Detecting Misfolded α-Synuclein in Blood Years before the Diagnosis of Parkinson's Disease.

BACKGROUND: Identifying individuals with Parkinson's disease (PD) already in the prodromal phase of the disease has become a priority objective for opening a window for early disease-modifying therapies. OBJECTIVE: The aim was to evaluate a blood-based α-synuclein seed amplification assay (α-syn SAA) as a novel biomarker for diagnosing PD in the prodromal phase. METHODS: In the TREND study (University of Tuebingen) biennial blood samples of n = 1201 individuals with/without increased risk for PD were taken prospectively over 4 to 10 years. We retrospectively analyzed blood samples of 12 participants later diagnosed with PD during the study to detect and amplify pathological α-syn conformers derived from neuronal extracellular vesicles using (1) immunoblot analyses with an antibody against these conformers and (2) an α-syn-SAA. Additionally, blood samples of n = 13 healthy individuals from the TREND cohort and n = 20 individuals with isolated rapid eye movement sleep behavior disorder (iRBD) from the University Hospital Cologne were analyzed. RESULTS: All individuals with PD showed positive immunoblots and a positive α-syn SAA at the time of diagnosis. Moreover, all PD patients showed a positive α-syn SAA 1 to 10 years before clinical diagnosis. In the iRBD cohort, 30% showed a positive α-syn SAA. All healthy controls had a negative SAA. CONCLUSIONS: We here demonstrate the possibility to detect and amplify pathological α-syn conformers in peripheral blood up to 10 years before the clinical diagnosis of PD in individuals with and without iRBD. The findings of this study indicate that this blood-based α-syn SAA assay has the potential to serve as a diagnostic biomarker for prodromal PD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Gut Microbiome Signatures of Risk and Prodromal Markers of Parkinson Disease.

OBJECTIVE: Alterations of the gut microbiome in Parkinson disease (PD) have been repeatedly demonstrated. However, little is known about whether such alterations precede disease onset and how they relate to risk and prodromal markers of PD. We investigated associations of these features with gut microbiome composition. METHODS: Established risk and prodromal markers of PD as well as factors related to diet/lifestyle, bowel function, and medication were studied in relation to bacterial α-/ß-diversity, enterotypes, and differential abundance in stool samples of 666 elderly TREND (Tübingen Evaluation of Risk Factors for Early Detection of Neurodegeneration) study participants. RESULTS: Among risk and prodromal markers, physical inactivity, occupational solvent exposure, and constipation showed associations with α-diversity. Physical inactivity, sex, constipation, possible rapid eye movement sleep behavior disorder (RBD), and smoking were associated with ß-diversity. Subthreshold parkinsonism and physical inactivity showed an interaction effect. Among other factors, age and urate-lowering medication were associated with α- and ß-diversity. Constipation was highest in individuals with the Firmicutes-enriched enterotype, and physical inactivity was most frequent in the Bacteroides-enriched enterotype. Constipation was lowest and subthreshold parkinsonism least frequent in individuals with the Prevotella-enriched enterotype. Differentially abundant taxa were linked to constipation, physical inactivity, possible RBD, smoking, and subthreshold parkinsonism. Substantia nigra hyperechogenicity, olfactory loss, depression, orthostatic hypotension, urinary/erectile dysfunction, PD family history, and the prodromal PD probability showed no significant microbiome associations. INTERPRETATION: Several risk and prodromal markers of PD are associated with gut microbiome composition. However, the impact of the gut microbiome on PD risk and potential microbiome-dependent subtypes in the prodrome of PD need further investigation based on prospective clinical and (multi)omics data in incident PD cases. ANN NEUROL 2021;90:E1-E12.

Gut Microbiome Signatures of Risk and Prodromal Markers of Parkinson Disease.

OBJECTIVE: Alterations of the gut microbiome in Parkinson disease (PD) have been repeatedly demonstrated. However, little is known about whether such alterations precede disease onset and how they relate to risk and prodromal markers of PD. We investigated associations of these features with gut microbiome composition. METHODS: Established risk and prodromal markers of PD as well as factors related to diet/lifestyle, bowel function, and medication were studied in relation to bacterial α-/ß-diversity, enterotypes, and differential abundance in stool samples of 666 elderly TREND (Tübingen Evaluation of Risk Factors for Early Detection of Neurodegeneration) study participants. RESULTS: Among risk and prodromal markers, physical activity, occupational solvent exposure, and constipation showed associations with α-diversity. Physical activity, sex, constipation, possible rapid eye movement sleep behavior disorder (RBD), and smoking were associated with ß-diversity. Subthreshold parkinsonism and physical activity showed an interaction effect. Among other factors, age and urate-lowering medication were associated with α- and ß-diversity. Physical inactivity and constipation were highest in individuals with the Firmicutes-enriched enterotype. Constipation was lowest and subthreshold parkinsonism least frequent in individuals with the Prevotella-enriched enterotype. Differentially abundant taxa were linked to constipation, physical activity, possible RBD, smoking, and subthreshold parkinsonism. Substantia nigra hyperechogenicity, olfactory loss, depression, orthostatic hypotension, urinary/erectile dysfunction, PD family history, and the prodromal PD probability showed no significant microbiome associations. INTERPRETATION: Several risk and prodromal markers of PD are associated with gut microbiome composition. However, the impact of the gut microbiome on PD risk and potential microbiome-dependent subtypes in the prodrome of PD need further investigation based on prospective clinical and (multi)omics data in incident PD cases. ANN NEUROL 2020;88:320-331.

