Age-dependent aspects of informativeness of surrogate indices of insulin resistance in the formation of menopausal metabolic syndrome

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Acesso é pago ou somente para assinantes

Resumo

Background. The need to assess insulin resistance (IR), which pathogenetically combines the components of the metabolic syndrome (MS), is of particular importance during its formation during the menopausal transition due to changes in the functional state of the pituitary–ovarian axis. In addition to the traditional indices of the HOMA family, the TyG index has attracted attention in recent years as surrogate indicator, showing close agreement with the clamp test, the gold standard for assessing the severity of IR. However, comparative studies of the informativity of surrogate insulin and non-insulin indices in the perimenopausal period are practically absent.

Objective. Evaluation of the effect of age on the relationship of surrogate indices of IR, TyG, and the HOMA2 family with the parameters of menopausal MS during its formation in a cohort of women aged 35–59 years without dysglycemia, depending on the presence of arterial hypertension (AH).

Methods. Body mass index (BMI), waist circumference (WC), blood pressure, triglycerides, HDL-C, IRI, FSH and estradiol levels, fasting glycemia, TyG and HOMA2 family indices were determined in 88 normoglycemic women aged 35–59 years with different functional state of the pituitary-ovarian axis and divided into 2 groups depending on the presence of arterial hypertension. Using SPSS (version 17), ME (25–75%); intergroup differences according to the Mann-Whitney test assessed; Spearman rank correlation analysis and partial correlation analysis to level the influence of age were carried out.

Results. The largest range of significant associations, independent of age and in tandem with it, in contrast to the HOMA2 family indices, was found in TyG in the group of patients with arterial hypertension: with WC and BMI, IRI and HDL-C, FSH and E2. The TyG index is also influenced by age-associated factors: the duration of arterial hypertension and postmenopause. In the general cohort of women (n=88), most of these correlations persist and increase, but associations with FSH and E2 persist only within the Spearman analysis. Stable associations of TyG with IRI in the absence of those with HOMA2-B in patients with hypertension without dysglycemia attract attention.

Conclusion. In the process of the formation of menopausal MS in patients with AH, the TyG index, in contrast to HOMA2-IR and HOMA2-S, forms a greater number of stable age-associated an age-independent correlations with MS markers and indicators of the functional activity of the pituitary-ovarian axis during the period of menopausal transition. Correlations of TyG with RI levels, which are stable when the effect of age is leveled in patients with AH without dysglycemia, indicate the threat of carbohydrate metabolism disorders through the phenomenon of lipoglucotoxicity with a further increase in RI against the background of estrogen deficiency.

Texto integral

Acesso é fechado

Sobre autores

Lyudmila Ruyatkina

Novosibirsk State Medical University

Autor responsável pela correspondência
Email: larut@list.ru
ORCID ID: 0000-0002-6762-5238

Dr. Sci. (Med.), Professor, Novosibirsk State Medical University, Novosibirsk, Russia

Rússia, Novosibirsk

D. Ruyatkin

Novosibirsk State Medical University

Email: larut@list.ru
ORCID ID: 0000-0003-3431-5943
Rússia, Novosibirsk

L. Shcherbakova

Research Institute of Therapy and Preventive Medicine - Branch Campus of the Federal Research Center, Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences

