Wayne State University

We have designed and characterized modular self-assembled hierarchical films containing a molecular catalyst tethered to an anchoring molecule by means of dipole–induced dipole interactions. In order to do so, two new CoIII-based molecular catalyst candidates were designed, namely, [CoIIIL1(pyrr)2]ClO4 (Co1) and [CoIIIL2(pyrr)2]ClO4 (Co2), where L1 and L2 are the respective deprotonated forms of N,N′-[4,5-bis(dodecyloxy)-1,2-phenylene]dipicolinamide and N,N′-[4,5-bis(methoxyethoxy)-1,2-phenylene]dipicolinamide and were characterized by electrochemical, electronic, and film formation properties. Species Co1 and Co2 were deposited onto an anchor molecule such as octylphosphonic acid (OPA) or the chromophoric [RuII(bpyPO3H)2(bpyC7)]Cl2 (Ru) previously attached onto conductive fluorine-doped tin oxide (FTO). Four hierarchical films of the form substrate|anchor-catalyst were obtained, namely, FTO|OPA-Co1, FTO|OPA-Co2, FTO|Ru–Co1, and FTO|Ru–Co2, and the role of dipole–dipole interactions between anchor and catalyst modules was assessed. These newly synthesized hierarchical films were characterized by a host of surface-specific methods that include X-ray photoelectron spectroscopy, ellipsometry, X-ray fluorescence, and water contact angle, thus enabling an unprecedented level of analysis. Compared to the weak C–H van der Waals interactions exhibited by Co1, the presence of alkoxy chains in Co2 ensures stronger dipole–dipole interactions with the alkyl chain of the anchors due to O···H formation. The persistence of their redox properties, which include metal oxidation, and directionality of electron transport were probed suggesting direct relevance to catalytic processes such as water oxidation.

Solid polymer electrolytes are promising alternatives to traditional liquid electrolytes for use in lithium batteries. Poly(ethylene glycol) diacrylate (PEGDA) is a versatile cross-linkable monomer that promotes easy incorporation of a variety of filler materials for solid electrolyte synthesis without the use of solvents. This study examines the effects that varying concentrations of SiO2, CeZrO4, V2O5, and succinonitrile have on the electrochemical performance of UV-cured PEGDA electrolytes. A composite polymer electrolyte containing 8% V2O5 and 30% succinonitrile in PEGDA was synthesized and exhibited a room-temperature ionic conductivity of 1.43 × 10–4 S cm⁻¹. The improvement in ionic conductivity of this electrolyte may be attributed to the synergistic effect of the two incorporated fillers resulting in decreased crystallinity of the polymer matrix. This study demonstrates that polymer electrolyte characteristics can be optimized by combining the benefits of multiple fillers.

The Clubhouse model of psychosocial rehabilitation has supported the recovery of people with serious mental illness for over 75 years, but many of the roughly 350 Clubhouses are not well-integrated into the larger health care system, limiting their reach. This article examines Clubhouses’ and psychiatric providers’ interactions and experiences to understand the nature of and barriers to partnerships. The directors of Clubhouses affiliated with Clubhouse International were surveyed, examining their attitudes and practices around collaboration with psychiatric providers. To provide context, psychiatric providers were also surveyed regarding their understanding of and experiences with Clubhouses. Findings reveal broad support among both Clubhouse directors and psychiatrists for enhancing partnerships, despite current barriers, limited interactions, and the need for greater mutual understanding. Key considerations that emerged include the importance of maintaining the Clubhouse model's distinct non-clinical, community-based, and member-directed identity in any integration efforts.

This Viewpoint discusses the need for consumer product safety regulation in the firearm industry.

