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NIDDK, JDRF, and the Diabetic Neuropathy Study Group of EASD sponsored a meeting to explore the current status of animal models of diabetic peripheral neuropathy. The goal of the workshop was to develop a set of consensus criteria for the phenotyping of rodent models of diabetic neuropathy. The discussion was divided into five areas: (1) status of commonly used rodent models of diabetes, (2) nerve structure, (3) electrophysiological assessments of nerve function, (4) behavioral assessments of nerve function, and (5) the role of biomarkers in disease phenotyping. Participants discussed the current understanding of each area, gold standards (if applicable) for assessments of function, improvements of existing techniques, and utility of known and exploratory biomarkers. The research opportunities in each area were outlined, providing a possible roadmap for future studies. The meeting concluded with a discussion on the merits and limitations of a unified approach to phenotyping rodent models of diabetic neuropathy and a consensus formed on the definition of the minimum criteria required for establishing the presence of the disease. A neuropathy phenotype in rodents was defined as the presence of statistically different values between diabetic and control animals in 2 of 3 assessments (nocifensive behavior, nerve conduction velocities, or nerve structure). The participants propose that this framework would allow different research groups to compare and share data, with an emphasis on data targeted toward the therapeutic efficacy of drug interventions.

Objective: To determine the association between triglyceride levels and lower-extremity amputation (LEA) risk in a large diabetic cohort.Research Design and Methods: This is a 10-year survey follow-up study (from 1995-2006) of 28,701 diabetic patients with a baseline triglyceride measure. All patients were fully insured members of the Kaiser Permanente Medical Care Program and responded to a survey at baseline that included information on ethnicity, socioeconomic status, education, behavioral factors, and information required to determine type of diabetes. The relationship between triglycerides and time to incident nontraumatic LEA, defined by primary hospitalization discharge or procedures, was evaluated using Cox proportional hazards models.Results: Triglyceride level was an independent, stepwise risk factor for nontraumatic LEAs within this large diabetic cohort: triglycerides 150-199 mg/dL, hazard ratio (HR) 1.10 (95% CI 0.92-1.32); 200-499 mg/dL, 1.27 (1.10-1.47); >500 mg/dL, 1.65 (1.30-2.10) (reference <150 mg/dL).Conclusions: Hypertriglyceridemia is a significant risk factor for LEA in diabetic patients even after controlling for known socioeconomic, health behavioral, and clinical factors. This previously unrecognized clinical risk needs to be further investigated to determine if treatment of triglycerides can reduce amputation risk. [ABSTRACT FROM AUTHOR]

Objective: To describe the sensitivity, specificity, positive predictive value, and negative predictive value of vibration perception threshold (VPT) testing in subjects with type 1 diabetes relative to gold standard assessments of peripheral neuropathy.Research Design and Methods: VPT was determined in 1,177 adults with type 1 diabetes 13-14 years after participating in a study of intensive (INT) versus conventional (CONV) diabetes treatment. Abnormal VPT was defined by values exceeding 2.5 SD above age-specific normal values. Signs and symptoms of peripheral neuropathy were assessed and electrodiagnostic studies were performed to establish definite clinical neuropathy, abnormal nerve conduction, and confirmed clinical neuropathy (the presence of both definite clinical neuropathy and abnormal nerve conduction).Results: Thirty-seven percent of subjects had definite clinical neuropathy, 61% had abnormal nerve conduction, and 30% had confirmed clinical neuropathy. Abnormal VPT was more common among former CONV than among INT subjects (64 vs. 57%, P < 0.05) and was associated with older age. VPT was a sensitive measure of confirmed clinical neuropathy (87%) and of definite clinical neuropathy (80%) and a specific measure of abnormal nerve conduction (62%). Higher VPT cut points improved test sensitivity and lower cut points improved specificity. Areas under the receiver operating characteristic curves ranged from 0.71-0.83 and were higher for older than for younger subjects and highest for those with confirmed clinical neuropathy.Conclusions: VPT was a sensitive measure of peripheral neuropathy. Future researchers may choose to select VPT cut points for defining abnormality based on the population studied and clinical outcome of interest. [ABSTRACT FROM AUTHOR]

