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Background Angiotensin receptor blockers (ARBs) may have protective effects against dementia occurrence in patients with hypertension (HTN). However, whether telmisartan, an ARB with peroxisome proliferator-activated receptor γ (PPAR-γ)–modulating effects, has additional benefits compared to other ARBs remains unclear. Methods and findings Between 1997 and 2013, 2,166,944 type 2 diabetes mellitus (T2DM) patients were identified from the National Health Insurance Research Database of Taiwan. Patients with HTN using ARBs were included in the study. Patients with a history of stroke, traumatic brain injury, or dementia were excluded. Finally, 65,511 eligible patients were divided into 2 groups: the telmisartan group and the non-telmisartan ARB group. Propensity score matching (1:4) was used to balance the distribution of baseline characteristics and medications. The primary outcome was the diagnosis of dementia. The secondary outcomes included the diagnosis of Alzheimer disease and occurrence of symptomatic ischemic stroke (IS), any IS, and all-cause mortality. The risks between groups were compared using a Cox proportional hazard model. Statistical significance was set at p < 0.05. There were 2,280 and 9,120 patients in the telmisartan and non-telmisartan ARB groups, respectively. Patients in the telmisartan group had a lower risk of dementia diagnosis (telmisartan versus non-telmisartan ARBs: 2.19% versus 3.20%; HR, 0.72; 95% CI, 0.53 to 0.97; p = 0.030). They also had lower risk of dementia diagnosis with IS as a competing risk (subdistribution HR, 0.70; 95% CI, 0.51 to 0.95; p = 0.022) and with all-cause mortality as a competing risk (subdistribution HR, 0.71; 95% CI, 0.53 to 0.97; p = 0.029). In addition, the telmisartan users had a lower risk of any IS (6.84% versus 8.57%; HR, 0.79; 95% CI, 0.67 to 0.94; p = 0.008) during long-term follow-up. Study limitations included potential residual confounding by indication, interpretation of causal effects in an observational study, and bias caused by using diagnostic and medication codes to represent real clinical data. Conclusions The current study suggests that telmisartan use in hypertensive T2DM patients may be associated with a lower risk of dementia and any IS events in an East-Asian population., Chi-Hung Liu and co-workers study associations between antihypertensive medication prescriptions and dementia risk in people with type 2 diabetes., Author summary Why was this study done? The drug of choice for antihypertensive treatment, and its association with dementia in patients with both type 2 diabetes mellitus (T2DM) and hypertension, is a valid question worthy of further investigations. Angiotensin receptor blockers (ARBs) modulate the renin–angiotensin–aldosterone system and potentially associate with cognitive preservation, but internal comparisons of the cognitive protective effects of different ARBs have not previously been performed. Telmisartan is the only ARB that, at a clinical dose, has agonistic effects on peroxisome proliferator-activated receptor γ (PPAR-γ), which maintains insulin sensitivity, reduces insulin resistance, and potentially has a protective impact on dementia. What did the researchers do and find? Using the electronic health records of the National Health Insurance Research Database of Taiwan between 1997 and 2013, we studied whether telmisartan was associated with a lower incidence of dementia compared to other ARBs in patients with T2DM and hypertension. Using telmisartan was associated with a lower risk of dementia diagnosis compared to using non-telmisartan ARBs (2.19% versus 3.20%). What do these findings mean? Telmisartan use in hypertensive T2DM patients may be associated with a lower risk of dementia and any ischemic stroke events in an East-Asian population. Further work will be needed to investigate the observed association, owing to the limitations inherent in this observational study.

Introduction COVID-19 has placed unprecedented pressure on dementia health and social care systems worldwide. This has resulted in reduced services and support for people with dementia and their family carers. There are gaps in the evidence on the impact of the pandemic on Quality of Life (QoL). We carried out a study on the impact of the pandemic on the QoL of a group of people with dementia and their family carers who were part of a larger existing cohort study. Methods We quantitatively measured QoL, on two occasions during the two national lockdowns in 2020 and compared these data with those obtained when they entered the study (before the pandemic). Measures used included: DEMQOL-Proxy, Clinical Dementia Rating Scale and C-DEMQOL. To understand how QoL changed over time, a repeated measures ANOVA was run for each dependent variable with the following variables entered as co-variates: duration in study, baseline dementia severity, gender of the family carer, gender of the person with dementia, family carer relationship, dementia type, living status, age of the person with dementia, and age of the family carer. Results 248 participants took part in the study. QoL scores did not significantly decline between either time period for the person with dementia or their family carer. There was variation in subgroups; with co-resident status, carer relationship, gender of the person with dementia, age of the person with dementia, and baseline cognitive status influencing QoL outcomes in family carers. Discussion It is striking that people with dementia and their carers did not report a decline in QoL during the pandemic or in the months following restrictions suggesting the possibility of resilience. Variation in subgroups suggests that specific groups of family carers were more vulnerable to lower QoL; indicating the need for more tailored, nuanced support during this period.