Background Over 15 million informal caregivers provide assistance to persons living with dementia. Despite increasing emergency department (ED) use within the population, little is known regarding the support required of older adults seeking acute care with varying degrees of cognitive impairment. Our objectives were to quantify the daily care hours that informal caregivers provide to older ED patients with diagnosed dementia, undiagnosed cognitive impairment, and intact cognition. Methods We conducted a cross‐sectional analysis of caregivers of community‐dwelling older adults aged 65+ years seeking emergency care. The caregiver‐completedAD8 (cAD8) was administered to screen for cognitive impairment among caregivers of older ED patients without a diagnosis of dementia indicated in the electronic health record. Based on the caregiver’s response, older adults were categorized into groups of: diagnosed dementia, undiagnosed cognitive impairment (cAD8 score of 2+), and cognitively intact (cAD8 score of <2). The primary outcome was mean self‐reported hours of care per day provided by the caregiver assessed using the Mann‐Whitney U test. Results Our analytic sample included caregivers of 439 older ED patients; 58 had diagnosed dementia, 69 had undiagnosed cognitive impairment, and 312 had intact cognition. Hours of care per day provided by caregivers were significantly greater for older adults with diagnosed dementia (13.8 hours, P<0.0001) and undiagnosed cognitive impairment (9.9 hours, P<0.0001) compared to those with intact cognition (1.7 hours). Conclusions Dementia and cognitive impairment, whether diagnosed or undiagnosed by the health system, were associated with increased daily care hours among caregivers of older adults seeking emergency care. Older adults with undiagnosed cognitive impairment required nearly ten hours of care daily, suggesting that improved diagnostic screening capabilities may optimize resource allocation to support caregivers.

Background Approaches to caregiving interventions are often “one‐size‐fits‐all”, yet family caregivers for individuals with dementia have unique caregiving styles with which they enact daily care. Mixed‐methods work by this team identified 5 distinct caregiving style profiles that vary in: orientation toward oneself or the care partner, adaptability, understanding of dementia, emotional expression, and behavioral management. This study seeks to develop a person‐centered assessment of caregiving style such that interventions and services can be targeted to caregivers’ unique styles of care. Method Person‐centeredness of the Style measure is assessed with the NIH‐funded LINC‐AD’s Person‐Centered Measure Evaluation Tool (PC‐Met). Development phases assessed included: mixed‐methods exploratory research on caregiving style, iterative development and refinement of an item pool, cognitive interviews with caregivers, expert review, literacy and translatability review, and field testing of the refined items in 200 family/friend caregivers for a person living with dementia. Result Person‐centered practices in the development of a caregiving style measure include: co‐creation (e.g., caregiver feedback throughout, caregiver interview), accommodation (e.g., full disclosure of topics, exploring across stage of disease), pragmatism (e.g., translatability review), incorporation (e.g., using positive versus loss language), biopsychosocial/cultural components (e.g., utility across multiple settings), and systemic focus (e.g., use of shared language). Conclusion Caregiving style is associated with key outcomes of care (e.g., caregiver burden) and thus a person‐centered assessment measure can help tailor services and interventions to best fit unique styles of care, ultimately improving well‐being of the caregiver and quality of care for the person living with dementia.

Background Examination of family caregiving and the stress process has focused on a “primary” caregiver (e.g., spouse, adult child) at the exclusion of other members of the caregiving network. Yet “non‐traditional” (e.g., grandchild, friend) caregivers are increasingly providing substantial caregiving support. This study explores whether non‐traditional dementia caregivers vary from traditional caregivers in terms of caregiving approaches and outcomes. Method Participants included 200 family caregivers (n = 156 traditional and n = 54 non‐traditional) for a person living with dementia from the Style Measure Study. Descriptive statistics and t‐tests were run to examine differences between traditional and non‐traditional caregivers. Linear regressions were run adjusting for caregiver demographics and the care context with caregiver type predicting use of dementia management strategies (criticism, encouragement, active management), Neuro‐QOL measures of caregiver strain, caregiver‐specific anxiety, feeling trapped, and positive affect, PROMIS measures of sleep‐related impairment, fatigue and pain intensity, and caregiver readiness. Result Non‐traditional caregivers were significantly younger, more likely to be Black, had higher levels of financial difficulty, had provided care for less time, and reported significantly greater pain intensity and significantly lower levels of caregiver readiness. In adjusted linear regressions however, caregiver type only predicted use of active management care strategies; traditional caregivers used more active management than non‐traditional caregivers (B = ‐2.24, p<.05). Conclusion Non‐traditional caregivers are notably distinct from their traditional counterparts demographically, yet non‐traditional status generally did not predict differences in caregiving outcomes. Future research should prioritize inclusion of diverse caregivers to represent the broader caregiver population and recognize that their care needs are just as extensive as traditional caregivers.

