Co-morbidity associated with dementia.

IntroductionLate-life difficult-to-treat depression (LL-DTD) and dementia frequently coexist in later life, but it remains unclear whether clinical and sociodemographic characteristics, as well as medication exposure patterns, differ across dementia subtypes among older adults with both conditions.MethodsWe analysed anonymised electronic health records from a south London catchment area. We included patients aged ≥60 years at first recorded depression diagnosis with a dementia diagnosis. LL-DTD was defined as inadequate response to ≥2 antidepressant trials. Dementia diagnoses were classified as Alzheimer’s disease (AD), vascular dementia (VD), mixed AD/VD, dementia with Lewy bodies (DLB), or other/unspecified dementia. Around the index depressive episode, we captured sociodemographics, depressive symptoms, physical comorbidity, and medication indicators. We used multivariate logistic regression to examine cross-sectional correlates distinguishing dementia subtypes (with AD as reference) within the LL-DTD and dementia sample. We conducted stratified analyses comparing non-AD versus AD dementia by the temporal order of dementia and depression diagnoses.ResultsAmong 890 older adults with LL-DTD and dementia, AD was the most common subtype (33.9%), followed by mixed AD/VD (22.5%), VD (22.2%), other/unspecified dementia (15.2%), and DLB (6.2%). Depressive symptom profiles and psychotropic treatment history were broadly similar across subtypes. Compared with AD, VD was associated with greater functional impairment, while greater physical comorbidity burden was more evident in VD and mixed AD/VD.DiscussionSomatic multimorbidity and functional impairment provided the clearest clinical separation between subgroups, while depressive symptom patterns and medication exposure appeared largely non-specific across dementia subtypes. This underscores the importance of multimorbidity and physical health burden in understanding heterogeneity of dementia outcomes in LL-DTD.

Dementia and stroke are major global causes of disability, with modifiable lifestyle factors playing a significant role in their development. The updated LIfestyle for BRAin health (LIBRA2) score integrates 15 modifiable risk and protective factors to quantify lifestyle-based dementia risk, but its relationship with stroke and dementia subtypes remains unexplored. We investigated LIBRA2's association with stroke and dementia subtypes while also assessing potential interactions with genetic susceptibility. Prospective data were used from the French multicenter Three-City (3C) Study, with participants aged 65 years and older followed for up to 17 years. Weighted LIBRA2 scores at baseline were constructed based on the presence of 15 modifiable risk and protective factors, with higher scores representing higher lifestyle-based dementia risk. Cox proportional hazards models were used to study the association of LIBRA2 with incident dementia (and subtypes) and stroke, adjudicated by expert neurologist panels. Genetic susceptibility to dementia and stroke was assessed using APOE ε4 carriership and disease-specific genetic risk scores. Analyses included 4,731 participants for stroke (mean age 73.8 years, 60.1% female) and 4,737 participants for dementia (mean age 73.8 years, 59.9% female). One-point increases in LIBRA2 scores (theoretical range -6.1 to +25.7) were associated with increased dementia risk (hazard ratio [HR] 1.08; 1.06-1.11), with a stronger association for vascular and mixed dementia (HR 1.13; 1.08-1.18) compared with Alzheimer disease (AD) dementia (HR 1.06; 1.03-1.09). LIBRA2 was not significantly associated with incident stroke risk (HR 1.03; 0.99-1.07). No significant interaction was found between LIBRA2 and APOE ε4 carriership or disease-specific genetic risk scores in relation to dementia subtypes or stroke. LIBRA2 serves as a valuable tool for assessing lifestyle-related dementia risk and its subtypes but showed no association with stroke, highlighting the potential for a stroke-specific risk reduction model. These associations were independent of genetic disease susceptibility, reinforcing the universal benefits of lifestyle modifications on dementia risk reduction. The stronger association between LIBRA2 (and some individual components) and vascular or mixed dementia, compared with AD dementia, highlights the pivotal role of vascular mechanisms in the relationship between lifestyle and brain health.

