Impaired GK-GKRP interaction rather than direct GK activation worsens lipid profiles and contributes to long-term complications: a Mendelian randomization study.

BACKGROUND: Glucokinase (GK) plays a key role in glucose metabolism. In the liver, GK is regulated by GK regulatory protein (GKRP) with nuclear sequestration at low plasma glucose level. Some GK activators (GKAs) disrupt GK-GKRP interaction which increases hepatic cytoplasmic GK level. Excess hepatic GK activity may exceed the capacity of glycogen synthesis with excess triglyceride formation. It remains uncertain whether hypertriglyceridemia associated with some GKAs in previous clinical trials was due to direct GK activation or impaired GK-GKRP interaction. METHODS: Using publicly available genome-wide association study summary statistics, we selected independent genetic variants of GCKR and GCK associated with fasting plasma glucose (FPG) as instrumental variables, to mimic the effects of impaired GK-GKRP interaction and direct GK activation, respectively. We applied two-sample Mendelian Randomization (MR) framework to assess their causal associations with lipid-related traits, risks of metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular diseases. We verified these findings in one-sample MR analysis using individual-level statistics from the Hong Kong Diabetes Register (HKDR). RESULTS: Genetically-proxied impaired GK-GKRP interaction increased plasma triglycerides, low-density lipoprotein cholesterol and apolipoprotein B levels with increased odds ratio (OR) of 14.6 (95% CI 4.57-46.4) per 1 mmol/L lower FPG for MASLD and OR of 2.92 (95% CI 1.78-4.81) for coronary artery disease (CAD). Genetically-proxied GK activation was associated with decreased risk of CAD (OR 0.69, 95% CI 0.54-0.88) and not with dyslipidemia. One-sample MR validation in HKDR showed consistent results. CONCLUSIONS: Impaired GK-GKRP interaction, rather than direct GK activation, may worsen lipid profiles and increase risks of MASLD and CAD. Development of future GKAs should avoid interfering with GK-GKRP interaction.

Status epilepticus management in patients with brain tumors. A cohort study.

PURPOSE: Status epilepticus (SE) represents a neurological emergency with significant morbidity and mortality. SE in patients with primary brain tumors received only limited attention to date; detailed analysis of treatment flow is lacking, especially as compared to other SE causes. This study aims to describe the frequency and treatment flow of tumor-related SE and compare it to other SE etiologies. METHODS: Retrospective cohort study based on an institutional SE registry (SERCH) comprising adult SE (excluding post-anoxic causes), treated between January 2013 and December 2022, comparing SE management, frequency of refractory SE, and clinical outcome, among four patients' groups stratified by SE etiology: Non-neoplastic, Gliomas, Brain metastases, Other brain tumors. RESULTS: We analyzed 961 episodes in 831 patients (Non-neoplastic: 649, Gliomas: 85, Metastases: 77, Other brain tumors: 20). Although tumor-patients presented more often with focal episodes and less consciousness impairment than non-neoplastic patients, administration of benzodiazepines as first-line treatment (>75% across all groups), and utilization of second-line ASM were similar across groups. Treatment adequacy was marginally higher in glioma patients compared to the non-neoplastic population (p: 0.049), while refractory SE was comparable in all groups (p: 0.269). No significant differences in clinical outcomes were observed (mortality: non-neoplastic (89/649, 13.7%), glioma (8/85, 9.4%), metastases (14/77, 18.2%), other tumors (5/20, 25.0%), p: 0.198; non-neoplastic vs. glioma, p: 0.271) CONCLUSION: Tumor-associated SE represents 1/5 of all SE episodes, and is managed similarly to other SE causes. Treatment responsiveness and short-term clinical outcomes also exhibit comparable results.

Association of dipeptidyl peptidase-4 inhibitor initiation at glycated haemoglobin <7.5% with reduced major clinical events mediated by low glycated haemoglobin variability.

