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Summary: 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.

Introduction: Whilst gut microbiome (GM) dysregulation and glymphatic dysfunction have been identified as emerging risk factors of dementia in symptomatic individuals, it is unknown whether GM is associated with the glymphatic system, especially in the pre-symptomatic stage of dementia. We therefore aimed to identify the associations of GM with neuroimaging markers of glymphatic function, [diffusion tensor imaging-along the perivascular vascular space (DTI-ALPS)] and cognitive functions in a cohort of neurologically healthy middle-aged individuals. Methods: We recruited middle-aged Hong Kong Chinese without known cognitive impairment or symptomatic vascular disorders. We studied the associations of their GM characterized by shotgun metagenomic sequencing with cerebral DTI-ALPS index and fractional anisotropy (FA) as determined by brain magnetic resonance imaging. Cognitive performances were assessed by Victoria Stroop Test and Hong Kong Montreal Cognitive Assessment (HK-MoCA). Permutational multivariate analysis of covariance based on Bray-Curtis Dissimilarity was performed to calculate the β-diversity of the samples. Shannon alpha diversity was examined to measure the GM diversity within the subjects and partial least squares-discriminatory analysis was performed to discriminate characteristics of species-level microbiota with DTI-ALPS index and cognitive assessments. Data were analyzed under the full model of covariate adjustment including age, sex, body mass index, educational years, hypertension and smoking status. Results: In 241 Hong Kong Chinese (mean age 54±6 years, 47% men; HK-MoCA M= 27.9±2.25; Stroop Interference M= 11.69±7.37), we identified significant positive correlations between Firmicutes/Bacteroidetes ratio and the DTI-ALPS index (p

Background: Systemic inflammation is a significant mechanism underpinning adverse cognitive changes. Sleep quality is a crucial factor associated with systemic inflammation and neurocognitive health. Elevated levels of pro-inflammatory cytokines in the periphery help mark inflammation. With this background, we examined the relationship between systemic inflammation, subjective sleep quality, and neurocognitive performance in adults. Method & Results: In 252 healthy adults, we measured the systemic inflammation reflected by serum levels of IL-6, IL-12, IL-18, TNF-α and IFN-γ, subjective sleep quality reflected by the global scores of the Pittsburgh Sleep Quality Index, and their neurocognitive performance measured by the Hong Kong Montreal Cognitive Assessment. We observed that neurocognitive performance was negatively related to IL-18 (p = 0.046) and positively related to sleep quality (p = 0.006). We did not observe significant associations between other cytokines and neurocognitive performance. Furthermore, we found that sleep quality as a mediator explained the relationship between IL-18 and neurocognitive performance depending on the levels of IL-12 (index of moderated mediation: 95% CI = [0.0047, 0.0664]). Better subjective sleep quality buffered the negative effect of IL-18 on neurocognitive performance when IL-12 was low (bootstrapping 95% CI: [- 0.0824, - 0.0018]). On the contrary, poor subjective sleep quality mediated the association between higher IL-18 and poorer neurocognitive performance when IL-12 was elevated (bootstrapping 95% CI: [0.0004, 0.0608]). Conclusion & Implications: Our findings indicate that systemic inflammation was negatively associated with neurocognitive performance. Sleep quality regulated by IL-18/IL-12 axis activation could be a potential mechanism underpinning neurocognitive changes. Our results illustrate the intricate relationships between immune functioning, sleep quality and neurocognitive performance. These insights are essential to understand the potential mechanisms underpinning neurocognitive changes, paving the way for the development of preventive interventions for the risk of cognitive impairment.

Summary: Red blood cell distribution width (RCDW) and mean corpuscular volume (MCV) are associated with different risk factors for hemorrhagic stroke. However, whether RCDW and MCV are causally related to hemorrhagic stroke remains poorly understood. Therefore, we explored the causality between RCDW/MCV and nontraumatic hemorrhagic strokes using Mendelian randomization (MR) methods. We extracted exposure and outcome summary statistics from the UK Biobank and FinnGen. We evaluated the causality of RCDW/MCV on four outcomes (subarachnoid hemorrhage [SAH], intracerebral hemorrhage [ICH], nontraumatic intracranial hemorrhage [nITH], and a combination of SAH, cerebral aneurysm, and aneurysm operations) using univariable MR (UMR) and multivariable MR (MVMR). We further performed colocalization and mediation analyses. UMR and MVMR revealed that higher genetically predicted MCV is protective of ICH (UMR: odds ratio [OR] = 0.89 [0.8–0.99], p = 0.036; MVMR: OR = 0.87 [0.78–0.98], p = 0.021) and nITH (UMR: OR = 0.89 [0.82–0.97], p = 0.005; MVMR: OR = 0.88 [0.8–0.96], p = 0.004). There were no strong causal associations between RCDW/MCV and any other outcome. Colocalization analysis revealed a shared causal variant between MCV and ICH; it was not reported to be associated with ICH. Proportion mediated via diastolic blood pressure was 3.1% (0.1%,14.3%) in ICH and 3.4% (0.2%,15.8%) in nITH. The study constitutes the first MR analysis on whether genetically elevated RCDW and MCV affect the risk of hemorrhagic strokes. UMR, MVMR, and mediation analysis revealed that MCV is a protective factor for ICH and nITH, which may inform new insights into the treatments for hemorrhagic strokes.

Summary: Background: This study aims to evaluate the association between thromboembolic events and hemorrhagic stroke following BNT162b2 and CoronaVac vaccination. Methods: Patients with incident thromboembolic events or hemorrhagic stroke within 28 days of covid-19 vaccination or SARS-CoV-2 positive test during 23 February to 30 September 2021 were included. The incidence per 100,000 covid-19 vaccine doses administered and SARS-CoV-2 test positive cases were estimated. A modified self-controlled case series (SCCS) analysis using the data from the Hong Kong territory-wide electronic health and vaccination records. Seasonal effect was adjusted by month. Findings: A total of 5,526,547 doses of BNT162b2 and 3,146,741 doses of CoronaVac were administered. A total of 334 and 402 thromboembolic events, and 57 and 49 hemorrhagic stroke cases occurred within 28 days after BNT162b2 and CoronaVac vaccination, respectively. The crude incidence of thromboembolic events and hemorrhagic stroke per 100,000 doses administered for both covid-19 vaccines were smaller than that per 100,000 SARS-CoV-2 test positive cases. The modified SCCS detected an increased risk of hemorrhagic stroke in BNT162b2 14-27 days after first dose with adjusted IRR of 2.53 (95% CI 1.48-4.34), and 0-13 days after second dose with adjusted IRR 2.69 (95% CI 1.54-4.69). No statistically significant risk was observed for thromboembolic events for both vaccines. Interpretation: We detected a possible safety signal for hemorrhagic stroke following BNT162b2 vaccination. The incidence of thromboembolic event or hemorrhagic stroke following vaccination is lower than that among SARS-CoV-2 test positive cases; therefore, vaccination against covid-19 remains an important public health intervention. Funding: This study was funded by a research grant from the Food and Health Bureau, The Government of the Hong Kong Special Administrative Region (reference COVID19F01).