabolic Syndrome, And Cardiovascular Disease: A Review. EJIFCC. 2024; 35(1):44-51. PMC: 11063788. View

16.

Panagiotakos D, Chrysohoou C, Pitsavos C, Tsioufis K . Prediction of 10-year cardiovascular disease risk by diabetes status and lipoprotein-a levels; the HellenicSCORE II. Hellenic J Cardiol. 2023; 79:3-14. DOI: 10.1016/j.hjc.2023.10.001. View

17.

Sharma P . Inflammation and the metabolic syndrome. Indian J Clin Biochem. 2012; 26(4):317-8. PMC: 3210244. DOI: 10.1007/s12291-011-0175-6. View

18.

McLaughlin T, Allison G, Abbasi F, Lamendola C, Reaven G . Prevalence of insulin resistance and associated cardiovascular disease risk factors among normal weight, overweight, and obese individuals. Metabolism. 2004; 53(4):495-9. DOI: 10.1016/j.metabol.2003.10.032. View

19.

Wilson P, Kannel W, Silbershatz H, DAgostino R . Clustering of metabolic factors and coronary heart disease. Arch Intern Med. 1999; 159(10):1104-9. DOI: 10.1001/archinte.159.10.1104. View

20.

Pereira M, Jacobs Jr D, Van Horn L, Slattery M, Kartashov A, Ludwig D . Dairy consumption, obesity, and the insulin resistance syndrome in young adults: the CARDIA Study. JAMA. 2002; 287(16):2081-9. DOI: 10.1001/jama.287.16.2081. View