Associations of numbers of teeth present and of missing teeth with Alzheimer’s disease were cross-sectionally analyzed using the National Database of Health Insurance Claims and Specific Health Checkups of Japan. Dental care claims data of patients aged 60 years or older diagnosed with periodontitis (n = 4,009,345) or missing teeth (n = 662,182) were used to obtain information about the numbers of teeth present and of missing teeth, respectively, and they were combined with medical care claims data including the diagnosis of Alzheimer’s disease. Numbers of teeth present and of missing teeth excluding third molars were calculated using the dental formula in the claims for periodontitis and missing teeth, respectively, and categorized into three groups each. Percentages of subjects treated for Alzheimer’s disease with 20–28, 10–19, and 1–9 teeth present were 1.95%, 3.87%, and 6.86%, respectively, in patients diagnosed as having periodontitis, and those treated for Alzheimer’s disease with 1–13, 14–27, and 28 missing teeth were 2.67%, 5.51%, and 8.70%, respectively, in patients diagnosed as having missing teeth. Logistic regression models using treatment for Alzheimer’s disease as an outcome variable and adjusting for age and sex showed that odds ratios (95% confidence intervals) for patients with 10–19 and 1–9 teeth (reference: 20–28 teeth) were 1.11 (1.10–1.13) and 1.34 (1.32–1.37), respectively, (pp

Background Alzheimer’s disease (AD) is assessed by carefully examining a patient’s cognitive impairment. However, previous studies reported inadequate diagnostic accuracy for dementia in primary care settings. Many hospitals use the automated quantitative evaluation method known as the Voxel-based Specific Regional Analysis System for Alzheimer’s Disease (VSRAD), wherein brain MRI data are used to evaluate brain morphological abnormalities associated with AD. Similarly, an automated quantitative evaluation application called the easy Z-score imaging system (eZIS), which uses brain SPECT data to detect regional cerebral blood flow decreases associated with AD, is widely used. These applications have several indicators, each of which is known to correlate with the degree of AD. However, it is not completely known whether these indicators work better when used in combination in real-world clinical practice. Methods We included 112 participants with mild cognitive impairment (MCI) and 128 participants with early AD in this study. All participants underwent MRI, SPECT, and the Mini-Mental State Examination (MMSE). Demographic and clinical characteristics were assessed by univariate analysis, and logistic regression analysis with a combination of MMSE, VSRAD and eZIS indicators was performed to verify whether the diagnostic accuracy in discriminating between MCI and early AD was improved. Results The area under the receiver operating characteristic curve (AUC) for the MMSE score alone was 0.835. The AUC was significantly improved to 0.870 by combining the MMSE score with two quantitative indicators from the VSRAD and eZIS that assessed the extent of brain abnormalities. Conclusion Compared with the MMSE score alone, the combination of the MMSE score with the VSRAD and eZIS indicators significantly improves the accuracy of discrimination between patients with MCI and early AD. Implementing VSRAD and eZIS does not require professional clinical experience in the treatment of dementia. Therefore, the accuracy of dementia diagnosis by physicians may easily be improved in real-world primary care settings.

Background The two biomarkers 2-[18F]FDG-PET and cerebrospinal fluid biomarkers are both recommended to support the diagnosis of Alzheimer’s disease. However, there is a lack of knowledge for the comparison of the two biomarkers in a routine clinical setting. Objective The aim was to compare the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers on diagnosis, prognosis, and patient management in patients suspected of Alzheimer’s disease. Methods Eighty-one patients clinically suspected of Alzheimer’s disease were retrospectively included from the Copenhagen Memory Clinic. As part of the clinical work-up all patients had a standard diagnostic program examination including MRI and ancillary investigations with 2-[18F]FDG-PET and cerebrospinal fluid biomarkers. An incremental study design was used to evaluate the clinical impact of the biomarkers. First, the diagnostic evaluation was based on the standard diagnostic program, then the diagnostic evaluation was revised after addition of either cerebrospinal fluid biomarkers or 2-[18F]FDG-PET. At each diagnostic evaluation, two blinded dementia specialists made a consensus decision on diagnosis, prediction of disease course, and change in patient management. Confidence in the decision was measured on a visual analogue scale (0–100). After 6 months, the diagnostic evaluation was performed with addition of the other biomarker. A clinical follow-up after 12 months was used as reference for diagnosis and disease course. Results The two biomarkers had a similar clinical value across all diagnosis when added individually to the standard diagnostic program. However, for the correctly diagnosed patient with Alzheimer’s disease cerebrospinal fluid biomarkers had a significantly higher impact on diagnostic confidence (mean scores±SD: 88±11 vs. 82±11, p = 0.046) and a significant reduction in the need for ancillary investigations (23 vs. 18 patients, p = 0.049) compared to 2-[18F]FDG-PET. Conclusion The two biomarkers had similar clinical impact on diagnosis, but cerebrospinal fluid biomarkers had a more significant value in corroborating the diagnosis of Alzheimer’s disease compared to 2-[18F]FDG-PET.