Email: larut@list.ru
ORCID ID: 0000-0001-9270-9188
Rússia, Novosibirsk

Bibliografia

  1. Tune J.D., Goodwill A.G., Sassoon D.J., Mather K.J. Cardiovascular consequences of metabolic syndrome. Transl Res. 2017;183:57–70. doi: 10.1016/j.trsl.2017.01.001.
  2. Lindberg S., Jensen J.S., Bjerre M., et al. Low adiponectin levels at baseline and decreasing adiponectin levels over 10 years of follow-up predict risk of the metabolic syndrome. Diabetes Metab. 2017;43(2):134–39. doi: 10.1016/j.diabet.2016.07.027.
  3. Lopez-Jaramillo P., Gomez-Arbelaez D., Martinez-Bello D., et al. Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. Lancet Healthy Longev. 2023;4(1):e23–e33. doi: 10.1016/S2666-7568(22)00247-1.
  4. Reaven G.M. The metabolic syndrome or the insulin resistance syndrome? Different names, different concepts, and different goals. Endocrinol Metab Clin N Am. 2004;33:283–303. doi: 10.1016/j.ecl.2004.03.002.
  5. Alberti K.G., Eckel R.H., Grundy S.M., et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–45. doi: 10.1161/CIRCULATIONAHA.109.192644.
  6. Jeong H.G., Park H. Metabolic Disorders in Menopause. Metabolites. 2022;12(10):954. Doi: org/10.3390/metabo12100954.
  7. Fisher G., Tay J., Warren J.L., et al. Sex and race contribute to variation in mitochondrial function and insulin sensitivity. Physiol Rep. 2021;9(19):e15049. doi: 10.14814/phy2.15049.
  8. Oya J., Nakagami T., Yamamoto Y., Fukushima S. et al. Effects of age on insulin resistance and secretion in subjects without diabetes. Intern Med. 2014;53(9):941–47. doi: 10.2169/internalmedicine.53.1580.
  9. Ahmed F., Kamble P.G., Hetty S., et al. Role of Estrogen and Its Receptors in Adipose Tissue Glucose Metabolism in Pre- and Postmenopausal Women. J Clin Endocrinol Metab. 2022;107(5):e1879-e1889. doi: 10.1210/clinem/dgac042.
  10. Stevenson J.C., Tsiligiannis S., Panay N. Cardiovascular Risk in Perimenopausal Women. Curr Vasc Pharmacol. 2019;17(6):591–594. doi: 10.2174/1570161116666181002145340.
  11. Kodoth V, Scaccia S, Aggarwal B. Adverse Changes in Body Composition During the Menopausal Transition and Relation to Cardiovascular Risk: A Contemporary Review. Womens Health Rep (New Rochelle). 2022;3(1):573–81. doi: 10.1089/whr.2021.0119.
  12. Park S.K., Harlow S.D., Zheng H., et al. Association between changes in oestradiol and follicle-stimulating hormone levels during the menopausal transition and risk of diabetes. Diabet Med. 2017;34(4):531–38. Doi: org/10.1111/dme.13301.
  13. Son M.K., Lim N.K., Lim J.Y., et al. Difference in blood pressure between early and late menopausal transition was significant in healthy Korean women. BMC Womens Health. 2015;15:64. doi: 10.1186/s12905-015-0219-9.
  14. Malmstrom H., Walldius G., Carlsson S., et al. Elevations of metabolic risk factors 20 years or more before diagnosis of type 2 diabetes: Experience from the AMORIS study. Diabetes Obes Metab. 2018;20(6):1419–26. doi: 10.1111/dom.13241.
  15. Garvey W.T., Garber A.J., Mechanick J.I., et al. The Aace Obesity Scientific Committee. American association of clinical endocrinologists and american college of endocrinology position statement on the 2014 advanced framework for a new diagnosis of obesity as a chronic disease. Endocr Pract. 2014;20(9):977-89. doi: 10.4158/EP14280.PS.
  16. Slopien R., Wender-Ozegowska E., Rogowicz-Frontczak A., et al. Menopause and diabetes: EMAS clinical guide. Maturitas. 2018;117:6–10. doi: 10.1016/j.maturitas.2018.08.009.
  17. Руяткина Л.А., Руяткин Д.С., Исхакова И.С. Возможности и варианты суррогатной оценки инсулинорезистентности. Ожирение и метаболизм. 2019;16(1):27–33. [Ruyatkina L.A., Ruyatkin D.S., Iskhakova I.S. Opportunities and options for surrogate assessment of insulin resistance. Ozhirenie i metabolizm=Obesity and metabolism. 2019;16(1):27–33. (In Russ.)]. doi: 10.14341/omet10082.