AIM: Therapeutic inertia, hypoglycaemia and poor treatment persistence can lead to glycaemic fluctuation and poor outcomes in type 2 diabetes (T2D). We compared glycated haemoglobin (HbA1c) variability, insulin initiation, severe hypoglycaemia and clinical events in patients with T2D initiated dipeptidyl peptidase-4 inhibitors (DPP4is) at low versus high HbA1c thresholds. METHODS: Using territory-wide electronic medical records in Hong Kong, we curated a propensity score-matched cohort of patients initiated DPP4i at HbA1c <7.5% versus ≥7.5% in 2007-2019. We expressed the HbA1c variability score (HVS) as a proportion of HbA1c varied by ≥0.5% compared with preceding values. We used the Cox model to compare the risks of insulin initiation and clinical outcomes, adjusted for time-varying variables between the two groups. Mediation analysis estimated the effects of HbA1c variability on outcomes. RESULTS: Among 6874 insulin-naïve patients who initiated DPP4i, 88.7% were treated with metformin and 79.6% with sulphonylureas at baseline (54.9% men; mean age 65.2 ± 11.4 years). After a median follow-up of 4.6 years, compared with the high-threshold plus high-HVS group (≥50%), the low-threshold plus low-HVS (<50%) group had reduced hazard ratios (95% confidence interval) of insulin initiation (0.35, 0.31-0.40), severe hypoglycaemia (0.38, 0.34-0.44), major adverse cardiovascular endpoints (0.76, 0.66-0.88), heart failure (0.42, 0.36-0.49), end-stage kidney disease (0.65, 0.36-0.49) and mortality (0.45, 0.35-0.57). Reduced HbA1c variability explained 31.1%-81.2% of the effect size of DPP4i initiation at HbA1c <7.5% versus ≥7.5% on outcomes. CONCLUSIONS: In Chinese patients with T2D, avoiding therapeutic inertia with intensified glycaemic control at HbA1c <7.5% using drugs with low risk of hypoglycaemia and good tolerability, such as DPP4i, delayed insulin treatment, reduced HbA1c variability and improved clinical events.

Monogenic diabetes in a Chinese population with young-onset diabetes: A 17-year prospective follow-up study in Hong Kong.

AIMS: Asians have a high prevalence of young-onset diabetes, but the pattern of monogenic diabetes is unknown. We aimed to determine the prevalence of monogenic diabetes in Chinese patients with young-onset diabetes and compare the clinical characteristics and outcome between patients with and without monogenic diabetes. MATERIALS AND METHODS: We sequenced a targeted panel of 33 genes related to monogenic diabetes in 1021 Chinese patients with non-type 1 diabetes diagnosed at age ≤40 years. Incident complications including cardiovascular disease (CVD), end-stage kidney disease (ESKD) and all-cause death were captured since enrolment (1995-2012) until 2019. RESULTS: In this cohort (mean ± SD age at diagnosis: 33.0 ± 6.0 years, median[IQR] diabetes duration 7.0[1.0-15.0] years at baseline, 44.9% men), 22(2.2%, 95% confidence interval[CI] 1.4%-3.2%) had monogenic diabetes. Pathogenic (P) or likely pathogenic (LP) variants were detected in GCK (n = 6), HNF1A (n = 9), HNF4A (n = 1), PLIN1 (n = 1) and PPARG (n = 2), together with copy number variations in HNF1B (n = 3). Over a median follow-up of 17.1 years, 5(22.7%) patients with monogenic diabetes (incidence rate 12.3[95% CI 5.1-29.4] per 1000 person-years) versus 254(25.4%) without monogenic diabetes (incidence rate 16.7[95% CI 14.8-18.9] per 1000 person-years) developed the composite outcome of CVD, ESKD and/or death (p = 0.490). The multivariable Cox model did not show any difference in hazards for composite events between groups. CONCLUSIONS: In Chinese with young-onset non-type 1 diabetes, at least 2% of cases were contributed by monogenic diabetes, over 80% of which were accounted for by P/LP variants in common MODY genes. The incidence of diabetes complications was similar between patients with and without monogenic diabetes.