Background The purpose of this study was to assess the impact of vendor-provided atlas-based MRAC on FDG PET/MR for the evaluation of Alzheimer’s disease (AD) by using simulated images. Methods We recruited 47 patients, from two institutions, who underwent PET/CT and PET/MR (GE SIGNA) examination for oncological staging. From the PET raw data acquired on PET/MR, two FDG-PET series were generated, using vendor-provided MRAC (atlas-based) and CTAC. The following simulation steps were performed in MNI space: After spatial normalization and smoothing of the PET datasets, we calculated the error map for each patient, PETMRAC/PETCTAC. We multiplied each of these 47 error maps with each of the 203 Alzheimer’s Disease Neuroimaging Initiative (ADNI) cases after the identical normalization and smoothing. This resulted in 203*47 = 9541 datasets. To evaluate the probability of AD in each resulting image, a cumulative t-value was calculated automatically using commercially-available software (PMOD PALZ) which has been used in multiple large cohort studies. The diagnostic accuracy for the discrimination of AD and predicting progression from mild cognitive impairment (MCI) to AD were evaluated in simulated images compared with ADNI original images. Results The accuracy and specificity for the discrimination of AD-patients from normal controls were not substantially impaired, but sensitivity was slightly impaired in 5 out of 47 datasets (original vs. error; 83.2% [CI 75.0%-89.0%], 83.3% [CI 74.2%-89.8%] and 83.1% [CI 75.6%-88.3%] vs. 82.7% [range 80.4–85.0%], 78.5% [range 72.9–83.3%,] and 86.1% [range 81.4–89.8%]). The accuracy, sensitivity and specificity for predicting progression from MCI to AD during 2-year follow-up was not impaired (original vs. error; 62.5% [CI 53.3%-69.3%], 78.8% [CI 65.4%-88.6%] and 54.0% [CI 47.0%-69.1%] vs. 64.8% [range 61.5–66.7%], 75.7% [range 66.7–81.8%,] and 59.0% [range 50.8–63.5%]). The worst 3 error maps show a tendency towards underestimation of PET scores. Conclusion FDG-PET/MR based on atlas-based MR attenuation correction showed similar diagnostic accuracy to the CT-based method for the diagnosis of AD and the prediction of progression of MCI to AD using commercially-available software, although with a minor reduction in sensitivity.

The precise diagnostics of complex diseases require to integrate a large amount of information from heterogeneous clinical and biomedical data, whose direct and indirect interdependences are notoriously difficult to assess. To this end, we propose an efficient computational approach to simultaneously compute and assess the significance of multivariate information between any combination of mixed-type (continuous/categorical) variables. The method is then used to uncover direct, indirect and possibly causal relationships between mixed-type data from medical records, by extending a recent machine learning method to reconstruct graphical models beyond simple categorical datasets. The method is shown to outperform existing tools on benchmark mixed-type datasets, before being applied to analyze the medical records of eldery patients with cognitive disorders from La Pitié-Salpêtrière Hospital, Paris. The resulting clinical network visually captures the global interdependences in these medical records and some facets of clinical diagnosis practice, without specific hypothesis nor prior knowledge on any clinically relevant information. In particular, it provides some physiological insights linking the consequence of cerebrovascular accidents to the atrophy of important brain structures associated to cognitive impairment., Author summary We developed a machine learning approach to analyze medical records and help clinicians visualize the direct and indirect interrelations between clinical examinations and the variety of syndromes implicated in complex diseases. The reconstruction of such clinical networks is illustrated on the spectrum of cognitive disorders, originating from either neurodegenerative, cerebrovascular or psychiatric dementias. This global network analysis is also shown to uncover novel direct associations and possible cause-effect relationships between clinically relevant information, such as medical examinations, diagnoses, treatments and personal data from patients’ medical records.

