Social health and subsequent cognitive functioning in people aged 50 years and older: examining the mediating roles of depressive symptoms and inflammatory biomarkers in two European longitudinal studies.

BACKGROUND: Social health markers, including marital status, contact frequency, network size, and social support, have been shown to be associated with cognition. However, the mechanisms underlying these associations remain poorly understood. We investigated whether depressive symptoms and inflammation mediated associations between social health and subsequent cognition. METHODS: In the English Longitudinal Study of Ageing (ELSA), a nationally representative longitudinal study in England, UK, we sampled 7136 individuals aged 50 years or older living in private households without dementia at baseline or at the intermediate mediator assessment timepoint, who had recorded information on at least one social health marker and potential mediator. We used four-way decomposition to examine to what extent depressive symptoms, C-reactive protein, and fibrinogen mediated associations between social health and subsequent standardised cognition (verbal fluency and delayed and immediate recall), including cognitive change, with slopes derived from multilevel models (12-year slope). We examined whether findings were replicated in the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), a population-based longitudinal study in Sweden, in a sample of 2604 individuals aged 60 years or older living at home or in institutions in Kungsholmen (central Stockholm) without dementia at baseline or at the intermediate mediator assessment timepoint (6-year slope). Social health exposures were assessed at baseline, potential mediators were assessed at an intermediate timepoint (wave 2 in ELSA and 6-year follow-up in SNAC-K); cognitive outcomes were assessed at a single timepoint (wave 3 in ELSA and 12-year follow-up in SNAC-K), and cognitive change (between waves 3 and 9 in ELSA and between 6-year and 12-year follow-ups in SNAC-K). FINDINGS: The study sample included 7136 participants from ELSA, of whom 3962 (55·5%) were women and 6934 (97·2%) were White; the mean baseline age was 63·8 years (SD 9·4). Replication analyses included 2604 participants from SNAC-K, of whom 1604 (61·6%) were women (SNAC-K did not collect ethnicity data); the mean baseline age was 72·3 years (SD 10·1). In ELSA, we found indirect effects via depressive symptoms of network size, positive support, and less negative support on subsequent verbal fluency, and of positive support on subsequent immediate recall (pure indirect effect [PIE] 0·002 [95% CI 0·001-0·003]). Depressive symptoms also partially mediated associations between less negative support and slower decline in immediate recall (PIE 0·001 [0·000-0·002]) and in delayed recall (PIE 0·001 [0·000-0·002]), and between positive support and slower decline in immediate recall (PIE 0·001 [0·000-0·001]). We did not observe mediation by inflammatory biomarkers. Findings of mediation by depressive symptoms in the association between positive support and verbal fluency and between positive support and change in immediate recall were replicated in SNAC-K. INTERPRETATION: The findings of this study provide new insights into mechanisms linking social health with cognition, suggesting that associations between interactional aspects of social health, especially social support, and cognition are partly underpinned by depressive symptoms. FUNDING: EU Joint Programme-Neurodegenerative Disease Research (JPND) and Alzheimer's Society. TRANSLATION: For the Swedish translation of the abstract see Supplementary Materials section.

Associations Between Mid- to Late-Life Body Mass Index and Chronic Disease-Free Survival: A Nationwide Twin Study.

