Coresh, Josef

AbstractBackgroundPrior evidence has suggested associations between hearing loss and smaller brain volumes particularly in the temporal lobe where the primary auditory cortex is located, probably due to changes in cortical organization associated with reduced auditory stimulation. Evidence regarding associations of central auditory processing with brain volumes is relatively limited. The study aims to investigate cross‐sectional associations of central auditory processing with brain volumes in older adults.MethodWe studied 587 cognitively normal (Mini‐Mental State Exam ≥23 if high school degree or less and ≥25 if above high school degree) older adults (mean age: 77±4 years, 56% female, 17% black), including 426 with hearing loss from the Aging and Cognitive Health Evaluation in Elders (ACHIEVE) baseline (2018‐19), which is an ancillary randomized controlled trial of the Atherosclerosis Risk in Communities (ARIC) Study and 161 with normal hearing, comparable age and cognitive status from the ARIC Visit 6/7 (2016‐17/2018‐19). Brain volumes were measured by magnetic resonance imaging (MRI) scans and were transformed to z‐scores. Central auditory processing was measured by the Quick Speech‐in‐Noise (QuickSIN) test. Total scores range from 0 to 30 with lower scores indicating worse speech‐in‐noise performance. Multivariable‐adjusted linear regression was used to estimate cross‐sectional differences in standardized brain volumes associated with differences in QuickSIN score. Models adjusted for age, sex, race, education, cohort, body mass index, smoking, hypertension, diabetes, stroke, intracranial volume, and pure‐tone average.ResultEvery 5‐point decrease in QuickSIN score (worse performance) was associated with smaller volumes of the temporal lobe (‐0.07SD, 95% CI: ‐0.13, ‐0.01). QuickSIN score was also associated with subregions of the temporal lobe related to auditory processing, memory and emotion, including amygdala (‐0.13SD, 95% CI: ‐0.21, ‐0.04), middle (‐0.08SD, 95% CI: ‐0.15, ‐0.00) and superior temporal gyri (‐0.08SD, 95% CI: ‐0.15, ‐0.01).ConclusionPoorer central auditory processing was associated with smaller temporal lobe volumes, including but not limited to subregions important for auditory processing. The ongoing collection of ACHIEVE data with longitudinal MRI data will facilitate understanding of association between hearing loss and brain atrophy as well as the potential promising effects of hearing treatment in mitigating brain aging.

Abstract We investigated the cross-sectional associations of speech-in-noise performance with magnetic resonance imaging brain volumes among 588 cognitively normal participants (77±4 years, 56% female) from the Aging and Cognitive Health Evaluation in Elders Study (randomized trial embedded in the Atherosclerosis Risk in Communities (ARIC) Study) baseline in 2018-19 (N=427, with hearing loss) and ARIC (N=161, normal hearing) Visit 6/7 in 2016-17/2018-19. Central auditory processing was measured by Quick Speech-in-Noise (QuickSIN) test; range: 0 to 30, lower scores=worse performance. In models adjusted for demographic and disease covariates, every 5-point decrease in QuickSIN score was associated with smaller volumes of the temporal lobe overall (-0.07SD, 95% CI:-0.13,-0.01) as well as subregions including but not limited to those important for auditory processing (amygdala:-0.13SD, 95% CI:-0.21,-0.04; middle temporal gyrus:-0.08SD, 95% CI:-0.15,-0.00; superior temporal gyrus:-0.08SD, 95% CI:-0.15,-0.01). Further research is needed to understand the mechanisms underlying these observed associations.

Introduction: Among older adults with diabetes, cognitive dysfunction is of particular concern as it has implications for treatment adherence and diabetes self-management. The prevalence of cognitive dysfunction has not been well characterized in this population. Methods: We conducted a cross-sectional analysis of 5509 participants (1815 with diabetes) from visit 5 (2011-2013) of the ARIC Study. Diabetes was defined based on self-reported physician diagnosis, diabetes medication use, or HbA1c ≥ 6.5%. Cognitive function was measured using 8 neuropsychological tests, which were grouped into three cognitive domains representing memory, executive function, and language. Participants were categorized as having cognitive dysfunction if test scores were more than 1.5 standard deviations below age-, race-, and education-adjusted scores derived from a cognitively healthy population. We calculated crude prevalence estimates and used Poisson regression to estimate adjusted prevalence ratios (PRs), comparing cognitive dysfunction in persons with and without diabetes. We adjusted for demographic and clinical characteristics. Results: The mean age of participants was 75 years, 59% were female, 79% were white, and 33% had diabetes. In each domain, the prevalence of cognitive dysfunction among persons with diabetes ranged from 14% to 27%. Persons with diabetes were more likely than persons without diabetes to have dysfunction in multiple domains (PR = 1.29, 95% CI: 1.12, 1.49). Prevalence of cognitive dysfunction was significantly higher in persons with versus without diabetes for memory (PR=1.13, 95% CI: 1.02, 1.25), language (PR=1.24, 95% CI: 1.09, 1.45), and executive function (PR=1.10, 95% CI: 1.00, 1.22)(Figure). PRs were similar in crude models. Conclusions: The prevalence of cognitive dysfunction among older adults with diabetes is high. These results have implications for how physicians educate patients in appropriate self-management practices and for the prevention of diabetes-related complications.

