CUT-OFF VALUES OF BODY FAT PERCENTAGE AND INSULIN FOR PREDICTING LOW ADIPONECTIN / LEPTIN RATIO

Authors

  • Ivona MITU ‟Grigore Popa” University of Medicine and Pharmacy Iași
  • Corina Paraschiva CIOBANU ‟Grigore Popa” University of Medicine and Pharmacy Iași
  • A.D. COSTACHE ‟Grigore Popa” University of Medicine and Pharmacy Iași
  • Minerva Codruta BADESCU ‟Grigore Popa” University of Medicine and Pharmacy Iași
  • Cristina Daniela DIMITRIU ‟Grigore Popa” University of Medicine and Pharmacy Iași

DOI:

https://doi.org/10.22551/xwj10j39

Abstract

This study aims to establish a reliable parameter and its optimal cut-off point for accurately estimating adipose tissue dysfunctionality in apparently healthy, but abdominally obese subjects. Materials and methods: The 104 subjects included in the study, all with no prior chronic diseases, underwent biochemical (TG, HDL-c, glucose, insulin, adiponectin, leptin) and anthropometric measurements (waist circumference, height, adipose tissue percentage). A ROC curve was performed to assess the overall performance of these parameters to determine the dysfunctionality of the adipocytes, as assessed by adiponectin: leptin ratio (ALR) lower than 0.5. Logistic regression identified models that included 2 or 3 parameters to further increase the accuracy. The software Excel and SPSS were used for the statistical analysis. Results: From all individual parameters, trunk adipose tissue percentage had an AUC value of 0.812, suggesting a high accuracy for predicting a value of ALR<0.5. The optimal cut-off point for trunk adipose tissue percentage was established at 42.45% with a sensitivity of 0.725 and a specificity of 0.766. A higher AUC (0.857) was observed for the model that included whole-body adipose tissue (cut-off 41.7%) and insulin (cut-off 14.6 μU/mL), while the highest accuracy (AUC=0.889) was reported in the model for metabolically unhealthy non-obese subjects. Conclusions: Apparently healthy individuals, but abdominally obese, report adipocyte dysfunctionality together with high cardiometabolic risk at values for trunk adipose tissue percentage surpassing 42.45%, or combined values of 41.7% for whole-body adipose tissue and 14.6 μU/mL for insulin

Author Biographies

  • Ivona MITU, ‟Grigore Popa” University of Medicine and Pharmacy Iași

    Faculty of Medicine
    Department of Morpho-Functional Sciences (II)

  • Corina Paraschiva CIOBANU, ‟Grigore Popa” University of Medicine and Pharmacy Iași

    Faculty of Medicine
    Department of Morpho-Functional Sciences (II)

  • A.D. COSTACHE, ‟Grigore Popa” University of Medicine and Pharmacy Iași

    Faculty of Medicine
    Department of Medical Specialties (I) 

  • Minerva Codruta BADESCU, ‟Grigore Popa” University of Medicine and Pharmacy Iași

    Faculty of Medicine
    Department of Medical Specialties (I)

  • Cristina Daniela DIMITRIU, ‟Grigore Popa” University of Medicine and Pharmacy Iași

    Faculty of Medicine
    Department of Morpho-Functional Sciences (II)

References

1. Kopelman PG. Obesity as a medical problem. Nature 2000; 404: 635-643 / doi: 10.1038 /35007508.

2. Seip M, Trygstad O. Generalized lipodystrophy, congenital and acquired (lipoatrophy). Acta Paediatr Suppl 1996; 413: 2-28 / doi: 10.1111/j.1651-2227.1996.tb 14262.x

3. Funcke JB, Scherer PE. Beyond adiponectin and leptin: adipose tissue-derived mediators of inter-organ communication. J Lipid Res 2019; 60: 1648-1684 / doi: 10.1194/jlr.R 09 4060.

4. Frühbeck G, Catalán V, Rodríguez A, et al. Adiponectin-leptin Ratio is a Functional Biomarker of Adipose Tissue Inflammation. Nutrients 2019; 11(2): 454 / doi: 10.3390/ nu11020454.

5. Tremblay EJ, Tchernof A, Pelletier M, Joanisse DR, Mauriège P. Plasma adiponectin/leptin ratio associates with subcutaneous abdominal and omental adipose tissue characteristics in women. BMC Endocr Disord 2024; 24(1): 39 / doi: 10.1186/s-024-01567-8.