BACKGROUND: Some studies have linked late-life overweight to a reduced mortality risk compared to normal body mass index (BMI). However, the impact of late-life overweight and its combination with mid-life BMI status on healthy survival remains unclear. We aimed to investigate whether and to what extent mid- and/or late-life overweight are associated with chronic disease-free survival. METHODS: Within the Swedish Twin Registry, 11 597 chronic disease-free twins aged 60-79 years at baseline were followed up for 18 years. BMI (kg/m2) was recorded at baseline and 25-35 years before baseline (ie, midlife) and divided as underweight (<20), normal (≥20-25), overweight (≥25-30), and obese (≥30). Incident chronic diseases (cardiovascular diseases, type 2 diabetes, and cancer) and deaths were ascertained via registries. Chronic disease-free survival was defined as years lived until the occurrence of any chronic diseases or death. Data were analyzed using multistate survival analysis. RESULTS: Of all participants, 5 640 (48.6%) were overweight/obese at baseline. During the follow-up, 8 772 (75.6%) participants developed at least 1 chronic disease or died. Compared to normal BMI, late-life overweight and obesity were associated with 1.1 (95% CI, 0.3, 2.0) and 2.6 (1.6, 3.5) years shorter chronic disease-free survival. Compared to normal BMI through mid- to late life, consistent overweight/obesity and overweight/obesity only in mid-life led to 2.2 (1.0, 3.4) and 2.6 (0.7, 4.4) years shorter disease-free survival, respectively. CONCLUSIONS: Late-life overweight and obesity may shorten disease-free survival. Further research is needed to determine whether preventing overweight/obesity from mid- to late life might favor longer and healthier survival.

The Association between Education and Cognitive Performance Varies at Different Levels of Cognitive Performance: A Quantile Regression Approach.

INTRODUCTION: Educational differences in cognitive performance among older adults are well documented. Studies that explore this association typically estimate a single average effect of education on cognitive performance. We argue that the processes that contribute to the association between education and cognitive performance are unlikely to have equal effects at all levels of cognitive performance. In this study, we employ an analytical approach that enables us to go beyond averages to examine the association between education and five measures of global and domain-specific cognitive performance across the outcome distributions. METHODS: This cross-sectional study included 1,780 older adults aged 58-68 years from the Longitudinal Aging Study Amsterdam. Conditional quantile regression was used to examine variation across the outcome distribution. Cognitive outcomes included Mini-Mental State Examination (MMSE) score, crystallized intelligence, information processing speed, episodic memory, and a composite score of global cognitive performance. RESULTS: The results showed that the associations between education and different cognitive measures varied across the outcome distributions. Specifically, we found that education had a stronger association with crystallized intelligence, MMSE, and a composite cognitive performance measure in the lower tail of performance distributions. The associations between education and information processing speed and episodic memory were uniform across the outcome distributions. CONCLUSION: Larger associations between education and some domains of cognitive performance in the lower tail of the performance distributions imply that inequalities are primarily generated among individuals with lower performance rather than among average and high performers. Additionally, the varying associations across some of the outcome distributions indicate that estimating a single average effect through standard regression methods may overlook variations in cognitive performance between educational groups. Future studies should consider heterogeneity across the outcome distribution.

Vascular cognitive impairment - Past, present, and future challenges.

Vascular cognitive impairment (VCI) is a lifelong process encompassing a broad spectrum of cognitive disorders, ranging from subtle or mild deficits to prodromal and fully developed dementia, originating from cerebrovascular lesions such as large and small vessel disease. Genetic predisposition and environmental exposure to risk factors such as unhealthy lifestyles, hypertension, cardiovascular disease, and metabolic disorders will synergistically interact, yielding biochemical and structural brain changes, ultimately culminating in VCI. However, little is known about the pathological processes underlying VCI and the temporal dynamics between risk factors and disease mechanisms (biochemical and structural brain changes). This narrative review aims to provide an evidence-based summary of the link between individual vascular risk/disorders and cognitive dysfunction and the potential structural and biochemical pathophysiological processes. We also discuss some key challenges for future research on VCI. There is a need to shift from individual risk factors/disorders to comorbid vascular burden, identifying and integrating imaging and fluid biomarkers, implementing a life-course approach, considering possible neuroprotective influences of positive life exposures, and addressing biological sex at birth and gender differences. Finally, this review highlights the need for future researchers to leverage and integrate multidimensional data to advance our understanding of the mechanisms and pathophysiology of VCI.

Social Health and Change in Cognitive Capability among Older Adults: Findings from Four European Longitudinal Studies.