Objective: To determine if baseline subgroups in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial can be identified for whom intensive compared with standard glycemia treatment had different effects on all-cause mortality.Research Design and Methods: Exploratory post hoc intention-to-treat comparisons were made between intensive and standard glycemia groups on all-cause mortality by subgroups defined by baseline characteristics.Results: There were few significant interactions between baseline characteristics and effects of intensive versus standard glycemia treatment on mortality: self-reported history of neuropathy (hazard ratio [HR] 1.95, 95% CI 1.41-2.69) versus no history of neuropathy (0.99, 0.79-1.26; P value for interaction 0.0008), higher A1C (A1C >8.5%: HR 1.64, 95% CI 1.22-2.22; A1C 7.5-8.4%: 1.00, 0.75-1.34; A1C <7.5%: 1.00, 0.67-1.50; P value for interaction 0.04), and aspirin use (HR 1.45, 95% CI 1.13-1.85, compared with 0.96, 0.72-1.27, in nonusers; P value for interaction 0.03).Conclusions: We found a remarkable similarity of effect from intensive compared with standard glycemia treatment on mortality across most baseline subgroups. No differential effect was found in subgroups defined by variables anticipated to have an interaction: age, duration of diabetes, and previous history of cardiovascular disease. The three baseline characteristics that defined subgroups for which there was a differential effect on mortality may help identify patients with type 2 diabetes at higher risk of mortality from intensive regimens for glycemic control. Further research is warranted. [ABSTRACT FROM AUTHOR]

Objective: We sought to evaluate the prevalence of subclinical neuropathy in intensive and conventional treatment groups at completion of the Diabetes Control and Complications Trial (DCCT).Research Design and Methods: We assessed neuropathy using nerve conduction results obtained at DCCT completion after stratifying the DCCT cohort to exclude subjects with progressively less severe degrees of diagnosable neuropathy. We began with those who had confirmed clinical neuropathy (the primary DCCT end point) and eventually excluded all subjects with any clinical or electrodiagnostic evidence of neuropathy.Results: After excluding subjects with confirmed clinical neuropathy at DCCT completion, 8 of 10 nerve conduction measures (including all lower-extremity measures) were significantly improved in the intensive treatment group (O'Brien rank-sum test across all nerve conduction measures, P < 0.0001). Conduction velocity group differences were substantial, and the peroneal conduction velocity averaged 3.1 m/s faster in the intensive compared with the conventional treatment group (45.1 vs. 42.0 m/s, P < 0.0001). Numerous significant differences in median and peroneal motor conduction velocities favoring the intensive treatment group persisted, regardless of the exclusion criteria applied.Conclusions: Intensive and conventional treatment group subjects without diagnosable neuropathy at DCCT completion had significant differences in electrophysiologic measurements favoring the intensive treatment group. Differences in subsequent incident neuropathy between the original treatment groups may reflect, in part, their levels of subclinical neuropathy at DCCT completion, rather than persistent metabolic effects. [ABSTRACT FROM AUTHOR]

BACKGROUND Preliminary evidence indicates that lamotrigine might be a useful treatment for neuropathic pain, including pain associated with diabetic neuropathy (DN). OBJECTIVE To evaluate the efficacy and tolerability of lamotrigine for DN pain. DESIGN AND INTERVENTION The investigators conducted two multicenter, placebo-controlled, randomized, double-blind studies in the US. Inclusion criteria were age ≥18 years, type 1 or type 2 diabetes, and severe neuropathic pain (mean weekly pain score ≥4 on an 11-point numerical rating scale, with 0 representing no pain and 10 representing worst pain possible) for at least 6 months. Patients with long-standing (≥5 years) refractory pain and those with pain attributable to conditions other than diabetes were excluded. The two studies had a 2-4-week screening phase, a 1-week baseline phase and a 19-week treatment phase; patients were randomly assigned to receive lamotrigine at 200, 300 or 400 mg/day or placebo (n=90 per treatment group in each study). The treatment phase began with a 7-week dose-escalation period, followed by a 12-week fixed-dose maintenance phase. Acetaminophen could be used as rescue medication; other analgesics and nonpharmacological pain therapies were prohibited. Efficacy data were analyzed for the intent-to-treat population (n=679); tolerability data were analyzed for the safety population (n=706). In addition, post hoc analyses were conducted using last-observation-carried-forward (LOCF) scores. OUTCOME MEASURES The primary efficacy variable was the mean daily pain intensity, as rated on the 11 -point numerical scale. Secondary efficacy measures included the McGill Pain Questionnaire, the Neuropathy Pain Scale, walking pain intensity, pain-related sleep interference as rated on an 11-point numerical scale, responder rates, and amount of rescue medication used. The main measure of tolerability was the occurrence of adverse events. RESULTS Patients receiving lamotrigine 400 mg/day (but not other doses) in Study 2 had a greater mean reduction in the primary efficacy measure from baseline to week 19 than did patients receiving placebo (observed change: -2.7 vs -1.6; P≤0.05). This finding was