Background Depression is associated with an increased risk of dementia. However, short follow-up times and lack of adjustment for familial factors in previous studies could influence this association. The purpose of the present study was to investigate the association between depression and subsequent dementia, while controlling for familial factors and with a follow-up of over 35 years. Methods and findings Two cohorts were formed from all individuals aged 50 years or older living in Sweden as of 31 December 2005 (n = 3,341,010). The Swedish National Patient Register was searched from 1964 through 2016 to identify diagnosis of depression and dementia. In the first cohort, individuals diagnosed with depression (n = 119,386) were matched 1:1 with controls without depression diagnosis. The second cohort was a sibling cohort (n = 50,644) consisting of same-sex full sibling pairs with discordant depression status. In the population matched cohort study, a total of 9,802 individuals were diagnosed with dementia during a mean follow-up time of 10.41 (range 0–35) years (5.5% of those diagnosed with depression and 2.6% of those without depression diagnosis (adjusted odds ratio [aOR] 2.47, 95% CI 2.35–2.58; p < 0.001), with a stronger association for vascular dementia (aOR 2.68, 95% CI 2.44–2.95; p < 0.001) than for Alzheimer disease (aOR 1.79, 95% CI 1.68–1.92; p < 0.001). The association with dementia diagnosis was strongest in the first 6 months after depression diagnosis (aOR 15.20, 95% CI 11.85–19.50; p < 0.001), then decreased rapidly but persisted over follow-up of more than 20 years (aOR 1.58, 95% CI 1.27–1.98; p < 0.001). Also in the sibling cohort, the association was strongest in the first 6 months (aOR 20.85, 95% CI 9.63–45.12; p < 0.001), then decreased rapidly but persisted over follow-up of more than 20 years (aOR 2.33, 95% CI 1.32–4.11; p < 0.001). The adjusted models included sex, age at baseline, citizenship, civil status, household income, and diagnoses at baseline. The main limitation of the study methodology is the use of observational data; hence, the associations found are not proof of causal effects. Conclusions Depression is associated with increased odds of dementia, even more than 20 years after diagnosis of depression, and the association remains after adjustment for familial factors. Further research is needed to investigate whether successful prevention and treatment of depression decrease the risk of dementia., In a nationwide longitudinal study, Sofie Holmquist and colleagues investigate the association between diagnosis of depression and subsequent diagnosis of dementia in population-based and sibling cohorts of Swedish individuals older than 50 years., Author summary Why was this study done? Dementia is a leading cause of increased need for assistance worldwide among older individuals. The risk of dementia has been associated with previous depression. However, the results from previous studies are not conclusive, there is a lack of studies with long follow-up, and no study has evaluated whether familial factors may influence the association. What did the researchers do and find? From all inhabitants living in Sweden aged 50 years or older, 2 cohorts were formed: a cohort of 119,386 individuals with depression matched 1:1 with controls without depression, and a cohort of 50,644 full sibling pairs discordant for depression. Both cohorts were evaluated for dementia during follow-up. In both cohorts, the risk of dementia was increased 10–20 times in the first year after a diagnosis of depression. Thereafter the risk decreased rapidly but was still evident more than 20 years after the diagnosis of depression. The risk of dementia was higher for those with a severe depression compared to those with mild depression, and a stronger association was seen with vascular dementia. What do these findings mean? The risk of dementia is increased for decades after a diagnosis of depression, where those diagnosed with especially severe depressions are at increased risk.

Increasing reliance on electronic medical records at large medical centers provides unique opportunities to perform population level analyses exploring disease progression and etiology. The massive accumulation of diagnostic, procedure, and laboratory codes in one place has enabled the exploration of co-occurring conditions, their risk factors, and potential prognostic factors. While most of the readily identifiable associations in medical records are (now) well known to the scientific community, there is no doubt many more relationships are still to be uncovered in EMR data. In this paper, we introduce a novel finding index to help with that task. This new index uses data mined from real-time PubMed abstracts to indicate the extent to which empirically discovered associations are already known (i.e., present in the scientific literature). Our methods leverage second-generation p-values, which better identify associations that are truly clinically meaningful. We illustrate our new method with three examples: Autism Spectrum Disorder, Alzheimer's Disease, and Optic Neuritis. Our results demonstrate wide utility for identifying new associations in EMR data that have the highest priority among the complex web of correlations and causalities. Data scientists and clinicians can work together more effectively to discover novel associations that are both empirically reliable and clinically understudied.

This large, retrospective case-control study of electronic health records from 56 million unique adult patients examined whether or not treatment with a Tumor Necrosis Factor (TNF) blocking agent is associated with lower risk for Alzheimer’s disease (AD) in patients with rheumatoid arthritis (RA), psoriasis, and other inflammatory diseases which are mediated in part by TNF and for which a TNF blocker is an approved treatment. The analysis compared the diagnosis of AD as an outcome measure in patients receiving at least one prescription for a TNF blocking agent (etanercept, adalimumab, and infliximab) or for methotrexate. Adjusted odds ratios (AORs) were estimated using the Cochran-Mantel-Haenszel (CMH) method and presented with 95% confidence intervals (CIs) and p-values. RA was associated with a higher risk for AD (Adjusted Odds Ratio (AOR) = 2.06, 95% Confidence Interval: (2.02-2.10), P-value P P P < 0.0001), ulcerative colitis (AOR = 1.82 (1.74-1.91), P P P P < 0.0001), or infliximab (AOR = 0.52 (0.39-0.69), P P