INTRODUCTION: In this study, we examine whether social health markers measured at baseline are associated with differences in cognitive capability and the rate of cognitive decline over an 11-to-18-year period among older adults and compare results across studies. METHODS: We applied an integrated data analysis approach to 16,858 participants (mean age 65 years; 56% female) from the National Survey for Health and Development (NSHD), the English Longitudinal Study of Aging (ELSA), the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), and the Rotterdam Study. We used multilevel models to examine social health in relation to cognitive capability and the rate of cognitive decline. RESULTS: Pooled estimates show distinct relationships between markers of social health and cognitive domains, e.g., a large network size (≥6 people vs. none) was associated with higher executive function (0.17 standard deviation [SD] [95% CI: 0.00, 0.34], I2 = 27%) but not with memory (0.08 SD [95% CI: -0.02, 0.18], I2 = 19%). We also observed pooled associations between being married or cohabiting, having a large network size, and participating in social activities with slower decline in cognitive capability. However, estimates were close to zero, e.g., 0.01 SD/year (95% CI: 0.01, 0.02) I2 = 19% for marital status and executive function. There were clear study-specific differences: results for average processing speed were the most homogenous, and results for average memory were the most heterogeneous. CONCLUSION: Overall, markers of good social health have a positive association with cognitive capability. However, we found differential associations between specific markers of social health and cognitive domains and differences between studies. These findings highlight the importance of examining between-study differences and considering the context specificity of findings in developing and deploying interventions.

Associations between dietary patterns and dementia-related neuroimaging markers.

BACKGROUND: The exploration of associations between dietary patterns and dementia-related neuroimaging markers can provide insights on food combinations that may impact brain integrity. METHODS: Data were derived from the Swedish Gothenburg H70 Birth Cohort Study (n = 610). Three dietary patterns were obtained using principal component analysis. Magnetic resonance imaging markers included cortical thickness, an Alzheimer's disease (AD) signature score, small vessel disease, and white matter microstructural integrity. Adjusted linear/ordinal regression analyses were performed. RESULTS: A high-protein and alcohol dietary pattern was negatively associated with cortical thickness in the whole brain (Beta: -0.011; 95% confidence interval [CI]: -0.018 to -0.003), and with an Alzheimer's disease cortical thickness signature score (Beta: -0.013; 95% CI: -0.024 to -0.001). A positive association was found between a Mediterranean-like dietary pattern and white matter microstructural integrity (Beta: 0.078; 95% CI: 0.002-0.154). No associations were found with a Western-like dietary pattern. DISCUSSION: Dietary patterns may impact brain integrity through neurodegenerative and vascular pathways. HIGHLIGHTS: Certain dietary patterns were associated with dementia-related neuroimaging markers. A Mediterranean dietary pattern was positively associated with white matter microstructure. A high-protein and alcohol pattern was negatively associated with cortical thickness.

The global burden of cerebral small vessel disease in low- and middle-income countries: A systematic review and meta-analysis.

BACKGROUND: Cerebral small vessel disease (cSVD) is a major cause of stroke and dementia. Previous studies on the prevalence of cSVD are mostly based on single geographically defined cohorts in high-income countries. Studies investigating the prevalence of cSVD in low- and middle-income countries (LMICs) are expanding but have not been systematically assessed. AIM: This study aims to systematically review the prevalence of cSVD in LMICs. RESULTS: Articles were searched from the Ovid MEDLINE and EMBASE databases from 1 January 2000 to 31 March 2022, without language restrictions. Title/abstract screening, full-text review, and data extraction were performed by two to seven independent reviewers. The prevalence of cSVD and study sample size were extracted by pre-defined world regions and health status. The Risk of Bias for Non-randomized Studies tool was used. The protocol was registered on PROSPERO (CRD42022311133). A meta-analysis of proportion was performed to assess the prevalence of different magnetic resonance imaging markers of cSVD, and a meta-regression was performed to investigate associations between cSVD prevalence and type of study, age, and male: female ratio. Of 2743 studies identified, 42 studies spanning 12 global regions were included in the systematic review. Most of the identified studies were from China (n = 23). The median prevalence of moderate-to-severe white matter hyperintensities (WMHs) was 20.5%, 40.5%, and 58.4% in the community, stroke, and dementia groups, respectively. The median prevalence of lacunes was 0.8% and 33.5% in the community and stroke groups. The median prevalence of cerebral microbleeds (CMBs) was 10.7% and 22.4% in the community and stroke groups. The median prevalence of moderate-to-severe perivascular spaces was 25.0% in the community. Meta-regression analyses showed that the weighted median age (51.4 ± 0.0 years old; range: 36.3-80.2) was a significant predictor of the prevalence of moderate-to-severe WMH and lacunes, while the type of study was a significant predictor of the prevalence of CMB. The heterogeneity of studies was high (>95%). Male participants were overrepresented. CONCLUSIONS: This systematic review and meta-analysis provide data on cSVD prevalence in LMICs and demonstrated the high prevalence of the condition. cSVD research in LMICs is being published at an increasing rate, especially between 2010 and 2022. More data are particularly needed from Sub-Saharan Africa and Central Europe, Eastern Europe, and Central Asia.

