Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study.

The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.

Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI.

Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.

White matter brain age as a biomarker of cerebrovascular burden in the ageing brain.

As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94 years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39 years, p < 0.001) among all risk factors followed by hypertension (0.87 years, p < 0.001) and smoking (0.69 years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual's cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors.

Associations of Osteoarthritis with Prevalence and Incidence of Cardiovascular Disease over 10 Years in Community-Dwelling Older Adults: The Sydney Memory and Ageing Study.

INTRODUCTION: The data are limited for the association between osteoarthritis (OA) and cardiovascular disease (CVD) in community-based older populations and whether there is sex difference. This study aimed to examine the relationship between OA and prevalence and incidence of CVD over 10 years in community-dwelling older adults. METHODS: Data on self-reported OA, high cholesterol, hypertension, and type 2 diabetes were collected from 1,025 community-dwelling participants aged 70-90 years in the Sydney Memory and Ageing Study. The presence of CVD at baseline was defined as self-reported presence of stroke, heart attack, transient ischaemic attack, angina, aortic aneurysm, or claudication. The incidence of CVD was defined by a combination of incident self-reported CVD or CVD mortality at different follow-up timepoints over 10 years. RESULTS: At baseline, 395 (38.5%) participants self-reported OA (252 [44.6%] women, 143 [31.1%] men). Self-reported OA was associated with increased prevalence of CVD in women (OR 1.67, 95% CI 1.12-2.47) but not men (1.26, 0.80-1.98). In the total population, self-reported OA at baseline was associated with increased incidence of CVD at 4 years (OR 1.77, 95% CI 1.10-2.83), 6 years (1.59, 1.03-2.46), 8 years (1.56, 1.02-2.38), and 10 years (1.66, 1.10-2.50), but not at 2 years (1.43, 0.79-2.57). Significant associations were observed in female participants at 4, 8, and 10 years, with no significant associations seen in male participants. CONCLUSION: OA was associated with increased prevalence at baseline and incidence of CVD over 10 years in community-based older adults, especially women. Identifying those with OA to target their cardiovascular risk factors while managing their OA has the potential to reduce the burden of CVD in older people, particularly women.

Longitudinal alcohol-related brain changes in older adults: The Sydney Memory and Ageing Study.

Increases in harmful drinking among older adults indicate the need for a more thorough understanding of the relationship between later-life alcohol use and brain health. The current study investigated the relationships between alcohol use and progressive grey and white matter changes in older adults using longitudinal data. A total of 530 participants (aged 70 to 90 years; 46.0% male) were included. Brain outcomes assessed over 6 years included total grey and white matter volume, as well as volume of the hippocampus, thalamus, amygdala, corpus callosum, orbitofrontal cortex and insula. White matter integrity was also investigated. Average alcohol use across the study period was the main exposure of interest. Past-year binge drinking and reduction in drinking from pre-baseline were additional exposures of interest. Within the context of low-level average drinking (averaging 11.7 g per day), higher average amount of alcohol consumed was associated with less atrophy in the left (B = 7.50, pFDR = 0.010) and right (B = 5.98, pFDR = 0.004) thalamus. Past-year binge-drinking was associated with poorer white matter integrity (B = -0.013, pFDR = 0.024). Consuming alcohol more heavily in the past was associated with greater atrophy in anterior (B = -12.73, pFDR = 0.048) and posterior (B = -17.88, pFDR = 0.004) callosal volumes over time. Across alcohol exposures and neuroimaging markers, no other relationships were statistically significant. Within the context of low-level drinking, very few relationships between alcohol use and brain macrostructure were identified. Meanwhile, heavier drinking was negatively associated with white matter integrity.

Cognitive and physical declines and falls in older people with and without mild cognitive impairment: a 7-year longitudinal study.