Background Diffusion magnetic resonance imaging (dMRI) permits characterizing differences in white matter microstructure associated with amnestic mild cognitive impairment (aMCI) and Alzheimer’s dementia (AD). However, most dMRI measures aggregate signals across multiple axonal fiber populations with varying spatial orientations, which limits the sensitivity and specificity of clinical diagnosis. To overcome this shortcoming, we estimated fiber density (FD) measures, independently from crossing fiber populations, and extracellular cerebral spinal fluid (CSF). We hypothesized that aMCI and AD diagnoses are associated with differential patterns of FD changes in larger and smaller diameter fiber populations. Method We evaluated cross‐sectional dMRI data from 179 clinically characterized participants enrolled in the University of Michigan Memory and Aging Project. Image processing leveraged the MRtrix3 multi‐shell multi‐tissue fixel‐based analysis framework to estimate FD, separately for three fiber orientations, and CSF. Data analysis used multi‐block partial least squares correlation (PLS‐C) to estimate factors from multidirectional FD and CSF images correlated with differences between cognitively unimpaired (CU; n = 98) and those diagnosed with aMCI (n = 52) or AD (n = 29). Result The PLS‐C model yielded three significant latent variables (LVs; Fig. 1), reflecting patterns of both significant positive and negative associations between diagnosis and FD in crossing fibers. LV1 explained 80% of the differences from CU to aMCI to AD, demonstrating a stepwise reduction of FD and increased CSF with greater disease severity. However, LV2 and LV3 showed FD differences in smaller crossing fibers, distinguishing clinical diagnoses. Intriguingly, participants in the aMCI and AD groups showed different regions with increased or decreased FD in smaller crossing fibers, relative to CU participants. Pairwise PLS‐C models showed aMCI and AD diagnoses were associated with similar patterns of FD changes in smaller crossing fibers in overlapping regions. Conclusion The present study found distinctive patterns of white matter alterations that systematically differ across diagnostic severity in MCI and Alzheimer’s dementia. These results highlight the value of decomposing signals from crossing fibers as a sensitive neuroimaging correlate to clinical diagnosis. These findings challenge the common perspective that MCI and Alzheimer’s dementia are associated with monotonic declines in white matter integrity.

Background Hispanic/Latino communities in the US are rapidly growing and aging and are at two‐fold risk of Alzheimer’s Disease and Related Dementia’s (ADRD) compared to non‐Hispanic Whites. This additional risk could be, in part, due to increased risk of cardiovascular disease. Hispanics/Latinos also have higher rate of diabetes compared to non‐Hispanic Whites and nearly 2 out of 5 individuals with diabetes go undiagnosed. While diabetes has been linked to white matter hyperintensities (WMHs), less is known about the magnitude in diverse Hispanics/Latinos and even fewer studies have considered links to other neurobiological endpoints in this population. This work aims to clarify these associations in a deeply characterized diverse middle‐aged and older Latino cohort. Method We used data from the Study of Latinos‐Investigation of Neurocognitive Aging (SOL‐INCA) MRI study which is an ancillary study of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The goal of SOL‐INCA MRI is to understand cerebrovascular pathology and ADRD etiology using MRI. A total of N = 2400 middle aged and older (50+ years) and N = 400 younger (35‐49 years) participants were recruited from the parent HCHS/SOL study. Diabetes was assessed using both continuous HbA1c % and recommended ADA cutoffs to assess prediabetes and diabetes status. Outcomes included structural (i.e., volumes; WMHs, total brain, regional and total gray matter volumes, lateral ventricles, hippocampus) and diffusion MRI (free water = FW, fractional anisotropy = FA). Survey weighted linear models (covariate adjusted by important confounders) were used to test the associations between our exposures and outcomes of interest. Result Compared to no diabetes, diabetes was linked to smaller total brain and occipital volumes, larger WMHs, larger lateral ventricles, lower FA, and higher FW (Figure 1). Higher levels of HbA1c % (treated continuously) were additionally associated with smaller occipital and larger hippocampal volumes (Figure 2). These findings were consistent across men and women. We did not find significant differences with the pre‐diabetes group. Conclusion Our findings suggest that elevated HbA1c and diabetes status are linked to cerebrovascular pathology and brain atrophy. Findings with hippocampal volume could be due to maladaptive process or hypertrophy. Further research is needed to understand different neurobiological mechanisms in understudied communities.