Young-Onset Diabetes in East Asians: From Epidemiology to Precision Medicine.

Precision diagnosis is the keystone of clinical medicine. In East Asians, classical type 1 diabetes is uncommon in patients with youngonset diabetes diagnosed before age of 40, in whom a family history, obesity, and beta-cell and kidney dysfunction are key features. Young-onset diabetes affects one in five Asian adults with diabetes in clinic settings; however, it is often misclassified, resulting in delayed or non-targeted treatment. Complex aetiologies, long disease duration, aggressive clinical course, and a lack of evidence-based guidelines have contributed to variable care standards and premature death in these young patients. The high burden of comorbidities, notably mental illness, highlights the numerous knowledge gaps related to this silent killer. The majority of adult patients with youngonset diabetes are managed as part of a heterogeneous population of patients with various ages of diagnosis. A multidisciplinary care team led by physicians with special interest in young-onset diabetes will help improve the precision of diagnosis and address their physical, mental, and behavioral health. To this end, payors, planners, and providers need to align and re-design the practice environment to gather data systematically during routine practice to elucidate the multicausality of young-onset diabetes, treat to multiple targets, and improve outcomes in these vulnerable individuals.

A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study.

BACKGROUND: Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. METHODS AND FINDINGS: We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. CONCLUSIONS: Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

Diving into CDC pregnancy data in the United States: longitudinal study and interactive application.

Objective: Preterm birth (PTB) is a major determinant of neonatal mortality, morbidity, and childhood disability. In this article, we present a longitudinal analysis of the risk factors associated with PTB and how they have varied over the years: starting from 1968 when the CDC first started, reporting the natality data, up until 2021. Along with this article, we are also releasing an RShiny web application that will allow for easy consumption of this voluminous dataset by the research community. Further, we hope this tool can aid clinicians in the understanding and prevention of PTB. Materials and Methods: This study used the CDC Natality data from 1968 to 2021 to analyze trends in PTB outcomes across the lens of various features, including race, maternal age, education, and interval length between pregnancies. Our interactive RShiny web application, CDC NatView, allows users to explore interactions between maternal risk factors and maternal morbidity conditions and the aforementioned features. Results: Our study demonstrates how CDC data can be leveraged to conduct a longitudinal analysis of natality trends in the United States. Our key findings reveal an upward trend in late PTBs, which is concerning. Moreover, a significant disparity exists between African American and White populations in terms of PTB. These disparities persist in other areas, such as education, body-mass index, and access to prenatal care later in pregnancy. Discussion: Another notable finding is the increase in maternal age over time. Additionally, we confirm that short interpregnancy intervals (IPIs) are a risk factor for PTBs. To facilitate the exploration of pregnancy risk factors, infections, and maternal morbidity, we developed an open-source RShiny tool called CDC NatView. This software offers a user-friendly interface to interact with and visualize the CDC natality data, which constitutes an invaluable resource. Conclusion: In conclusion, our study has shed light on the rise of late PTBs and the persistent disparities in PTB rates between African American and White populations in the US. The increase in maternal age and the confirmation of a short IPI as a risk factor for PTB are noteworthy findings. Our open-source tool, CDC NatView, can be a valuable resource for further exploration of the CDC natality data to enhance our understanding of pregnancy risk factors and the interaction of PTB outcomes and maternal morbidities.

Comparison of ß-Cell Function and Insulin Sensitivity Between Normal-Weight and Obese Chinese With Young-Onset Type 2 Diabetes.