Early diagnosis of dementia is crucial for timely intervention. However, frequently, there is a substantial delay in diagnosis. Therefore, it is essential to recognise and address the barriers to early diagnosis. These have not been systematically studied in India. We at a specialist memory clinic in India investigated the time from symptom onset to diagnosis of dementia and factors contributing to the delay. In this cross-sectional study, consecutive patients with dementia (n=855) seen at a private hospital underwent a standard clinical assessment and investigations. The primary outcome variable was time from symptom onset to diagnosis (TTD). The association of age, education, gender, dementia subtype, and age of onset on TTD were examined using a univariate analysis of covariance. The median TTD was 24months; 43% were diagnosed after 24months. There was a significant association between TTD and age at onset (young onset-median 36months vs. late onset-24months) and dementia subtype. Patients with vascular dementia were diagnosed significantly earlier as compared to patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD) [median 18, 24, and 30months, respectively]. There was no effect of gender or education on the TTD. About 40% of patients with dementia were diagnosed more than 2years after symptom onset, particularly young onset dementias and FTD. Our study findings highlight the gaps in diagnosing patients with dementia in urban India and have significant implications for developing and implementing multifaceted interventions to improve the early diagnosis of dementia.

To assess the association between new-onset atrial fibrillation and dementia among patients with type 2 diabetes, a group with a high prevalence of atrial fibrillation. This cohort study included 22 989 patients with type 2 diabetes from the UK Biobank. New-onset atrial fibrillation was ascertained from hospital admission records. We used an algorithm officially released by the UK Biobank to identify all-cause dementia, Alzheimer's disease and vascular dementia. The algorithm was developed using multiple sources, including hospital admissions and the death registry. Time-varying Cox regression analyses were performed to investigate the association between new-onset atrial fibrillation and dementia. A total of 2843 participants developed atrial fibrillation, whereas the remaining 20 146 did not. During the median of 12.3 years of follow-up, 844 all-cause dementia, 342 Alzheimer's disease and 246 vascular dementia cases occurred. Compared with participants without atrial fibrillation, those with atrial fibrillation had higher risks of all-cause dementia (hazard ratio [HR] 2.15, 95% confidence interval [CI] 1.80-2.57), Alzheimer's disease (HR 1.44, 95% CI 1.06-1.96) and vascular dementia (HR 3.11, 95% CI 2.32-4.17). New-onset atrial fibrillation was associated with a substantially higher risk of all-cause dementia, Alzheimer's disease and vascular dementia in patients with type 2 diabetes. Our findings highlight the significance of atrial fibrillation management in mitigating the risk of dementia in this demographic.

Dementia is the most common neurological disease in older adults; headaches, including migraines, are the most common neurological disorder across all ages. The objective of this study was to explore the relationship between migraines and dementia, including Alzheimer's disease (AD) and vascular dementia (VaD). Analyses were based on 679 community-dwelling participants 65+ years from the Manitoba Study of Health and Aging, a population-based, prospective cohort study. Participants screened as cognitively intact at baseline had complete data on migraine history and all covariates at baseline and were assessed for cognitive outcomes (all-cause dementia, AD, and VaD) 5 years later. The association of exposure (lifetime history of migraines), confounding (age, gender, education, and depression), and intervening variables (hypertension, myocardial infarction, other heart conditions, stroke, and diabetes) with all-cause dementia and dementia subtypes (AD and VaD) was assessed using multiple logistic regression models. A history of migraines was significantly associated with both all-cause dementia (odds ratio [OR]=2.97; 95% confidence interval [CI]=1.25-6.61) and AD (OR=4.22; 95% CI=1.59-10.42), even after adjustment for confounding and intervening variables. Migraines were not significantly associated with VaD either before (OR=1.83; 95% CI=0.39-8.52) or after (OR=1.52; 95% CI=0.20-7.23) such adjustment. Migraines were a significant risk factor for AD and all-cause dementia. Despite the vascular mechanisms involved in migraine physiology, migraines were not significantly associated with VaD in this study. Recognition of the long-term detrimental consequences of migraines for AD and dementia has implications for migraine management, as well as for our understanding of AD etiology.