OBJECTIVES: We examined longitudinal changes in cognitive and physical function and associations between change in function and falls in people with and without mild cognitive impairment (MCI). DESIGN: Prospective cohort study with assessments every 2 years (for up to 6 years). SETTING: Community, Sydney, Australia. PARTICIPANTS: Four hundred and eighty one people were classified into three groups: those with MCI at baseline and MCI or dementia at follow-up assessments (n = 92); those who fluctuated between cognitively normal and MCI throughout follow-up (cognitively fluctuating) (n = 157), and those who were cognitively normal at baseline and all reassessments (n = 232). MEASUREMENTS: Cognitive and physical function measured over 2-6 years follow-up. Falls in the year following participants' final assessment. RESULTS: In summary, 27.4%, 38.5%, and 34.1% of participants completed 2, 4, and 6 years follow-up of cognitive and physical performance, respectively. The MCI and cognitive fluctuating groups demonstrated cognitive decline, whereas the cognitively normal group did not. The MCI group had worse physical function than the cognitively normal group at baseline but decline over time in physical performance was similar across all groups. Decline in global cognitive function and sensorimotor performance were associated with multiple falls in the cognitively normal group and decline in mobility (timed-up-and-go test) was associated with multiple falls across the whole sample. CONCLUSIONS: Cognitive declines were not associated with falls in people with MCI and fluctuating cognition. Declines in physical function were similar between groups and decline in mobility was associated with falls in the whole sample. As exercise has multiple health benefits including maintaining physical function, it should be recommended for all older people. Programs aimed at mitigating cognitive decline should be encouraged in people with MCI.

Network analysis of neuropsychiatric, cognitive, and functional complications of stroke: implications for novel treatment targets.

AIM: Recovery from stroke is adversely affected by neuropsychiatric complications, cognitive impairment, and functional disability. Better knowledge of their mutual relationships is required to inform effective interventions. Network theory enables the conceptualization of symptoms and impairments as dynamic and mutually interacting systems. We aimed to identify interactions of poststroke complications using network analysis in diverse stroke samples. METHODS: Data from 2185 patients were sourced from member studies of STROKOG (Stroke and Cognition Consortium), an international collaboration of stroke studies. Networks were generated for each cohort, whereby nodes represented neuropsychiatric symptoms, cognitive deficits, and disabilities on activities of daily living. Edges characterized associations between them. Centrality measures were used to identify hub items. RESULTS: Across cohorts, a single network of interrelated poststroke complications emerged. Networks exhibited dissociable depression, apathy, fatigue, cognitive impairment, and functional disability modules. Worry was the most central symptom across cohorts, irrespective of the depression scale used. Items relating to activities of daily living were also highly central nodes. Follow-up analysis in two studies revealed that individuals who worried had more densely connected networks than those free of worry (CASPER [Cognition and Affect after Stroke: Prospective Evaluation of Risks] study: S = 9.72, P = 0.038; SSS [Sydney Stroke Study]: S = 13.56, P = 0.069). CONCLUSION: Neuropsychiatric symptoms are highly interconnected with cognitive deficits and functional disabilities resulting from stroke. Given their central position and high level of connectedness, worry and activities of daily living have the potential to drive multimorbidity and mutual reinforcement between domains of poststroke complications. Targeting these factors early after stroke may have benefits that extend to other complications, leading to better stroke outcomes.

Umbrella review and Delphi study on modifiable factors for dementia risk reduction.

A 2013 systematic review and Delphi consensus study identified 12 modifiable risk and protective factors for dementia, which were subsequently merged into the "LIfestyle for BRAin health" (LIBRA) score. We systematically evaluated whether LIBRA requires revision based on new evidence. To identify modifiable risk and protective factors suitable for dementia risk reduction, we combined an umbrella review of systematic reviews and meta-analyses with a two-round Delphi consensus study. The review of 608 unique primary studies and opinions of 18 experts prioritized six modifiable factors: hearing impairment, social contact, sleep, life course inequalities, atrial fibrillation, and psychological stress. Based on expert ranking, hearing impairment, social contact, and sleep were considered the most suitable candidates for inclusion in updated dementia risk scores. As such, the current study shows that dementia risk scores need systematic updates based on emerging evidence. Future studies will validate the updated LIBRA score in different cohorts. HIGHLIGHTS: An umbrella review was combined with opinions of 18 dementia experts. Various candidate targets for dementia risk reduction were identified. Experts prioritized hearing impairment, social contact, and sleep. Re-assessment of dementia risk scores is encouraged. Future work should evaluate the predictive validity of updated risk scores.

