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.
Background
In old age, cardiovascular and metabolic disorders rarely occur in isolation, but rather cluster together, in the so‐called metabolic syndrome (MetS). Although individual MetS components ...(obesity, hypertension, diabetes, and dyslipidemia) have been linked to dementia, their combined effect on cognition remains poorly explored. We recently showed that MetS was associated with clinically relevant cognitive decrements in otherwise cognitively healthy septuagenarians. However, the mechanistic underpinnings are unknown. This study investigates the association between MetS and markers of neurodegenerative and cerebrovascular pathologies, considering sex‐differences.
Method
Within the Gothenburg H70‐Birth cohort 1944, we included 741 cognitively intact septuagenarians with available brain MRI (n = 286 with cerebrospinal fluid CSF samples). MetS was identified according to standard criteria as the presence of at least three conditions: central obesity, raised blood pressure, raised blood glucose, reduced HDL‐cholesterol, or elevated triglycerides. MRI markers of brain pathology included overall (mean cortical thickness) and Alzheimer’s disease (AD; average cortical thickness in signature areas) neurodegeneration as well as cerebrovascular pathology including small vessel disease (SVD) and DTI’s microstructural white‐matter changes. CSF biomarkers included t‐tau, neurofilament light, and neurogranin (overall neurodegeneration), Aβ42 and p‐tau (AD), and CSF/serum albumin ration (blood‐brain barrier integrity). Analyses included linear, quantile, and logistic regression models as well as interaction and stratification by sex.
Result
Overall, 410 participants (53%) had MetS; 196 were women. In multi‐adjusted regressions (by sex, education, heart disease, and APOE‐ɛ4 allele), MetS was associated with increased likelihood of neurodegeneration (OR = 2.0, 95%CI 1.4–2.9) and AD (OR = 1.8, 95%CI 1.4–2.6), high SVD burden (presence of ≥3 SVD compared to none: OR = 2.1, 95%CI 1.1‐4.3), and greater microstructural white‐matter changes. In the CSF sample, MetS was associated with higher CSF/serum albumin ratio. In analyses stratified by sex, men with MetS (reference: no‐MetS) had increased odds of higher SVD burden, particularly of enlarged perivascular spaces (ePVS; OR = 2.9, 95%CI 1.7–5.0). Such differences were not observed between women with and without MetS. No associations were observed between MetS and amyloid and tau biomarkers.
Conclusion
MetS in late‐life increased the likelihood of neurodegenerative and cerebrovascular pathologies. SVD, especially ePVS, appears more prominent in men with MetS than women.
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.
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.
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).
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.
Background
The interplay of genetic and environmental factors can trigger a cascade of neuropathological changes leading to individual differences in brain aging. The apparent age of the brain (brain ...age) can differ from chronological age, potentially reflecting different resilience mechanisms. Brain age has also the potential as biomarker to predict future cognitive impairments and dementia. In this study, we developed a biological measure of brain age, based on the differences between predicted brain age (PBA) and chronological age (CA) using deep learning model (DLM).
Method
The sample included 16734 T1‐w MRI from multiple time‐points of 15115 healthy individuals, aged 32‐96 yrs., from: 1) ADNI (n = 1489); 2) AIBL (n = 957); GENIC (n = 406); 4) UK Biobank (n = 13882). Healthy status was defined as absence of dementia/cognitive impairment, neuro‐psychiatric disorders, and/or self‐report of good health. Medical diagnoses were collected through physician‐/self‐reports or medical records following the International Classification of Diseases. A DLM based on convolutional neural networks was trained to develop an algorithm to predict brain age from raw MRI images registered to the MNI space using training (90%) and hold‐out test (10%) sets. To assess the model generalizability, we applied the algorithm in the Gothenburg H70‐Birth Cohort 1944 (n = 792 septuagenarians).
Result
The hold‐out method achieved a mean absolute error (MAE) error, between PBA and CA, of 3.04 yrs. In the H70 cohort the mean PBA was 73.9±1.49 64.7‐86.1 years.
Conclusion
The model shows accurate predictions, comparable with those from previous studies using other computation methods. As next steps, we will evaluate hyperparameter optimization and implementation of a cross‐validation methodology. We also seek to include further cohorts of healthy individuals with heterogeneous age‐span to increase the model reliability and generalizability. This will enable to address e.g., differences between normal and pathological aging as well as exploring resilience mechanisms.
Background
Cognitive and physical deficits independently raise the risk for negative events in older adults. Less is known about whether their co-occurrence constitutes a distinct risk profile. This ...study quantifies the association between cognitive impairment, no dementia (CIND), slow walking speed (WS) and their combination and disability and mortality.