Background The medial temporal lobe (MTL) has distinct cortical subregions that are differentially vulnerable to pathology and neurodegeneration in diseases such as Alzheimer’s disease. However, previous protocols for segmentation of MTL cortical subregions on magnetic resonance imaging (MRI) vary substantially across research groups, and have been informed by different cytoarchitectonic definitions, precluding consistent interpretations. The Hippocampal Subfields Group aims to create a harmonized, histology‐based protocol for segmentation of MTL cortical subregions that can reliably be applied to T2‐weighted MRI with high in‐plane resolution. Method Nissl‐stained sections from the temporal lobes of three human specimens (66‐90 years old; 2 female) were annotated by four expert neuroanatomists for the following MTL subregions: entorhinal cortex (ERC), Brodmann’s Area 35 (BA35; largely corresponding to “transentorhinal” cortex), Brodmann’s Area 36 (BA36), and parahippocampal cortex (PHC). On each histology section, the number of annotations and the spatial overlap of annotations were analyzed to determine the consensus of the anterior to posterior range of each structure. Gross anatomical landmarks, detectable on MRI and reliably corresponding with each range, were then selected to create an MRI ranging protocol. Feasibility of this MRI protocol was tested by two independent raters across four MRI scans (two healthy adults, two older adults), and agreement in range selection was assessed using Cohen’s kappa statistic. Result The proposed MTL ranging protocol is shown in Fig. 1, and corresponding histology data substantiating the protocol is shown in Fig. 2. MRI‐visible gross anatomical landmarks that reliably corresponded with the anterior or posterior range of each subregion on histology included the anterior‐most appearance of the collateral sulcus (Fig. 3A), hippocampal head (Fig. 3B), hippocampal body, and anterior calcarine fissure (Fig. 3C). This protocol demonstrated high feasibility when applied to MRI, with average kappa values of 0.75 ± 0.07, representing a “substantial” level of agreement of range selection. Conclusion Future directions include obtaining consensus on this protocol from the larger research community through a Delphi procedure, and expansion of the protocol to include slice‐by‐slice segmentation guidelines for full delineation. This harmonized, histology‐based protocol will facilitate critical research on MTL subregion vulnerability and their contributions to memory deficits in Alzheimer’s disease.

Background Serum AD biomarkers are becoming useful to the early and accurate diagnosis of neurodegenerative disease, but much of this work has been done with clinic‐based studies of mostly non‐Hispanic Whites. For this study, we examined relations between plasma biomarkers Aβ 42/40, pTau 181, NfL, GFAP, ApoE genotype and cognitive state in the SOL‐INCA‐MRI study. Given that prior work in SOL‐INCA found vascular risk to be associated with mild cognitive impairment¹, we included vascular risk measured by the Framingham CVD risk score² and white matter hyperintensity (WMH) burden from MRI as additional predictors. Method 2288 individuals enriched for cognitive impairment (29%), mean age 64.5 ± 6.8 years of which 69% were female and 60% had at least a high school education were studied. All subjects had MRI as well as plasma measures of Aβ 42/40, pTau 181, GFAP and NfL and a diagnosis of normal (71%), questionable (16%), or mild cognitive impairment (13%). Multi‐variate linear regression adjusted for age, sex, heritage, and education was used to build models of associations between each of the measures and cognition. Finaly, structural equation modeling was used to assess the structural relationship between measured variables. Result Higher serum pTau181 and Nfl concentrations were significantly associated with diagnosis (Table 1). PTau181, NfL and GFAP were also associated with MRI measures, particularly WMH (Table 2). CVD risk also was significantly associated with pTau181 (β=1.23 ± 0.34, p <0.0001) and NfL (β=25.4 ± 2.8, p <0.0001). In a final model (Table 3), sex, CVD risk, WMH and pTau181 remained significantly associated with cognition. Best fit SEM summarizes the relationship of these measures to each other and cognition (Figure). ApoE4 genotype and Abeta 42/40 ratio were not associated with diagnosis in this group. Conclusion In a group of Latinos, enriched for cognitive impairment, vascular risk, WMH and pTau181 were significantly associated with diagnosis. The lack of association with serum Abeta 42/40 ratio suggest that vascular disease and not AD pathology are most strongly associated with cognition. The relationship between CVD risk, WMH, and pTau181is consistent with emerging evidence relating tau phosphorylation to vascular disease3, 4.