IntroductionPrevious studies evaluating the association between clinically diagnosed Alzheimer's disease (AD) and age-related macular degeneration (AMD) have generated conflicting results. This study is the first to assess whether AMD prevalence is higher in AD patients than non-AD controls by using histopathology to definitively diagnose AD.MethodsThis was a retrospective case-control study utilizing diagnostic information extracted from autopsy reports of patients age 75 and above, including 115 with a neuropathological diagnosis of AD and 57 age-matched normal controls.ResultsThe rate of AMD was not significantly higher in AD cases (53.0%) than in controls (59.6%) (z = 0.820, p = 0.794). AMD severity as determined by Sarks score was similar between AD patients and controls (χ2 = 2.96, p = 0.706). There was also no significant association between Braak stage of AD severity and AMD (χ2 = 4.55, p = 0.602).DiscussionNo significant effect of AD diagnosis or pathologic severity on AMD comorbidity was found, suggesting that any shared mechanisms between AMD and AD may be nondeterministic.

The assessment of daily living activities could provide information about daily functions and participation restrictions to develop intervention strategies. The purposes of this study were to assess the scores of the Barthel Index (BI) and Lawton Instrumental Activities of Daily Living (IADL) scale in older adults with cognitive impairment and to explore the different effects that levels of cognitive functions have on changes in IADL functions. We recruited 31 participants with dementia, 36 with mild cognitive impairment (MCI), and 35 normal controls (NCs) from the neurology outpatient department of a regional hospital. The results of the demographic and clinical characteristics through the Lawton IADL scale, BI, Quick Mild Cognitive Impairment (Qmci) screen, Montreal Cognitive Assessment (MoCA), and Mini-Mental State Examination (MMSE), were collected on the same day and compared with the Kruskal-Wallis test, Wilcoxon rank-sum test, Fisher's exact test, and a multiple linear regression analysis, as appropriate. In the BI, bathing was the most discriminating activity to differentiate patients with MCI and dementia; in the Lawton IADL scale, medication responsibility and shopping were the most discriminating activities to differentiate NCs and patients with MCI, and patients with MCI and dementia, respectively. In addition, the predictors of changes in Lawton IADL scale scores were the problem-solving score of the Clinical Dementia Rating scale, a Qmci score of > 20.4 and an age of ≤ 81.2 years, a MoCA score of < 9.4 and an age of > 81.2 years, and the MMSE score and an age of > 81.2 years. This study adds to the evidence that the description of basic and instrumental daily activities is integrated in older adults with cognitive impairment. Notably, the Qmci is the most significant predictor of changes in IADL function for "young" older adults, as are the MoCA and MMSE for "old" older adults.

BackgroundSocial engagement protects against dementia onset. Less is known about patterns of social engagement around the time of dementia diagnosis. We investigated face-to-face and telephone contact at three times (pre-diagnosis, at report of diagnosis, 2 years post-diagnosis) in individuals who developed dementia and a comparison group.MethodsSocial engagement was assessed at waves 2-7 of the English Longitudinal Study of Ageing in 4171 individuals aged 50 and older. Dementia was ascertained by either self-reported physician diagnosis or through an informant evaluation of a participant's functional and cognitive performance compared with a few years earlier. Generalized estimating equations were used to examine differences by group, time, and group-by-time interactions.ResultsThe dementia group reported less face-to-face (p < 0.001) and telephone contact (p < 0.001) than the dementia-free group pre-diagnosis. The dementia group experienced greater reductions in social engagement leading up to dementia diagnosis and in the 2 years following diagnosis (p's < 0.001).ConclusionGiven that social engagement reduces dementia risk and supports the lived experience of people with dementia, it is important to find ways of promoting social interaction in older adults.

Background: Alzheimer’s disease (AD) remains a clinical diagnosis but biomarkers from cerebrospinal fluid (CSF) and more lately amyloid imaging with positron emission tomography (PET), are important to support a diagnosis of AD. Objective: To compare amyloid-β (Aβ) PET imaging with biomarkers in CSF and evaluate the prediction of Aβ PET on diagnosis in a memory clinic setting. Methods: We included 64 patients who had lumbar puncture and Aβ PET with 18F-Flutemetamol performed within 190 days. PET was binary classified (Flut+ or Flut-) and logistic regression analyses for correlation to each CSF biomarker; Aβ 42 (Aβ42), total tau (T-tau) and phosphorylated tau (P-tau), were performed. Cut-off values were assessed by receiver operating characteristic (ROC) curves. Logistic regression was performed for prediction of clinical AD diagnosis. We assessed the interrater agreement of PET classification as well as for diagnoses, which were made both with and without knowledge of PET results. Results: Thirty-two of the 34 patients (94%) in the Flut+ group and nine of the 30 patients (30%) in the Flut- group had a clinical AD diagnosis. There were significant differences in all CSF biomarkers in the Flut+ and Flut- groups. Aβ42 showed the highest correlation with 18F-Flutemetamol PET with a cut-off value of 706.5 pg/mL, corresponding to sensitivity of 88% and specificity of 87%. 18F-Flutemetamol PET was the best predictor of a clinical AD diagnosis. We found a very high interrater agreement for both PET classification and diagnosis. Conclusions: The present study showed an excellent correlation of Aβ42 in CSF and 18F-Flutemetamol PET and the presented cut-off value for Aβ42 yields high sensitivity and specificity for 18F-Flutemetamol PET. 18F-Flutemetamol PET was the best predictor of clinical AD diagnosis. EGM (Ebba Gløersen Müller) received grants from Civitan Norway Research Foundation for Alzheimer´s disease.

