Comparing different metabolic indexes to predict type 2 diabetes mellitus in a five years follow-up cohort: The Baependi Heart Study

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Citações na Scopus
1
Tipo de produção
article
Data de publicação
2022
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ISSN da Revista
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Editora
PUBLIC LIBRARY SCIENCE
Autores
OLIVEIRA, Camila Maciel de
PAVANI, Jessica Leticia
LIU, Chunyu
BALCELLS, Mercedes
CAPASSO, Robson
ALVIM, Rafael de Oliveira
MOURAO-JUNIOR, Carlos Alberto
Citação
PLOS ONE, v.17, n.6, article ID e0267723, 9p, 2022
Projetos de Pesquisa
Unidades Organizacionais
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Resumo
This study evaluates the association of anthropometric indexes and the incidence of type 2 diabetes mellitus (T2DM) after a 5-year follow-up. This analysis included 1091 middle-aged participants (57% women, mean age 47 +/- 15 years) who were free of T2DM at baseline and attended two health examinations cycles [cycle 1 (2005-2006) and cycle 2 (2010-2013)]. As expected, the participants who developed T2DM after five years (3.8%) had the worst metabolic profile with higher hypertension, dyslipidemia, and obesity rates. Besides, using mixed-effects logistic regression and adjustment for sex, age, and glucose, we found that one unit increase in body adiposity index (BAI) was associated with an 8% increase in their risk of developing T2DM (odds ratio [OR] = 1.08 [95% CI, 1.02-1.14]) and visceral adiposity index (VAI) was associated with a risk increase of 11% (OR = 1.11 [95% CI, 1.00-1.22]). Moreover, a one-unit increase in the triglycerides-glucose index (TyG) was associated with more than four times the risk of developing T2DM (OR = 4.27 [95% CI, 1.01-17.97]). The interquartile range odds ratio for the continuous predictors showed that TyG had the best discriminating performance. However, when any of them were additionally adjusted for waist circumference (WC) or even body mass index (BMI), all adiposity indexes lost the effect in predicting T2DM. In conclusion, TyG had the most substantial predictive power among all three indexes. However, neither BAI, VAI, nor TyG were superior to WC or BMI for predicting the risk of developing T2DM in a middle-aged normoglycemic sample in this rural Brazilian population.
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Referências
  1. Alvim RO., 2014, PLOSONE
  2. Amato MC, 2010, DIABETES CARE, V33, P920, DOI 10.2337/dc09-1825
  3. Bergman RN, 2011, OBESITY, V19, P1083, DOI 10.1038/oby.2011.38
  4. Chamroonkiadtikun P, 2020, PRIM CARE DIABETES, V14, P161, DOI 10.1016/j.pcd.2019.08.004
  5. de Oliveira CM, 2008, BMC MED GENET, V9, DOI 10.1186/1471-2350-9-32
  6. de Oliveira CM, 2020, PREV MED REP, V20, DOI 10.1016/j.pmedr.2020.101172
  7. de Oliveira CM, 2019, DIABETOL METAB SYNDR, V11, DOI 10.1186/s13098-019-0467-1
  8. diabetesatlas.org, IDF DIABETES ATLAS
  9. Egan KJ, 2016, BMJ OPEN, V6, DOI 10.1136/bmjopen-2016-011598
  10. Flor Luisa Sorio, 2017, Rev. bras. epidemiol., V20, P16, DOI 10.1590/1980-5497201700010002
  11. Freitas Isabel Cristina Martins de, 2016, Rev. bras. epidemiol., V19, P433, DOI 10.1590/1980-5497201600020018
  12. Guerrero-Romero F, 2010, J CLIN ENDOCR METAB, V95, P3347, DOI 10.1210/jc.2010-0288
  13. Hameed EK, 2019, DIABETES METAB SYND, V13, P1241, DOI 10.1016/j.dsx.2019.01.039
  14. Hudish LI, 2019, J CLIN INVEST, V129, P4001, DOI 10.1172/JCI129188
  15. Klein S, 2007, DIABETES CARE, V30, P1647, DOI 10.2337/dc07-9921
  16. Lopez AA, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0035281
  17. Low S, 2018, DIABETES RES CLIN PR, V143, P43, DOI 10.1016/j.diabres.2018.06.006
  18. Malta Deborah Carvalho, 2017, Rev. bras. epidemiol., V20, P217, DOI 10.1590/1980-5497201700050018
  19. Nick Todd G., 2007, V404, P273, DOI 10.1007/978-1-59745-530-5_14
  20. Nusrianto R, 2019, DIABETES RES CLIN PR, V155, DOI 10.1016/j.diabres.2019.107798
  21. Nusrianto R, 2019, DIABETES METAB SYND, V13, P1231, DOI 10.1016/j.dsx.2019.01.056
  22. Padilha K, 2016, METABOLOMICS, V12, DOI 10.1007/s11306-016-1107-5
  23. R Development Core Team, 2022, R LANG ENV STAT COMP
  24. Ramirez-Velez R, 2019, NUTRIENTS, V11, DOI 10.3390/nu11112654
  25. Sanchez-Garcia A, 2020, INT J ENDOCRINOL, V2020, DOI 10.1155/2020/4678526
  26. Wu JS, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-14251-w