Background Several epigenetic clocks based on DNA methylation (DNAm) have been developed to estimate an individual’s biological age. Age acceleration, the deviation of the DNAm‐estimated age from the chronological age, has been proposed as a novel biomarker to predict age‐associated conditions and life expectancy. Due to the paucity of longitudinal DNAm data, especially among diverse Hispanic/Latino adults, the association between changes in age acceleration over time and cognitive aging phenotypes has not been investigated. Method We estimated epigenetic age acceleration from 5 PC‐based epigenetic clocks in 2667 Hispanic and Latino adults (58.2 years; 55.9% women) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), who had available blood DNAm data and neurocognitive assessment at two visits (approximately 6 years apart). These included first generation clocks, Horvath and Hannum clocks; second generation PhenoAge and GrimAge; and third generation DunedinPACE (Pace of Aging). We used survey linear regression weighted least square models to estimate the association of each of these measures with change in general cognitive function score and cognitive decline status between the two visits. All models adjusted for age, gender, Hispanic background, and years of education. Result For all epigenetic clocks, measures of age acceleration at visit 1 (V1) and visit 2 (V2) were strongly correlated (r=0.80 to 0.93; P <0.001). A higher GrimAge acceleration at V1 and V2 was associated with a greater decline in general cognitive function score between the two visits, while a higher V1 and V2 GrimAge acceleration and a higher V2 PhenoAge and DunedinPACE (POA) acceleration were associated with a greater likelihood of having cognitive decline at V2 (Table). An increase in GrimAge and PhenoAge acceleration between visits was associated with greater decline in global cognitive function score and presence of cognitive decline at V2 and these effects were stronger than single‐time point estimates. Conclusion Biological aging is associated with lower cognitive function score and greater cognitive decline in diverse Hispanic and Latino adults. Longitudinal assessment of change in age acceleration for second generations clocks, GrimAge and PhenoAge may provide additional value in predicting cognitive decline beyond single time point assessment.

Background Few large microbiome studies on Alzheimer’s Disease and Related Dementia (AD/ADRD) have been conducted, especially among US Latinos. We conducted a study within the Study of Latinos‐ Investigation of Neurocognitive Aging (SOL‐INCA) cohort to examine the role of the gut microbiota in cognitive function. Methods We analyzed the fecal metagenomes of 2,470 SOL‐INCA participants to, cross‐sectionally, identify microbial taxonomic and functional features associated with cognitive function. Global cognition was defined as an aggregate score based on a cognitive battery (executive function, working memory, among others). Omnibus (PERMANOVA) and feature‐wise analyses (MaAsLin2) were conducted to identify microbiome‐cognition associations, and specific microbial species and pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG modules) associated with cognition. We assessed the accuracy of a Random Forest classifier to distinguish SOL participants with the best (>=1SD above mean) vs. the worst (>=1SD below mean) cognition. We also tested the association of identified taxa and KEGG modules with concurrently collected serum metabolites. Result We identified several taxa and pathways significantly associated with cognitive function in SOL. B. longum was the taxa most strongly associated with worse cognition, whereas Eubacterium species (E. siraeum and E.eligens), were associated with better cognition. Several KEGG modules, most strongly Ornithine and Serine biosynthesis, were associated with worse cognition. A microbiome species‐based Random Forest classifier had moderate accuracy (AUC = 0.62) to discriminate between high (1SD or more above mean) vs low (1SD or more below mean) cognition. Conclusion In a large Latino cohort, we identified several microbial taxa and KEGG pathways associated with cognition, further implicating the microbiome in AD/ADRD risk.

