Impact of glycemic control on circulating endothelial progenitor cells and arterial stiffness in patients with type 2 diabetes mellitus

<p>Abstract</p> <p>Background</p> <p>Patients with type 2 diabetes mellitus (DM) have increased risk of endothelial dysfunction and arterial stiffness. Levels of circulating endothelial progenitor cells (EPCs) are also reduced in hyperglycemic states. However, the relationships between glycemic control, levels of EPCs and arterial stiffness are unknown.</p> <p>Methods</p> <p>We measured circulating EPCs and brachial-ankle pulse wave velocity (baPWV) in 234 patients with type 2 DM and compared them with 121 age- and sex-matched controls.</p> <p>Results</p> <p>Patients with DM had significantly lower circulating Log CD34/KDR⁺and Log CD133/KDR⁺EPC counts, and higher Log baPWV compared with controls (all <it>P < 0.05</it>). Among those 120/234 (51%) of DM patients with satisfactory glycemic control (defined by Hemoglobin A1c, HbA1c < 6.5%), they had significantly higher circulating Log CD34/KDR⁺and Log CD133/KDR⁺EPC counts, and lower Log baPWV compared with patients with poor glycemic control (all <it>P < 0.05)</it>. The circulating levels of Log CD34/KDR⁺EPC (r = -0.46, <it>P < 0.001</it>) and Log CD133/KDR⁺EPC counts (r = -0.45, <it>P < 0.001</it>) were negatively correlated with Log baPWV. Whilst the level of HbA1c positively correlated with Log baPWV (r = 0.20, <it>P < 0.05</it>) and negatively correlated with circulating levels of Log CD34/KDR⁺EPC (r = -0.40, <it>P < 0.001</it>) and Log CD133/KDR⁺EPC (r = -0.41, <it>P < 0.001</it>). Multivariate analysis revealed that HbA1c, Log CD34/KDR⁺and Log CD133/KDR⁺EPC counts were independent predictors of Log baPWV (<it>P < 0.05</it>).</p> <p>Conclusions</p> <p>In patients with type 2 DM, the level of circulating EPCs and arterial stiffness were closely related to their glycemic control. Furthermore, DM patients with satisfactory glycemic control had higher levels of circulating EPCs and were associated with lower arterial stiffness.</p

Predicting dementia diagnosis from cognitive footprints in electronic health records: a case-control study protocol

INTRODUCTION: Dementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case-control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history. METHODS AND ANALYSIS: We will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared. ETHICS AND DISSEMINATION: This study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients' records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities' Action in Response to Dementia project (https://www.tip-card.hku.hk/)

Prognostic implications of surrogate markers of atherosclerosis in low to intermediate risk patients with type 2 diabetes.

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Roles of the CHADS2 and CHA2DS2-VASc scores in post-myocardial infarction patients: Risk of new occurrence of atrial fibrillation and ischemic stroke

Background: Patients with myocardial infarction (MI) are at risk of the development of atrial fibrillation (AF) and ischemic stroke. We sought to evaluate the prognostic performance of the CHADS2 and CHA2DS2-VASc scores in predicting new AF and/or ischemic stroke in post-ST segment elevation MI (STEMI) patients. Six hundred and seven consecutive post-STEMI patients with no previously documented AF were studied.Methods and Results: After a follow-up of 63 months (3,184 patient-years), 83 (13.7%) patients developed new AF (2.8% per year). Patients with a high CHADS2 and/or CHA2DS2-VASc score were more likely to develop new AF. The annual incidence of new AF was 1.18%, 2.10%, 4.52%, and 7.03% in patients with CHADS2 of 0, 1, 2, and ≥ 3; and 0.39%, 1.72%, 1.83%, and 5.83% in patients with a CHA2DS2-VASc score of 1, 2, 3 and ≥ 4. The CHA2DS2-VASc score (C-statistic = 0.676) was superior to the CHADS2 (C-statistic = 0.632) for discriminating new AF. Ischemic stroke occurred in 29 patients (0.9% per year), the incidence increasing in line with the CHADS2 (0.41%, 1.02%, 1.11%, and 1.95% with score of 0, 1, 2, and ≥ 3) and CHA2DS2-VASc scores (0.39%, 0.49%, 1.02%, and 1.48% with score of 1, 2, 3 and ≥ 4). The C-statistic of the CHA2DS2-VASc score as a predictor of ischemic stroke was 0.601, superior to that of CHADS2 score (0.573). CHADS2 and CHA2DS2-VASc scores can identify post-STEMI patients at high risk of AF and stroke.Conclusions: The CHADS2 and CHA2DS2-VASc scores can identify post-STEMI patients at high risk of AF and ischemic stroke. This enables close surveillance and prompt anticoagulation for stroke prevention

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively. Funding: The Research Grants Council of Hong Kong under the Early Career Scheme 27110519

Age-Specific Associations of Renal Impairment With Magnetic Resonance Imaging Markers of Cerebral Small Vessel Disease in Transient Ischemic Attack and Stroke

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The default mode network is disrupted in Parkinson's disease with visual hallucinations.