  18. Guerrero-Romero F., Simental-Mendia L.E., Gonzalez-Ortiz M., et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(7):3347–51. doi: 10.1210/jc.2010-0288.
  19. Massimino M., Monea G., Marinaro G., et al. The Triglycerides and Glucose (TyG) Index Is Associated with 1-Hour Glucose Levels during an OGTT. Int J Environ Res Public Health. 2022;20(1):787. doi: 10.3390/ijerph20010787.
  20. Клинические рекомендации. Менопауза и климактерическое состояние у женщин. 2021. [Clinical guidelines. Menopause and menopause in women. 2021. (In Russ.)]. URL: https://minzdrav.orb.ru/documents/active/33083/.
  21. Wallace T.M., Levy J.C., Matthews D.R. Use and Abuse of HOMA Modeling. Diabetes Care. 2004;27(6):1487–95. doi: 10.2337/diacare.27.6.1487.
  22. Karvonen-Gutierrez C., Kim C. Association of Mid-Life Changes in Body Size, Body Composition and Obesity Status with the Menopausal Transition. Healthcare (Basel). 2016;4(3):42. doi: 10.3390/healthcare4030042.
  23. Randolph J.F. Jr., Zheng H., Sowers M.R., et al. Change in Follicle-Stimulating Hormone and Estradiol Across the Menopausal Transition: Effect of Age at the Final Menstrual Period. J Clin Endocrin & Metab. 2011;96(3):746–74. doi: 10.1210/jc.2010-1746.
  24. Yu Z., Yang J., Huang, W.J., et al. Follicle stimulating hormone promotes production of renin through its receptor in juxtaglomerular cells of kidney. Diabetol Metab Syndr. 2022;14(1):65. doi: 10.1186/s13098-022-00816-x.
  25. Barton M., Meyer M.R. Postmenopausal Hypertension: Mechanisms and Therapy. Hypertension. 2009;54(1):11–8. doi: 10.1161/HYPERTENSIONAHA.108.120022.
  26. Руяткина Л.А., Руяткин Д.С. Ожирение: «перекрестки» мнений, знаний и возможностей. Медицинский Совет. 2020;(7):108-120. [Ruyatkina L.A., Ruyatkin D.S. Obesity: The Crossroads of Opinion, Knowledge, and Opportunity. Meditsinskiy sovet=Medical Council. 2020;(7):108–20. (In Russ.)]. doi: 10.21518/2079-701X-2020-7-108-120.
  27. Lopez-Lopez J.P., Cohen D.D., Ney-Salazar D., et al. The prediction of Metabolic Syndrome alterations is improved by combining waist circumference and handgrip strength measurements compared to either alone. Cardiovasc Diabetol. 2021;20(1):68. doi: 10.1186/s12933-021-01256-z.
  28. Karakelides H., Irving B.A., Short K.R., O’Brien P., Nair KS. Age, obesity, and sex effects on insulin sensitivity and skeletal muscle mitochondrial function. Diabetes. 2010;59(1):89–97. doi: 10.2337/db09-0591.
  29. Exebio J.C., Ajabshir S., Zarini G.G., et al. Use of Homeostatic Model Assessment Indexes for the Identification of Metabolic Syndrome and Insulin Resistance among Cuban-Americans: A Cross Sectional Study. Br J Med Med Res. 2014;4(29):4824–33. doi: 10.9734/BJMMR/2014/8988.
  30. Wu Z., Cui H., Li W., et al. Comparison of three non-insulin-based insulin resistance indexes in predicting the presence and severity of coronary artery disease. Front Cardiovasc Med. 2022;29(9):918359. doi: 10.3389/fcvm.2022.918359.
  31. Cerf M.E. Developmental Programming and Glucolipotoxicity: Insights on Beta Cell Inflammation and Diabetes. Metabolites. 2020;10(11):444. doi: 10.3390/metabo10110444.
  32. Lee Y., Hirose H., Ohneda M., Johnson J.H. et al. Beta-cell lipotoxicity in the pathogenesis of non-insulin-dependent diabetes mellitus of obese rats: impairment in adipocyte-beta-cell relationships. Proc Natl Acad Sci U S A. 1994;91(23):10878–82. doi: 10.1073/pnas.91.23.10878.
  33. Somesh B.P., Verma M.K., Sadasivuni M.K., et al. Chronic Glucolipotoxic Conditions in Pancreatic Islets Impair Insulin Secretion Due to Dysregulated Calcium Dynamics, Glucose Responsiveness and Mitochondrial Activity. BMC Cell Biol. 2013;14:31. Doi: 10.1186/ 1471-2121-14-31.
