Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Sep 21;14(9):1197.
doi: 10.3390/life14091197.

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

Affiliations

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

Christos Sotiropoulos et al. Life (Basel). .

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.

Keywords: BMI categorization; NHR Index; data visualization; insulin resistance; metabolic syndrome.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flowchart of study design.
Figure 2
Figure 2
Responses to the questionnaire about the visualization of the laboratory results. Question 1: Each test is accompanied by a short explanatory sentence regarding its definition and role. Do you think it helped you to understand better its definition and role? Question 2: Each test is presented as a bullet bar with three colors: green (normal)—orange (borderline)—red (abnormal). Do you think it helped you to understand better how normal or how abnormal your test value is? Question 3: This format includes a test which is called non-HDL cholesterol. How well did you know its meaning? Question 4: Do you think the explanation of non-HDL cholesterol helped you understand its role and value? Question 5: This format includes a section with the title “Metabolic Syndrome Assessment”. How well did you know its existence? Question 6: Do you think the explanation and visualization of “Metabolic Syndrome Assessment” help you understand its role and value? Question 7: This format includes a section with the title “Insulin Resistant Assessment”. How well did you know its existence? Question 8: Do you think the explanation and visualization of “Insulin Resistance Assessment” help you understand its role and value? Question 9: Overall, do you think that our initiative has helped you gain a more complete picture of the usefulness of your tests and your metabolic profile?

References

    1. Fitriyani N.L., Syafrudin M., Ulyah S.M., Alfian G., Qolbiyani S.L., Yang C.-K., Rhee J., Anshari M. Performance Analysis and Assessment of Type 2 Diabetes Screening Scores in Patients with Non-Alcoholic Fatty Liver Disease. Mathematics. 2023;11:2266. doi: 10.3390/math11102266. - DOI
    1. Friedrich N., Thuesen B., Jørgensen T., Juul A., Spielhagen C., Wallaschofksi H., Linneberg A. The Association Between IGF-I and Insulin Resistance. Diabetes Care. 2012;35:768–773. doi: 10.2337/dc11-1833. - DOI - PMC - PubMed
    1. Qu H.-Q., Li Q., Rentfro A.R., Fisher-Hoch S.P., McCormick J.B. The Definition of Insulin Resistance Using HOMA-IR for Americans of Mexican Descent Using Machine Learning. PLoS ONE. 2011;6:e21041. doi: 10.1371/journal.pone.0021041. - DOI - PMC - PubMed
    1. Freeman A.M., Acevedo L.A., Pennings N. StatPearls. StatPearls Publishing; Treasure Island, FL, USA: 2024. Insulin Resistance. - PubMed
    1. Hostalek U. Global Epidemiology of Prediabetes—Present and Future Perspectives. Clin. Diabetes Endocrinol. 2019;5:5. doi: 10.1186/s40842-019-0080-0. - DOI - PMC - PubMed

LinkOut - more resources