Environmental risk factors for dementia: a systematic review

Background - Dementia risk reduction is a major and growing public health priority. While certain modifiable risk factors for dementia have been identified, there remains a substantial proportion of unexplained risk. There is evidence that environmental risk factors may explain some of this risk. Thus, we present the first comprehensive systematic review of environmental risk factors for dementia. Methods - We searched the PubMed and Web of Science databases from their inception to January 2016, bibliographies of review articles, and articles related to publically available environmental data. Articles were included if they examined the association between an environmental risk factor and dementia. Studies with another outcome (for example, cognition), a physiological measure of the exposure, case studies, animal studies, and studies of nutrition were excluded. Data were extracted from individual studies which were, in turn, appraised for methodological quality. The strength and consistency of the overall evidence for each risk factor identified was assessed. Results - We screened 4784 studies and included 60 in the review. Risk factors were considered in six categories: air quality, toxic heavy metals, other metals, other trace elements, occupational-related exposures, and miscellaneous environmental factors. Few studies took a life course approach. There is at least moderate evidence implicating the following risk factors: air pollution; aluminium; silicon; selenium; pesticides; vitamin D deficiency; and electric and magnetic fields. Conclusions - Studies varied widely in size and quality and therefore we must be circumspect in our conclusions. Nevertheless, this extensive review suggests that future research could focus on a short list of environmental risk factors for dementia. Furthermore, further robust, longitudinal studies with repeated measures of environmental exposures are required to confirm these associations

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

Cerebrospinal Fluid TAR DNA-Binding Protein 43 Combined with Tau Proteins as a Candidate Biomarker for Amyotrophic Lateral Sclerosis and Frontotemporal Dementia Spectrum Disorders

Background: Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are nowadays recognized as spectrum disorders with a molecular link, the TAR DNA-binding protein 43 (TDP-43), rendering it a surrogate biomarker for these disorders. Methods: We measured cerebrospinal fluid (CSF) levels of TDP-43, beta-amyloid peptide with 42 amino acids (Aβ42), total tau protein (τT), and tau protein phosphorylated at threonine 181 (τP-181) in 32 patients with ALS, 51 patients with FTD, and 17 healthy controls. Double-sandwich commercial enzyme-linked immunosorbent assays were used for measurements. Results: Both ALS and FTD patients presented with higher TDP-43 and τT levels compared to the control group. The combination of biomarkers in the form of the TDP-43 × τT / τP-181 formula achieved the best discrimination between ALS or FTD and controls, with sensitivities and specificities >0.8. Conclusion: Combined analysis of TDP-43, τT, and τP-181 in CSF may be useful for the antemortem diagnosis of ALS and FTD. © 2017 S. Karger AG, Basel

TARDBP pathogenic variants in patients with amyotrophic lateral sclerosis, frontotemporal dementia and Alzheimer's disease phenotypes