Examining the validity of the Mini-Mental State Examination (MMSE) and its domains using network analysis.

BACKGROUND: The Mini-Mental State Examination (MMSE) is the most widely used standardised screener for impairments across a range of cognitive domains. However, the degree to which its domains (orientation, registration, attention, recall, language, and visuospatial) capture cognitive functioning measured using standardised neuropsychological tests is unclear. METHOD: A longitudinal research design with four biannual assessments over a 6-year period was used with an initial sample of 1037 older adults (aged above 70 years). Participants completed MMSE and neuropsychological tests at each assessment. Network analysis was utilised to investigate unique associations among the MMSE and its domains and neuropsychological test performance at each time point. RESULTS: The total MMSE and two of its domains, language and recall, were associated with neuropsychological memory performance. The MMSE orientation, registration and visuospatial domains did not have any unique associations with neuropsychological performance. No stable internal interconnections between MMSE domains were found over time. The association of total MMSE as well as its recall domain with neuropsychological memory performance remained very similar over the 6-year period. CONCLUSIONS: The present study adds evidence to the validity of the MMSE and supports the clinical usage of the MMSE, whereby the total score is used for screening patients with or without cognitive impairments, with repeated administration to monitor cognitive changes over time, to inform intervention. However, the tool is not able to diagnose the cases for changes in specific cognitive domains and as such, should not replace a complete neuropsychological assessment.

Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery.

BACKGROUND: Feature selection is often used to identify the important features in a dataset but can produce unstable results when applied to high-dimensional data. The stability of feature selection can be improved with the use of feature selection ensembles, which aggregate the results of multiple base feature selectors. However, a threshold must be applied to the final aggregated feature set to separate the relevant features from the redundant ones. A fixed threshold, which is typically used, offers no guarantee that the final set of selected features contains only relevant features. This work examines a selection of data-driven thresholds to automatically identify the relevant features in an ensemble feature selector and evaluates their predictive accuracy and stability. Ensemble feature selection with data-driven thresholding is applied to two real-world studies of Alzheimer's disease. Alzheimer's disease is a progressive neurodegenerative disease with no known cure, that begins at least 2-3 decades before overt symptoms appear, presenting an opportunity for researchers to identify early biomarkers that might identify patients at risk of developing Alzheimer's disease. RESULTS: The ensemble feature selectors, combined with data-driven thresholds, produced more stable results, on the whole, than the equivalent individual feature selectors, showing an improvement in stability of up to 34%. The most successful data-driven thresholds were the robust rank aggregation threshold and the threshold algorithm threshold from the field of information retrieval. The features identified by applying these methods to datasets from Alzheimer's disease studies reflect current findings in the AD literature. CONCLUSIONS: Data-driven thresholds applied to ensemble feature selectors provide more stable, and therefore more reproducible, selections of features than individual feature selectors, without loss of performance. The use of a data-driven threshold eliminates the need to choose a fixed threshold a-priori and can select a more meaningful set of features. A reliable and compact set of features can produce more interpretable models by identifying the factors that are important in understanding a disease.

Past antihypertensive medication use is associated with lower levels of small vessel disease and lower Aß plaque stage in the brains of older individuals.