BackgroundReliable epidemiological data on Alzheimer's disease are scarce. However, these are necessary to adapt healthcare policy in terms of prevention, care and social needs related to this condition. To estimate the prevalence rate in the Alpes-Maritimes on the French Riviera, with a population of one million, we present a capture-recapture procedure applied to cases of Alzheimer's disease, based on two epidemiological surveillance systems.MethodsTo estimate the total number of patients affected by Alzheimer's disease, a capture-recapture study included a cohort of patients with Alzheimer's disease or receiving medications only eligible for use for this condition, recorded by a specific health insurance information system (Health Insurance Cohort, HIC), and those registered in the French National Alzheimer's Data Bank ("Banque Nationale Alzheimer", BNA) in 2010 and 2011. We applied Bayesian estimation of the Mt ecological model, taking into account age and gender as covariates, i.e. factors of inhomogeneous catchability.ResultsOverall, 5,562 patients with Alzheimer's disease were recorded, of whom only 856 were common to both information systems. Mean age and F/M sex ratio differed between BNA and HIC surveillance systems, 81 vs 84 years and 2.7 vs 3.2, respectively. A Bayesian estimation, with age and gender as covariates, yields an estimate of 15,060 cases of Alzheimer's disease [95%HPDI: 14,490-15,630] in the Alpes-Maritimes. The completeness of the HIC and BNA databases were respectively of 25.4% and 17.2%. The estimated prevalence rate among the population over 65 years old was 6.3% in 2010-2011.ConclusionsThis study demonstrates that it is possible to determine the number of subjects affected by Alzheimer's disease in a geographical unit, using available data from two existing surveillance systems in France, i.e. 15,060 cases in the Alpes-Maritimes. This is the first stage of a population-based approach in view of adapting available resources to the population's needs.

Among a variety of comorbidities of ankylosing spondylitis (AS), the association between dementia and AS by using an extensive dataset from the Korean National Health Insurance System was evaluated in this study. We extracted 15,547 newly diagnosed AS subjects among the entire Korean population and excluded wash-out patients (n = 162) and patients that were inappropriate for cohort match (n = 1192). Finally, 14,193 subjects were chosen as the AS group, and through 1:5 age- and sex-stratified matching, 70,965 subjects were chosen as the control group. We evaluated patient demographics, household incomes, and comorbidities, including hypertension, diabetes, and dyslipidemia. The prevalence of overall dementia (1.37%) and Alzheimer's dementia (AD) (0.99%) in the AS group was significantly higher than in the control group (0.87% and 0.63%), respectively. The adjusted hazard ratio of the AS group for overall dementia (1.758) and AD (1.782) showed statistical significance also. On the other hand, the prevalence of vascular dementia did not differ significantly between the two groups. Subgroup analyses revealed the following risk factors for dementia in the AS group: male gender, greater than 65 years in age, fair income (household income greater than 20% of the median), urban residency, no diabetes, and no hypertension. From the nationwide, population-based, retrospective, longitudinal cohort study, AS patients showed a significantly higher prevalence of overall dementia and Alzheimer's dementia. Comprehensive patient assessment using our subgroup analysis could help to prevent dementia in patients suffering from AS.

IntroductionThis Dutch study is a qualitative interview study. It aims to contribute to our understanding of the day-to-day experiences by providing an idiographic description of what it means existentially to be in the world as a person affected by a form of dementia, taking into account the contextual nature of these embodied experiences.MethodsWe used a combination of narrative accounts of people from dementia. We first collected 322 recorded messages of 16 diarists who joined the Dutch Dementia Diaries project. This data was supplemented with 37 interview accounts. Our data analysis was inspired by Van Manen's existential phenomenological approach.ResultsThe findings show that living with dementia-from a first-person perspective-can be understood as a severely unsettling experience: the people concerned enter a very uncertain, unpredictable and ambiguous period of life. They have to face all kinds of losses that considerably change and disrupt their relationships with 1) their own body, 2) with others and 3) with the surrounding world. This experience is explicated in the following themes: la) scrutinizing your disrupted body; 1b) trying to control your bodily loss-of-control; 2a) feeling scrutinized by the suspicious gaze of others; 2b) drifting away from significant others; 2c) having difficulties sharing the struggle; 2d) longing to be taken seriously; 2e) engaging in a world of peers; and 3a) sensing disorientation in an alien place; 3b) feeling closed in within a shrinking space; 3c) trying to control a dreaded future; 3d) trying to control a dreaded future by means of euthanasia.DiscussionOur study demonstrates how the people with dementia are affected by 'the eyes of others'. They longed for a safe and accepting environment, but quite often felt scrutinized by inquisitive and disapproving looks. The outcomes also reveal a connection between dominant social imaginaries and people's self-understanding of dementia. Much of the suffering stems from living under the shadow of negative imaginaries. Furthermore, our study supports the demand for a socio-relational approach by demonstrating that-from a first-person perspective-dementia can be seen a disorder that is related in particular to questions about self-hood, social relations and social roles. For the people involved, instead of what dementia is, the focus is on what Alzheimer's disease means and does and how it affects daily life.