Methods
We examined 2546 dementia-free people aged ≥ 60 years, part of the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) up to 12 years. The following four profiles were created: (1) healthy profile; (2) isolated CIND (scoring 1.5 SD below age-specific means on at least one cognitive domain); (3) isolated slow WS (< 0.8 m/s); (4) CIND+ slow WS. Disability was defined as the sum of impaired activities of daily living and trajectories of disability were derived from mixed-effect linear regression models. Piecewise proportional hazard models were used to estimate mortality rate hazard ratios (HRs). Population attributable risks of death were calculated.
Results
Participants with both CIND and slow WS had the worst prognosis, especially in the short-term period. They experienced the steepest increase in disability and five times the mortality rate (HR 5.1; 95% CI 3.5–7.4) of participants free from these conditions. Similar but attenuated results were observed for longer follow-ups. Co-occurring CIND and slow WS accounted for 30% of short-term deaths.
Conclusions
Co-occurring cognitive and physical limitations constitute a distinct risk profile in older people, and account for a large proportion of short-term deaths. Assessing cognitive and physical function could enable early identification of people at high risk for adverse events.
Abstract INTRODUCTION Cognitive reserve might mitigate the risk of Alzheimer's dementia among memory clinic patients. No study has examined the potential modifying role of stress on this relation. ...METHODS We examined cross‐sectional associations of the cognitive reserve index (CRI; education, occupational complexity, physical and leisure activities, and social health) with cognitive performance and AD‐related biomarkers among 113 memory clinic patients. The longitudinal association between CRI and cognition over a 3‐year follow‐up was assessed. We examined whether associations were influenced by perceived stress and five measures of diurnal salivary cortisol. RESULTS Higher CRI scores were associated with better cognition. Adjusting for cortisol measures reduced the beneficial association of CRI on cognition. A higher CRI score was associated with better working memory in individuals with higher (favorable) cortisol AM/PM ratio, but not among individuals with low cortisol AM/PM ratio. No association was found between CRI and AD‐related biomarkers. DISCUSSION Physiological stress reduces the neurocognitive benefits of cognitive reserve among memory clinic patients. Highlights Physiological stress may reduce the neurocognitive benefits accrued from cognitively stimulating and enriching life experiences (cognitive reserve CR) in memory clinic patients. Cortisol awakening response modified the relation between CR and P‐tau 181 , a marker of Alzheimer's disease (AD). Effective stress management techniques for AD and related dementia prevention are warranted.
Abstract
Background
Despite the well‐established link between type 2 diabetes and dementia, its impact on the prodromal dementia phase remains controversial, as does the impact of comorbid ...cardiovascular disease (CVD). In this study, we assessed the impact of diabetes and CVD on the development of cognitive impairment no dementia (CIND) and its progression to dementia.
Methods
In the Swedish National Study on Aging and Care‐Kungsholmen (SNAC‐K), a cohort of cognitively‐intact individuals (n=1840) and a cohort of individuals with CIND (n=682) aged ≥60 years were followed over 15 years. At baseline and each follow‐up (every 3 or 6 years), a neuropsychological test battery was administered to assess five cognitive domains (episodic memory, processing speed, executive function, verbal fluency, visuospatial abilities). CIND was defined as having no dementia and cognitive performance ≥1.5 SDs below age group‐specific means in at least one domain. Dementia was diagnosed according to international criteria. Diabetes (controlled and uncontrolled: HbA1c <7.5% vs. ≥7.5%) was assessed based on medical history, clinical records, and glycated hemoglobin. CVD (atrial fibrillation, heart failure, ischemic heart disease, cardiac valve diseases, and bradycardias) was ascertained through medical examinations and medical records. Data were analyzed with multivariable Cox regression models.
Results
At baseline, 135 (7%) participants in the cognitively‐intact cohort and 85 (12%) in the CIND cohort had diabetes. During follow‐up (mean 9.2 ± 3.1 years 2.2–15.6 years), 544 (30%) participants in the cognitively‐intact cohort developed CIND. Diabetes was associated with a 35% higher risk of CIND (HR 1.35, 95% CI: 0.98‐1.88) compared to the diabetes‐free group. This risk rose to 75% in people with comorbid diabetes and CVD (HR 1.75, 95% CI: 1.01‐3.04) and was doubled in people with uncontrolled diabetes (HR 1.97, 95% CI: 1.12‐3.49). In the CIND cohort, 151 (22%) individuals progressed to dementia during follow‐up. Participants with uncontrolled diabetes had triple the risk of progressing to dementia (HR 3.00, 95% CI: 1.26‐7.13) vs. the diabetes‐free group, and the HR of dementia was 4.36 (95% CI: 1.51‐12.59) in individuals with uncontrolled diabetes and comorbid CVD.