Normal-weight individuals with usual-onset type 2 diabetes have reduced ß-cell function and greater insulin sensitivity compared with their obese counterparts. The relative contribution of ß-cell dysfunction and insulin resistance to young-onset type 2 diabetes (YOD) among normal-weight individuals is not well established. In 44 individuals with YOD (24 with normal weight and 20 with obesity) and 24 healthy control individuals with normoglycemia (12 with normal weight and 12 with obesity), we conducted 2-h 12 mmol/L hyperglycemic clamps to measure acute (0-10 min) and steady-state (100-120 min) insulin and C-peptide responses, as well as insulin sensitivity index. Normal-weight individuals with YOD had lower acute insulin response, steady-state insulin and C-peptide responses, and a higher insulin sensitivity index compared with their obese counterparts with YOD. Compared with BMI-matched healthy control individuals, normal-weight individuals with YOD had lower acute and steady-state insulin and C-peptide responses but a similar insulin sensitivity index. The impairment of steady-state ß-cell response relative to healthy control individuals was more pronounced in normal-weight versus obese individuals with YOD. In conclusion, normal-weight Chinese with YOD exhibited worse ß-cell function but preserved insulin sensitivity relative to obese individuals with YOD and BMI-matched healthy individuals with normoglycemia. The selection of glucose-lowering therapy should account for pathophysiological differences underlying YOD between normal-weight and obese individuals.

Enhanced prediction of abnormal glucose tolerance using an extended non-invasive risk score incorporating routine renal biochemistry.

INTRODUCTION: Type 2 diabetes is preventable in subjects with impaired glucose tolerance based on 2-hour plasma glucose (2hPG) during 75 g oral glucose tolerance test (OGTT). We incorporated routine biochemistry to improve the performance of a non-invasive diabetes risk score to identify individuals with abnormal glucose tolerance (AGT) defined by 2hPG≥7.8 mmol/L during OGTT. RESEARCH DESIGN AND METHODS: We used baseline data of 1938 individuals from the community-based "Better Health for Better Hong Kong - Hong Kong Family Diabetes Study (BHBHK-HKFDS) Cohort" recruited in 1998-2003. We incorporated routine biochemistry in a validated non-invasive diabetes risk score, and evaluated its performance using area under receiver operating characteristics (AUROC) with internal and external validation. RESULTS: The AUROC of the original non-invasive risk score to predict AGT was 0.698 (95% CI, 0.662 to 0.733). Following additional inclusion of fasting plasma glucose, serum potassium, creatinine, and urea, the AUROC increased to 0.778 (95% CI, 0.744 to 0.809, p<0.001). Net reclassification improved by 31.9% (p<0.001) overall, by 30.8% among people with AGT and 1.1% among people without AGT. The extended model showed good calibration (χ2=11.315, p=0.1845) and performance on external validation using an independent data set (AUROC=0.722, 95% CI, 0.680 to 0.764). CONCLUSIONS: The extended risk score incorporating clinical and routine biochemistry can be integrated into an electronic health records system to select high-risk subjects for evaluation of AGT using OGTT for prevention of diabetes.

Taxogenomic analyses of Starmerella gilliamiae f.a, sp. nov. and Starmerella monicapupoae f.a., sp. nov., two novel species isolated from plant substrates and insects.

Four yeast isolates collected from flowers from different ecosystems in Brazil, one from fruit of Nothofagus alpina in Argentina, three from flowers of Neltuma chilensis in Chile and one obtained from the proventriculus of a female bumblebee in Canada were demonstred, by analysis of the sequences of the internal transcribed spacer (ITS) region and D1/D2 domains of the large subunit rRNA gene, to represent two novel species of the genus Starmerella. These species are described here as Starmerella gilliamiae f.a, sp. nov. (CBS 16166T; Mycobank MB 851206) and Starmerella monicapupoae f.a., sp. nov. (PYCC 8997T; Mycobank MB 851207). The results of a phylogenomic analysis using 1037 single-copy orthogroups indicated that S. gilliamiae is a member of a subclade that contains Starmerella opuntiae, Starmerella aceti and Starmerella apicola. The results also indicated that S. monicapupoae is phylogenetically related to Starmerella riodocensis. The two isolates of S. monicapupoae were obtained from flowers in Brazil and were probably vectored by insects that visit these substrates. Starmerella gilliamiae has a wide geographical distribution having been isolated in flowers from Brazil and Chile, fruit from Argentina and a bumblebee from Canada.