Gait analysis with wearables predicts conversion to parkinson disease.

OBJECTIVE: Quantification of gait with wearable technology is promising; recent cross-sectional studies showed that gait characteristics are potential prodromal markers for Parkinson disease (PD). The aim of this longitudinal prospective observational study was to establish gait impairments and trajectories in the prodromal phase of PD, identifying which gait characteristics are potentially early diagnostic markers of PD. METHODS: The 696 healthy controls (mean age = 63 ± 7 years) recruited in the Tubingen Evaluation of Risk Factors for Early Detection of Neurodegeneration study were included. Assessments were performed longitudinally 4 times at 2-year intervals, and people who converted to PD were identified. Participants were asked to walk at different speeds under single and dual tasking, with a wearable device placed on the lower back; 14 validated clinically relevant gait characteristics were quantified. Cox regression was used to examine whether gait at first visit could predict time to PD conversion after controlling for age and sex. Random effects linear mixed models (RELMs) were used to establish longitudinal trajectories of gait and model the latency between impaired gait and PD diagnosis. RESULTS: Sixteen participants were diagnosed with PD on average 4.5 years after first visit (converters; PDC). Higher step time variability and asymmetry of all gait characteristics were associated with a shorter time to PD diagnosis. RELMs indicated that gait (lower pace) deviates from that of non-PDC approximately 4 years prior to diagnosis. INTERPRETATION: Together with other prodromal markers, quantitative gait characteristics can play an important role in identifying prodromal PD and progression within this phase. ANN NEUROL 2019;86:357-367.

Application of the movement disorder society prodromal Parkinson's disease research criteria in 2 independent prospective cohorts.

BACKGROUND: The research criteria for prodromal PD of the MDS propose a new approach for the assessment of the individual probability of prodromal PD. These criteria require a testing of their reliability in different prospective cohorts. OBJECTIVES: The objective was to evaluate the MDS prodromal PD criteria in 2 independent prospective studies. METHODS: Prodromal PD probabilities of the Tübingen Evaluation of Risk Factors for Early Detection of Neurodegeneration cohort (TREND study, n = 650, recruited by the presence of probable rapid eye movement sleep behavior disorder, depression, and/or hyposmia or none of these at baseline and 2-, 4-, and 6-year follow-up) and the population-based Prospective Evaluation of Risk Factors for Idiopathic Parkinson's Syndrome cohort (PRIPS Tübingen subsample; n = 715, baseline and 3- and 5-year follow-up) were calculated. Baseline posttest probabilities, time to PD diagnosis, marker constellations, and longitudinal changes of prodromal PD probabilities were analyzed. RESULTS: Incident PD cases (TREND, n = 10; PRIPS = 7) showed significantly higher likelihood ratios of risk and prodromal markers at baseline when compared with nonconverters. Only 2 of 17 incident PD cases met the criteria for probable prodromal PD (ie, posttest probability > 80%) and 5 had possible prodromal PD (ie, > 50%) 1.4 to 3.8 years before diagnosis. The criteria showed high specificity and negative predictive values (>98%), but low sensitivity (TREND, 30%; PRIPS, 14%) and positive predictive values (TREND, 19%, PRIPS, 50%). The individual risk for prodromal PD in incident PD cases showed an inverse correlation with the time to conversion (Spearman rho = .80, P = .006) and unlike in nonconverters, increased during follow-up. CONCLUSION: The MDS prodromal criteria provide a practical framework for the calculation of prodromal PD risk. Although specificity of the criteria is high, most patients will not meet the criteria before diagnosis unless testing is thoroughly performed with numerous and highly specific markers objectively assessed. © 2017 International Parkinson and Movement Disorder Society.

Structural Ultrasound of the Medial Temporal Lobe in Alzheimer's Disease.