Cardiometabolic multimorbidity and incident dementia: the Swedish twin registry.

AIMS: Cardiometabolic diseases (CMDs), including diabetes, heart disease, and stroke, are established risk factors for dementia, but their combined impact has been investigated only recently. This study aimed to examine the association between mid- and late-life cardiometabolic multimorbidity and dementia and explore the role of genetic background in this association. METHODS AND RESULTS: Within the Swedish Twin Registry, 17 913 dementia-free individuals aged ≥60 were followed for 18 years. CMDs [including age of onset in mid (60) or late (≥60) life] and dementia were ascertained from medical records. Cardiometabolic multimorbidity was defined as having ≥2 CMDs. Cox regression was used to estimate the CMD-dementia association in (i) a classical cohort study design and (ii) a co-twin study design involving 356 monozygotic and dizygotic pairs. By comparing the strength of the association in the two designs, the contribution of genetic background was estimated. At baseline, 3,312 (18.5%) participants had 1 CMD and 839 (4.7%) had ≥2 CMDs. Over the follow-up period, 3,020 participants developed dementia. In the classic cohort design, the hazard ratio (95% confidence interval) of dementia was 1.42 (1.27-1.58) for 1 CMD and 2.10 (1.73-2.57) for ≥2 CMDs. Dementia risk was stronger with mid-life as opposed to late-life CMDs. In the co-twin design, the CMD-dementia association was attenuated among monozygotic [0.99 (0.50-1.98)] but not dizygotic [1.55 (1.15-2.09)] twins, suggesting that the association was in part due to genetic factors common to both CMDs and dementia. CONCLUSION: Cardiometabolic multimorbidity, particularly in mid-life, is associated with an increased risk of dementia. Genetic background may underpin this association.

Social Health and Cognitive Change in Old Age: Role of Brain Reserve.

OBJECTIVE: Individual aspects of social health (SH; eg, network, engagement, support) have been linked to cognitive health. However, their combined effect and the role of the structural properties of the brain (brain reserve [BR]) remain unclear. We investigated the interplay of SH and BR on cognitive change in older adults. METHODS: Within the Swedish National Study on Aging and Care-Kungsholmen, 368 dementia-free adults aged ≥60 years with baseline brain magnetic resonance imaging were followed over 12 years to assess cognitive change. A measure of global cognition was computed at each of the 5 waves of assessment by averaging domain-specific Z scores for episodic memory, perceptual speed, semantic memory, and letter and category fluency. An SH composite score was computed at baseline by combining leisure activities and social network. BR was proxied by total brain tissue volume (TBTV). Linear mixed models (adjusted for sociodemographic, vascular, and genetic factors) were used to estimate cognitive trajectories in relation to SH and TBTV. Interaction analysis and stratification were used to examine the interplay between SH and TBTV. RESULTS: Moderate-good SH (n = 245; vs poor, ß-slope = 0.01, 95% confidence interval [CI] = 0.002-0.02, p = 0.018) and moderate-to-large TBTV (n = 245; vs small, ß-slope = 0.03, 95% CI = 0.02-0.04, p < 0.001) were separately associated with slower cognitive decline. In stratified analysis, moderate-good SH was associated with higher cognitive levels (but not change) only in participants with moderate-to-large TBTV (ß-intercept = 0.21, 95% CI = 0.06-0.37, p < 0.01; interaction SH * TBTV, p < 0.05). INTERPRETATION: Our findings highlight the interplay between SH and BR that likely unfolds throughout the entire life course to shape old-age cognitive outcomes. ANN NEUROL 2023;93:844-855.