Diabetes and infection: review of the epidemiology, mechanisms and principles of treatment.

An association between diabetes and infection has been recognised for many years, with infection being an important cause of death and morbidity in people with diabetes. The COVID-19 pandemic has re-kindled an interest in the complex relationship between diabetes and infection. Some infections occur almost exclusively in people with diabetes, often with high mortality rates without early diagnosis and treatment. However, more commonly, diabetes is a complicating factor in many infections. A reciprocal relationship occurs whereby certain infections and their treatments may also increase the risk of diabetes. People with diabetes have a 1.5- to 4-fold increased risk of infection. The risks are the most pronounced for kidney infection, osteomyelitis and foot infection, but are also increased for pneumonia, influenza, tuberculosis, skin infection and general sepsis. Outcomes from infection are worse in people with diabetes, with the most notable example being a twofold higher rate of death from COVID-19. Hyperglycaemia has deleterious effects on the immune response. Vascular insufficiency and neuropathy, together with altered skin, mucosal and gut microbial colonisation, contribute to the increased risk of infection. Vaccination is important in people with diabetes although the efficacy of certain immunisations may be compromised, particularly in the presence of hyperglycaemia. The principles of treatment largely follow those of the general population with certain notable exceptions.

Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank.

AIMS/HYPOTHESIS: The aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers. METHODS: From a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m2) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts. RESULTS: At false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts. CONCLUSIONS/INTERPRETATION: Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.

The predictive value of highly malignant EEG patterns after cardiac arrest: evaluation of the ERC-ESICM recommendations.

PURPOSE: The 2021 guidelines endorsed by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) recommend using highly malignant electroencephalogram (EEG) patterns (HMEP; suppression or burst-suppression) at > 24 h after cardiac arrest (CA) in combination with at least one other concordant predictor to prognosticate poor neurological outcome. We evaluated the prognostic accuracy of HMEP in a large multicentre cohort and investigated the added value of absent EEG reactivity. METHODS: This is a pre-planned prognostic substudy of the Targeted Temperature Management trial 2. The presence of HMEP and background reactivity to external stimuli on EEG recorded > 24 h after CA was prospectively reported. Poor outcome was measured at 6 months and defined as a modified Rankin Scale score of 4-6. Prognostication was multimodal, and withdrawal of life-sustaining therapy (WLST) was not allowed before 96 h after CA. RESULTS: 845 patients at 59 sites were included. Of these, 579 (69%) had poor outcome, including 304 (36%) with WLST due to poor neurological prognosis. EEG was recorded at a median of 71 h (interquartile range [IQR] 52-93) after CA. HMEP at > 24 h from CA had 50% [95% confidence interval [CI] 46-54] sensitivity and 93% [90-96] specificity to predict poor outcome. Specificity was similar (93%) in 541 patients without WLST. When HMEP were unreactive, specificity improved to 97% [94-99] (p = 0.008). CONCLUSION: The specificity of the ERC-ESICM-recommended EEG patterns for predicting poor outcome after CA exceeds 90% but is lower than in previous studies, suggesting that large-scale implementation may reduce their accuracy. Combining HMEP with an unreactive EEG background significantly improved specificity. As in other prognostication studies, a self-fulfilling prophecy bias may have contributed to observed results.

Recent advances in clinical electroencephalography.

PURPOSE OF REVIEW: Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients. RECENT FINDINGS: Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field. SUMMARY: This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.

Validation of the RBD Symptom Severity Scale in the North American Prodromal Synucleinopathy Consortium.

