ArticleMetabolism: clinical and experimental2015
Associations between metabolic dysregulation and circulating biomarkers of fibrosis: the Cardiovascular Health Study.
Article in Metabolism: clinical and experimental, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Blood PAI-1 and cardiovascular and metabolic risk factors among the middle-aged women from SWAN study.Scientific reports · 2024Article
- Laboratory and Metabolomic Fingerprint in Heart Failure with Preserved Ejection Fraction: From Clinical Classification to Biomarker Signature.Biomolecules · 2023Review
- Sick fat: the good and the bad of old and new circulating markers of adipose tissue inflammation.Journal of endocrinological investigation · 2019Review
- Immune Cells and Inflammation in Diabetic Nephropathy.Journal of diabetes research · 2016Review
Corrections and comments
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Authors and funding
11 authors at 10 institutions in 1 country.
Funding
Abstract
aimFibrosis is one postulated pathway by which diabetes produces cardiac and other systemic complications. Our aim was to determine which metabolic parameters are associated with circulating fibrosis-related biomarkers transforming growth factor-β (TGF-β) and procollagen type III N-terminal propeptide (PIIINP).
methodsWe used linear regression to determine the cross-sectional associations of diverse metabolic parameters, including fasting glucose, fasting insulin, body mass index, fatty acid binding protein 4, and non-esterified fatty acids, with circulating levels of TGF-β (n = 1559) and PIIINP (n = 3024) among community-living older adults in the Cardiovascular Health Study.
resultsAmong the main metabolic parameters we examined, only fasting glucose was associated with TGF-β (P = 0.03). In contrast, multiple metabolic parameters were associated with PIIINP, including fasting insulin, body mass index, and non-esterified fatty acids (P<0.001, P<0.001, P=0.001, respectively). These associations remained statistically significant after mutual adjustment, except the association between BMI and PIIINP.
conclusionsIsolated hyperglycemia is associated with higher serum concentrations of TGF-β, while a broader phenotype of insulin resistance is associated with higher serum PIIINP. Whether simultaneous pharmacologic targeting of these two metabolic phenotypes can synergistically reduce the risk of cardiac and other manifestations of fibrosis remains to be determined.
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