not replicated in Study 1. For the secondary efficacy measures, the two highest lamotrigine doses tended to be superior to placebo, but the difference between lamotrigine and placebo was only occasionally statistically significant. Some 20-30% of patients across treatment groups and studies dropped out before reaching their target dose. In a post hoc analysis of pooled LOCF data from patients who did reach their target dose, lamotrigine 300 mg/day and 400 mg/day were significantly more effective than placebo at reducing pain throughout much of the maintenance period, with a reduction in pain intensity scores from baseline to week 19 for lamotrigine 400 mg/day vs placebo of -2.7 vs -2.0 (P≤0.05). The most common lamotrigine-associated adverse events were headache and rash. CONCLUSION Lamotrigine (300 and 400 mg/day) provided inconsistent relief from DN pain; the drug was generally well tolerated. [ABSTRACT FROM AUTHOR]

SGLT-2i and GLP-1RA are recommended for persons with type 2 diabetes and atherosclerotic cardiovascular disease (ASCVD) but are underused in clinical practice. The COORDINATE-Diabetes randomized clincal trial evaluated a multi-faceted intervention to increase the use of evidence-based therapies for reducing cardiovascular risk among participants with diabetes and atherosclerotic cardiovascular disease. This analysis reports the discontinuation rate of SGLT-2i and GLP-1RA in follow up and summarises the clinician-reported reasons underlying these decisions., Competing Interests: Declaration of competing interest AJN: Advisory board panel/consulting for Amgen, Boehringer Ingelheim, Eli Lilly, GSK, Merck, Novartis, Novo Nordisk, Sanofi, VAXXINITY. Grants from National Heart Foundation of Australia, Diabetes Australia and the Royal Australasian College of Physicians.; LK – None; DKM - Personal fees from Novo Nordisk, Lilly USA, Pfizer, Boehringer Ingelheim, NewAmsterdam, AstraZeneca, CSL Behring, Esperion, Bayer, Lexicon, Altimmune, Esperion, Intercept Pharmaceuticals, Amgen, Neurotronics, Applied Therapeutics, Merck & Co. ML – None; HRA – None; LW – None; RDL - Grants and personal fees from Bristol-Myers Squibb and Pfizer, personal fees from Boehringer Ingelheim, Novo Nordisk, and Bayer AG and grants from Amgen Inc, GlaxoSmithKline, Medtronic PLC, and Sanofi Aventis; RP-B - Research support (to the University of Michigan) from AstraZeneca, and grant support from the National Institutes of Health (NIDDK-1-R01-DK-107956-01, UC4 DK101108); MAC - Consulting fees from AstraZeneca, Boehringer Ingelheim, Edwards Lifesciences, Merck, and Sanofi-Aventis. He has received research funding (nonsalary) from Amgen, AstraZeneca, Bristol-Myers Squibb, Chiesi, GlaxoSmithKline, Novartis, and Takeda and research funding (salary) from Novo-Nordisk; VRA - Consulting fees from Duke University, Liberum, Novo Nordisk, and Sanofi; research grant support paid to her institution from Applied Therapeutics, Fractyl/Premier, Novo Nordisk, and Sanofi; and her spouse was/is an employee of Merck and Janssen; MLM - Consulting fees from Boehringer Inhelheim and Novo Nordisk; CRR – None; IL - Grant support from Merck, Mylan, Novo Nordisk, Pfizer and Sanofi, personal fees from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Intarcia, Janssen, Mannkind, Novo Nordisk, Sanofi, TARGETPharma and Valeritas; and non-financial support from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Novo Nordisk, Pfizer and Sanofi; JKK – None; AP - Supported by the Texas Health Resources Clinical Scholarship, the Gilead Sciences Research Scholar Program, the National Institute of Aging GEMSSTAR Grant (1R03AG067960-01), and Applied Therapeutics; has served on the advisory board for Roche Diagnostics; and has received nonfinancial support from Pfizer and Merck; TG - Employee of Boehringer Ingelheim; JP - Employee of Boehringer Ingelheim; AW - Employee of Lilly Pharmaceuticals; CS - Employee of Lilly Pharmaceuticals; JBG - Grants from AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline and Sanofi, and personal fees from AstraZeneca, Merck, Boehringer‐Ingelheim, Sanofi/Regeneron, and NovoNordisk; CBG - Research grants and consulting/speaker fees from Boehringer Ingelheim, Bristol-Myers Squibb, Janssen Pharmaceutica Products, L.P., and Pfizer, research grants from AKROS, Apple, AstraZeneca, Daichii-Sankyo, US Food & Drug Administration, GlaxoSmithKline, Medtronic Foundation, and Novartis Pharmaceutical Company, consulting/speaker fees from Abbvie, Bayer Corp US, Boston Scientific Corp, CeleCor Therapeutics, Correvio, Espero BioPharma, Medscape, Medtronic Inc., Merck, National Institutes of Health, NovoNordisk, Rhoshan Pharmaceuticals, and Roche Diagnostics; NJP - Grants from Alnylam, Amgen, Bayer, Boehringer Ingelheim, Eggland's Best, Eli Lilly, Novartis, Novo Nordisk, Merck; Amgen, Bayer, Boehringer Ingelheim, CRISPR Therapeutics, Eli Lilly, Esperion, AstraZeneca, Merck, Novartis, and Novo Nordisk; Executive Committee member for trials sponsored by Novo Nordisk and by Amgen and by AstraZeneca; DSMB for trials sponsored by J+J and Novartis; Medical advisory board for Miga Health., (Copyright © 2024. Published by Elsevier Inc.)