BACKGROUND AND OBJECTIVES: REM sleep behavior disorder (RBD) is a parasomnia characterized by dream enactment. The International RBD Study Group developed the RBD Symptom Severity Scale (RBDSSS) to assess symptom severity for clinical or research use. We assessed the psychometric and clinimetric properties of the RBDSSS in participants enrolled in the North American Prodromal Synucleinopathy (NAPS) Consortium for RBD. METHODS: NAPS participants, who have polysomnogram-confirmed RBD, and their bedpartners completed the RBDSSS (participant and bedpartner versions). The RBDSSS contains 8 questions to assess the frequency and severity/impact of (1) dream content, (2) vocalizations, (3) movements, and (4) injuries associated with RBD. Total scores for participant (maximum score = 54) and bedpartner (maximum score = 38) questionnaires were derived by multiplying frequency and severity scores for each question. The Clinical Global Impression Scale of Severity (CGI-S) and RBD symptom frequency were assessed by a physician during a semistructured clinical interview with participants and, if available, bedpartners. Descriptive analyses, correlations between overall scores, and subitems were assessed, and item response analysis was performed to determine the scale's validity. RESULTS: Among 261 study participants, the median (interquartile range) score for the RBDSSS-PT (participant) was 10 (4-18) and that for the RBDSSS-BP (bedpartner) was 8 (4-15). The median CGI-S was 3 (3-4), indicating moderate severity. RBDSSS-BP scores were significantly lower in women with RBD (6 vs 9, p = 0.02), while there were no sex differences in RBDSSS-PT scores (8 vs 10.5, p = 0.615). Positive correlations were found between RBDSSS-PT vs RBDSSS-BP (Spearman rs = 0.561), RBDSSS-PT vs CGI-S (rs = 0.556), and RBDSSS-BP vs CGI-S (rs = 0.491, all p < 0.0001). Item response analysis showed a high discriminatory value (range 1.40-2.12) for the RBDSSS-PT and RBDSSS-BP (1.29-3.47). DISCUSSION: We describe the RBDSSS with adequate psychometric and clinimetric properties to quantify RBD symptom severity and good concordance between participant and bedpartner questionnaires and between RBDSSS scores and clinician-assessed global severity.

1-year weight change after diabetes diagnosis and long-term incidence and sustainability of remission of type 2 diabetes in real-world settings in Hong Kong: An observational cohort study.

BACKGROUND: Clinical trials have demonstrated that remission of type 2 diabetes can be achieved following sustained weight loss. However, the feasibility of achieving diabetes remission through weight management in real-world settings remains unclear. In this study, we aimed to examine the association of weight change at 1 year after diabetes diagnosis with long-term incidence and sustainability of type 2 diabetes remission in real-world settings in Hong Kong. METHODS AND FINDINGS: This was a population-based observational cohort study. The territory-wide Risk Assessment and Management Programme for Diabetes Mellitus (RAMP-DM) provides regular comprehensive assessments of metabolic control and complication screening for people with diabetes in Hong Kong. We included 37,326 people with newly diagnosed type 2 diabetes who were enrolled in the RAMP-DM between 2000 and 2017, followed until 2019. Diabetes remission was defined as 2 consecutive HbA1c <6.5% measurements at least 6 months apart in the absence of glucose-lowering drugs (GLDs) and with no record of GLDs at least 3 months before these measurements. During a median follow-up of 7.9 years, 6.1% (2,279) of people achieved diabetes remission, with an incidence rate of 7.8 (95% CI: 7.5, 8.1) per 1,000 person-years. After adjusting for age at diabetes diagnosis, sex, assessment year, body mass index, other metabolic indices, smoking, alcohol drinking, and medication use, the hazard ratio (HR) for diabetes remission was 3.28 (95% CI: 2.75, 3.92; p < 0.001) for people with ≥10% weight loss within 1 year of diagnosis, 2.29 (95% CI: 2.03, 2.59; p < 0.001) for those with 5% to 9.9% weight loss, and 1.34 (95% CI: 1.22, 1.47; p < 0.001) for those with 0% to 4.9% weight loss compared to people with weight gain. During a median follow-up of 3.1 years, 67.2% (1,531) of people who had achieved diabetes remission returned to hyperglycaemia, with an incidence rate of 184.8 (95% CI: 175.5, 194.0) per 1,000 person-years. The adjusted HR for returning to hyperglycaemia was 0.52 (95% CI: 0.41, 0.65; p < 0.001) for people with ≥10% weight loss, 0.78 (95% CI: 0.68, 0.92; p = 0.002) for those with 5% to 9.9% weight loss, and 0.90 (95% CI: 0.80, 1.01; p = 0.073) for those with 0% to 4.9% weight loss compared to people with weight gain. Diabetes remission was associated with a 31% (HR: 0.69, 95% CI: 0.52, 0.93; p = 0.014) decreased risk of all-cause mortality. The main limitation of the study is that the reliability of HbA1c used to define diabetes remission can be affected by other medical conditions. Furthermore, we did not have data on bariatric surgery. CONCLUSIONS: In this study, greater weight loss within the first year of diabetes diagnosis was associated with an increased likelihood of achieving diabetes remission and a decreased risk of returning to hyperglycaemia among those who had achieved diabetes remission. However, both the incidence of diabetes remission and the probability of its long-term sustainability were low with conventional management in real-world settings, in an era when the importance of weight loss was not fully appreciated. Our study provides evidence for policymakers to design and implement early weight management interventions and diabetes remission initiatives.

