Brain Health Outcomes in Sexual and Gender Minority Groups: Results From the All of Us Research Program.

BACKGROUND AND OBJECTIVES: Sexual and gender minority (SGM) groups have been historically underrepresented in neurologic research, and their brain health disparities are unknown. We aim to evaluate whether SGM persons are at higher risk of adverse brain health outcomes compared with cisgender straight (non-SGM) individuals. METHODS: We conducted a cross-sectional study in the All of Us Research Program, a US population-based study, including all participants with information on gender identity and sexual orientation. We used baseline questionnaires to identify sexual minority (lesbian, gay, bisexual, diverse sexual orientation; nonstraight sexual orientation) and gender minority (gender diverse and transgender; gender identity different from sex assigned at birth) participants. The primary outcome was a composite of stroke, dementia, and late-life depression, assessed using electronic health record data and self-report. Secondarily, we evaluated each disease separately. Furthermore, we evaluated all subgroups of gender and sexual minorities stratified by sex assigned at birth. We used multivariable logistic regression (adjusted for age, sex assigned at birth, race/ethnicity, cardiovascular risk factors, other relevant comorbidities, and neighborhood deprivation index) to assess the relationship between SGM groups and the outcomes. RESULTS: Of 413,457 US adults enrolled between May 31, 2017, and June 30, 2022, we included 393,041 participants with available information on sexual orientation and gender identity (mean age 51 [SD 17] years), of whom 39,632 (10%) belonged to SGM groups. Of them, 38,528 (97%) belonged to a sexual minority and 4,431 (11%) to a gender minority. Compared with non-SGM, SGM persons had 15% higher odds of the brain health composite outcome (odds ratio [OR] 1.15, 95% CI 1.08-1.22). In secondary analyses, these results persisted across sexual and gender minorities separately (all 95% CIs > 1). Assessing individual diseases, all SGM groups had higher odds of dementia (SGM vs non-SGM: OR 1.14, 95% CI 1.00-1.29) and late-life depression (SGM vs non-SGM: OR 1.27, 95% CI 1.17-1.38) and transgender women had higher odds of stroke (OR 1.68, 95% CI 1.04-2.70). DISCUSSION: In a large US population study, SGM persons had higher odds of adverse brain health outcomes. Further research should explore structural causes of inequity to advance inclusive and diverse neurologic care.

Brain Care Score and Neuroimaging Markers of Brain Health in Asymptomatic Middle-Age Persons.

OBJECTIVES: To investigate associations between health-related behaviors as measured using the Brain Care Score (BCS) and neuroimaging markers of white matter injury. METHODS: This prospective cohort study in the UK Biobank assessed the BCS, a novel tool designed to empower patients to address 12 dementia and stroke risk factors. The BCS ranges from 0 to 21, with higher scores suggesting better brain care. Outcomes included white matter hyperintensities (WMH) volume, fractional anisotropy (FA), and mean diffusivity (MD) obtained during 2 imaging assessments, as well as their progression between assessments, using multivariable linear regression adjusted for age and sex. RESULTS: We included 34,509 participants (average age 55 years, 53% female) with no stroke or dementia history. At first and repeat imaging assessments, every 5-point increase in baseline BCS was linked to significantly lower WMH volumes (25% 95% CI [23%-27%] first, 33% [27%-39%] repeat) and higher FA (18% [16%-20%] first, 22% [15%-28%] repeat), with a decrease in MD (9% [7%-11%] first, 10% [4%-16%] repeat). In addition, a higher baseline BCS was associated with a 10% [3%-17%] reduction in WMH progression and FA decline over time. DISCUSSION: This study extends the impact of the BCS to neuroimaging markers of clinically silent cerebrovascular disease. Our results suggest that improving one's BCS could be a valuable intervention to prevent early brain health decline.

APOE epsilon variants and composite risk of dementia, disability, and death in the health and retirement study.