6. Amato MC, Giordano C, Galia M, et al. Visceral Adiposity Index. Diabetes Care 2010; 33(4): 920-922.

7. Ghesmaty Sangachin M, Cavuoto LA, Wang Y. Use of various obesity measurement and classification methods in occupational safety and health research: a systematic review of the literature. BMC Obes 2018; 5(1): 28.

8. Bawadi H, Hassan S, Shanbeh Zadeh A, et al. Age and gender specific cut-off points for body fat parameters among adults in Qatar. Nutr J 2020; 19(1): 75 / doi: 10.1186/s 12937-020-00569-1.

9. Macek P, Biskup M, Terek-Derszniak M, et al. Optimal cut-off values for anthropometric measures of obesity in screening for cardiometabolic disorders in adults. Sci Rep 2020; 10(1): 11253 / doi: 10.1038/ s41598-020-68265-y.

10. Sweatt K, Garvey WT, Martins C. Strengths and Limitations of BMI in the Diagnosis of Obesity: What is the Path Forward? Curr Obes Rep 2024; 13: 584-595 / doi: 10.1007/s 13679-024-00580-1

11. Neeland IJ, Ross R, Després JP, et al, International Atherosclerosis Society; International Chair on Cardiometabolic Risk Working Group on Visceral Obesity. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol 2019; 7(9): 715-725 / doi: 10.1016/ S2213-8587(19)30084-1.

12. Fox CS, Massaro JM, Hoffmann U, et al. Abdominal visceral and subcutaneous adipose tissue com-partments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007; 116(1): 39-48 / doi: 10.1161/CIRCULATIONAHA.106. 675355.

13. Zhao S, Kusminski CM, Scherer PE. Adiponectin, Leptin and Cardiovascular Disorders. Circ Res 2021; 128(1): 136-149 / doi: 10.1161/CIRCRESAHA.120.314458.

14. Assunção M, Guimarães JT, Faria M, Monteiro R. Adipokines as emerging biomarkers for adipose tissue dysfunction. In: Understanding Obesity: from Its Causes to Impact on Life, edited by Monteiro R, Martins MJ. Singapore: Bentham Science Publishers, 2020, 81-99.

15. Burhans MS, Hagman DK, Kuzma JN, Schmidt KA, Kratz M. Contribution of Adipose Tissue In-flammation to the Development of Type 2 Diabetes Mellitus. Compr Physiol 2018; 9(1): 1-58 / doi: 10.1002/cphy.c170040.

16. Ouchi N, Parker JL, Lugus JJ, Walsh K. Adipokines in inflammation and metabolic disease. Nat Rev Immunol 2011; 11(2): 85-97 / doi: 10.1038/nri2921.

17. Antuna-Puente B, Feve B, Fellahi S, Bastard JP. Adipokines: the missing link between insulin re-sistance and obesity. Diabetes Metab 2008; 34(1): 2-11 / doi: 10.1016/j.diabet.2007.09.004.

18. Hassanzad, M, Hajian-Tilaki, K. Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: an update review. BMC Med Res Methodol 2024; 24: 84 / doi: 10.1186/s12874-024-02198-2.

19. Mitu I, Dimitriu C-D, Mitu O, Preda C, Mitu F, Ciocoiu M. Artificial Neural Network Models for Accurate Predictions of Fat-Free and Fat Masses, Using Easy-to-Measure Anthropometric Parame-ters. Biomedicines 2023; 11(2): 489 / doi: 10.3390/biomedicines11020489.

20. Cichosz, S.L, Rasmussen, N.H, Vestergaard, P, Hejlesen, O. Precise Prediction of Total Body Lean and Fat Mass from Anthropometric and Demographic Data: Development and Validation of Neural Network Models. J Diabetes Sci Technol 2021; 15: 1337-1343.

21. Cichosz SL, Rasmussen NH, Vestergaard P, Hejlesen O. Is predicted body-composition and relative fat mass an alternative to body-mass index and waist circumference for disease risk estimation? Diabetes Metab. Syndr 2022; 16: 102590.

22. Agrawal S, Klarqvist MDR, Diamant N, et al. Association of machine learning-derived measures of body fat distribution with cardiometabolic diseases in > 40,000 individuals. medRxiv 2021.

Additional Files

Published

2025-04-07

Issue

Section

INTERNAL MEDICINE - PEDIATRICS