Early treatment with dipeptidyl-peptidase 4 inhibitors reduces glycaemic variability and delays insulin initiation in type 2 diabetes: A propensity score-matched cohort study.

AIMS: To examine whether early treatment intensification using dipeptidyl-peptidase 4 inhibitors (DPP4i) delays insulin initiation in Chinese patients diagnosed with type 2 diabetes for less than 5 years. MATERIALS AND METHODS: In a territory-wide prospective cohort study, patients with type 2 diabetes initiating DPP4i at diabetes duration <2 years (early intensification) and 3-5 years (late intensification) were matched using 1:1 propensity-score matching (n = 908 in each arm). We used Cox regression to compare the risk of insulin initiation between the two groups. We explored the interactive and mediation effects of glycated haemoglobin (HbA1c) variability score (HVS), defined as the percentage of HbA1c varying by ≥0.5% compared with preceding values. RESULTS: Of 1816 patients (60.7% men, mean age 54.4 ± 11.9 years), 92.4% and 71.9% were treated with metformin and sulphonylureas respectively at DPP4i initiation. Early DPP4i intensification [hazard ratio (HR) 0.71, (95% CI 0.58-0.68)] and low HVS (<50%) (HR = 0.40, 0.33-0.50) were associated with delayed insulin initiation during a median 4.08 years of follow-up. Early intensification with low HVS had the lowest risk versus late intensification with high HVS (HR = 0.30, 0.22-0.40) (pinteraction  = 0.013). HVS mediated 19.5% of the total effect of early DPP4i intensification on delaying insulin initiation. The late and early intensification groups had similar HbA1c at month 0 (8.4 ± 1.3% vs. 8.4 ± 1.5%) and month 3 (7.6 ± 1.2% vs. 7.6 ± 1.3%) after DPP4i initiation. By month 12, HbA1c in the late intensification group deteriorated (7.9 ± 1.4%) but remained stable in the early intensification group (7.6 ± 1.4%, p = 0.001) with persistent between-group difference over 72 months (8.2 ± 1.7% vs. 7.7 ± 1.6%, p = 0.001). CONCLUSIONS: In type 2 diabetes, early DPP4i intensification delayed insulin initiation, partially explained by reduced glycaemic variability.

Impact of Body Composition and Anemia on Accuracy of a Real-Time Continuous Glucose Monitor in Diabetes Patients on Continuous Ambulatory Peritoneal Dialysis.