BACKGROUND: Clinical trials in older adults are increasingly focused on functional outcomes, and the composite outcome of dementia, disability, and death is gaining pivotal importance. Genetic variation, particularly the APOE epsilon(ε) variants, may modify responses to new treatments. Although APOE ε4 is known to influence these outcomes separately, the magnitude of its effect on this composite outcome remains unknown. We tested the hypothesis that APOE ε4 increases, whereas APOE ε2 decreases, the risk of a composite outcome of dementia, disability, and death. METHODS: We evaluated clinical and genomic data from the Health and Retirement Study collected from 1992 to 2020. We used variants rs429358 and rs7412 to determine APOE genotypes, modeled dominantly (carriers/noncarriers). We conducted survival analysis, using multivariable Cox proportional hazards models with a composite endpoint of dementia, disability, and death. Our primary analysis evaluated participants with genetic data and no previous dementia or disability. In secondary analyses, we focused on persons aged > = 75 years without heart disease or stroke, a subpopulation increasingly important in clinical trials of older adults. RESULTS: We included 14,527 participants in the primary analysis. Over a median of 18 (Interquartile Range [IQR] 12-24) years, 6711 (46%) participants developed the composite outcome. In Cox analyses, APOE ε4 associated with higher risk (HR:1.15, 95%CI:1.09-1.22) of the composite outcome, whereas APOE ε2 associated with lower risk (HR:0.92, 95%CI:0.86-0.99). In the secondary analysis, we included 3174 participants. Over a median of 7 (IQR 4-11) years, 1326 participants (42%) developed the composite outcome. In Cox analyses, APOE ε4 associated with higher risk (HR:1.25, 95%CI:1.10-1.41) of the composite outcome, whereas APOE ε2 associated with lower risk (HR:0.84, 95%CI:0.71-0.98). CONCLUSIONS: APOE ε variants are linked to the risk of dementia, disability, and death in older adults. By examining these variants in clinical trials, we can better elucidate how they might alter the effectiveness of tested interventions. Importantly, this genetic information could help identify participants who may have greater absolute benefit from such interventions.

Bidirectional relationship between epigenetic age and brain health events.

Chronological age offers an imperfect estimate of the molecular changes that occur with aging. Epigenetic age, which is derived from DNA methylation data, provides a more nuanced representation of aging-related biological processes. This study examines the bidirectional relationship between epigenetic age and the occurrence of brain health events (stroke, dementia, and late-life depression). Using data from the Health and Retirement Study, we analyzed blood samples from over 4,000 participants to determine how epigenetic age relates to past and future brain health events. Study participants with a prior brain health event prior to blood collection were 4% epigenetically older (beta 0.04, SE 0.01), suggesting that these conditions are associated with faster aging than that captured by chronological age. Furthermore, a one standard deviation increase in epigenetic age was associated with 70% higher odds of experiencing a brain health event in the next four years after blood collection (OR 1.70, 95%CI 1.16-2.50), indicating that epigenetic age is not just a consequence but also a predictor of poor brain health. Both results were replicated through Mendelian Randomization analyses, supporting their causal nature. Our findings support the utilization of epigenetic age as a useful biomarker to evaluate the role of interventions aimed at preventing and promoting recovery after a brain health event.

A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data.

OBJECTIVE: In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP). The authors aimed to develop an ML-based algorithm that automatically classifies normal signals, artifacts, and drainages in high-resolution ICP monitoring data from EVDs, making the data suitable for real-time artifact removal and for future ML applications. METHODS: In their 2-center retrospective cohort study, the authors used labeled ICP data from 40 patients in the first neurocritical care unit (University Hospital Zurich) for model development. The authors created 94 descriptive features that were used to train the model. They compared histogram-based gradient boosting with extremely randomized trees after building pipelines with principal component analysis, hyperparameter optimization via grid search, and sequential feature selection. Performance was measured with nested 5-fold cross-validation and multiclass area under the receiver operating characteristic curve (AUROC). Data from 20 patients in a second, independent neurocritical care unit (Charité - Universitätsmedizin Berlin) were used for external validation with bootstrapping technique and AUROC. RESULTS: In cross-validation, the best-performing model achieved a mean AUROC of 0.945 (95% CI 0.92-0.969) on the development dataset. On the external validation dataset, the model performed with a mean AUROC of 0.928 (95% CI 0.908-0.946) in 100 bootstrapping validation cycles to classify normal signals, artifacts, and drainages. CONCLUSIONS: Here, the authors developed a well-performing supervised model with external validation that can detect normal signals, artifacts, and drainages in ICP signals from patients in neurocritical care units. For future analyses, this is a powerful tool to discard artifacts or to detect drainage events in ICP monitoring signals.

Intraparenchymal near-infrared spectroscopy for detection of delayed cerebral ischemia in poor-grade aneurysmal subarachnoid hemorrhage.