Conclusions
Uncontrolled diabetes increases the risk of cognitive impairment and accelerates its progression to dementia, particularly in older adults with comorbid CVD.
Background
Despite the well‐established link between type 2 diabetes and dementia, its impact on the prodromal dementia phase remains controversial, as does the impact of comorbid cardiovascular ...disease (CVD). In this study, we assessed the impact of diabetes and CVD on the development of cognitive impairment no dementia (CIND) and its progression to dementia.
Methods
In the Swedish National Study on Aging and Care‐Kungsholmen (SNAC‐K), a cohort of cognitively‐intact individuals (n=1840) and a cohort of individuals with CIND (n=682) aged ≥60 years were followed over 15 years. At baseline and each follow‐up (every 3 or 6 years), a neuropsychological test battery was administered to assess five cognitive domains (episodic memory, processing speed, executive function, verbal fluency, visuospatial abilities). CIND was defined as having no dementia and cognitive performance ≥1.5 SDs below age group‐specific means in at least one domain. Dementia was diagnosed according to international criteria. Diabetes (controlled and uncontrolled: HbA1c <7.5% vs. ≥7.5%) was assessed based on medical history, clinical records, and glycated hemoglobin. CVD (atrial fibrillation, heart failure, ischemic heart disease, cardiac valve diseases, and bradycardias) was ascertained through medical examinations and medical records. Data were analyzed with multivariable Cox regression models.
Results
At baseline, 135 (7%) participants in the cognitively‐intact cohort and 85 (12%) in the CIND cohort had diabetes. During follow‐up (mean 9.2 ± 3.1 years 2.2–15.6 years), 544 (30%) participants in the cognitively‐intact cohort developed CIND. Diabetes was associated with a 35% higher risk of CIND (HR 1.35, 95% CI: 0.98‐1.88) compared to the diabetes‐free group. This risk rose to 75% in people with comorbid diabetes and CVD (HR 1.75, 95% CI: 1.01‐3.04) and was doubled in people with uncontrolled diabetes (HR 1.97, 95% CI: 1.12‐3.49). In the CIND cohort, 151 (22%) individuals progressed to dementia during follow‐up. Participants with uncontrolled diabetes had triple the risk of progressing to dementia (HR 3.00, 95% CI: 1.26‐7.13) vs. the diabetes‐free group, and the HR of dementia was 4.36 (95% CI: 1.51‐12.59) in individuals with uncontrolled diabetes and comorbid CVD.
Conclusions
Uncontrolled diabetes increases the risk of cognitive impairment and accelerates its progression to dementia, particularly in older adults with comorbid CVD.
Abstract
Background
Cognitive deficits can occur years or even decades before a clinical diagnosis of dementia, with a rate of decline which differs from that in normal aging. This study aimed to ...investigate differences in trajectories of cognitive decline in multiple cognitive domains in the preclinical dementia phase, and whether early rates of decline can be used to predict dementia.
Method
Repeated neuropsychological assessments were conducted for 1652 participants (age ≥60 years) from the Swedish National Study on Aging and Care–Kungsholmen (SNAC‐K). In this sample, 220 developed dementia and 1432 remained dementia free or died without dementia over 12 years. Piecewise linear mixed models were used to compare rates of cognitive decline between people who developed dementia and those who remained dementia free. Individual rates of decline from baseline over six years follow‐up were extrapolated from the mixed models for episodic memory, semantic memory, verbal fluency, and perceptual speed. Fast decliners were identified as those whose rate of decline was >1.5SDs faster than the mean of the no‐dementia group. Multinomial logistic regressions were used to estimate the association between domain‐specific rate of decline and dementia at 9 or 12 year.
Result
Significantly faster decline was observed in all cognitive domains in those who developed dementia vs. those who did not. In piecewise linear mixed‐models, participants in a preclinical dementia phase showed disproportionally accelerated decline as they neared the time of diagnosis in all cognitive domains except episodic memory, where decline was more linear. Being a fast decliner was associated with increased dementia risk and being a fast decliner in at least two cognitive domains further increased the risk of dementia. For the individual domains, however, only rate of decline in verbal fluency added independent variance above that of baseline scores.
Conclusion
The preclinical dementia phase is associated with accelerated cognitive decline, especially in the last 6 years prior to diagnosis. Using early rates of decline (12‐6 years before diagnosis) add some information in the prediction of future dementia and can help identify individuals at increased risk of developing the disease.
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