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1. Logic-based modeling of inflammatory macrophage cross talk with glomerular endothelial cells in diabetic kidney disease

   https://doi.org/10.1152/ajprenal.00362.2024  

   Patidar, Krutika; Ford_Versypt, Ashlee N (July 2025, American Journal of Physiology-Renal Physiology)

   This work provides a novel analysis of signaling cross talk between macrophages and glomerular endothelial cells in the early stage of diabetic kidney disease. A logic-based mathematical modeling approach identified vital signaling molecules and interactions that regulate glucose-mediated inflammation in glomerular endothelial cells and cause endothelial dysfunction in the diabetic kidney. Simulated interactions among vascular endothelial growth factor receptor 1, nitric oxide, and calcium significantly affected the intercellular junction proteins between glomerular endothelial cells. 

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2. Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

   https://doi.org/10.1136/bmjmed-2023-000651  

   Khera, Rohan; Dhingra, Lovedeep Singh; Aminorroaya, Arya; Li, Kelly; Zhou, Jin J; Arshad, Faaizah; Blacketer, Clair; Bowring, Mary G; Bu, Fan; Cook, Michael; et al (October 2023, BMJ Medicine)

   ObjectiveTo assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin. DesignFederated pharmacoepidemiological evaluation in LEGEND-T2DM. Setting10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021. Participants4.8 million patients (=18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments. ExposureThe exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort. Main outcomes measuresThe outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated. Results4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease. ConclusionsDespite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus. 

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3. Cost-Effectiveness of Continuous Glucose Monitoring With Remote Patient Monitoring in Pediatric Patients With Newly Diagnosed Type 1 Diabetes in the U.S.

   https://doi.org/10.2337/dc25-2825  

   Dupenloup, Paul; Chen, Yupeng; Prahalad, Priya; Johari, Ramesh; Addala, Ananta; Zaharieva, Dessi P; Lee, Ming Yeh; Maahs, David M; Scheinker, David; Brandeau, Margaret L (March 2026, Diabetes Care)

   OBJECTIVEThe use of continuous glucose monitoring (CGM) with remote patient monitoring (RPM) continues to grow. We evaluated the cost-effectiveness of CGM with RPM compared with self-monitoring of blood glucose (SMBG) and CGM alone. RESEARCH DESIGN AND METHODSWe simulated type 1 diabetes progression with a Markov model in 5-year-old patients over a 20-year, 50-year, and lifetime horizon. We tracked diabetic ketoacidosis (DKA), severe hypoglycemia (SH), and seven chronic complications: retinopathy, neuropathy, nephropathy, cardiovascular disease, end-stage renal disease, lower-extremity amputation, and blindness. We compared three interventions: SMBG, CGM, and CGM with RPM. Efficacy estimates were derived from meta-analyses of pediatric CGM studies and the results of the Teamwork, Targets, Technology, and Tight Glycemia Study (4T Study 1). We evaluated quality-adjusted life years (QALYs) and health care costs (2022 U.S. dollars) discounted at 3% annually. We performed extensive sensitivity analyses. RESULTSCompared with SMBG, CGM increased QALYs by 0.09 and costs by $8,900 over 20 years; CGM with RPM increased QALYs by 0.37, and costs by $10,300. CGM with RPM yielded more QALYs at a lower incremental cost-effectiveness ratio compared with CGM ($27,400/QALY vs. $103,700/QALY, respectively). Results were robust across sensitivity analyses and time horizons. CGM with RPM remained cost-effective when achieving at least 30% of 4T’s clinical efficacy. CONCLUSIONSCGM with RPM delivers superior health outcomes compared with SMBG and CGM and is likely cost-effective for patients with newly diagnosed type 1 diabetes. Despite higher intervention costs, CGM with RPM can reduce complications costs and generate net health care savings. 

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4. “Brain age” predicts disability accumulation in multiple sclerosis

   https://doi.org/10.1002/acn3.51782  

   Brier, Matthew R; Li, Zhuocheng; Ly, Maria; Karim, Helmet T; Liang, Leda; Du, Weixin; McCarthy, John E; Cross, Anne H; Benzinger, Tammie_L S; Naismith, Robert T; et al (June 2023, Annals of Clinical and Translational Neurology)

   Abstract ObjectiveNeurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well‐developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of “brain age” analysis on disability in MS using a large, real‐world dataset. MethodsBrain age analysis is predicated on the over‐estimation of predicted brain age in patients with more advanced pathology. We compared the performance of three brain age algorithms in a large, longitudinal dataset (>13,000 imaging sessions from >6,000 individual MS patients). Effects of MS, MS disease course, disability, lesion burden, and DMT efficacy were assessed using linear mixed effects models. ResultsMS was associated with advanced predicted brain age cross‐sectionally and accelerated brain aging longitudinally in all techniques. While MS disease course (relapsing vs. progressive) did contribute to advanced brain age, disability was the primary correlate of advanced brain age. We found that advanced brain age at study enrollment predicted more disability accumulation longitudinally. Lastly, a more youthful appearing brain (predicted brain age less than actual age) was associated with decreased disability. InterpretationBrain age is a technically tractable and clinically relevant biomarker of disease pathology that correlates with and predicts increasing disability in MS. Advanced brain age predicts future disability accumulation. 

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5. Transglutaminase-mediated stiffening of the glomerular basement membrane mitigates pressure-induced reductions in molecular sieving coefficient by reducing compression

   https://doi.org/10.1016/j.matbio.2024.05.002  

   Wang, Dan; Ferrell, Nicholas (June 2024, Matrix Biology)

   Proteinuria, the presence of high molecular weight proteins in the urine, is a primary indicator of chronic kidney disease. Proteinuria results from increased molecular permeability of the glomerular filtration barrier combined with saturation or defects in tubular protein reabsorption. Any solute that passes into the glomerular filtrate traverses the glomerular endothelium, the glomerular basement membrane, and the podocyte slit diaphragm. Damage to any layer of the filter has reciprocal effects on other layers to increase glomerular permeability. The GBM is thought to act as a compressible ultrafilter that has increased molecular selectivity with increased pressure due to compression that reduced the porosity of the GBM with increased pressure. In multiple forms of chronic kidney disease, crosslinking enzymes are upregulated and may act to increase GBM stiffness. Here we show that enzymatically crosslinking porcine GBM with transglutaminase increases the stiffness of the GBM and mitigates pressure-dependent reductions in molecular sieving coefficient. This was modeled mathematically using a modified membrane transport model accounting for GBM compression. Changes in the mechanical properties of the GBM may contribute to proteinuria through pressure-dependent effects on GBM porosity. 

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