Managing atrial fibrillation in the CRT patient: Controversy or consensus?

A dvances in our understanding of vascular cognitive impairment (VCI) have springboarded from further elucidation of the role of vascular risk factors, surgical procedures, medications, and neuroimaging studies that are related to this condition. The main focus of this VCI review will highlight the relation of diabetes mellitus and hippocampal dysfunction in VCI; coronary artery bypass surgery and cognitive decline; the role of cerebral amyloid angiopathy (CAA) in vascular dysfunction; and a discussion of other cardiovascular risk factors, treatment, and neuroimaging findings related to disease progression; and histopathologic and genetic correlates.

According to the latest revised National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (now known as the Alzheimer's Association) (NINCDS-ADRDA) diagnostic criteria for Alzheimer's disease dementia, the confidence in diagnosing mild cognitive impairment (MCI) due to Alzheimer's disease dementia is raised with the application of imaging biomarkers. These tests, added to core clinical criteria, might increase the sensitivity or specificity of a testing strategy. However, the accuracy of biomarkers in the diagnosis of Alzheimer's disease dementia and other dementias has not yet been systematically evaluated. A formal systematic evaluation of the sensitivity, specificity, and other properties of positron emission tomography (PET) imaging with the (11)C-labelled Pittsburgh Compound-B ((11)C-PIB) ligand was performed. To determine the diagnostic accuracy of the (11)C- PIB-PET scan for detecting participants with MCI at baseline who will clinically convert to Alzheimer's disease dementia or other forms of dementia over a period of time. The most recent search for this review was performed on 12 January 2013. We searched MEDLINE (OvidSP), EMBASE (OvidSP), BIOSIS Previews (ISI Web of Knowledge), Web of Science and Conference Proceedings (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We also requested a search of the Cochrane Register of Diagnostic Test Accuracy Studies (managed by the Cochrane Renal Group).No language or date restrictions were applied to the electronic searches and methodological filters were not used so as to maximise sensitivity. We selected studies that had prospectively defined cohorts with any accepted definition of MCI with baseline (11)C-PIB-PET scan. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis for example NINCDS-ADRDA or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria. We screened all titles generated by electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. The identified full papers were assessed for eligibility and data were extracted to create two by two tables. Two independent assessors performed quality assessment using the QUADAS 2 tool. We used the hierarchical summary receiver operating characteristic (ROC) model to produce a summary ROC curve. Conversion from MCI to Alzheimer's disease dementia was evaluated in nine studies. The quality of the evidence was limited. Of the 274 participants included in the meta-analysis, 112 developed Alzheimer's dementia. Based on the nine included studies, the median proportion converting was 34%. The studies varied markedly in how the PIB scans were done and interpreted.The sensitivities were between 83% and 100% while the specificities were between 46% and 88%. Because of the variation in thresholds and measures of (11)C-PIB amyloid retention, we did not calculate summary sensitivity and specificity. Although subject to considerable uncertainty, to illustrate the potential strengths and weaknesses of (11)C-PIB-PET scans we estimated from the fitted summary ROC curve that the sensitivity was 96% (95% confidence interval (CI) 87 to 99) at the included study median specificity of 58%. This equated to a positive likelihood ratio of 2.3 and a negative likelihood ratio of 0.07. Assuming a typical conversion rate of MCI to Alzheimer's dementia of 34%, for every 100 PIB scans one person with a negative scan would progress and 28 with a positive scan would not actually progress to Alzheimer's dementia.There were limited data for formal investigation of heterogeneity. We performed two sensitivity analyses to assess the influence of type of reference standard and the use of a pre-specified threshold. There was no effect on our findings. Although the good sensitivity achieved in some included studies is promising for the value of (11)C-PIB-PET, given the heterogeneity in the conduct and interpretation of the test and the lack of defined thresholds for determination of test positivity, we cannot recommend its routine use in clinical practice.(11)C-PIB-PET biomarker is a high cost investigation, therefore it is important to clearly demonstrate its accuracy and standardise the process of the (11)C-PIB diagnostic modality prior to it being widely used.