Background Socioeconomic disadvantage at different life‐course stages has been associated with later life cognitive impairment. However, its association with changes in cognitive function needs to be further elucidated. We assessed the association between socioeconomic position (SEP) throughout the life‐course and cognitive function change in middle‐aged and older Hispanic/Latino adults. Method We used data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a multicenter community‐based cohort of Latinos in the US (n = 6351; age range 45‐74) and a follow up visit conducted 7 years later by the SOL‐INCA ancillary study. Childhood SEP was determined using parental education (<high school [HS], HS, >HS). An index combining participants’ education, household income, occupation, and assets determined midlife SEP (low, middle, high SEP). Using dichotomized childhood and midlife SEP, we classified participants into socioeconomic mobility categories (stable low or high SEP, upward or downward mobility). Cognitive function changes (7‐years change) were ascertained as the difference in test scores between visits. Cognitive function assessments included the Six‐Item Screener, Brief‐Spanish English Verbal Learning Test Sum and Recall, Controlled Oral Word Association, and the Digit Symbol Substitution Test. Cognitive measures were z‐score standardized and a global cognition (GC) score was created using confirmatory factor analysis. Survey linear regression models were performed accounting for demographic, behavioral, and clinical covariates, baseline cognitive function, and years from baseline. Result Higher midlife SEP was associated with a slower decline in cognitive function in later life (b for GC middle vs. low SEP: 0.12, 95% CI: 0.05, 0.19; and high vs. low SEP: 0.26, 95% CI: 0.15, 0.35). Upward socioeconomic mobility vs. stable low SEP (GC b: 0.29, 95% CI: 0.20, 0.39) and stable high SEP vs. stable low (GC b: 0.43, 95% CI: 0.32, 0.54) were also associated with slower cognitive decline. Results were similar for each individual cognitive test. We did not find associations between childhood SEP and changes in cognitive function. Conclusion This study highlights the benefits on cognitive aging of advantageous SEP over the life‐course. Having a higher midlife SEP, upward socioeconomic mobility, or a stable high SEP over the life‐course showed protection for cognitive decline in adulthood.

Background Increasingly, research evidence is identifying subjective cognitive decline (SCD) as a precursor for cognitive impairment and dementia. Identifying predictors of SCD is essential for understanding its utility as a preclinical indicator for impairment and especially pertinent for Hispanics/Latinos who have limited access to healthcare resources and clinical diagnostics and are disproportionally affected by Alzheimer’s disease and related dementias. We extend work on predictors of Mild Cognitive Impairment (MCI) in diverse Hispanics/Latinos in the US by modeling multidomain predictors of SCD. Method We use data (n = 4347, average baseline age = 56.4‐years) from the Hispanic Community Health Study/ Study of Latinos (HCHS/SOL; 2008‐2011; Visit 1), a multisite prospective cohort study of diverse Hispanics/Latinos, and its ancillary study, the SOL‐Investigation of Neurocognitive Aging (SOL‐INCA; average 7‐years after Visit 1). Our outcome is a composite SCD measured at SOL‐INCA by averaging the component items of the Everyday Cognition (ECog‐12) scale and is modeled using 37 cross‐domain Visit 1 indicators, previously linked to MCI, reflecting (1) sociodemographic characteristics, (2) childhood factors, (3) acculturation factors, (4) biological and (5) behavioral markers, and (6) mental and (7) functional health factors. We use supervised machine learning (ML: Random Forest = RF; regression = ML‐Reg) and standard statistical techniques (regression = Reg) for identifying leading predictors of SCD. In secondary analysis, we assess enhancement in predictive performance by accounting for Visit 1 global cognitive (GC) function. Result Our best performing (i.e., R‐squared) ML model (ML‐Reg) explained only 17% of the variance in SCD. Leading identified predictors of SCD included physical health scores, airflow obstruction, anxiety, mental health scores, Hispanic/Latino heritage, education, depression, income, and language and social acculturation. GC was predictive of SCD and explained an additional 5% of the variance. Conclusion Our findings indicate that multidomain factors contribute to SCD prediction, but the explained variance was relatively low. Biological markers, previously linked to MCI in our cohort, played a less significant role. Notably, the fit of the ML models for SCD was low relative to MCI specific models in the same population. Follow‐up work investigates how incorporating contemporaneous measures of the factors (vs. baseline alone) may improve the predictive capacity of the ML models.