  34. Руяткина Л.А., Руяткин Д.С., Исхакова И.С. Анализ формирования дисгликемии в обосновании ранней патогенетической терапии сахарного диабета. Медицинский совет. 2021;(7):33–44. [Ruyatkina L.A., Ruyatkin D.S., Iskhakova I.S. Analysis of the formation of dysglycemia in the substantiation of early pathogenetic therapy of diabetes mellitus. Meditsinskiy sovet=Medical Council. 2021;(7):33–44. (In Russ.)]. doi: 10.21518/2079-701X-2021-7-33-44.
  35. Li X., Li G., Cheng T., et al. Association between triglyceride-glucose index and risk of incident diabetes: a secondary analysis based on a Chinese cohort study: TyG index and incident diabetes. Lipids Health Dis. 2020;19(1):236. doi: 10.1186/s12944-020-01403-7.
  36. Lee D.Y., Lee E.S., Kim J.H., Park S.E. et al. Predictive Value of Triglyceride Glucose Index for the Risk of Incident Diabetes: A 4-Year Retrospective Longitudinal Study. PLoS One. 2016;11(9):e0163465. doi: 10.1371/journal.pone.0163465.
  37. Wang Q., Jokelainen J., Auvinen J., et al. Insulin resistance and systemic metabolic changes in oral glucose tolerance test in 5340 individuals: an interventional study. BMC Med. 2019;17(1):217. doi: 10.1186/s12916-019-1440-4.
  38. Abdul-Ghani M.A., Abdul-Ghani T., Ali N., Defronzo R.A. One-hour plasma glucose concentration and the metabolic syndrome identify subjects at high risk for future type 2 diabetes. Diabetes Care. 2008;31(8):1650–55. doi: 10.2337/dc08-0225.
  39. Correia E.S., Godinho-Mota J.C.M., Schincaglia R.M., et al. Metabolic Syndrome in postmenopausal women: prevalence, sensibility, and specificity of adiposity indices. Clinical Nutrition Open Science. 2022;41:106–14. Doi: org/10.1016/j.nutos.2022.01.001
  40. Seghieri M., Trico D., Natali A. The impact of triglycerides on glucose tolerance: Lipotoxicity revisited. Diabetes Metab. 2017;43(4):314–22. Doi: org/10.1016/j.diabet.2017.04.010.
  41. Agarwal T., Lyngdoh T., Dudbridge F., et al. Causal relationships between lipid and glycemic levels in an Indian population: A bidirectional Mendelian randomization approach. PLoS One. 2020;15(1):e0228269. doi: 10.1371/journal.pone.0228269.
  42. Yu W., Zhou G., Fan B., et al. Temporal sequence of blood lipids and insulin resistance in perimenopausal women: the study of women’s health across the nation. BMJ Open Diab Res Care. 2022;10(2):e002653. doi: 10.1136/bmjdrc-2021-002653.
  43. Mahdavi-Roshan M., Shoaibinobarian N.., Noormohammadi M, et al. Inflammatory Markers and Atherogenic Coefficient: Early Markers of Metabolic Syndrome. Int J Endocrinol Metab. 2022;20(4):e127445. doi: 10.5812/ijem-127445.
  44. Pang S., Miao G., Zhou Y., et al. Addition of TyG index to the GRACE score improves prediction of adverse cardiovascular outcomes in patients with non-ST-segment elevation acute coronary syndrome undergoing percutaneous coronary intervention: A retrospective study. Front Cardiovasc Med. 2022;9:957626. doi: 10.3389/fcvm.2022.957626.
  45. Lipke K., Kubis-Kubiak A., Piwowar A. Molecular Mechanism of Lipotoxicity as an Interesting Aspect in the Development of Pathological States-Current View of Knowledge. Cells. 2022;11(5):844. doi: 10.3390/cells11050844.
  46. Cho A.R., Kwon Y.J., Kim J.K. Pre-Metabolic Syndrome and Incidence of Type 2 Diabetes and Hypertension: From the Korean Genome and Epidemiology Study. J Pers Med. 2021;11(8):700. doi: 10.3390/jpm11080700.
  47. Strack C., Behrens G., Sag S., et al. Gender differences in cardiometabolic health and disease in a cross-sectional observational obesity study. Biol Sex Differ. 2022;13(1):8. doi: 10.1186/s13293-022-00416-4.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar
2. Fig. Relationships of the TyG index with markers and factors for the formation of menopausal metabolic syndrome in a cohort of patients with arterial hypertension (group 2)

Baixar (144KB)
Metadados

Declaração de direitos autorais © Bionika Media, 2023

Este site utiliza cookies

Ao continuar usando nosso site, você concorda com o procedimento de cookies que mantêm o site funcionando normalmente.

Informação sobre cookies