Introduction: Understanding the role of risk factors associated with accelerated cognitive decline has important public health relevance for the aging US population. To test hypotheses that mid-life cardiovascular (CV) risk factors increase risk of dementia in old age, the Atherosclerosis Risk in Communities (ARIC) cohort was evaluated for dementia in 2011-2013. Anticipating challenges of complete dementia ascertainment in an elderly population, supplementary data sources were collected to aid analysis accounting for potentially informative attrition. Methods: ARIC enrolled 15,792 participants 45-64 years old at baseline (1987-1989) from four US communities and collected baseline CV exposure data. A complete neurocognitive battery including informant interview was completed 2011-2013, yielding an algorithmic diagnosis of dementia, mild cognitive impairment (MCI) or normal cognition. Cognitively impaired and a random sample of cognitively normal were selected for further clinical evaluation. Syndromic diagnoses (dementia/MCI/normal) and etiologic diagnoses were made by a panel of experts using standardized criteria. Standardized protocols, timely reports of reviewer reliability, ongoing training of reviewers and a web-based data management system were developed to ensure reliability, consistency and efficiency of data collection and review. Living participants not attending the visit were asked to complete the Telephone Interview for Cognitive Status (TICS). Based on dementia discharge codes and death codes identified during cohort surveillance, interviewer impression of hearing loss or cognitive impairment or need for a proxy during semi-annual calls, or random sampling, participants refusing the TICS are eligible for proxy dementia interview. Medicare claims are being examined to identify missed dementia discharge codes. Results: Of 11,017 participants alive as of 2011, 6495 (59%) completed the neurocognitive battery; 2937 of these (45%) were selected for, and completed, additional clinical assessments and classification by committee (algorithmic or reviewer diagnosis: dementia 5%, MCI 21%). Of the 4522 participants who did not undergo the neurocognitive battery, 1463 (32%) completed the TICS (dementia 7%), while 1627 of the remaining participants were eligible for proxy calls (903 completed to date, dementia 58%). Conclusions: Participants who were not examined were more likely to be cognitively impaired, highlighting the importance of collecting supplementary data to support analyses of midlife CV risk factors accounting for informative attrition. Collection of exam data in an elderly cohort is difficult and requires a multi-pronged approach, especially for an outcome highly correlated with attrition. The strategy described here could be applied in other settings.

Background: Brain infarcts, white matter hyperintensities (WMH), and brain atrophy appear to contribute to the development of mild cognitive impairment (MCI) and dementia in selected populations, but few prospective studies are available in general populations. Methods: The study included 732 black and white participants, sampled from the original ARIC cohort, who had 3T brain Magnetic Resonance Imaging (MRI) scans, were free of MCI and dementia during 2011-13 (Visit 5), and were evaluated for cognitive impairment during 2016-17 (Visit 6, about 90% complete). We assessed the associations of brain infarcts, WMH, and brain volumes measured on MRI from 2011-2013 with incident MCI and dementia (combined), diagnosed using a computer algorithm based on specified criteria and comprehensive assessment at Visit 6. We used logistic regression, incorporating sampling weights, to evaluate the risk for incident MCI/dementia with adjustment for demographic covariates, major dementia risk factors, and history of cardiovascular diseases. Results: Participants had a median age of 75 (IQR: 71, 79) at Visit 5 (baseline); 62% (453) were female and 33% (242) were African American. There were 156 newly identified cases of MCI or dementia during a median follow-up time of 4.9 (IQR: 3.4, 5.2) years. The presence of any brain infarct was associated with a nonsignificantly higher risk of MCI/dementia (OR: 1.50; 95%CI: 0.87 to 2.58), but the only significant individual association was with cortical infarcts (OR: 1.99; 95%CI: 1.03 to 3.85). WMH volume above the median value was nonsignificantly associated with conversion to MCI/dementia. Smaller Alzheimer’s disease (AD) signature region was an independent predictor for greater risk of MCI/dementia (OR: 1.49; 95%CI: 1.00 to 2.22). A similar but nonsignificant association was observed for total brain volume. Conclusions: Brain infarcts (cortical infarcts) and lower brain volume (AD signature region), measured in community-based older residents, are risk factors for MCI and dementia incidence.