BACKGROUND: Genetic, cerebrospinal fluid and histopathological studies have highlighted the importance of TDP-43 (the protein product of the TARDBP gene) in the pathophysiology of neurodegeneration. Specifically, TDP-43 pathology has been associated with Frontotemporal Dementia (FTD), Amyotrophic Lateral Sclerosis (ALS) and, lately, Alzheimer's Disease (AD). Here we searched for TARDBP pathogenic variants in a cohort of Greek patients with AD, FTD, ALS or FTD/ALS. METHOD: A total of 192 patients were included in the study. These participants were 1) referred for testing to the Neurology/Neurogenetics Laboratory of the University of Crete, 2) identified through the Cretan Aging Cohort (CAC), 3) referred to the 1st Department of Neurology of the National and Kapodistrian University of Athens at Eginition Hospital, Athens, Greece. Of these, 95 were clinically characterized as AD, 45 as FTD, 44 as ALS and 8 as FTD-ALS. Patients' DNA samples were analyzed through Whole Exome Sequencing (WES). Subsequently, we analyzed WES data for the presence of TARDBP pathogenic variants. All TARDBP pathogenic variants identified were verified by Sanger sequencing. RESULT: We found three different TARDBP pathogenic variants in five apparently unrelated patients, two of whom had a family history of dementia or ALS. Two of the five patients, an 80-year-old male and a 82-year-old female, members of the CAC, were initially diagnosed as suffering from late-onset AD on the basis of their clinical presentation. Both were found to harbor the p.Ile383Val (c.1147A>G) TARDBP variant. The same variant had been found in another patient, also from Crete, presenting with a combined FTD/ALS phenotype. In addition, in 2 patients with pure ALS, we found the p.Met337Val (c.1009A>G) and the p.Asn352Ser (c.1055A>G) TARDBP pathogenic variants, respectively. CONCLUSION: Our analyses unraveled five patients with pathogenic TARDBP variants presenting with heterogeneous phenotypes, namely apparent late-onset AD, FTD/ALS and pure ALS phenotypes. Our findings further expand the phenotypic variability of the TARDBP gene variants and draw attention to the possibility that patients diagnosed with possible typical AD could harbor pathogenic TARDBP variants. Furthermore, our study showed that TARDBP pathogenic variants are a rather frequent cause of dementia in the Greek population. © 2021 the Alzheimer's Association

A novel variant in DYNC1H1 could contribute to human amyotrophic lateral sclerosis-frontotemporal dementia spectrum

Amyotrophic lateral sclerosis (ALS) belongs to the ALS-frontotemporal dementia (FTD) spectrum and is hallmarked by upper and lower motor neuron degeneration. Here, we present a patient with a cytoplasmic dynein 1 heavy chain 1 (DYNC1H1) pathogenic variant who fulfilled the ALS El Escorial criteria, and we review relevant literature. Using whole-exome sequencing, we identified a deleterious point variant in DYNC1H1 (c.4106A > G (p. Q1369R)) as a likely contributor to the ALS phenotype. In silico structural analysis, molecular dynamics simulation, and protein stability analysis predicted that this variant may increase DYNC1H1 protein stability. Moreover, this variant may disrupt binding of the transcription factor TFAP4, thus potentially acting as duon. Because (a) DYNC1H1 forms part of a ubiquitous eukaryotic motor protein complex, and (b) disruption of dynein function by perturbation of the dynein–dynactin protein complex is implicated in other motor neuron degenerative conditions, this variant could disrupt processes like retrograde axonal transport, neuronal migration, and protein recycling. Our findings expand the heterogenous spectrum of the DYNC1H1 pathogenic variant−associated phenotype and prompt further investigations of the role of this gene in ALS. © 2022 Mentis et al

Cognitive Impairment and Dementia in Primary Care: Current Knowledge and Future Directions Based on Findings From a Large Cross-Sectional Study in Crete, Greece

Introduction: Dementia severely affects the quality of life of patients and their caregivers; however, it is often not adequately addressed in the context of a primary care consultation, especially in patients with multi-morbidity. Study Population and Methods: A cross-sectional study was conducted between March-2013 and December-2014 among 3,140 consecutive patients aged >60 years visiting 14 primary health care practices in Crete, Greece. The Mini-Mental-State-Examination [MMSE] was used to measure cognitive status using the conventional 24-point cut-off. Participants who scored low on MMSE were matched with a group of elders scoring >24 points, according to age and education; both groups underwent comprehensive neuropsychiatric and neuropsychological assessment. For the diagnosis of dementia and Mild-Cognitive-Impairment (MCI), the Diagnostic and Statistical Manual-of-Mental-Disorders (DSM-IV) criteria and the International-Working-Group (IWG) criteria were used. Chronic conditions were categorized according to ICD-10 categories. Logistic regression was used to provide associations between chronic illnesses and cognitive impairment according to MMSE scores. Generalized Linear Model Lasso Regularization was used for feature selection in MMSE items. A two-layer artificial neural network model was used to classify participants as impaired (dementia/MCI) vs. non-impaired. Results: In the total sample of 3,140 participants (42.1% men; mean age 73.7 SD = 7.8 years), low MMSE scores were identified in 645 (20.5%) participants. Among participants with low MMSE scores 344 (54.1%) underwent comprehensive neuropsychiatric evaluation and 185 (53.8%) were diagnosed with Mild-Cognitive-Impairment (MCI) and 118 (34.3%) with dementia. Mental and behavioral disorders (F00-F99) and diseases of the nervous system (G00-G99) increased the odds of low MMSE scores in both genders. Generalized linear model lasso regularization indicated that 7/30 MMSE questions contributed the most to the classification of patients as impaired (dementia/MCI) vs. non-impaired with a combined accuracy of 82.0%. These MMSE items were questions 5, 13, 19, 20, 22, 23, and 26 of the Greek version of MMSE assessing orientation in time, repetition, calculation, registration, and visuo-constructive ability. Conclusions: Our study identified certain chronic illness-complexes that were associated with low MMSE scores within the context of primary care consultation. Also, our analysis indicated that seven MMSE items provide strong evidence for the presence of dementia or MCI. © Copyright © 2020 Bertsias, Symvoulakis, Tziraki, Panagiotakis, Mathioudakis, Zaganas, Basta, Boumpas, Simos, Vgontzas and Lionis