OBJECTIVE: Detection of delayed cerebral ischemia (DCI) is challenging in comatose patients with poor-grade aneurysmal subarachnoid hemorrhage (aSAH). Brain tissue oxygen pressure (PbtO2) monitoring may allow early detection of its occurrence. Recently, a probe for combined measurement of intracranial pressure (ICP) and intraparenchymal near-infrared spectroscopy (NIRS) has become available. In this pilot study, the parameters PbtO2, Hboxy, Hbdeoxy, Hbtotal and rSO2 were measured in parallel and evaluated for their potential to detect perfusion deficits or cerebral infarction. METHODS: In patients undergoing multimodal neuromonitoring due to poor neurological condition after aSAH, Clark oxygen probes, microdialysis and NIRS-ICP probes were applied. DCI was suspected when the measured parameters in neuromonitoring deteriorated. Thus, perfusion CT scan was performed as follow up, and DCI was confirmed as perfusion deficit. Median values for PbtO2, Hboxy, Hbdeoxy, Hbtotal and rSO2 in patients with perfusion deficit (Tmax > 6 s in at least 1 vascular territory) and/or already demarked infarcts were compared in 24- and 48-hour time frames before imaging. RESULTS: Data from 19 patients (14 University Hospital Zurich, 5 Charité Universitätsmedizin Berlin) were prospectively collected and analyzed. In patients with perfusion deficits, the median values for Hbtotal and Hboxy in both time frames were significantly lower. With perfusion deficits, the median values for Hboxy and Hbtotal in the 24 h time frame were 46,3 [39.6, 51.8] µmol/l (no perfusion deficits 53 [45.9, 55.4] µmol/l, p = 0.019) and 69,3 [61.9, 73.6] µmol/l (no perfusion deficits 74,6 [70.1, 79.6] µmol/l, p = 0.010), in the 48 h time frame 45,9 [39.4, 51.5] µmol/l (no perfusion deficits 52,9 [48.1, 55.1] µmol/l, p = 0.011) and 69,5 [62.4, 74.3] µmol/l (no perfusion deficits 75 [70,80] µmol/l, p = 0.008), respectively. In patients with perfusion deficits, PbtO2 showed no differences in both time frames. PbtO2 was significantly lower in patients with infarctions in both time frames. The median PbtO2 was 17,3 [8,25] mmHg (with no infarctions 29 [22.5, 36] mmHg, p = 0.006) in the 24 h time frame and 21,6 [11.1, 26.4] mmHg (with no infarctions 31 [22,35] mmHg, p = 0.042) in the 48 h time frame. In patients with infarctions, the median values of parameters measured by NIRS showed no significant differences. CONCLUSIONS: The combined NIRS-ICP probe may be useful for early detection of cerebral perfusion deficits and impending DCI. Validation in larger patient collectives is needed.

APOE ε4 and Intracerebral Hemorrhage in Patients With Brain Arteriovenous Malformation.

Importance: Intracerebral hemorrhage (ICH) is a serious complication of brain arteriovenous malformation (AVM). Apolipoprotein E (APOE) ε4 is a well-known genetic risk factor for ICH among persons without AVM, and cerebral amyloid angiopathy is a vasculopathy frequently observed in APOE ε4 carriers that may increase the risk of ICH. Objective: To assess whether APOE ε4 is associated with a higher risk of ICH in patients with a known AVM. Design, Setting, and Participants: This cross-sectional study including 412 participants was conducted in 2 stages (discovery and replication) using individual-level data from the UK Biobank (released March 2012 and last updated October 2023) and the All of Us Research Program (commenced on May 6, 2018, with its latest update provided in October 2023). The occurrence of AVM and ICH was ascertained at the time of enrollment using validated International Classification of Diseases, Ninth Revision and Tenth Revision, codes. Genotypic data on the APOE variants rs429358 and rs7412 were used to ascertain the ε status. Main Outcomes and Measures: For each study, the association between APOE ε4 variants and ICH risk was assessed among patients with a known AVM by using multivariable logistic regression. Results: The discovery phase included 253 UK Biobank participants with known AVM (mean [SD] age, 56.6 [8.0] years, 119 [47.0%] female), of whom 63 (24.9%) sustained an ICH. In the multivariable analysis of 240 participants of European ancestry, APOE ε4 was associated with a higher risk of ICH (odds ratio, 4.58; 95% CI, 2.13-10.34; P < .001). The replication phase included 159 participants with known AVM enrolled in All of Us (mean [SD] age, 57.1 [15.9] years; 106 [66.7%] female), of whom 29 (18.2%) sustained an ICH. In multivariable analysis of 101 participants of European ancestry, APOE ε4 was associated with higher risk of ICH (odds ratio, 4.52; 95% CI, 1.18-19.38; P = .03). Conclusions and Relevance: The results of this cross-sectional study of patients from the UK Biobank and All of Us suggest that information on APOE ε4 status may help identify patients with brain AVM who are at particularly high risk of ICH and that cerebral amyloid angiopathy should be evaluated as a possible mediating mechanism of the observed association.

Depressive symptoms and anti-N-methyl-D-aspartate-receptor GluN1 antibody seropositivity in the PROSpective cohort with incident stroke.