Background: Midlife vascular risk factors (MVRF) are associated with incident dementia. Similarly, amyloid β(Aβ) and neurodegeneration (e.g.brain volumes), as parts of the Alzheimer’s Disease (AD) ATN framework, are associated with cognition. Whether vascular and AD-associated factors contribute to dementia independently or interact synergistically to reduce cognitive ability is not well understood. Methods: Recruited from 3 U.S. communities, ARIC-PET participants were followed from 1987-89 (45-64 yo) through 2016-17 (74-94 yo). Cognition was evaluated in 2011-13 (ages 69-88), and twice more, every 2-3 years. In 2011-13, nondemented ARIC-PET participants had a brain MRI, with measurement of white matter hyperintensities (WMH) and brain volumes, with florbetapir (Aβ) PET scans in 2012-14; global cortical standardized uptake value ratio (SUVR) was log-transformed and standardized. Dementia was classified by expert review, as well as phone and medical record surveillance. The relative contributions of vascular risk (MVRF, WMH volume) and AD pathology (elevated Aβ SUVR, smaller AD signature region volumes) to incident dementia were evaluated with Cox proportional hazards regression. Results: In 298 individuals, 36 developed dementia. In models with key MVRF, demographics, and Aβ SUVR, hypertension and Aβ each independently predicted dementia risk (per SD of Aβ SUVR: HR 2.57, 95% CI 1.72-3.84; hypertension: HR 2.57, 95% CI 1.16-5.67), but didn’t interact on dementia risk. WMH (per SD: HR 1.51, 95% CI 1.03-2.20) and Aβ SUVR (per SD: HR 2.52, 95% CI 1.83-3.47) each contributed to incident dementia but WMH lost significance when MVRF were added to the model. Smaller AD signature regions were associated with incident dementia, independent of Aβ SUVR, and remained significant after adjustment for MVRF (HR per SD 2.18, 95% CI 1.18-4.01). Conclusions: Midlife hypertension and late-life Aβ independently contribute to dementia risk, but don’t synergize on a multiplicative scale. Neurodegeneration (e.g.smaller AD signature region volume) is also associated with incident dementia, independent of Aβ and MVRF. Multiple pathways leading to dementia should be considered when evaluating risk factors and interventions to reduce the burden of dementia.

Background: Vascular risk factors have been associated with risk of cognitive decline, with increasing evidence that midlife exposure to these risk factors may be most important in conferring late-life risk of cognitive impairment. We explored associations between vascular risk factors measured in midlife and the development of dementia over 25 years in the biracial Atherosclerosis Risk in Communities (ARIC) cohort. Methods: Participants in the ARIC study were recruited from four U.S. communities in 1987-1989, at ages 45-64, with four additional in-person visits, surveillance for hospitalizations, annual phone calls, and repeated cognitive evaluations over a 25-year period. In 2011-2013, ARIC participants were seen for the ARIC Neurocognitive Study (ARIC-NCS), and underwent a detailed neurocognitive battery and informant interviews. Through adjudicated review, dementia cases were defined. Additional dementia cases were identified through the telephone interview for cognitive status (TICS) or informant interview for those participants not attending ARIC-NCS; or by a prior dementia ICD-9 code during a hospitalization. Results: Of 15,744 participants in the cohort, 1516 cases of dementia were identified, of whom over one third were among black participants. Risk of dementia was highest in individuals of black race, with less than a high school education, older age, APOE ε4 carriage, and who, at ARIC baseline, had hypertension, diabetes, or were current smokers (table). APOE ε4 and smoking were each stronger risk factors for dementia in whites than in blacks. Discussion: Vascular risk factors measured in midlife are associated with increased risk of dementia in this biracial cohort, in black and white participants. The risk associated with diabetes nears the increased risk associated with having an APOE ε4 allele. Further studies are needed to evaluate the mechanism of and opportunities for prevention of the cognitive sequelae of these risk factors in midlife.

