Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort: a secondary analysis of pooled data from insulin clinical trials.

AIMS/HYPOTHESIS: The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events. METHODS: Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed. RESULTS: For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period. CONCLUSIONS/INTERPRETATION: Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.

Ongoing burden and recent trends in severe hospitalised hypoglycaemia events in people with type 1 and type 2 diabetes in Scotland: A nationwide cohort study 2016-2022.

AIMS: We examined severe hospitalised hypoglycaemia (SHH) rates in people with type 1 and type 2 diabetes in Scotland during 2016-2022, stratifying by sociodemographics. METHODS: Using the Scottish National diabetes register (SCI-Diabetes), we identified people with type 1 and type 2 diabetes alive anytime during 2016-2022. SHH events were determined through linkage to hospital admission and death registry data. We calculated annual SHH rates overall and by age, sex, and socioeconomic status. Summary estimates of time and stratum effects were obtained by fitting adjusted generalised additive models using R package mgcv. RESULTS: Rates for those under 20 with type 1 diabetes reached their minimum at the 2020-2021 transition, 30% below the study period average. A gradual decline over time also occurred among 20-49-year-olds with type 1 diabetes. Overall, females had 15% higher rates than males with type 2 diabetes (rate ratio 1.15, 95% CI 1.08-1.22). People in the most versus least deprived quintile experienced 2.58 times higher rates (95% CI 2.27-2.93) in type 1 diabetes and 2.33 times higher (95% CI 2.08-2.62) in type 2 diabetes. CONCLUSIONS: Despite advances in care, SHH remains a significant problem in diabetes. Future efforts must address the large socioeconomic disparities in SHH risks.

Tracking down the White Plague. Chapter three: Revision of endocranial abnormally pronounced digital impressions as paleopathological diagnostic criteria for tuberculous meningitis.

Abnormally pronounced digital impressions (APDIs) on the endocranial surface develop secondary to a prolonged rise in the intracranial pressure. This can result from a number of pathological conditions, including hydrocephalus due to tuberculous meningitis (TBM). APDIs have been described with relation to TBM not only in the modern medical literature but also in several paleopathological studies. However, APDIs are not pathognomonic for TBM and their diagnostic value for identifying TBM in past human populations has not been evaluated in identified pre-antibiotic era skeletons. To assess the diagnostic value of APDIs for the first time, a macroscopic investigation was performed on skeletons from the Terry Collection (Smithsonian Institution, Washington, DC, USA). Our material consisted of 234 skeletons with tuberculosis (TB) as the cause of death (TB group) and 193 skeletons with non-tuberculous (NTB) causes of death (NTB group). The macroscopic examination focused on the stage of the prominence and frequency of APDIs in the TB group and NTB group. To determine the significance of difference (if any) in the frequency of APDIs between the two groups, χ2 testing of our data was conducted. We found that APDIs were twice as common in the TB group than in the NTB group. The χ2 comparison of the frequencies of APDIs revealed a statistically significant difference between the two groups. In addition, APDIs with more pronounced stages were recorded more frequently in the TB group. Our results indicate that APDIs can be considered as diagnostic criteria for TBM in the paleopathological practice. With suitable circumspection, their utilization provides paleopathologists with a stronger basis for identifying TB and consequently, with a more sensitive means of assessing TB frequency in past human populations.

Verification of tuberculosis infection among Vác mummies (18th century CE, Hungary) based on lipid biomarker profiling with a new HPLC-HESI-MS approach.

Tuberculosis (TB) was a large burden of infections that peaked during the 19th century in Europe. Mummies from the 18th century CE, discovered in the crypt of a church at Vác, Hungary, had high TB prevalence, as revealed by amplification of key fragments of TB DNA and genome-wide TB analysis. Complementary methods are needed to confirm these diagnoses and one approach uses the identification of specific lipid biomarkers, such as TB mycocerosic acids (MCs). Previously, MC derivatives were profiled by specialised gas chromatography-mass spectrometry (GC-MS), so an alternative more direct approach has been developed. Underivatized MCs are extracted and analysed by high-performance liquid chromatography linked to a mass spectrometer, in heated electrospray ionisation mode (HPLC-HESI-MS). The method was validated using representatives of the Mycobacterium tuberculosis complex and other mycobacteria and tested on six Vác mummy cases, previously considered positive for TB infection. Analysing both rib and soft tissue samples, four out of six cases gave profiles of main C32 and major C29 and C39 mycocerosates correlating well with those of M. tuberculosis. Multidisciplinary methods are needed in the diagnosis of ancient tuberculosis; this new protocol accesses important confirmatory evidence, as demonstrated by the confirmation of TB in the Vác mummies.