Background: Statins are lipid-lowering agents with anti-inflammatory effects. Data surrounding the benefits of statins in patients with coronavirus disease 2019 (COVID-19) are conflicting. We sought to better understand the impact of statins in the context of COVID-19-related inflammation., Methods: We leveraged the International Study of Inflammation in COVID-19, a prospective multicenter cohort of patients hospitalized for COVID-19 between February 2020 and October 2022. Participants underwent systematic assessment of biomarkers of inflammation. We used logistic regression modeling and inverse probability-of-treatment weighting (IPTW) to examine the association between prior statin use and the composite outcome of in-hospital death, need for mechanical ventilation, and need for renal replacement therapy., Results: A total of 4464 patients were included in the study, of whom 1364 (27.5%) were taking a statin prior to admission. There were 1061 primary outcome events, including 540 deaths, 854 mechanical ventilation and 313 renal replacement therapy. Amongst biomarkers of inflammation, statin use was associated solely with lower levels of soluble urokinase plasminogen activator receptor (suPAR) after adjusting for known confounders. In multivariable logistic regression analysis, statin use was associated with lower odds of the composite outcome (adjusted odds ratio (aOR) 0.63, 95% CI [0.53-0.76]) compared to patients not on statins. Findings were consistent with IPTW (aOR 0.92, 95% CI [0.89- 0.95]). The proportion of the effect of statin on the primary outcome mediated by suPAR was estimated at 31.5%., Conclusion: Prior-statin use is associated with improved outcomes and lower inflammation as measured by suPAR levels in patients hospitalized for COVID-19., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs)., Methods: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool-the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy., Results: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose., Conclusion: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: D.C.K. is a consultant for Afon, Lifecare, Novo, Samsung, embecta, Glucotrack, SynchNeuro, and Thirdwayv. G.F. is CEO of IfDT. G.F./IfDT has/have received research support, speakers’ honoraria or consulting fees in the last three years from Abbott, Ascensia, Berlin Chemie, Boydsense, Dexcom, Glucoset, i-SENS, Lilly, Menarini, Novo Nordisk, Perfood, Pharmasens, Roche, Sinocare, Terumo, Ypsomed. S.P. is an employee of IfDT. B.P.K. declares patent royalties handled by UVA from: DexCom, J&J, Novo Nordisk, and Sanofi; and research support handled by UVA: Dexcom, Novo Nordisk, Tandem Diabetes Care. D.K. has received research support from Abbott Diabetes Care. M.S.D.A. is currently receiving in kind support (CGM devices) from Dexcom Inc for an investigator-initiated clinical study. K.D. receives research funding from Dexcom, Abbott, Sanofi, Viacyte, Insulet, consulting fees from Eli Lilly, Dexcom, Insulet, Oppenheimer, Elsevier, honoraria from Academy for Continued Healthcare Learning, Med Learning Group, Medscape, Impact Education, and royalties from UptoDate. S.M. is in receipt of an investigator-initiated grant from DexCom, has received speaker fees from Sanofi & Lilly, is funded by a Wellcome Trust (223024/Z/21/Z), and is supported by the NIHR Imperial Biomedical Research Centre. A.S. was supported by the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (#1648451). V.N.S. reports receiving personal fees from Sanofi, Embecta, NovoNordisk, Dexcom, Insulet, Tandem Diabetes Care, Ascensia Diabetes Care, Genomelink and LumosFit for consulting, advising or speaking. N.K.T. is a consultant for Roche Diagnostics / Roche Molecular Systems / Radiometer. UC Davis is a Roche Diagnostics Center of Excellence. N.K.T. has received speaking honoraria for Roche Diagnostics, Nova Biomedical, Thermo Fisher, and Radiometer. N.K.T. is also Chair-Elect for the Association for Diagnostic and Laboratory Medicine (ADLM) Critical and Point-of-Care Testing Division, Co-Founder of the Machine Intelligene Learning Optimizer (MILO), Inc, and a member of the