AIMS: This study assesses the association of antihypertensive medication use on the severities of neuropathological cerebrovascular disease (CVD excluding lobar infarction) in older individuals. METHODS: Clinical and neuropathological data were retrieved for 149 autopsy cases >75 years old with or without CVD or Alzheimer's disease and no other neuropathological diagnoses. Clinical data included hypertension status, hypertension diagnosis, antihypertensive medication use, antihypertensive medication dose (where available) and clinical dementia rating (CDR). Neuropathological CVD severity was evaluated for differences with anti-hypertensive medication usage. RESULTS: Antihypertensive medication use was associated with less severe white matter small vessel disease (SVD, mainly perivascular dilatation and rarefaction), with a 5.6-14.4 times greater likelihood of less severe SVD if medicated. No significant relationship was detected between infarction (presence, type, number and size), lacunes or cerebral amyloid angiopathy and antihypertensive medication use. Only increased white matter rarefaction/oedema and not perivascular dilation was associated with Alzheimer's pathology, with a 4.3 times greater likelihood of reduced Aß progression through the brain if white matter rarefaction severity was none or mild. Antihypertensive medication use was associated with reduced Aß progression but only in those with moderate to severe white matter SVD. CONCLUSIONS: This histopathological study provides further evidence that antihypertensive medication use in older individuals is associated with white matter SVD and not with other CVD pathologies. This is mainly due to a reduction in white matter perivascular dilation and rarefaction/oedema. Even in those with moderate to severe white matter SVD, antihypertensive medication use reduced rarefaction and Aß propagation through the brain.

Using network analysis to validate domains of the modified telephone interview for cognitive status.

BACKGROUND: The modified Telephone Interview for Cognitive Status (TICS-M) is a widely used tool for assessing global cognitive functions and screening for cognitive impairments. The tool was conceptualised to capture various cognitive domains, but the validity of such domains has not been investigated against comprehensive neuropsychological assessments tools. Therefore, this study aimed to explore the associations between the TICS-M domains and neuropsychological domains to evaluate the validity of the TICS-M domains using network analysis. MATERIALS AND METHODS: A longitudinal research design was used with a large sample of older adults (aged above 70 years; n = 1037 at the baseline assessment) who completed the TICS-M and comprehensive neuropsychological assessments biennially. We applied network analysis to identify unique links between the TICS-M domains and neuropsychological test scores. RESULTS: At baseline, there were weak internal links between the TICS-M domains. The TICS-M memory and language domains were significantly related to their corresponding neuropsychological domains. The TICS-M attention domain had significant associations with executive function and visuospatial abilities. The TICS-M orientation domain was not significantly associated with any of the five neuropsychological domains. Despite an attrition of almost 50% at wave four, weak internal links between the TICS-M domains and most associations between TICS-M and neuropsychological domains that were found initially, remained stable at least over two waves within the 6-year period. CONCLUSIONS: This study supports the overall structural validity of the TICS-M screener in assessing enduring global cognitive function. However, separate TICS-M cognitive domains should not be considered equivalent to the analogous neuropsychological domains.

Defining exceptional cognition in older adults: A systematic review of cognitive super-ageing.

OBJECTIVE: A consistent approach to defining cognitive super-ageing is needed to increase the value of research insights that may be gained from studying this population including ageing well and preventing and treating neurodegenerative conditions. This review aims to evaluate the existing definitions of 'super-ageing' with a focus on cognition. METHODS: A systematic literature search was conducted across PubMed, Embase, Web of Science, Scopus, PsycINFO and Google Scholar from inception to 24 July 2023. RESULTS: Of 44 English language studies that defined super-ageing from a cognitive perspective in older adults (60-97 years), most (n = 33) were based on preserved verbal episodic memory performance comparable to that of younger adult in age range 16-65 years. Eleven studies defined super-agers as the top cognitive performers for their age group based upon standard deviations or percentiles above the population mean. Only nine studies included longitudinal cognitive performance in their definitions. CONCLUSIONS: Equivalent cognitive abilities to younger adults, exceptional cognition for age and a lack of cognitive deterioration over time are all meaningful constructs and may provide different insights into cognitive ageing. Using these criteria in combination or individually to define super-agers, with a clear rationale for which elements have been selected, could be fit for purpose depending on the research question. However, major discrepancies including the age range of super-agers and comparator groups and the choice of cognitive domains assessed should be addressed to reach some consensus in the field.