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Background/Introduction Patients with atrial fibrillation (AF) are associated with higher risk for cognitive decline and progression to dementia. There are limited data whether sodium-glucose cotransporter-2 inhibitor (SGLT2i) reduces new-onset cognitive dysfunction, especially in Asian patients with AF who also have diabetes mellitus. Purpose This study aimed to investigate whether SGLT2i reduces the risks of cardiovascular events and dementia among AF patients with diabetes. Methods We identified AF patients with diabetes based on the Taiwan's National Health Insurance Research Database (2016). AF patients without prior dementia history and aged 55-85 years were studied: 810 patients were prescribed SGLT2i for at least 180 days and were matched with 1620 patients without SGLT2i using propensity scores (Figure 1). Cardiovascular and dementia events were followed through the end of 2021. Subtypes of dementia were investigated using competing risk models. Results During a follow-up period of 5 years of AF patients with diabetes receiving SGLT2i, as shown in Figure 2, this study provides a detailed insight into the results of survival analyses for clinical outcomes of AF, stroke, total dementia, vascular dementia, all-cause death, and cardiovascular (CV) death. After multivariable adjustment, the risks of AF-related hospitalization (HR: 0.741, 95% CI: 0.563-0.976), stroke (HR: 0.748, 95% CI: 0.599-0.934) and all-cause death (HR: 0.338, 95% CI: 0.245-0.466), were significantly decreased. Both CV death (HR: 0.449, 95% CI: 0.268-0.755) and non-CV death (HR: 0.298, 95% CI: 0.198-0.448) were significantly reduced. SGLT2i use was associated with a significantly decreased risk of total dementia (HR: 0.615, 95% CI: 0.436-0.869) and vascular dementia (HR: 0.419, 95% CI: 0.221-0.793). The reduced risks of Alzheimer’s dementia and other/mixed dementia were not statistically significant. After multivariable adjustment, total dementia and vascular dementia were associated with higher age and stroke events. The beneficial effect on dementia was irrespective of novel oral anticoagulants (NOAC) use, although the SGLT2i benefits were greater in non-warfarin and non-dipeptidyl peptidase 4 (DPP4) users compared to users. Conclusions There were decreased risks of AF-related hospitalization, stroke event, all-cause death, and vascular dementia in the users of SGLT2i in AF patients with diabetes. This was irrespective of NOAC use, although the SGLT2i benefits were greater in non-warfarin users compared to warfarin users.Figure 1:Study flow chart

IntroductionDementia is characterized by significant declines in cognitive, physical, social, and behavioral functioning, and includes multiple subtypes that differ in etiology. There is limited evidence of the influence of psychiatric and substance use history on the risk of dementia subtypes among older underrepresented racial/ethnic minorities in the United States. Our study explored the role of psychiatric and substance use history on the risk of etiology-specific dementias: Alzheimer’s disease (AD) and vascular dementia (VaD), in the context of a racially and ethnically diverse sample based on national data.MethodsWe conducted secondary data analyses based on the National Alzheimer’s Coordinating Center Uniform Data Set (N = 17,592) which is comprised a large, racially, and ethnically diverse cohort of adult research participants in the network of US Alzheimer Disease Research Centers (ADRCs). From 2005 to 2019, participants were assessed for history of five psychiatric and substance use disorders (depression, traumatic brain injury, other psychiatric disorders, alcohol use, and other substance use). Cox proportional hazard models were used to examine the influence of psychiatric and substance use history on the risk of AD and VaD subtypes, and the interactions between psychiatric and substance use history and race/ethnicity with adjustment for demographic and health-related factors.ResultsIn addition to other substance use, having any one type of psychiatric and substance use history increased the risk of developing AD by 22–51% and VaD by 22–53%. The risk of other psychiatric disorders on AD and VaD risk varied by race/ethnicity. For non-Hispanic White people, history of other psychiatric disorders increased AD risk by 27%, and VaD risk by 116%. For African Americans, AD risk increased by 28% and VaD risk increased by 108% when other psychiatric disorder history was present.ConclusionThe findings indicate that having psychiatric and substance use history increases the risk of developing AD and VaD in later life. Preventing the onset and recurrence of such disorders may prevent or delay the onset of AD and VaD dementia subtypes. Prevention efforts should pay particular attention to non-Hispanic White and African American older adults who have history of other psychiatric disorders.Future research should address diagnostic shortcomings in the measurement of such disorders in ADRCs, especially with regard to diverse racial and ethnic groups.