Brain Changes and Fast Cognitive and Motor Decline in Older Adults.

BACKGROUND: To identify brain magnetic resonance imaging (MRI) signatures characterizing people with different patterns of decline in cognition and motor function. METHODS: In the Swedish National Study on Aging and Care in Kungsholmen, Stockholm, 385 participants had available repeated brain MRI examinations, where markers of brain volumes and white matter integrity were assessed. The speed of cognitive and motor decline was estimated as the rate of a Mini-Mental State Examination and gait speed decline over 12 years (linear mixed models), and further dichotomized into the upper (25% fastest rate of decline) versus the lower quartiles. Participants were grouped in slow/no decliners (reference), isolated motor decliners, isolated cognitive decliners, and cognitive and motor decliners. We estimated the associations between changes in brain markers (linear mixed models) and baseline diffusion tensor imaging measures (linear regression model) and the 4 decline patterns. RESULTS: Individuals with concurrent cognitive and motor decline (n = 51) experienced the greatest loss in the total brain (ß: -12.3; 95% confidence interval [CI]: -18.2; -6.38) and hippocampal (ß: -0.25; 95% CI: -0.34; -0.16) volumes, the steepest accumulation of white matter hyperintensities (ß: 1.61; 95% CI: 0.54; 2.68), and the greatest ventricular enlargement (ß: 2.07; 95% CI: 0.67; 3.47). Compared to the reference, those only experiencing cognitive decline presented with steeper hippocampal volume loss, whereas those exhibiting only motor decline displayed a greater white matter hyperintensities burden. Lower microstructural white matter integrity was associated with concurrent cognitive and motor decline. CONCLUSION: Concurrent cognitive and motor decline is accompanied by rapidly evolving and complex brain pathology involving both gray and white matter. Isolated cognitive and motor declines seem to exhibit brain damage with different qualitative features.

Association Between Late-Life Weight Change and Dementia: A Population-based Cohort Study.

BACKGROUND: The impact of late-life weight changes on incident dementia is unclear. We aimed to investigate the associations of body mass index (BMI) and weight changes with dementia and to explore the role of APOE ɛ4 in these associations. METHODS: A total of 1 673 dementia-free participants aged ≥60 and older were followed for an initial 6 years to detect changes in BMI/weight and then for an additional 6 years to detect incident dementia. BMI change ([BMIfirst 6-year follow-up - BMIbaseline]/BMIbaseline) was categorized as stable (≤5%), and moderate (5%-10%) or large (>10%) gain or loss. Weight change (weightfirst 6-year follow-up - weightbaseline) was categorized as stable (≤2.5 kg), and moderate (2.5-7.5 kg) or large (>7.5 kg) gain or loss. Dementia was diagnosed following standard criteria. Data were analyzed using Cox regression models. RESULTS: Over the second 6-year follow-up period, 102 incident dementia cases were identified. Compared with stable BMI, the hazard ratios (95% CI) of dementia were 2.61 (1.09-5.54) and 2.93 (1.72-4.91) for BMI gain or loss >10%, respectively. The risk of dementia was higher among APOE ɛ4 carriers experiencing a large BMI gain (9.93 [3.49-24.6]) or loss (6.66 [2.83-14.4]) than APOE ɛ4 noncarriers with stable BMI. Similar results were observed for weight change and dementia associations. CONCLUSIONS: BMI and weight changes showed U-shaped associations with dementia risk. Large bodyweight gain and loss alike are associated with an almost 3-fold higher risk of dementia, which may be amplified by APOE ɛ4.