Frequency of cognitive "super-aging" in three Australian samples using different diagnostic criteria.

OBJECTIVES: To investigate the frequency of exceptional cognition (cognitive super-aging) in Australian older adults using different published definitions, agreement between definitions, and the relationship of super-aging status with function, brain imaging markers, and incident dementia. DESIGN: Three longitudinal cohort studies. SETTING: Participants recruited from the electoral roll, Australian Twins Registry, and community advertisements. PARTICIPANTS: Older adults (aged 65-106) without dementia from the Sydney Memory and Ageing Study (n = 1037; median age 78), Older Australian Twins Study (n = 361; median age 68), and Sydney Centenarian Study (n = 217; median age 97). MEASUREMENTS: Frequency of super-aging was assessed using nine super-aging definitions based on performance on neuropsychological testing. Levels of agreement between definitions were calculated, and associations between super-aging status for each definition and functioning (Bayer ADL score), structural brain imaging measures, and incident dementia were explored. RESULTS: Frequency of super-aging varied between 2.9 and 43.4 percent with more stringent definitions associated with lower frequency. Agreement between different criteria varied from poor (K = 0.04, AC1 = .24) to very good (K = 0.83, AC1 = .91) with better agreement between definitions using similar tests and cutoffs. Super-aging was associated with better functional performance (4.7-11%) and lower rates of incident dementia (hazard ratios 0.08-0.48) for most definitions. Super-aging status was associated with a lower burden of white matter hyperintensities (3.8-33.2%) for all definitions. CONCLUSIONS: The frequency of super-aging is strongly affected by the demographic and neuropsychological testing parameters used. Greater consistency in defining super-aging would enable better characterization of this exceptional minority.

Genetic and environmental influences on fruit and vegetable consumption and depression in older adults.

BACKGROUND: Prior work suggests that higher fruit and vegetable consumption may protect against depression in older adults. Better understanding of the influence of genetic and environmental factors on fruit and vegetable intakes may lead to the design of more effective dietary strategies to increase intakes. In turn this may reduce the occurrence of depression in older adults. OBJECTIVES: The primary aim of this study is to estimate the genetic and environmental influences on the consumption of fruit and vegetables in older adults. The secondary aim is an exploratory analysis into possible shared genetic influences on fruit and vegetable intakes and depression. METHODS: Analysis of observational data from 374 twins (67.1% female; 208 monozygotic (MZ); 166 dizygotic (DZ)) aged ≥ 65 years drawn from the Older Australian Twins Study. Dietary data were obtained using a validated food frequency questionnaire and depressive symptoms were measured using the 15-item short form Geriatric Depression Scale. The contribution of genetic and environmental influences on fruit and vegetable intake were estimated by comparing MZ and DZ twin intakes using structural equation modelling. A tri-variate twin model was used to estimate the genetic and environmental correlation between total fruit and vegetable intakes and depression. RESULTS: In this study, vegetable intake was moderately influenced by genetics (0.39 95%CI 0.22, 0.54). Heritability was highest for brassica vegetables (0.40 95%CI 0.24, 0.54). Overall fruit intake was not significantly heritable. No significant genetic correlations were detected between fruit and vegetable intake and depressive symptoms. CONCLUSIONS: Vegetable consumption, particularly bitter tasting brassica vegetables, was significantly influenced by genetics, although environmental influences were also apparent. Consumption of fruit was only influenced by the environment, with no genetic influence detected, suggesting strategies targeting the food environment may be particularly effective for encouraging fruit consumption.

Parental history of dementia and the risk of dementia: A cross-sectional analysis of a global collaborative study.

