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- BIOCROSS (1)
- Cross-sectional studies (1)
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- adipose tissue (1)
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- extracellular matrix (1)
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- periostin (1)
Remodeling of the extracellular matrix is a key component of the metabolic adaptations of adipose tissue in response to dietary and physiological challenges. Disruption of its integrity is a well-known aspect of adipose tissue dysfunction, for instance, during aging and obesity. Adipocyte regeneration from a tissue-resident pool of mesenchymal stem cells is part of normal tissue homeostasis. Among the pathophysiological consequences of adipogenic stem cell aging, characteristic changes in the secretory phenotype, which includes matrix-modifying proteins, have been described. Here, we show that the expression of the matricellular protein periostin, a component of the extracellular matrix produced and secreted by adipose tissue-resident interstitial cells, is markedly decreased in aged brown and white adipose tissue depots. Using a mouse model, we demonstrate that the adaptation of adipose tissue to adrenergic stimulation and high-fat diet feeding is impaired in animals with systemic ablation of the gene encoding for periostin. Our data suggest that loss of periostin attenuates lipid metabolism in adipose tissue, thus recapitulating one aspect of age-related metabolic dysfunction. In human white adipose tissue, periostin expression showed an unexpected positive correlation with age of study participants. This correlation, however, was no longer evident after adjusting for BMI or plasma lipid and liver function biomarkers. These findings taken together suggest that age-related alterations of the adipose tissue extracellular matrix may contribute to the development of metabolic disease by negatively affecting nutrient homeostasis.
Background
Biomarker-based analyses are commonly reported in observational epidemiological studies; however currently there are no specific study quality assessment tools to assist evaluation of conducted research. Accounting for study design and biomarker measurement would be important for deriving valid conclusions when conducting systematic data evaluation.
Methods
We developed a study quality assessment tool designed specifically to assess biomarker-based cross-sectional studies (BIOCROSS) and evaluated its inter-rater reliability. The tool includes 10-items covering 5 domains: ‘Study rational’, ‘Design/Methods’, ‘Data analysis’, ‘Data interpretation’ and ‘Biomarker measurement’, aiming to assess different quality features of biomarker cross-sectional studies. To evaluate the inter-rater reliability, 30 studies were distributed among 5 raters and intraclass correlation coefficients (ICC-s) were derived from respective ratings.
Results
The estimated overall ICC between the 5 raters was 0.57 (95% Confidence Interval (CI): 0.38–0.74) indicating a good inter-rater reliability. The ICC-s ranged from 0.11 (95% CI: 0.01–0.27) for the domain ‘Study rational’ to 0.56 (95% CI: 0.40–0.72) for the domain ‘Data interpretation’.
Conclusion
BIOCROSS is a new study quality assessment tool suitable for evaluation of reporting quality from cross-sectional epidemiological studies employing biomarker data. The tool proved to be reliable for use by biomedical scientists with diverse backgrounds and could facilitate comprehensive review of biomarker studies in human research.