A deep learning model for brain age prediction using minimally preprocessed T1w images as input.

Introduction: In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods: We used a multicohort dataset of cognitively healthy individuals (age range = 32.0-95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2-4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset's images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results: The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1-4, respectively. The model's performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63-2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion: We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.

Associations between social connections and cognition: a global collaborative individual participant data meta-analysis.

BACKGROUND: Poor social connections (eg, small networks, infrequent interactions, and loneliness) are modifiable risk factors for cognitive decline. Existing meta-analyses are limited by reporting aggregate responses, a focus on global cognition, and combining social measures into single constructs. We aimed to investigate the association between social connection markers and the rate of annual change in cognition (ie, global and domain-specific), as well as sex differences, using an individual participant data meta-analysis. METHODS: We harmonised data from 13 longitudinal cohort studies of ageing in North America, South America, Europe, Africa, Asia, and Australia. Studies were eligible for inclusion if they had baseline data for social connection markers and at least two waves of cognitive scores. Follow-up periods ranged from 0 years to 15 years across cohorts. We included participants with cognitive data for at least two waves and social connection data for at least one wave. We then identified and excluded people with dementia at baseline. Primary outcomes were annual rates of change in global cognition and cognitive domain scores over time until final follow-up within each cohort study analysed by use of an individual participant data meta-analysis. Linear mixed models within cohorts used baseline social connection markers as predictors of the primary outcomes. Effects were pooled in two stages using random-effects meta-analyses. We assessed the primary outcomes in the main (partially adjusted) and fully adjusted models. Partially adjusted models controlled for age, sex, and education; fully adjusted models additionally controlled for diabetes, hypertension, smoking, cardiovascular risk, and depression. FINDINGS: Of the 40 006 participants in the 13 cohort studies, we excluded 1392 people with dementia at baseline. 38 614 individual participants were included in our analyses. For the main models, being in a relationship or married predicted slower global cognitive decline (b=0·010, 95% CI 0·000-0·019) than did being single or never married; living with others predicted slower global cognitive (b=0·007, 0·002-0·012), memory (b=0·017, 0·006-0·028), and language (b=0·008, 0·000-0·015) decline than did living alone; and weekly interactions with family and friends (b=0·016, 0·006-0·026) and weekly community group engagement (b=0·030, 0·007-0·052) predicted slower memory decline than did no interactions and no engagement. Never feeling lonely predicted slower global cognitive (b=0·047, 95% CI 0·018-0·075) and executive function (b=0·047, 0·017-0·077) decline than did often feeling lonely. Degree of social support, having a confidante, and relationship satisfaction did not predict cognitive decline across global cognition or cognitive domains. Heterogeneity was low (I2=0·00-15·11%) for all but two of the significant findings (association between slower memory decline and living with others [I2=58·33%] and community group engagement, I2=37·54-72·19%), suggesting robust results across studies. INTERPRETATION: Good social connections (ie, living with others, weekly community group engagement, interacting weekly with family and friends, and never feeling lonely) are associated with slower cognitive decline. FUNDING: EU Joint Programme-Neurodegenerative Disease Research grant, funded by the National Health and Medical Research Council Australia, and the US National Institute on Aging of the US National Institutes of Health.

What Do We Know about Social and Non-Social Factors Influencing the Pathway from Cognitive Health to Dementia? A Systematic Review of Reviews.

