Association of Peripheral Insulin Resistance and Other Markers of Type 2 Diabetes Mellitus with Brain Amyloid Deposition in Healthy Individuals at Risk of Dementia

We explored the association of type 2 diabetes related blood markers with brain amyloid accumulation on PiB-PET scans in 41 participants from the FINGER PET sub-study. We built logistic regression models for brain amyloid status with12 plasma markers of glucose and lipid metabolism, controlled for diabetes and APOE epsilon 4 carrier status. Lower levels of insulin, insulin resistance index (HOMA-IR), C-peptide, and plasminogen activator (PAI-1) were associated with amyloid positive status, although the results were not significant after adjusting for multiple testing. None of the models found evidence for associations between amyloid status and fasting glucose or HbA1c.Peer reviewe

Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline

The importance of early interventions in Alzheimer's disease (AD) emphasizes the need to accurately and efficiently identify at-risk individuals. Although many dementia prediction models have been developed, there are fewer studies focusing on detection of brain pathology. We developed a model for identification of amyloid-PET positivity using data on demographics, vascular factors, cognition,APOEgenotype, and structural MRI, including regional brain volumes, cortical thickness and a visual medial temporal lobe atrophy (MTA) rating. We also analyzed the relative importance of different factors when added to the overall model. The model used baseline data from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) exploratory PET sub-study. Participants were at risk for dementia, but without dementia or cognitive impairment. Their mean age was 71 years. Participants underwent a brain 3T MRI and PiB-PET imaging. PiB images were visually determined as positive or negative. Cognition was measured using a modified version of the Neuropsychological Test Battery. Body mass index (BMI) and hypertension were used as cardiovascular risk factors in the model. Demographic factors included age, gender and years of education. The model was built using the Disease State Index (DSI) machine learning algorithm. Of the 48 participants, 20 (42%) were rated as A beta positive. Compared with the A beta negative group, the A beta positive group had a higher proportion ofAPOE epsilon 4 carriers (53 vs. 14%), lower executive functioning, lower brain volumes, and higher visual MTA rating. AUC [95% CI] for the complete model was 0.78 [0.65-0.91]. MRI was the most effective factor, especially brain volumes and visual MTA rating but not cortical thickness.APOEwas nearly as effective as MRI in improving detection of amyloid positivity. The model with the best performance (AUC 0.82 [0.71-0.93]) was achieved by combiningAPOEand MRI. Our findings suggest that combining demographic data, vascular risk factors, cognitive performance,APOEgenotype, and brain MRI measures can help identify A beta positivity. Detecting amyloid positivity could reduce invasive and costly assessments during the screening process in clinical trials

Accelerated inbreeding depression suggests synergistic epistasis for deleterious mutations in Drosophila melanogaster

Epistasis may have important consequences for a number of issues in quantitative genetics and evolutionary biology. In particular, synergistic epistasis for deleterious alleles is relevant to the mutation load paradox and the evolution of sex and recombination. Some studies have shown evidence of synergistic epistasis for spontaneous or induced deleterious mutations appearing in mutation-accumulation experiments. However, many newly arising mutations may not actually be segregating in natural populations because of the erasing action of natural selection. A demonstration of synergistic epistasis for naturally segregating alleles can be achieved by means of inbreeding depression studies, as deleterious recessive allelic effects are exposed in inbred lines. Nevertheless, evidence of epistasis from these studies is scarce and controversial. In this paper, we report the results of two independent inbreeding experiments carried out with two different populations of Drosophila melanogaster. The results show a consistent accelerated inbreeding depression for fitness, suggesting synergistic epistasis among deleterious alleles. We also performed computer simulations assuming different possible models of epistasis and mutational parameters for fitness, finding some of them to be compatible with the results observed. Our results suggest that synergistic epistasis for deleterious mutations not only occurs among newly arisen spontaneous or induced mutations, but also among segregating alleles in natural populationsWe acknowledge the support by Uvigo Marine Research Centre funded by the “Excellence in Research (INUGA)” Programme from the Regional Council of Culture, Education and Universities, with co-funding from the European Union through the ERDF Operational Programme Galicia 2014-2020. This work was funded by Agencia Estatal de Investigación (AEI) (CGL2016-75904-C2-1-P), Xunta de Galicia (ED431C 2016-037) and Fondos Feder: “Unha maneira de facer Europa.” SD was founded by a predoctoral (FPI) grant from Ministerio de Economía y Competitividad, SpainS

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD

Quantity adjustments in the regional labour markets of EU candidate countries

The article comprises an analysis of the adjustment of regional labour markets of former (and present) EU candidate countries to asymmetric shocks. As in EU member states, a substantial part of the adjustment to changes in employment in candidate countries is carried by participation decisions, and migration plays a small role. Candidate countries, however, have experienced larger region-specific shocks to labour demand than member states, and these shocks lead to higher long-run changes in employment. Furthermore, adjustment mechanisms partly explain high regional unemployment. High unemployment regions exhibit a lower capability to absorb region-specific shocks through mechanisms other than higher unemployment. Copyright RSAI 2005.