Results from animal and human studies suggest that lithium in therapeutic doses may improve learning and memory and modify the risk of developing dementia. Additional preliminary studies suggest that subtherapeutic levels, including microlevels of lithium, may influence human cognition. To investigate whether the incidence of dementia in the general population covaries with long-term exposure to microlevels of lithium in drinking water. This Danish nationwide, population-based, nested case-control study examined longitudinal, individual geographic data on municipality of residence and data from drinking water measurements combined with time-specific data from all patients aged 50 to 90 years with a hospital contact with a diagnosis of dementia from January 1, 1970, through December 31, 2013, and 10 age- and sex-matched control individuals from the Danish population. The mean lithium exposure in drinking water since 1986 was estimated for all study individuals. Data analysis was performed from January 1, 1995, through December 31, 2013. A diagnosis of dementia in a hospital inpatient or outpatient contact. Diagnoses of Alzheimer disease and vascular dementia were secondary outcome measures. In primary analyses, distribution of lithium exposure was compared between patients with dementia and controls. A total of 73 731 patients with dementia and 733 653 controls (median age, 80.3 years; interquartile range, 74.9-84.6 years; 44 760 female [60.7%] and 28 971 male [39.3%]) were included in the study. Lithium exposure was statistically significantly different between patients with a diagnosis of dementia (median, 11.5 µg/L; interquartile range, 6.5-14.9 µg/L) and controls (median, 12.2 µg/L; interquartile range, 7.3-16.0 µg/L; P < .001). A nonlinear association was observed. Compared with individuals exposed to 2.0 to 5.0 µg/L, the incidence rate ratio (IRR) of dementia was decreased in those exposed to more than 15.0 µg/L (IRR, 0.83; 95% CI, 0.81-0.85; P < .001) and 10.1 to 15.0 µg/L (IRR, 0.98; 95% CI, 0.96-1.01; P = .17) and increased with 5.1 to 10.0 µg/L (IRR, 1.22; 95% CI, 1.19-1.25; P < .001). Similar patterns were found with Alzheimer disease and vascular dementia as outcomes. Long-term increased lithium exposure in drinking water may be associated with a lower incidence of dementia in a nonlinear way; however, confounding from other factors associated with municipality of residence cannot be excluded.

Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.

Patients with clinical diagnoses of Alzheimer's disease, vascular dementia, or undifferentiated dementia were rated on standardized measures of depression, cognitive impairment, and functional impairment. Logistic regression was used to evaluate the relationship between functional or cognitive impairment, as well as their interaction, and depressive features in each group. This analysis revealed notable differences by type of dementia. The results imply that the mechanisms underlying depression in Alzheimer's disease may be different from those in vascular and other types of dementia. These results also provide indicators to the clinician for further evaluation of depression in different dementia subtypes.

IntroductionEvidence supporting cardiovascular diseases could increase the risk of dementia remains fragmented. A comprehensive study to illuminate the distinctive associations across different dementia types is still lacking. This study is...