The heterogeneous and multi-factorial nature of dementia requires the consideration of all health aspects when predicting the risk of its development and planning strategies for its prevention. This systematic review of reviews provides a comprehensive synthesis of those factors associated with cognition in the context of dementia, identifying the role of social aspects and evidencing knowledge gaps in this area of research. Systematic reviews and meta-analyses from 2009-2021 were searched for within Medline, PsycINFO, CINAHL Complete, Cochrane, and Epistemonikos. Reviewers independently screened, reviewed, and assessed the records, following the PRISMA-2020 guidelines. From 314 included studies, 624 cognitive-related factors were identified, most of them risk factors (61.2%), mainly belonging to the group of 'somatic comorbidities' (cardiovascular disease and diabetes) and 'genetic predispositions'. The protective factors (20%) were mainly related to lifestyle, pointing to the Mediterranean diet, regular physical activity, and cognitively stimulating activities. Social factors constituted 9.6% of all identified factors. Research on biological and medical factors dominates the reviewed literature. Greater social support and frequent contact may confer some protection against cognitive decline and dementia by delaying its onset or reducing the overall risk; however, overall, our findings are inconsistent. Further research is needed in the fields of lifestyle, psychology, social health, and the protective factors against cognitive decline and dementia.

Trajectories of cognitive decline and dementia development: A 12-year longitudinal study.

INTRODUCTION: Mapping the preclinical dementia phase is important for early detection and evaluation of interventions. We assessed the trajectories of cognitive decline in preclinical dementia over 12 years and investigated whether being a fast decliner across 6 years is associated with increased risk of dementia the following 6 years. METHODS: Rates of cognitive decline were determined using mixed-effects models for 1646 participants from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) cohort. Cox regression was used to assess the future likelihood of dementia for fast decliners (declining ≥1.5 standard deviations [SDs] faster than the age-specific rates). RESULTS: Participants in a preclinical phase of dementia showed increased rates of decline in all cognitive tests compared to the no-dementia group, particularly closer (0-6 years) to diagnosis. Participants declining fast in three or more cognitive tests 12-6 years before diagnosis demonstrated a high risk of dementia 6 years later (hazard ratio [HR] 3.90, 95% confidence interval [CI] 2.28-6.69). DISCUSSION: Being a fast decliner is linked to increased risk of future dementia.

Cardiometabolic multimorbidity accelerates cognitive decline and dementia progression.

INTRODUCTION: Cardiometabolic diseases (CMDs) have been individually associated with adverse cognitive outcomes, but their combined effect has not been investigated. METHODS: A total of 2577 dementia-free participants 60 years of age or older were followed for 12 years to observe changes in cognitive function and to detect incident cognitive impairment, no dementia (CIND) and dementia. CMDs (including type 2 diabetes, heart disease, and stroke) were assessed at baseline through medical records and clinical examinations. Cardiometabolic multimorbidity was defined as the presence of two or more CMDs. Data were analyzed using multi-adjusted linear mixed-effects models, Cox regression, and Laplace regression. RESULTS: CMD multimorbidity was associated with cognitive decline, CIND (hazard ratio [HR] 1.73; 95% confidence interval CI 1.23 to 2.44), and its progression to dementia (HR 1.86; 95% CI 1.17 to 2.97). CMD multimorbidity accelerated the onset of CIND by 2.3 years and dementia by 1.8 years. CONCLUSIONS: CMD multimorbidity accelerates cognitive decline and increases the risk of both CIND and its conversion to dementia. HIGHLIGHTS: We explored the combined impact of cardiometabolic diseases (CMDs) on cognition. An increasing number of CMDs dose-dependently accelerated cognitive decline. CMD multimorbidity increased the risk of both cognitive impairment and dementia. Co-morbid CMDs could be ideal targets for interventions to protect cognitive health.

Recognition of social health: A conceptual framework in the context of dementia research.

Objective: The recognition of dementia as a multifactorial disorder encourages the exploration of new pathways to understand its origins. Social health might play a role in cognitive decline and dementia, but conceptual clarity is lacking and this hinders investigation of associations and mechanisms. The objective is to develop a conceptual framework for social health to advance conceptual clarity in future studies. Process: We use the following steps: underpinning for concept advancement, concept advancement by the development of a conceptual model, and exploration of its potential feasibility. An iterative consensus-based process was used within the international multidisciplinary SHARED project. Conceptual framework: Underpinning of the concept drew from a synthesis of theoretical, conceptual and epidemiological work, and resulted in a definition of social health as wellbeing that relies on capacities both of the individual and the social environment. Consequently, domains in the conceptual framework are on both the individual (e.g., social participation) and the social environmental levels (e.g., social network). We hypothesize that social health acts as a driver for use of cognitive reserve which can then slow cognitive impairment or maintain cognitive functioning. The feasibility of the conceptual framework is demonstrated in its practical use in identifying and structuring of social health markers within the SHARED project. Discussion: The conceptual framework provides guidance for future research and facilitates identification of modifiable risk and protective factors, which may in turn shape new avenues for preventive interventions. We highlight the paradigm of social health in dementia as a priority for dementia research.