Self-reported fatigue as a risk index for dementia diagnosis

Introduction: Cognitive impairment and frailty are major problems of older age. This study aims to explore the association between frailty and cognitive impairment in a rural cohort of older subjects in southern Europe (Cretan Aging Cohort). Methods: Community-based, primary care, cross-sectional, study in the Heraklion Prefecture, Crete, Greece. Four hundred and two persons aged 60–100 years from the Cretan Aging Cohort [100 with dementia, 175 with mild cognitive impairment (MCI) and 127 cognitively non-impaired] were enrolled, mostly rural dwellers (86.2%). Frailty was assessed with the Simple “Frail” Questionnaire Screening Tool. Demographic data, BMI, Mini-Mental State Examination scores (MMSE), severity of dementia according to the Clinical Dementia Rating Scale, and depressive symptoms according to the Geriatric Depression Scale (GDS) were recorded. Results: Frailty was present in 17% of persons with dementia (73.8% of mild severity), in 6.3% of persons with MCI and in 8.7% of cognitively non-impaired persons (P < 0.05). Among the various frailty variables, fatigue and difficulty walking were significantly more frequently reported by persons with dementia. Each frailty variable and the frailty score correlated negatively with MMSE score and positively with GDS score and polypharmacy. Multivariate analysis revealed that reported fatigue improved the identification of dementia in addition to MMSE, significantly and independently of symptoms of depression (P = 0.04). Conclusion: Frailty rates are significantly higher in persons with dementia. In this predominantly rural cohort of older subjects, reported fatigue could serve as a marker of physical decline and a complementary index for referral for further neuropsychological and neuropsychiatric evaluation. © 2018, European Geriatric Medicine Society

The Cretan Aging Cohort: Cohort Description and Burden of Dementia and Mild Cognitive Impairment

Our aim was to explore the burden of dementia in the Cretan Aging Cohort, comprised of 3140 persons aged ≥60 years (56.8% women, 5.8 ± 3.3 years formal education, 86.2% living in rural areas) who attended selected primary health-care facilities on the island of Crete, Greece. In the first study phase, a formal diagnosis of dementia had been reached in 4.0% of the participants. However, when selected 505 participants underwent thorough neuropsychiatric evaluation in the second phase of this study (344 with Mini-Mental State Examination [MMSE] <24 and 161 with MMSE ≥24), and results were extrapolated to the entire cohort, the prevalence of dementia and mild cognitive impairment was estimated at 10.8% (9.7%-11.9%) and 32.4% (30.8%-34.0%), respectively. Using both the field diagnostic data and the extrapolated data, the highest dementia prevalence (27.2%) was found in the 80- to 84-year-old group, who also showed the lowest educational level, apparently due to lack of schooling during World War II. © The Author(s) 2018