BACKGROUND: Visual hallucinations (VH) are one of the most striking nonmotor symptoms in Parkinson's disease (PD), and predict dementia and mortality. Aberrant default mode network (DMN) is associated with other psychoses. Here, we tested the hypothesis that DMN dysfunction contributes to VH in PD. METHODS: Resting state functional data was acquired from individuals with PD with VH (PDVH) and without VH (PDnonVH), matched for levodopa drug equivalent dose, and a healthy control group (HC). Independent component analysis was used to investigate group differences in functional connectivity within the DMN. In addition, we investigated whether the functional changes associated with hallucinations were accompanied by differences in cortical thickness. RESULTS: There were no group differences in cortical thickness but functional coactivation within components of the DMN was significantly lower in both PDVH and PDnonVH groups compared to HC. Functional coactivation within the DMN was found to be greater in PDVH group relative to PDnonVH group. CONCLUSION: Our study demonstrates, for the first time that, within a functionally abnormal DMN in PD, relatively higher "connectivity" is associated with VH. We postulate that aberrant connectivity in a large scale network affects sensory information processing and perception, and contributes to "positive" symptom generation in PD.Contract grant sponsor: Research Grant Council of Hong Kong (General Research Fund awarded to Chua and McAlonan); Infrastructural support: National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and [Institute of Psychiatry] King's College London (McAlonan); Wellcome Trust; Contract grant number: 088324 (Rowe); National Institute for Health Research Cambridge Biomedical Research Centre (Suckling).This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/hbm.2257

The Spill-Over Impact of the Novel Coronavirus-19 Pandemic on Medical Care and Disease Outcomes in Non-communicable Diseases: A Narrative Review

OBJECTIVES: The coronavirus-19 (COVID-19) pandemic has claimed more than 5 million lives worldwide by November 2021. Implementation of lockdown measures, reallocation of medical resources, compounded by the reluctance to seek help, makes it exceptionally challenging for people with non-communicable diseases (NCD) to manage their diseases. This review evaluates the spill-over impact of the COVID-19 pandemic on people with NCDs including cardiovascular diseases, cancer, diabetes mellitus, chronic respiratory disease, chronic kidney disease, dementia, mental health disorders, and musculoskeletal disorders. METHODS: Literature published in English was identified from PubMed and medRxiv from January 1, 2019 to November 30, 2020. A total of 119 articles were selected from 6,546 publications found. RESULTS: The reduction of in-person care, screening procedures, delays in diagnosis, treatment, and social distancing policies have unanimously led to undesirable impacts on both physical and psychological health of NCD patients. This is projected to contribute to more excess deaths in the future. CONCLUSION: The spill-over impact of COVID-19 on patients with NCD is just beginning to unravel, extra efforts must be taken for planning the resumption of NCD healthcare services post-pandemic

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively

Association between the risk of seizure and COVID-19 vaccinations: A self-controlled case-series study

OBJECTIVE: The risk of seizure following BNT162b2 and CoronaVac vaccinations has been sparsely investigated. This study aimed to evaluate this association. METHOD: Patients who had their first seizure-related hospitalization between February 23, 2021 and January 31, 2022 were identified in Hong Kong. All seizure episodes happening on the day of vaccination (day 0) were excluded since clinicians validated that most of the cases on day 0 were syncopal episodes. Within-individual comparison using a modified self-controlled case series analysis was applied to estimate the incidence rate ratio (IRR) with 95% confidence intervals (CI) of seizure using conditional Poisson regression. RESULTS: We identified 1656 individuals who had their first seizure-related hospitalization (BNT162b2: 426; CoronaVac: 263; unvaccinated: 967) within the observation period. The incidence of seizure was 1.04 (95% CI: 0.80-1.33) and 1.11 (95% CI: 0.80-1.50) per 100,000 doses of BNT162b2 and CoronaVac administered respectively. 16 and 17 individuals received second dose after having first seizure within 28 days after first dose of BNT162b2 and CoronaVac vaccinations, respectively. None had recurrent seizures after the second dose. There was no increased risk during day 1-6 after the first (BNT162b2: IRR=1.39, 95% CI=0.75-2.58; CoronaVac: IRR=1.19, 95% CI=0.50-2.83) and second doses (BNT162b2: IRR=1.36, 95% CI 0.72-2.57; CoronaVac: IRR=0.71, 95% CI=0.22-2.30) of vaccinations. During 7-13, 14-20- and 21-27-days post-vaccination, no association was observed for both vaccines. SIGNIFICANCE: The findings demonstrated no increased risk of seizure following BNT162b2 and CoronaVac vaccinations. Future studies will be warranted to evaluate the risk of seizure following COVID-19 vaccinations in different populations with subsequent doses to ensure the generalizability