ObjectivesTo identify factors associated with QOL in carers of persons with young-onset Alzheimer's (AD) and frontotemporal dementia (FTD) and explore development in QOL over a two-year period.MethodsEighty-eight family carers of community-dwelling people with young-onset AD (n = 50) and FTD (n = 38) recruited from Nordic memory clinics. Carer QOL was assessed using the Quality of Life-Alzheimer's Disease questionnaire. Carer burden was assessed by the Relatives' Stress scale and depressive symptoms by the Montgomery-Åsberg Depression Rating Scale. Factors associated with QOL in YOD and development in QOL over time were explored with growth mixture model trajectories and mixed model analyses.ResultsWe identified two carer groups of persons with YOD following trajectories with better (n = 53) versus poorer (n = 30) QOL. Carers who reported more burden at baseline had greater odds of belonging to the poorer QOL group (OR 1.1 (1.0-1.2), p = 0.004). Analyses of the development in QOL showed a significant decline in QOL-AD scores among the AD-carers from baseline to two-year follow-up (p = 0.044), while the score remained stable among the FTD-carers. The FTD-carer group had significantly higher mean QOL-AD scores at one- and two-year follow-up (p = 0.022 and 0.045, respectively). However, the difference between the two groups regarding time trend was non-significant. Poorer QOL was associated with increased carer burden (p = 0.01), more depressive symptoms (p = 0.024), and being male carer (p = 0.038).ConclusionHigher care burden, more depressive symptoms, and being a male carer was associated with poorer QOL in family carers for persons with YOD. Carers of persons with AD may experience greater challenges in preserving QOL compared to carers of persons with FTD.

The Addenbrooke’s Cognitive Examination III is one of the most widely used tests to assess cognitive impairment. Although previous studies have shown adequate levels of diagnostic utility to detect severe impairment, it has not shown sensitivity to detect mild decline. The aim of this study was to evaluate the psychometric properties of Addenbrooke’s Cognitive Examination III in a large sample of elderly people through Item Response Theory, due to the lack of studies using this approach. A cross-sectional study was conducted with 1164 people from the age of 60 upwards, of which 63 had a prior diagnosis of Alzheimer dementia. The results showed that, globally, the Addenbrooke’s Cognitive Examination III possesses adequate psychometrics properties. Furthermore, the information function test shows that the subscales have different sensitivity to different levels of impairment. These results can contribute to determining patterns of cognitive deterioration for the adequate detection of different levels of dementia. An optimized version is suggested which may be an economic alternative in the applied field.

It has been quite a challenge to diagnose Mild Cognitive Impairment due to Alzheimer's disease (MCI) and Alzheimer-type dementia (AD-type dementia) using the currently available clinical diagnostic criteria and neuropsychological examinations. As such we propose an automated diagnostic technique using a variant of deep neural networks language models (DNNLM) on the verbal utterances of affected individuals. Motivated by the success of DNNLM on natural language tasks, we propose a combination of deep neural network and deep language models (D2NNLM) for classifying the disease. Results on the DementiaBank language transcript clinical dataset show that D2NNLM sufficiently learned several linguistic biomarkers in the form of higher order n-grams to distinguish the affected group from the healthy group with reasonable accuracy on very sparse clinical datasets.

There has been a growing interest in the relationship between sedative-hypnotics use and the risk of Alzheimer’s dementia (AD) risk. This study aimed to evaluate the risk of AD associated with the use of sedative-hypnotics. A retrospective cohort study was conducted with randomly selected 5% samples from ≥50 years old beneficiaries of National Health Insurance Service (NHIS) of Korea from January 2002 to December 2015. The exposure to sedative-hypnotics was defined when prescribed over 30 defined daily dose (DDD) after January 2004 and it was categorized by prescribed dosage, types and half-lives of benzodiazepines. Time-dependent Cox regression model with a lag period of 5-years was used to evaluate the association between use of sedative-hypnotics and the risk of subsequent AD. Sensitivity analysis was performed for restricting sedative-hypnotics only when prescribed with insomnia. A total of 268,170 subjects were identified and subjects exposed to sedative-hypnotics showed a higher risk of AD (HR: 1.79; 95% CI: 1.72–1.86) than those who were not. There was an increased risk of AD among subjects exposed to benzodiazepines or zolpidem (HR: 1.75; 95% CI: 1.67–1.82) and antidepressants or low-dose antipsychotics (HR: 1.63; 95% CI: 1.42–1.87). The risk of AD was increased regardless of dose of sedative-hypnotics and half-life among benzodiazepines, especially in exposure to more than 360 DDD of sedative-hypnotics (HR: 1.78; 95% CI: 1.60–1.99) and the long-acting benzodiazepine (HR:1.77; 95% CI: 1.65–1.89).

Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a machine learning method to learn the relationship between pairs of data points at different time separations. The input vector comprises a summary of the time series history and includes both demographic and non-time varying variables such as genetic data. The dataset used is from the 2017 TADPOLE grand challenge which aims to predict the onset of Alzheimer's disease using including demographic, physical and cognitive data. The challenge is a three-fold diagnosis classification into AD, MCI and control groups, the prediction of ADAS-13 score and the normalised ventricle volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73. The results show that the method is effective and comparable with other methods., Comment: 6 pages, 1 figure, 6 tables

INTRODUCTIONAlzheimer’s disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts to develop predictive models have relied on demographic and clinical risk factors using traditional logistical regression methods. We hypothesized that machine-learning algorithms using administrative claims data may represent a novel approach to predicting ADRD.METHODSUsing a national de-identified dataset of more than 125 million patients including over 10,000 clinical, pharmaceutical, and demographic variables, we developed a cohort to train a machine learning model to predict ADRD 4-5 years in advance.RESULTSThe Lasso algorithm selected a 50-variable model with an area under the curve (AUC) of 0.693. Top diagnosis codes in the model were memory loss (780.93), Parkinson’s disease (332.0), mild cognitive impairment (331.83) and bipolar disorder (296.80), and top pharmacy codes were psychoactive drugs.DISCUSSIONMachine learning algorithms can rapidly develop predictive models for ADRD with massive datasets, without requiring hypothesis-driven feature engineering.RESEARCH IN CONTEXTSystematic review: Previous attempts to predict incident dementia have relied on extensive clinical evaluations, cognitive testing, laboratory testing, neuro-imaging, genetic factors, demographics, and lifestyle variables. Applying machine learning to a large administrative claims dataset to identify individuals at increased likelihood for near-term diagnosis of dementia had not been tested.Interpretation: A 50-variable model to identify those at risk for near-term diagnosis of dementia was created and validated. Based on AUC analysis, the model compared favorably with other historical attempts at modeling more traditional forms of data.Future direction: Models, such as the one developed here, could be used to identify populations of higher prior probability for near-term diagnosis of dementia. These could then be subjected to more in-depth scrutiny for intervention or dementia-related research eligibility.

Introduction Globally, dementia is one of the leading causes of death. Given the growing elderly population in the world, the yearly number of deaths by dementia is expected to increase. Patients dying from dementia are reported to suffer from a burden of symptoms similar to that of patients with cancer, but receive less medication against symptoms, have a lower probability of palliative care planning and seldom have access to specialised palliative care. Studies investigating the quality of palliative care in dementia are scarce. The aim of this Swedish national study was to compare the quality of end-of-life care between patients with dementia and patients with cancer regardless of place of care. Methods Thirteen end-of-life care quality indicators collected by the Swedish Register of Palliative Care (SRPC) were compared between patients dying from dementia and patients dying from cancer. Data were collected from deaths occurring in nursing homes, hospitals, specialised and general palliative home care, and palliative in-patient units during a three-year period (during March 2012 to February 2015). Analyses were performed using a multivariable logistic regression model, adjusted for age and gender. A subgroup of patients with Alzheimer's disease was identified and compared to patients with other and unspecified types of dementia. Results A total of 4624 deaths from Alzheimer's disease, 11 804deaths from other dementia diagnoses and 51 609 deaths from cancer were included. For six of the 13 quality indicators examined (prescription of PRN drugs against nausea and anxiety, information and bereavement support offered to next of kin, pain assessment and specialised palliative care consultations), poorer outcomes were shown for the dementia group in comparison to the cancer group. Two outcomes (prevalence of pressure ulcers and fluid therapy during the last 24 hours in life) showed better outcomes for the dementia group. The outcomes for the 13 quality indicators were similar for patients with Alzheimer's disease compared to patients with other and unspecified types of dementia. Conclusions The findings in this study indicates that patients dying from Alzheimer's disease and other types of dementia receive a poorer quality of end-of-life care concerning several important end-of-life care areas when compared to patients dying from cancer. Guidelines for end-of-life care in Sweden cannot explain or justify these differences. Further studies are needed to find possible ways to improve end-of-life care in the large and growing group of patients dying from dementia.