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Association of Galectin-3 With Diabetes Mellitus in the Dallas Heart Study.

Publication ,  Journal Article
Vora, A; de Lemos, JA; Ayers, C; Grodin, JL; Lingvay, I
Published in: J Clin Endocrinol Metab
October 1, 2019

CONTEXT: Galectin-3 is a biomarker associated with inflammation and fibrosis in cardiac, liver, and renal disease. Galectin-3 is higher in overweight and obese individuals; whether an association with diabetes exists independent of weight is unknown. OBJECTIVE: To evaluate if galectin-3 is associated with diabetes mellitus. DESIGN: We performed measurements of galectin-3 among participants in the Dallas Heart Study (DHS) Phases 1 and 2 (DHS-1 and DHS-2; n = 3392, and n = 3194, respectively). Of these, 1989 participants were evaluated longitudinally in both studies. Associations of galectin-3 with prevalent and incident type 2 diabetes were determined using logistic regression models. Associations of galectin-3 with relevant biomarkers and fat compartments were evaluated using Spearman correlation coefficients and multivariable linear regression models, respectively. SETTING AND PARTICIPANTS: DHS is a population-based, single-site, multiethnic study conducted in Dallas County, Texas, with oversampling to comprise 50% blacks. RESULTS: Galectin-3 levels were associated with diabetes prevalence in DHS-1 [OR 1.56 per SD change in log-galectin (95% CI 1.41 to 1.73)] and DHS-2 [OR 1.86 (95% CI 1.67 to 2.06)]. Galectin-3 levels in DHS-1 also associated with incident diabetes mellitus over the 7.1 (interquartile range 6.6 to 7.6)-year follow-up period [OR 1.34 (95% CI 1.14 to 1.58)]. These associations maintained significance in models adjusted for traditional metabolic risk factors (age, sex, race, body mass index, and hypertension) and renal function. Galectin-3 levels correlated with levels of biomarkers implicated in inflammation (high-sensitivity C-reactive peptide, IL-18, monocyte chemoattractant protein 1, soluble TNF receptor 1A, myeloperoxidase), insulin secretion (C-peptide and C-peptide/homeostatic model assessment for insulin resistance), and subcutaneous adiposity. CONCLUSIONS: Galectin-3 is associated with diabetes prevalence and incidence, possibly through the inflammatory pathway contributing to β-cell fibrosis and impaired insulin secretion.

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Published In

J Clin Endocrinol Metab

DOI

EISSN

1945-7197

Publication Date

October 1, 2019

Volume

104

Issue

10

Start / End Page

4449 / 4458

Location

United States

Related Subject Headings

  • Texas
  • Severity of Illness Index
  • Risk Assessment
  • Retrospective Studies
  • Prevalence
  • Population Surveillance
  • Obesity
  • Multivariate Analysis
  • Middle Aged
  • Male
 

Citation

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Vora, A., de Lemos, J. A., Ayers, C., Grodin, J. L., & Lingvay, I. (2019). Association of Galectin-3 With Diabetes Mellitus in the Dallas Heart Study. J Clin Endocrinol Metab, 104(10), 4449–4458. https://doi.org/10.1210/jc.2019-00398
Vora, Amy, James A. de Lemos, Colby Ayers, Justin L. Grodin, and Ildiko Lingvay. “Association of Galectin-3 With Diabetes Mellitus in the Dallas Heart Study.J Clin Endocrinol Metab 104, no. 10 (October 1, 2019): 4449–58. https://doi.org/10.1210/jc.2019-00398.
Vora A, de Lemos JA, Ayers C, Grodin JL, Lingvay I. Association of Galectin-3 With Diabetes Mellitus in the Dallas Heart Study. J Clin Endocrinol Metab. 2019 Oct 1;104(10):4449–58.
Vora, Amy, et al. “Association of Galectin-3 With Diabetes Mellitus in the Dallas Heart Study.J Clin Endocrinol Metab, vol. 104, no. 10, Oct. 2019, pp. 4449–58. Pubmed, doi:10.1210/jc.2019-00398.
Vora A, de Lemos JA, Ayers C, Grodin JL, Lingvay I. Association of Galectin-3 With Diabetes Mellitus in the Dallas Heart Study. J Clin Endocrinol Metab. 2019 Oct 1;104(10):4449–4458.
Journal cover image

Published In

J Clin Endocrinol Metab

DOI

EISSN

1945-7197

Publication Date

October 1, 2019

Volume

104

Issue

10

Start / End Page

4449 / 4458

Location

United States

Related Subject Headings

  • Texas
  • Severity of Illness Index
  • Risk Assessment
  • Retrospective Studies
  • Prevalence
  • Population Surveillance
  • Obesity
  • Multivariate Analysis
  • Middle Aged
  • Male