Background: Anti-NMDA-receptor GluN1 antibodies (NMDAR1-abs) are present in an autoimmune encephalitis with severe neuropsychiatric symptoms. We aimed to estimate the impact of serum NMDAR1-abs on depressive symptoms years after first-ever ischemic stroke (IS). Methods: Data were used from the PROSpective Cohort with Incident Stroke-Berlin (PROSCIS-B; NCT01363856). Serum NMDAR1-abs (IgM/IgA/IgG) were measured within 7 days after IS using cell-based assays. We defined seropositivity as titers ≥1:10, thereof low titers as ≤1:100 and high titers as >1:100. We used the Center for Epidemiological Studies-Depression (CES-D) scale to measure depressive symptoms at year one, two and three following IS. We calculated crude and confounder adjusted weighted generalized linear models to quantify the impact of NMDAR1-abs on CES-D assessed at three annual time-points. Results: NMDAR1-abs were measured in 583 PROSCIS-B IS patients (mean age = 67 [SD = 13]; 42%female; median NIHSS = 2 [IQR = 1-4]) of whom 76 (13%; IgM: n = 49/IgA: n = 43/IgG: n = 2) were seropositive, 55 (9%) with low and 21 (4%) with high titers. CES-D regarded over all follow-up time-points was higher in seropositive patients (ßcrude = 2.56 [95%CI = -0.34 to 5.45]; ßadjusted = 2.26 [95%CI = -0.68 to 5.20]) and effects were highest in patients with high titer (low titers: ßcrude = 1.42 [95%CI = -1.79 to 4.62], ßadjusted = 0.53 [95%CI = -2.47 to 3.54]; high titers: ßcrude = 5.85 [95%CI = 0.20 to 11.50]; ßadjusted = 7.20 [95%CI = 0.98 to 13.43]). Conclusion: Patients with serum NMDAR1-abs (predominantly IgM&IgA) suffer more severe depressive symptoms after mild-to-moderate IS compared to NMDAR1-abs seronegative patients.

Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke.

Background: Accurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of functional recovery, cognitive function, depression, and mortality of first-ever ischemic stroke patients and to identify the leading prognostic factors. Methods: We predicted clinical outcomes for 307 patients (151 females, 156 males; 68 ± 14 years) from the PROSpective Cohort with Incident Stroke Berlin study using 43 baseline features. Outcomes included modified Rankin Scale (mRS), Barthel Index (BI), Mini-Mental State Examination (MMSE), Modified Telephone Interview for Cognitive Status (TICS-M), Center for Epidemiologic Studies Depression Scale (CES-D) and survival. The ML models included a Support Vector Machine with a linear kernel and a radial basis function kernel as well as a Gradient Boosting Classifier based on repeated 5-fold nested cross-validation. The leading prognostic features were identified using Shapley additive explanations. Results: The ML models achieved significant prediction performance for mRS at patient discharge and after 1 year, BI and MMSE at patient discharge, TICS-M after 1 and 3 years and CES-D after 1 year. Additionally, we showed that National Institutes of Health Stroke Scale (NIHSS) was the top predictor for most functional recovery outcomes as well as education for cognitive function and depression. Conclusion: Our machine learning analysis successfully demonstrated the ability to predict clinical outcomes after first-ever ischemic stroke and identified the leading prognostic factors that contribute to this prediction.

Associations of clonal hematopoiesis with recurrent vascular events and death in patients with incident ischemic stroke.

Clonal hematopoiesis (CH) is common among older people and is associated with an increased risk of atherosclerosis, inflammation, and shorter overall survival. Age and inflammation are major risk factors for ischemic stroke, yet the association of CH with risk of secondary vascular events and death is unknown. We investigated CH in peripheral blood DNA from 581 patients with first-ever ischemic stroke from the Prospective Cohort With Incident Stroke-Berlin study using error-corrected targeted sequencing. The primary composite end point (CEP) consisted of recurrent stroke, myocardial infarction, and all-cause mortality. A total of 348 somatic mutations with a variant allele frequency ≥1% were identified in 236 of 581 patients (41%). CH was associated with large-artery atherosclerosis stroke (P = .01) and white matter lesion (P < .001). CH-positive patients showed increased levels of proinflammatory cytokines, such as interleukin-6 (IL-6), interferon gamma, high-sensitivity C-reactive protein, and vascular cell adhesion molecule 1. CH-positive patients had a higher risk for the primary CEP (hazard ratio [HR], 1.55; 95% confidence interval [CI], 1.04-2.31; P = .03), which was more pronounced in patients with larger clones. CH clone size remained an independent risk factor (HR, 1.30; 95% CI, 1.04-1.62; P = .022) in multivariable Cox regression. Although our data show that, in particular, larger and TET2- or PPM1D-mutated clones are associated with increased risk of recurrent vascular events and death, this risk is partially mitigated by a common germline variant of the IL-6 receptor (IL-6R p.D358A). The CH mutation profile is accompanied by a proinflammatory profile, opening new avenues for preventive precision medicine approaches to resolve the self-perpetuating cycle of inflammation and clonal expansion.