BACKGROUND: Parental history of dementia appears to increase the risk of dementia, but there have been inconsistent results. We aimed to investigate whether the association between parental history of dementia and the risk of dementia are different by dementia subtypes and sex of parent and offspring. METHODS: For this cross-sectional study, we harmonized and pooled data for 17,194 older adults from nine population-based cohorts of eight countries. These studies conducted face-to-face diagnostic interviews, physical and neurological examinations, and neuropsychological assessments to diagnose dementia. We investigated the associations of maternal and paternal history of dementia with the risk of dementia and its subtypes in offspring. RESULTS: The mean age of the participants was 72.8 ± 7.9 years and 59.2% were female. Parental history of dementia was associated with higher risk of dementia (odds ratio [OR] = 1.47, 95% confidence interval [CI] = 1.15-1.86) and Alzheimer's disease (AD) (OR = 1.72, 95% CI = 1.31-2.26), but not with the risk of non-AD. This was largely driven by maternal history of dementia, which was associated with the risk of dementia (OR = 1.51, 95% CI = 1.15-1.97) and AD (OR = 1.80, 95% CI = 1.33-2.43) whereas paternal history of dementia was not. These results remained significant when males and females were analyzed separately (OR = 2.14, 95% CI = 1.28-3.55 in males; OR = 1.68, 95% CI = 1.16-2.44 for females). CONCLUSIONS: Maternal history of dementia was associated with the risk of dementia and AD in both males and females. Maternal history of dementia may be a useful marker for identifying individuals at higher risk of AD and stratifying the risk for AD in clinical trials.

Dose-response relationship between late-life physical activity and incident dementia: A pooled analysis of 10 cohort studies of memory in an international consortium.

INTRODUCTION: Though consistent evidence suggests that physical activity may delay dementia onset, the duration and amount of activity required remains unclear. METHODS: We harmonized longitudinal data of 11,988 participants from 10 cohorts in eight countries to examine the dose-response relationship between late-life physical activity and incident dementia among older adults. RESULTS: Using no physical activity as a reference, dementia risk decreased with duration of physical activity up to 3.1 to 6.0 hours/week (hazard ratio [HR] 0.88, 95% confidence interval [CI] 0.67 to 1.15 for 0.1 to 3.0 hours/week; HR 0.68, 95% CI 0.52 to 0.89 for 3.1 to 6.0 hours/week), but plateaued with higher duration. For the amount of physical activity, a similar pattern of dose-response curve was observed, with an inflection point of 9.1 to 18.0 metabolic equivalent value (MET)-hours/week (HR 0.92, 95% CI 0.70 to 1.22 for 0.1 to 9.0 MET-hours/week; HR 0.70, 95% CI 0.53 to 0.93 for 9.1 to 18.0 MET-hours/week). DISCUSSION: This cross-national analysis suggests that performing 3.1 to 6.0 hours of physical activity and expending 9.1 to 18.0/MET-hours of energy per week may reduce dementia risk.

Social connections and risk of incident mild cognitive impairment, dementia, and mortality in 13 longitudinal cohort studies of ageing.

INTRODUCTION: Previous meta-analyses have linked social connections and mild cognitive impairment, dementia, and mortality. However, these used aggregate data from North America and Europe and examined a limited number of social connection markers. METHODS: We used individual participant data (N = 39271, Mage  = 70.67 (40-102), 58.86% female, Meducation  = 8.43 years, Mfollow-up  = 3.22 years) from 13 longitudinal ageing studies. A two-stage meta-analysis of Cox regression models examined the association between social connection markers with our primary outcomes. RESULTS: We found associations between good social connections structure and quality and lower risk of incident mild cognitive impairment (MCI); between social structure and function and lower risk of incident dementia and mortality. Only in Asian cohorts, being married/in a relationship was associated with reduced risk of dementia, and having a confidante was associated with reduced risk of dementia and mortality. DISCUSSION: Different aspects of social connections - structure, function, and quality - are associated with benefits for healthy aging internationally. HIGHLIGHTS: Social connection structure (being married/in a relationship, weekly community group engagement, weekly family/friend interactions) and quality (never lonely) were associated with lower risk of incident MCI. Social connection structure (monthly/weekly friend/family interactions) and function (having a confidante) were associated with lower risk of incident dementia. Social connection structure (living with others, yearly/monthly/weekly community group engagement) and function (having a confidante) were associated with lower risk of mortality. Evidence from 13 longitudinal cohort studies of ageing indicates that social connections are important targets for reducing risk of incident MCI, incident dementia, and mortality. Only in Asian cohorts, being married/in a relationship was associated with reduced risk of dementia, and having a confidante was associated with reduced risk of dementia and mortality.

