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Objective:To investigate the mortality risk linked to prescription of different anti-diabetic medication classes.Design:Prospective population-based study.Setting:UK Biobank.Participants:410 389 of the 502 536 participants in UK Biobank with covariate data, clinical and prescription records were included in the analyses, 43 610 of which had been diagnosed type 2 diabetes (T2D). A nearest neighbour covariate matching (NNCM) algorithm based on covariates with relevant effects on survival was applied to match cohorts of anti-diabetic medication class users to minimally differing control cohorts, either with a T2D diagnosis or without. Kaplan Meier estimates and Cox proportional models were used to evaluate survival differences and hazard ratio between drug classes and controls.Main outcome measures:All-cause mortality and causes of death.Results:13667 (3.3%) individuals died during a median of 12.2 years of follow-up. After applying NNCM, participants with T2D on metformin (average hazard ratio 0.39, 95% confidence interval 0.31 to 0.49) or SGLT2I (average hazard ratio 0.58, 95% confidence interval 0.36 to 0.93) have an increased survival probability compared to matched individuals with T2D. When compared to matched individuals without T2D, the survival probability of individuals with T2D increases only if prescribed SGLT2I (average hazard ratio 0.31, 95% confidence interval 0.19 to 0.51). NNCM based analysis of matched individuals with T2D on both SGLT2I and metformin versus metformin only reveals increased survival in the presence of SGLT2I (average hazard ratio 0.29, 95% confidence interval 0.09 to 0.91), also when compared to matched identical individuals without T2D (average hazard ratio 0.05, 95% confidence interval 0.01 to 0.19). All the other anti-diabetic drugs analyzed are either detrimental in prolonging lifespan (insulin, thiazolidinediones, and sulfonylureas), or have no effect (DPP4 inhibitors and GLP1 receptor agonists).Conclusion:The use of the current first-line anti-diabetic treatment, metformin, or sodium glucose cotransporter 2 inhibitors (SGLT2I) increases the survival probability compared to matched individuals with diabetes using other anti-diabetic drugs. Only individuals on SGLT2I experience increased survival when compared to individuals without T2D.

BackgroundNearly every individual sustaining traumatic spinal cord injury receives multiple types and classes of medications to manage a litany of secondary complications. Prior clinical studies and evidence from animal models suggest that several of these medications could enhance or impede endogenous neurological recovery. However, there is a knowledge gap surrounding the spectrum of pharmacologic agents typically administered in the routine management of spinal cord injury.ObjectiveTo systematically determine the types of medications commonly administered, alone or in combination, in the acute to subacute phase of spinal cord injury.MethodsWe conducted an analysis of two largescale cohorts (the Sygen interventional trial and the SCIRehab observational cohort study) to determine what constitutes “ standards of acute pharmacological care” after spinal cord injury. Concomitant medication use, including dosage, timing and reason for administration, was tracked. Descriptive statistics were used to describe the medications administered within the first 60 days after spinal cord injury.ResultsAcross 2040 individuals with spinal cord injury, 775 unique medications were administered within the two months after injury. On average, patients enrolled in the Sygen trial received 9.9 ± 4.9 (range 0-34), 14.3 ± 6.3 (range 1-40), 18.6 ± 8.2 (range 0-58), and 21.5 ± 9.7 (range 0-59) medications within the first 7, 14, 30, and 60 days post-injury, respectively. Patients enrolled in the SCIRehab cohort study received on average 1.7 ± 1.7 (range 0-11), 3.7 ± 3.7 (range 0-24), 8.5 ± 6.3 (range 0-42), and 13.5 ± 8.3 (range 0-52) medications within the first 7, 14, 30, and 60 days post-injury, respectively. Polypharmacy was commonplace (up to 43 medications per day per patient). Approximately 10% of medications were administered acutely as prophylaxis (e.g., against the development of pain or infections).ConclusionsTo our knowledge, this was the first time acute pharmacological practices have been comprehensively examined after spinal cord injury. Our study revealed a high degree of polypharmacy in the acute stages of spinal cord injury, with potential to both positively and negatively impact neurological recovery. This data may provide key insight to achieve better understanding of how the acute pharmacological management of spinal cord injury affects long-term recovery. All results can be interactively explored on theRXSCIweb site (https://jutzelec.shinyapps.io/RxSCI/) and GitHub repository (https://github.com/jutzca/Acute-Pharmacological-Treatment-in-SCI/).

ObjectiveTo track and quantify the natural course of hematological markers over the first year following spinal cord injury.MethodsData on hematological markers, demographics, and injury characteristics were extracted from medical records of a clinical trial (Sygen) and an ongoing observational cohort study (Murnau Study). The primary outcomes were concentration/levels/amount of commonly collected hematological markers at multiple time-points. Two-way ANOVA and mixed-effects regression techniques were used to account for the longitudinal data and adjust for potential confounders. Trajectories of hematological markers contained in both data sources were compared using the slope of progression.ResultsAt baseline (≤ 2 weeks post-injury), most hematological markers were at pathological levels, but returned to normal values over the course of six to twelve months post-injury. The baseline levels and longitudinal trajectories were dependent on injury severity. More complete injuries were associated with more pathological values (e.g. hematocrit, ANOVA test; Chisq = 77.10, df = 3, adjusted p-valueConclusionsDue to trauma-induced physiological perturbations, hematological markers undergo marked changes over the course of recovery, from initial pathological levels that normalize within a year. The findings from this study are important as they provide a benchmark for clinical decision making and prospective clinical trials. All results can be interactively explored on the Haemosurveillance website (https://jutzelec.shinyapps.io/Haemosurveillance/).Code availabilityhttps://github.com/jutzca/Systemic-effects-of-Spinal-Cord-Injury