Continuous glucose monitoring (CGM) is proposed as an alternative for glycemic assessment in peritoneal dialysis, but volume overload and anemia may affect sensor accuracy. This is an exploratory analysis of a study of Guardian Connect™ with Guardian Sensor™ 3 in 30 participants with diabetes on continuous ambulatory peritoneal dialysis (CAPD) (age [mean ± standard deviation] 64.7 ± 5.6 years, 23 men, body mass index [BMI] 25.4 ± 3.9 kg/m2, blood hemoglobin [Hb] 10.7 ± 1.3 g/dL). The mean absolute relative difference (MARD) was calculated between paired sensor and YSI 2300 STAT venous glucose readings (n = 941) during an 8-h in-clinic session with glucose challenge. Body composition was evaluated using bioimpedance. The overall MARD was 10.4% (95% confidence interval 9.6-11.7). There were no correlations between BMI, extracellular water, relative hydration index, and lean or fat mass with MARD. No correlations were observed between MARD and Hb (r = 0.016, P > 0.05). In summary, this real-time CGM demonstrated good accuracy in CAPD with minimal influence from body composition and anemia.

Continuous versus Routine Electroencephalography in the Intensive Care Unit: A Review of Current Evidence.

BACKGROUND: Electroencephalography (EEG) has long been used to detect seizures in patients with disorders of consciousness. In recent years, there has been a drastically increased adoption of continuous EEG (cEEG) in the intensive care units (ICUs). Given the resources necessary to record and interpret cEEG, this is still not available in every center and widespread recommendations to use continuous instead of routine EEG (typically lasting 20 min) are still a matter of some debate. Considering recent literature and personal experience, this review offers a rationale and practical advice to address this question. SUMMARY: Despite the development of increasingly performant imaging techniques and several validated biomarkers, EEG remains central to clinicians in the intensive care unit and has been experiencing expanding popularity for at least 2 decades. Not only does EEG allow seizure or status epilepticus detection, which in the ICU often present without clinical movements, but it is also paramount for the prognostic evaluation of comatose patients, especially after cardiac arrest, and for detecting delayed ischemia after subarachnoid hemorrhage. At the end of the last Century, improvements of technical and digital aspects regarding recording and storage of EEG tracings have progressively led to the era of cEEG and automated quantitative analysis. KEY MESSAGES: As compared to repeated rEEG, cEEG in comatose patients does not seem to improve clinical prognosis to a relevant extent, despite allowing a more performant of detection ictal events and consequent therapeutic modifications. The choice between cEEG and rEEG must therefore always be patient-tailored.

Staged treatment response in status epilepticus: Lessons from the SENSE registry.

OBJECTIVES: Although in epilepsy patients the likelihood of becoming seizure-free decreases substantially with each unsuccessful treatment, to our knowledge this has been poorly investigated in status epilepticus (SE). We aimed to evaluate the proportion of SE cessation and functional outcome after successive treatment steps. METHODS: We conducted a post hoc analysis of a prospective, observational, multicenter cohort (Sustained Effort Network for treatment of Status Epilepticus [SENSE]), in which 1049 incident adult SE episodes were prospectively recorded at nine European centers. We analyzed 996 SE episodes without coma induction before the third treatment step. Rates of SE cessation, mortality (in ongoing SE or after SE control), and favorable functional outcome (assessed with modified Rankin scale) were evaluated after each step. RESULTS: SE was treated successfully in 838 patients (84.1%), 147 (14.8%) had a fatal outcome (36% of them died while still in SE), and 11 patients were transferred to palliative care while still in SE. Patients were treated with a median of three treatment steps (range 1-13), with 540 (54.2%) receiving more than two steps (refractory SE [RSE]) and 95 (9.5%) more than five steps. SE was controlled after the first two steps in 45%, with an additional 21% treated after the third, and 14% after the fourth step. Likelihood of SE cessation (p < 0.001), survival (p = 0.003), and reaching good functional outcome (p < 0.001) decreased significantly between the first two treatment lines and the third, especially in patients not experiencing generalized convulsive SE, but remained relatively stable afterwards. SIGNIFICANCE: The significant worsening of SE prognosis after the second step clinically supports the concept of RSE. However, and differing from findings in human epilepsy, RSE remains treatable in about one third of patients, even after several failed treatment steps. Clinical judgment remains essential to determine the aggressiveness and duration of SE treatment, and to avoid premature treatment cessation in patients with SE.