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Response to the Letter to the Editor by Jorge Violante-Cumpa; Marina Norde & Geloneze Geloneze “Fat-free mass index is a feasible predictor of insulin resistance in women with polycystic ovary syndrome: evidence from a cross-sectional study”

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References

  1. L. Xu et al. Comparisons of body-composition prediction accuracy: a study of 2 bioelectric impedance consumer devices in healthy Chinese persons using DXA and MRI as criteria methods. J. Clin. Densitom. 14(4), 458–464 (2011)

    Article  PubMed  Google Scholar 

  2. T.B. VanItallie et al. Height-normalized indices of the body’s fat-free mass and fat mass: potentially useful indicators of nutritional status. Am. J. Clin. Nutr. 52(6), 953–959 (1990)

    Article  CAS  PubMed  Google Scholar 

  3. N. Macias et al. Accuracy of body fat percent and adiposity indicators cut off values to detect metabolic risk factors in a sample of Mexican adults. BMC Public Health 14, 341 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  4. F.Q. Nuttall, Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr. Today 50(3), 117–128 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

  5. J.C. Lagacé et al. Increased odds of having the metabolic syndrome with greater fat-free mass: counterintuitive results from the National Health and Nutrition Examination Survey database. J. Cachexia Sarcopenia Muscle 13(1), 377–385 (2022)

    Article  PubMed  Google Scholar 

  6. J.F. Ascaso et al. Diagnosing insulin resistance by simple quantitative methods in subjects with normal glucose metabolism. Diabetes Care 26(12), 3320–3325 (2003)

    Article  CAS  PubMed  Google Scholar 

  7. S. Martin et al. HOMA-IR increase after antidepressant treatment in depressed patients with the Met allele of the Val66Met BDNF genetic polymorphism. Psychol. Med. 49(14), 2364–2369 (2019)

    Article  PubMed  Google Scholar 

  8. Chinese Diabetes Society Insulin Resistance Study Group (preparatory), Expert guidance on insulin resistance assessment methods and applications. Chin. J. Diabetes Mellitus 10(6), 377–385 (2018)

  9. W.P. JIA et al. Study of insulin resistance among Chinese population over40in Shanghai area. Shanghai Med. J. 24(4), 199–202 (2001)

    CAS  Google Scholar 

  10. X.Y. Xing et al. The diagnostic significance of homeostasis model assessment of insulin resistance in metabolic syndrome among subjects with different glucose tolerance. Chin. J. Diabetes 2004(03), 31–35 (2004)

    Google Scholar 

  11. J. Bai et al. Mixed exposure to phenol, parabens, pesticides, and phthalates and insulin resistance in NHANES: A mixture approach. Sci. Total Environ. 851(Pt 2), 158218 (2022)

    Article  CAS  PubMed  Google Scholar 

  12. A.G. Feroe et al. Acrolein metabolites, diabetes and insulin resistance. Environ. Res. 148, 1–6 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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JG and RN wrote the main manuscript and CL edited it. All authors reviewed the manuscript.

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Correspondence to Changqin Liu.

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The authors declare no competing interests.

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Guo, J., Niu, R. & Liu, C. Response to the Letter to the Editor by Jorge Violante-Cumpa; Marina Norde & Geloneze Geloneze “Fat-free mass index is a feasible predictor of insulin resistance in women with polycystic ovary syndrome: evidence from a cross-sectional study”. Endocrine (2024). https://doi.org/10.1007/s12020-024-03848-7

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  • DOI: https://doi.org/10.1007/s12020-024-03848-7

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