International Federation for Clinical Chemistry (IFCC) continuous glucose monitoring workgroup. T.T. is a consultant for Clinical ink. R.E.A. is a consultant for Clinical ink. D.T.A. has received speaker’s honoraria from Abbott, Ascensia Diabetes Care, Insulet, Lilly Diabetes, Mannkind, Novo Nordisk, and Xeris Pharmaceuticals. D.T.A. has received consulting fees from Ascensia Diabetes Care, Lilly Diabetes, and Senseonics. H.K.A. received research grants through University of Colorado from Dexcom, Tandem Diabetes, Medtronic, Mannkind, IM Therapeutics, IAFMS, and received honorarium through University of Colorado for consulting from Dexcom, Tandem Diabetes, and Medtronic. M.E.A.-S. has served on the advisory boards for Abbott, Medtronic, Insulet, VitalAire, Sanofi, Eli Lilly, and Dexcom; has received honoraria for speaking from Abbott, Eli Lilly, Medtronic, Novo Nordisk, Sanofi, VitalAire, and Eli Lilly; and received research support from Medtronic and Sanofi. T.S.B. has received research support from Abbott Rapid Diagnostics, Biolinq, Dexcom, Eli Lilly, Medtronic, Medtrum, Novo Nordisk, Sanofi, Senseonics, and vTv Therapeutics and consulting honoraria from Abbott Diabetes, Abbott Rapid Diagnostics, ACON, CeQur, HagarTech, Intuity Medical, Lifescan, Mannkind, Medtronic, Novo, Perspirion, Sanofi, Sequel Med Tech, and Ypsomed. M.B. currently participates on the Hospital Advisory board with Dexcom and has received research support from Dexcom. L.B. has received research/product support from Dexcom, Ypsomed, and Boehringer Ingelheim; speaker honoraria from Dexcom and Oviva; and participated in advisory boards of Dexcom, Novo Nordisk, Sanofi, Ypsomed, Oviva, and Roche Diabetes Care. Y.M.B. has received honoraria for lectures and scientific advisory from Roche and AstraZeneca in the past 12 months. J.E.B. is on the Speaker’s bureau for Insulet and on the Advisory Board for Cardinal Health and Lifescan. J.E.B. is a consultant for Embecta. J.E.B. receives research support from the Leona M. and Harry B. Helmsley Charitable Trust. E.C. is on the scientific advisory board for Novo Nordisk, Eli-Lilly, Adocia, Arecor, Proventionbio, Portal Insulin, and MannKind. D.C. was a Dexcom employee from August 2018 to May 2024. D.C. is a shareholder of Dexcom, Lilly, Abbott and Tandem. M.C. has received consulting fees from Glooko and research support from Dexcom and Abbott Diabetes Care. G.L.C. is a shareholder of BioTex, Inc., Basepair Biotechnologies Inc., Coordination Centric, Inc., and Shape Memory Medical, Inc. G.L.C. is supported by the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (#1648451). K.K.D. has received honoraria, travel, or fees for advisory boards from AstraZeneca, Novo Nordisk, Boehringer Ingelheim, Eli Lilly, Abbott Diabetes, Menarini, and Sanofi in the last 12 months. J.E. receives federal funding from FDA, NIMHD, and NCATS and is a consultant for Sanofi. C.F. receives royalties from Dexcom and Novo Nordisk managed through her institution. G.A.F. is an advisor for 180 Life Sciences, 89bio, Adocia, Abbott, AdipoPharma, Aerami, Amolyt, Carthera, Catalyst, Cohen Global, CMC Magnetics, Diasome, Eleos, Entera Bio, Enterin, Glyscend, Hagar, Hogan Lovells, IM Therapeutics, Innoneo, Intarcia, Levicure, Lilly Asia Ventures, Lumos, Mars Symbioscience, Melior, metaLead Therapeutics, Microbiotix, MMD, Modular Medical, New Amsterdam, Northwestern, NuSirt, NuVox, Oramed, Pano, Pasithea, PhylloPharma, Pleiogenix, Recordati, Regor, Remodeless, Renaissance, RenovoRx, Rivus, RxMP, Sera Biopharma, Seraxis, Serpin, SFC Fluidics, Skinject, SROne, Stalicla, Surf Bio, TIXiMED, Veroscience, Verthermia, WaveBreak, Zealand Pharma, and Zucara. M.A.L.G. is a consultant for Abbott, Medtronic, Novonordisk, and Roche. R.J.G. received research support from Novo Nordisk, Dexcom and Eli Lilly and consulting fees/advisory fees from Abbott Diabetes Care, Aztra Zeneca, Bayer, Boehringer, Dexcom, Eli Lilly, Novo Nordisk, and Medtronic, outside of this work. R.J.G. is partially supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Numbers P30DK111024, K23DK123384, R03DK138255, and U2CD137135. A.M.G.