Introduction: Studies have found that smaller brain volumes, cerebral infarcts, and white matter abnormalities are associated with dementia and mild cognitive impairment. However, these studies have been limited by short follow-up precluding a strong establishment of temporality. Therefore, it is unknown whether brain imaging findings are preceded by long-term changes in cognition. We sought to address this gap by examining brain imaging and two decades of cognitive changes in and a large, representative population-based cohort of older adults of black and white race. Hypothesis: We hypothesized that 22-year declines in global cognitive factor scores (GCFS) would be associated with a pattern of smaller total brain and temporal lobe meta region of interest (likely to be affected by Alzheimer’s disease) volumes, larger white matter hyperintensity volumes, and greater odds of ≥1 lacunar infarct and elevated brain β-amyloid deposition. Methods: ARIC participants with brain imaging data, complete cognitive factor score, and not missing key covariates were included. GCFS were collected at three visits across 22 years (1990-2013), and brain MRI and florbetapir PET imaging were collected in 2011-13; PET in subset of n=327. Mixed effects models with random intercepts and slopes predicted individual change in GCFS. Outcomes of interest were total brain volume (cc), temporal lobe meta region volume, log 2 (white matter hyperintensity volume), ≥1 lacunar infarct, and elevated brain β-amyloid deposition (SUVR >1.2). Multivariable linear and logistic regression was used to relate outcomes to GCFS slopes after adjusting for confounders, including vascular risk factors. As appropriate, models were also adjusted for total intracranial volume. Results: Among 1957 with complete brain MRI imaging, 1830 were included in the study, 60% (n=1096) women and 26% (n=480) black. At the first visit, the mean (SD) baseline age was 55 (5.2) yrs. The mean (SD) observed GCFS at the three visits were 0.16 (0.79), 0.05 (0.75), and -0.78 (0.86). After adjustment, a 1-SD larger decline in GCFS was significantly associated with a smaller brain volume by 1.6% [95%CI: 1.3, 1.8] relative to mean brain volume, a smaller temporal lobe meta region volume by 2.4% [2.1, 2.8] relative to the mean volume, a 15% [11, 19] larger volume of white matter hyperintensities, 1.3-fold [1.2, 1.4] higher odds of having ≥1 lacune, and 1.8-fold [1.4, 2.4] higher odds of elevated brain β-amyloid deposition. Associations remained significant after further adjustment for first or last GCFS. Conclusions: Greater declines in long-term cognitive functioning were significantly associated with smaller brain volumes and dementia-related brain characteristics and were independent of last visit GCFS. This suggests long-term changes in cognition may precede late-life brain morphology and outperform cross-sectional cognitive measures.

Background: Ischemic Stroke (IS) is associated with an increased risk of dementia, but the relative contribution of IS severity or recurrence to cognition is not known. We aimed to determine the risk of dementia after incident IS and how it varies by stroke severity and recurrence in the Atherosclerosis Risk in Communities (ARIC) study. Methods: 15,405 ARIC participants free of stroke and dementia at baseline (1987-9) were followed for IS and dementia through 2019. Incident and recurrent IS were classified by expert review of hospital records, with stroke severity by the National Institutes of Health Stroke Scale (NIHSS) classified as NIHSS≤5, 6-10, 11-15, ≥16. Dementia cases were adjudicated through expert review of in-person evaluations, informant interviews, phone assessments, hospitalization code or death certificates. Poisson regression models with robust error variance were used to estimate dementia incidence in participants with and without IS, and associations between time-dependent IS incidence (excluding dementia in the first year after stroke), frequency and severity, and dementia were studied with Cox proportional hazards models, adjusting for demographics, APOE ε4 and vascular risk factors . Results: 1151 IS (970 incident) and 2807 dementia cases were identified. NIHSS was available for 877 IS (76%). Adjusted incidence rates (95% CI) of dementia per 100 person-years were 0.45 (0.42-0.49) in participants without IS vs. 1.33 (1.15-1.55) in those with IS. Compared to no IS, risk of dementia (adjusted HR, 95% CI) increased with IS number and severity from 1.71 (1.47-1.99) for participants with one IS to 6.68 (3.58-12.46) for those with ≥3 events, and from 1.64 (1.36-1.98) for NIHSS≤5 to 4.43 (1.84-10.68) for NIHSS≥16 ( Table ). Conclusion: Risk of dementia is significantly increased after stroke, independent of vascular risk factors. These data suggest a dose-response relationship between number of stroke events and stroke severity, and risk of dementia.

Purpose The purpose of this study was to investigate associations between performance on a clinical speech-in-noise measure with a comprehensive neurocognitive battery of tests. Method A group of older adults ( N = 250, M age = 77 years, age range: 67.3–89.1 years) enrolled in the Atherosclerosis Risk in Communities Neurocognitive Study took part in the hearing pilot study (2013) that included testing for audiometric thresholds and speech-in-noise performance (Quick Speech-in-Noise Test; Killion, Niquette, Gudmundsen, Revit, & Banerjee, 2004). This research study analyzed the associations between domain-specific cognitive function and speech-in-noise performance after adjusting for hearing thresholds and other demographic and cardiovascular factors. Results Multivariable-adjusted associations were found between all cognitive domains and speech-in-noise performance in the full sample, but the observed associations varied when participants with varying levels of moderate to moderately severe hearing loss were excluded from the analysis. Conclusions The findings are discussed in terms considering the cognitive status of older adults in relation to their speech-in-noise performance during audiological evaluation and implications for aural rehabilitation.