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The measurement of the blood insulin concentration, and comparison to cut-offs, is essential in diagnosing insulin dysregulation, a common equine endocrinopathy. However, different insulin assays provide disparate results. We aimed to ease comparison between assays by compiling original and published data into a web app to convert insulin measurements from one assay to another. Data were available for ADVIA Centaur insulin chemiluminescent immunoassay (CLIA), Beckman Coulter insulin radioimmunoassay (RIA), Immulite 1000 CLIA, Immulite 2000 CLIA, Immulite 2000 XPi CLIA, Mercodia equine insulin enzyme-linked immunosorbent assay (ELISA), and Millipore porcine insulin RIA. Linear models were fitted for 13 assay pairs using non-decreasing splines, and integrated into this app. Assay comparisons including data from several studies showed a lower performance. This indicates technical variation between laboratories, which has not been described before, but is relevant when diagnostic measurements and cut-offs are provided by different laboratories. Nevertheless, the models' overall high performance (median = 0.94; range 0.57-1.00) supports their use to interpret results from diagnostic insulin measurements when the reference assay is unavailable, and to compare values obtained from different assays.
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http://dx.doi.org/10.3390/ani13172704 | DOI Listing |
Lipids Health Dis
September 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
Background: The CRP-albumin-lymphocyte (CALLY) index has potential clinical value as a novel marker integrating inflammatory, nutritional and immune status in the development of colorectal polyps. This study examined whether gender factors influence the association between CALLY and colorectal polyps; in addition to elucidating whether metabolic pathways mediate this relationship.
Methods: This is a cross-sectional study including 5409 adult health screening participants who completed colonoscopy.
EMBO J
September 2025
School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW, Australia.
Insulin resistance is a heritable risk factor for many chronic diseases; however, the genetic drivers remain elusive. In seeking these, we performed genetic mapping of insulin sensitivity in 670 chow-fed Diversity Outbred in Australia (DOz) mice and identified a genome-wide significant locus (QTL) on chromosome 8 encompassing 17 defensin genes. By taking a systems genetics approach, we identified alpha-defensin 26 (Defa26) as the causal gene in this region.
View Article and Find Full Text PDFSci Rep
September 2025
College of First Clinical Medical, Shandong University of Traditional Chinese Medicine, Jinan, China.
Obstructive sleep apnea (OSA) is associated with metabolic disorders such as insulin resistance and liver fat accumulation. However, the specific mediating role of liver-related metabolic indicators in this association has not been fully studied. The purpose of this study was to investigate the relationship between Metabolic Score for Insulin Resistance (METS-IR) and OSA, focusing on the mediating effects of liver fat percentage (PLF) and hepatic steatosis index (HSI).
View Article and Find Full Text PDFInt J Obes (Lond)
September 2025
Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Aims And Background: Relative fat mass (RFM) is strongly associated with type 2 diabetes (T2DM) and has been shown to be a better predictor than body mass index (BMI) and waist circumference (WC). This study aims to investigate the association between RFM and incident T2DM among adults in the Tehran Lipid and Glucose Study cohort.
Methods: Data from 8419 participants (4716 women; mean age, 40.
Diabet Med
September 2025
Edinburgh Centre for Endocrinology & Diabetes, NHS Lothian, Edinburgh, UK.
Aims: This study aimed to assess the impact of the Omnipod 5 automated insulin delivery (AID) system on continuous glucose monitoring (CGM) metrics, HbA1c, and weight in a real-world setting. Additionally, independent predictors of glycaemic response were assessed.
Methods: Observational analysis of adults with type 1 diabetes using Omnipod 5 (n = 353).