-M. reports speaker fees from Novo Nordisk, Sanofi, Elli Lilly, Boehringer Ingelheim, Abbott, and Medtronic. L.H. is a consultant for a number of companies that are developing innovative solutions for the treatment of people with diabetes. N.H. reports Advisory Board member fees from Abbott Diabetes Care and Insulet as well as honoraria for lectures from Berlin-Chemie AG, Becton Dickenson, Sanofi Germany, Roche Diabetes Care, and Dexcom Germany. T.H. serves on the advisory board for Acella and Horizons Therapeutics (no financial compensation). S.H. has served on the advisory board for Tandem, Dexcom, and Medtronic, and received honoraria for nonpromotional educational and/or consultancy work from Abbott, Insulet, Dexcom, Roche, and Sanofi. P.G.J. is a shareholder of Pacific Diabetes Technologies Inc. and serves on the advisory board for Eli Lilly. P.G.J. received grant support from SFC Fluidics and research support from Eli Lilly and Dexcom. J.J. has received fees from lectures and or advisory boards from Abbott, Astra Zeneca, Boeringer Ingelheim, Eli Lilly, Medtronic, Nordic Infucare, Novo Nordisk, and Sanofi. S.R.J is a consultant for USV, Marico, Glenmark, Franco Indian, Twin Health, Biocon, and Zydus Lifesciences. S.R.J. received speaking honoraria from Abbott, Novo Nordisk, MSD, Sanofi, Boehringer Ingelheim, AstraZeneca, Lupin, Bayer Zydus, USV, DRL, Meyer Organics, Servier, Natco. R.A.L. is a consultant for Abbott Diabetes Care, Biolinq, Capillary Biomedical, Deep Valley Labs, Gluroo, PhysioLogic Devices, Portal Insulin, Sanofi, and Tidepool. R.A.L. has served on advisory boards for. ProventionBio and Lilly. R.A.L. receives research support through his institution from Insulet, Medtronic, Tandem, and Sinocare. L.A.L. has received research funding from, has provided CME on behalf of, and/or has acted as an advisor to Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Lexicon, Merck, Novo Nordisk, Pfizer, and Sanofi. M.L. has been a consultant or received honoraria from Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Nordic InfuCare, and Novo Nordisk, and has received research grants from Eli Lilly and Novo Nordisk, all outside of the submitted work. J.K.M. is a member of advisory boards of Abbott Diabetes Care, Becton-Dickinson, Biomea Fusion, Eli Lilly, Embecta, Medtronic, NovoNordisk A/S, Roche Diabetes Care, Sanofi-Aventis, and Viatris and received speaker honoraria from A. Menarini Diagnostics, Abbott Diabetes Care, AstraZeneca, Boehringer Ingelheim, Dexcom, Eli Lilly, Medtrust, MSD, NovoNordisk A/S, Roche Diabetes Care, Sanofi, Viatris, and Ypsomed. J.K.M. is a shareholder of decide Clinical Software GmbH and elyte Diagnostics. A.M. reports speaker fees from Novo Nordisk. M.M. has an existing research & development relationship with Scientific Bioprocessing, Inc and has been a consultant for Abbott. S.-J.M. is a consultant for Abbott, Curestream, Daewoong, EOFlow, G2e, Huons, iSense, Medtronic, Novo Nordisk, and Sanofi. D.N.O. has served on advisory boards for Abbott Laboratories, Medtronic, Merck Sharp & Dohme, Novo Nordisk, Roche, and Sanofi; received research support from Medtronic, Novo Nordisk, Roche, Eli Lilly and Company, and Sanofi; and received travel support from Novo Nordisk and Merck Sharp & Dohme. F.J.P. has received research support (to Emory University) from Dexcom, Insulet, Novo Nordisk, Tandem, and Ideal Medical Technologies, and has received consulting fees from Dexcom. A.L.P. is on the advisory board of Medscape, Vertex, and Lilly; receives research support from Insulin and Abbott; and has stock options in Omada Health. R.P.