Association of body mass index and its long-term changes with cardiometabolic diseases: A nationwide twin study.

BACKGROUND & AIMS: The association between higher body mass index (BMI) and cardiometabolic diseases (CMDs, including type 2 diabetes and cardiovascular diseases) is not well understood. We aimed to examine the association of BMI and its long-term changes with cardiometabolic diseases (CMDs) and explore the role of familial background and healthy lifestyle in this association. METHODS: Within the Swedish Twin Registry, 36 622 CMD-free individuals aged ≥40 were followed for up to 16 years. BMI data was collected at baseline and 25-35 years prior to baseline. Healthy lifestyle (non-smoking, no/mild alcohol consumption, and regular physical activity) was assessed as unfavourable (none or only one of these factors) vs. favourable (two or three). Incident CMDs were identified by linkage with the Swedish National Patient Registry. Two strategies were followed: 1) Cox models in all twin individuals; 2) stratified Cox models in CMD-discordant twin pairs. RESULTS: At baseline, 16 195 (44.2%) study participants had overweight/obesity (BMI ≥ 25 kg/m2) and 11 202 (30.6%) developed CMDs over follow-up. Among all participants, the hazard ratio (HR) and 95% confidence interval (CI) of developing any CMD was 1.52 (1.45-1.58) for people with overweight/obesity compared to normal BMI (20-25 kg/m2). Compared to stable normal BMI, HRs (95% CIs) of CMDs were 1.28 (1.02-1.59) and 1.33 (1.24-1.43) for only earlier life or only later life overweight/obesity, respectively, and 1.69 (1.55-1.85) for overweight/obesity both in earlier and later life. In stratified Cox analyses conducted among all CMD-discordant twin pairs, overweight/obesity was associated with greater risk of CMDs (1.37, 95% CI 1.18-1.61). In joint effect analysis, the risk of CMDs related to overweight/obesity was diminished 32% among people with a favourable lifestyle (1.51, 95% CI 1.44-1.58) compared to those with overweight/obesity and an unfavourable lifestyle (2.20, 95% CI 2.03-2.38). CONCLUSIONS: Overweight/obesity is associated with an increased risk of CMDs, and shared genetic and early-life environmental factors might not account for this association. However, a favourable lifestyle could attenuate the risk of high BMI-related CMDs.

The impact of diabetes on cognitive impairment and its progression to dementia.

INTRODUCTION: Diabetes is a well-established risk factor for dementia, but its impact on the prodromal phase of dementia is unclear. METHODS: Cohorts of older adults who were cognitively healthy (n = 1840) or had cognitive impairment-no dementia (CIND; n = 682) were followed over 12 years to detect incident CIND and dementia, respectively. RESULTS: Poorly controlled diabetes (glycated hemoglobin [HbA1c] ≥7.5%; reference = normoglycemia) was associated with double the risk of CIND (Cox regression multi-adjusted hazard ratio [HR] 2.01, 95% confidence interval [CI] 1.13-3.58) and triple the risk CIND progressing to dementia (HR 2.87, 95% CI 1.20-6.85). Co-morbid diabetes and heart disease doubled the risk of incident CIND and dementia, although neither disease conferred a significant risk of either outcome alone. Elevated systemic inflammation contributed to the diabetes-associated increased dementia risk. CONCLUSIONS: Diabetes characterized by poor glycemic control or cardiovascular complications is related to a greater risk of the development and progression of cognitive impairment. Inflammation may play a role in these relationships.