Establishing conversion of the 16-item Informant Questionnaire on Cognitive Decline in the Elderly scores into interval-level data across multiple samples using Rasch methodology.

BACKGROUND: The 16-item Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE-16) is a well-validated and widely-used measure of cognitive changes (CCs) among older adults. This study aimed to use Rasch methodology to establish psychometric properties of the IQCODE-16 and validate the existing ordinal-to-interval transformation algorithms across multiple large samples. METHODS: A Partial Credit Rasch model was employed to examine psychometric properties of the IQCODE-16 using data (n = 918) from two longitudinal studies of participants aged 57-99 years: the Older Australian Twins Study (n = 450) and the Canberra Longitudinal Study (n = 468), and reusing the Sydney Memory and Ageing Study (MAS) sample (n = 400). RESULTS: Initial analyses indicated good reliability for the IQCODE-16 (Person Separation Index range: 0.82-0.90). However, local dependency was identified between items, with several items showing misfit to the model. Replicating the existing Rasch solution could not reproduce the best Rasch model fit for all samples. Combining locally dependent items into three testlets resolved all misfit and local dependency issues and resulted in the best Rasch model fit for all samples with evidence of unidimensionality, strong reliability, and invariance across person factors. Accordingly, new ordinal-to-interval transformation algorithms were produced to convert the IQCODE-16 ordinal scores into interval data to improve the accuracy of its scores. CONCLUSIONS: The findings of this study support the reliability and validity of the IQCODE-16 in measuring CCs among older adults. New ordinal-to-interval conversion tables generated using samples from multiple independent datasets are more generalizable and can be used to enhance the precision of the IQCODE-16 without changing its original format. An easy-to-use converter has been made available for clinical and research use.

Texture Features of Magnetic Resonance Images Predict Poststroke Cognitive Impairment: Validation in a Multicenter Study.

BACKGROUND: Imaging features derived from T1-weighted (T1w) images texture analysis were shown to be potential markers of poststroke cognitive impairment, with better sensitivity than atrophy measurement. However, in magnetic resonance images, the signal distribution is subject to variations and can limit transferability of the method between centers. This study examined the reliability of texture features against imaging settings using data from different centers. METHODS: Data were collected from 327 patients within the Stroke and Cognition Consortium from centers in France, Germany, Australia, and the United Kingdom. T1w images were preprocessed to normalize the signal intensities and then texture features, including first- and second-order statistics, were measured in the hippocampus and the entorhinal cortex. Differences between the data led to the use of 2 methods of analysis. First, a machine learning modeling, using random forest, was used to build a poststroke cognitive impairment prediction model using one dataset and this was validated on another dataset as external unseen data. Second, the predictive ability of the texture features was examined in the 2 remaining datasets by ANCOVA with false discovery rate correction for multiple comparisons. RESULTS: The prediction model had a mean accuracy of 90% for individual classification of patients in the learning base while for the validation base it was ≈ 77%. ANCOVA showed significant differences, in all datasets, for the kurtosis and inverse difference moment texture features when measured in patients with cognitive impairment and those without. CONCLUSIONS: These results suggest that texture features obtained from routine clinical MR images are robust early predictors of poststroke cognitive impairment and can be combined with other demographic and clinical predictors to build an accurate prediction model.