-B. receives grant support from Novo Nordisk, Lexicon Pharmaceuticals, and Medtronic and received consulting fees from Bayer, Lexicon Pharmaceuticals, Novo Nordisk, and Roche. C.M.R. has received honoraria and/or grant support from AstraZeneca, Dexcom, Fresenius, and Vifor. D.B.S. is supported by the Intramural Research Program of the National Institutes of Health. S.S. was a Novo Nordisk employee from 2022-2023. S.S. has served as advisor for Novo Nordisk and has received speaker fees from Novo Nordisk and Nordic Infucare within the past three years. G.D.S’s employer, nonprofit International Diabetes Center, HealthPartners Institute, has received educational grant funds from Abbott Diabetes Care, Medscape, and Sanofi-Aventis Groupe. G.D.S receives no personal income/honorarium from these activities. K.S. received grant supports from Arkray, Terumo, and Dexcom. E.K.S. is partly supported by the VA Merit Award (1I01CX001825) from the US Department of Veterans Affairs Clinical Sciences Research and has received unrestricted research support from Dexcom and Tandem Diabetes (to Baltimore VA Medical Center and to University of Maryland) for the conduction of clinical trials. N.L.S. received funding from Novo Nordisk for an investigator-initiated research grant unrelated to the current project. G.E.U. is partly supported by research grants from National Institutes of Health (NIH/NATS UL 3UL1TR002378-05S2) from the Clinical and Translational Science Award program, and from National Institutes of Health and National Center for Research Resources (NIH/NIDDK 2P30DK111024-06). G.E.U. has received research support (to Emory University) from Abbott, Bayer, and Dexcom; and has participated in advisory boards for Dexcom and GlyCare. E.W. has received consulting fees from Abbott Diabetes Care, Ascensia, Bayer, Boehringer, Ingelheim, Embecta, GlaxoSmithKline, Lilly, Medtronic, Renalytix, and Sanofi. E.W. has received honoraria from Abbott Diabetes Care, Bayer, Boehringer Ingelheim, GlaxoSmithKline, Lilly, Medtronic, Renalytix, and Sanofi. E.W. is on the Speakers’ Bureau from Abbott Diabetes Care, Bayer, Boehringer, Ingelheim, GlaxoSmithKline, Lilly, Renalytix, and Sanofi. M.Z. is a consultant for DexCom, Inc. M.A.K. is Chief Medical Officer of QuesGen, Inc. C.T., C.L., B.V.H., J.S.K., W.A.L., S.Y.R., J.J.S., K.W., C.W., K.R., C.N.H., A.T.A., A.A., R.R.B., E.M.B., J.G.C., K.Y.C., A.D., N.E., S.K.K., U.M., N.M., C.M., J.H.N., A.P., P.R., S.M.S., B.S., M.V., H.W.V., J.W., A.H.B.W., and S.Y. have nothing to disclose.

Background: The optimal criteria to select individuals with type 1 diabetes mellitus (T1D) and albuminuric or normoalbuminuric diabetic kidney disease (DKD), who are at risk of rapid kidney function decline, for clinical trials are unclear., Methods: This study analyzed data from the Preventing Early Renal Loss in Diabetes (PERL) clinical trial, which investigated whether allopurinol slowed kidney function decline in persons with T1D and early-to-moderate DKD. Rates of iohexol GFR (iGFR) and estimated GFR (eGFR) decline during the three-year study were compared by linear mixed effect regression between participants enrolled based on a history of moderately or severely increased albuminuria (N=394) and those enrolled based on a recent history of rapid kidney function decline (≥3 ml/min/1.73 m2/year) in the absence of a history of albuminuria (N=124). The association between baseline albuminuria and iGFR/eGFR decline during the trial was also evaluated., Results: Rates of eGFR decline during the trial were higher in participants with a history of albuminuria than in those with a history of rapid kidney function decline (-3.56 [95% confidence intervals {CI} -3.17, -3.95] versus -2.35 [95% CI: -1.86, -2.84] ml/min/1.73 m2/year, p=0.001). Results were similar for iGFR decline, although the difference was not significant (p=0.07). Within the history of albuminuria group, the rate of eGFR decline was -5.30 (95% CI -4.52, -6.08) ml/min/1.73m2/year in participants with severely increased albuminuria as compared to -2.97 (95% CI 2.44, -3.50) and -2.32 (95% CI -1.61, -3.03) ml/min/1.73m2/year in those with moderately increased or normal/mildly increased albuminuria at baseline (p<0.001)., Conclusions: Severely increased albuminuria at screening is a powerful criterion for selecting persons with T1D at high risk of kidney function decline. A history of rapid eGFR decline without a history of albuminuria is less effective for this purpose but it can still identify individuals with T1D who will lose kidney function more rapidly than expected from physiological aging., Clinical Trail Registration: ClinicalTrials.gov, NCT02017171., (Copyright © 2024 by the American Society of Nephrology.)

Background: Type 2 diabetes (T2D) is one of the most prevalent chronic diseases worldwide and a leading cause of cardiorenal disease and mortality. Only one-third of individuals with T2D receive care as recommended by the American Diabetes Association's clinical practice guidelines. Effective strategies are needed to accelerate the implementation of guideline concordant T2D care., Methods: The Michigan Collaborative for Type 2 Diabetes (MCT2D) is a statewide population health collaborative quality initiative (CQI) developed to improve the care of all people with T2D in Michigan. MCT2D has developed a learning health system with physician organizations and their constituent practices to support quality improvement initiatives focused on (1) improving use of guideline-directed pharmacotherapy to improve cardiorenal outcomes, (2) increasing evidence-based use of continuous glucose monitoring, and (3) supporting use of lower carbohydrate eating patterns., Results: Between 2021 and 2022, MCT2D recruited 28 of the 40 Michigan-based physician organizations participating in Blue Cross' Physician Group Incentive Program with 336 constituent practices and 1357 physicians in primary care (304), endocrinology (21) and nephrology (11). In January 2022, baseline data included a sample of 96,140 unique individuals with T2D. The baseline HbA1c was ≤ 7.0% for 66.3% of patients (n = 32,787), while 14.9% of patients had a most recent HbA1c ≥ 8.0% (n = 7,393). The most recent body mass index (BMI) was ≥ 30.0 for 64.8% of patients (n = 38,516)., Discussion: MCT2D has organized a statewide collaborative to recruit and engage a diverse and large set of physician organizations and their constituent practices. This is a promising opportunity to accelerate adoption of guideline-concordant care for people with T2D and may be a model for other state or regional collaboratives. Future directions include specific evidence-based interventions targeted at reducing diabetes-linked comorbidities and associated healthcare costs as well as strategies focused on T2D prevention among at-risk populations., (© 2024. The Author(s).)

Context: Obesity is a risk factor for coronavirus disease 2019 (COVID-19)-related outcomes; however, the mechanism remains unclear., Objective: The objective of this analysis was to determine whether inflammation mediates the association between obesity and COVID-19 outcomes., Methods: The International Study of Inflammation in COVID-19 (ISIC): A Prospective Multi-Center Observational Study Examining the Role of Biomarkers of Inflammation in Predicting Covid-19 Related Outcomes in Hospitalized Patients, was conducted at 10 hospitals in the United States and Europe. Participants were adults hospitalized specifically for COVID-19 between February 1, 2020, through October 19, 2022. Inflammatory biomarkers, including soluble urokinase plasminogen activator receptor (suPAR), were measured at admission. Associations were examined between body mass index (BMI, kg/m2) and a composite of death, need for mechanical ventilation, and renal replacement therapy, stratified by pre- and post-Omicron variants. The contribution of inflammation to the relationship between obesity and outcomes was assessed., Results: Among 4644 participants (mean age 59.3, 45.6% male, 21.8% BMI ≥ 35), those with BMI > 40 (n = 485) had 55% higher odds of the composite outcome (95% CI, 1.21-1.98) compared with nonobese individuals (BMI < 30, n = 2358) in multivariable analysis. In multiple mediation analysis, only suPAR remained a significant mediator between BMI and composite outcome. Associations were amplified for participants younger than 65 years and with pre-Omicron variants., Conclusion: Obesity is associated with worse outcomes in COVID-19, notably in younger participants and in the pre-Omicron era. Inflammation, as measured by suPAR, is a significant mediator of the association between obesity and COVID-19 outcomes., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society.)

Objective: To determine bidirectional associations between the timing of chronic diabetes complications (CDCs) and mental health disorders (MHDs) in individuals with type 1 or type 2 diabetes., Research Design and Methods: We used a nationally representative health care claims database to identify matched individuals with type 1 or 2 diabetes or without diabetes using a propensity score quasirandomization technique stratified by age (0-19, 20-39, 40-59, and ≥60 years). CDCs and MHDs were identified using ICD-9/10 codes. We fit Cox proportional hazards models with time-varying diagnoses of CDCs or MHDs to investigate their association with the hazard of developing MHDs or CDCs, respectively., Results: From 2001 to 2018, a total of 553,552 individuals were included (44,735 with type 1 diabetes, 152,187 with type 2 diabetes, and 356,630 without diabetes). We found that having a CDC increased the hazard of developing an MHD (hazard ratio [HR] 1.9-2.9; P < 0.05, with higher HRs in older age strata), and having an MHD increased the hazard of developing a CDC (HR 1.4-2.5; P < 0.05, with the highest HR in age stratum 0-19 years). In those aged <60 years, individuals with type 1 diabetes were more likely to have CDCs, whereas individuals with type 2 diabetes were more likely to have MHDs. However, the relationship between CDCs and MHDs in either direction was not affected by diabetes type (P > 0.05 for interaction effects)., Conclusions: We found a consistent bidirectional association between CDCs and MHDs across the life span, highlighting the important relationship between CDCs and MHDs. Prevention and treatment of either comorbidity may help reduce the risk of developing the other., (© 2024 by the American Diabetes Association.)