Purpose One of the anatomical hallmarks of Alzheimer's disease (AD) is the atrophy of the medial temporal lobe (MTL), yet cost-effective and broadly available methodological alternatives to the current imaging tools for screening of this brain area are not currently available. Materials and Methods Using structural transcranial ultrasound (TCS), we attempted to visualize and measure the MTL, and compared the results of 32 AD patients and 84 healthy controls (HC). The MTL and the surrounding space were defined in the coronal plane on TCS. A ratio of the height of the MTL/height of the choroidal fissure (M/F) was calculated in order to obtain a regional proportion. Results An insufficient temporal bone window was identified in 22 % of the AD patients and 12 % of the HCs. The results showed that the ratio of M/F was significantly smaller in the AD group on both sides (p = 0.004 right, p = 0.007 left side). Furthermore, the M/F ratio made it possible to discriminate AD patients from HCs with a sensitivity of 83 % (right)/73 % (left) and a specificity of 76 % (right)/72 % (left) which is basically comparable to results published for magnetic resonance imaging. The measurements showed substantial intra/interrater reliability (ICC:0.79/0.69). Conclusion These results suggest that utilization of structural TCS may possibly constitute a cheap and easy-to-use supplement to other techniques for the diagnosis of AD. It may be especially useful as a screening tool in the large population of individuals with cognitive decline. Further studies are needed to validate this novel method.

Elevated fecal calprotectin is associated with gut microbial dysbiosis, altered serum markers and clinical outcomes in older individuals.

Fecal calprotectin is an established marker of gut inflammation in inflammatory bowel disease (IBD). Elevated levels of fecal calprotectin as well as gut microbial dysbiosis have also been observed in other clinical conditions. However, systemic and multi-omics alterations linked to elevated fecal calprotectin in older individuals remain unclear. This study comprehensively investigated the relationship between fecal calprotectin levels, gut microbiome composition, serum inflammation and targeted metabolomics markers, and relevant lifestyle and medical data in a large sample of older individuals (n = 735; mean age ± SD: 68.7 ± 6.3) from the TREND cohort study. Low (0-50 µg/g; n = 602), moderate (> 50-100 µg/g; n = 64) and high (> 100 µg/g; n = 62) fecal calprotectin groups were stratified. Several pro-inflammatory gut microbial genera were significantly increased and short-chain fatty acid producing genera were decreased in high vs. low calprotectin groups. In serum, IL-17C, CCL19 and the toxic metabolite indoxyl sulfate were increased in high vs. low fecal calprotectin groups. These changes were partially mediated by the gut microbiota. Moreover, the high fecal calprotectin group showed increased BMI and a higher disease prevalence of heart attack and obesity. Our findings contribute to the understanding of fecal calprotectin as a marker of gut dysbiosis and its broader systemic and clinical implications in older individuals.

Bayesian network modeling of risk and prodromal markers of Parkinson's disease.

Parkinson's disease (PD) is characterized by a long prodromal phase with a multitude of markers indicating an increased PD risk prior to clinical diagnosis based on motor symptoms. Current PD prediction models do not consider interdependencies of single predictors, lack differentiation by subtypes of prodromal PD, and may be limited and potentially biased by confounding factors, unspecific assessment methods and restricted access to comprehensive marker data of prospective cohorts. We used prospective data of 18 established risk and prodromal markers of PD in 1178 healthy, PD-free individuals and 24 incident PD cases collected longitudinally in the Tübingen evaluation of Risk factors for Early detection of NeuroDegeneration (TREND) study at 4 visits over up to 10 years. We employed artificial intelligence (AI) to learn and quantify PD marker interdependencies via a Bayesian network (BN) with probabilistic confidence estimation using bootstrapping. The BN was employed to generate a synthetic cohort and individual marker profiles. Robust interdependencies were observed for BN edges from age to subthreshold parkinsonism and urinary dysfunction, sex to substantia nigra hyperechogenicity, depression, non-smoking and to constipation; depression to symptomatic hypotension and excessive daytime somnolence; solvent exposure to cognitive deficits and to physical inactivity; and non-smoking to physical inactivity. Conversion to PD was interdependent with prior subthreshold parkinsonism, sex and substantia nigra hyperechogenicity. Several additional interdependencies with lower probabilistic confidence were identified. Synthetic subjects generated via the BN based representation of the TREND study were realistic as assessed through multiple comparison approaches of real and synthetic data. Altogether our work demonstrates the potential of modern AI approaches (specifically BNs) both for modelling and understanding interdependencies between PD risk and prodromal markers, which are so far not accounted for in PD prediction models, as well as for generating realistic synthetic data.

Older adults' coping strategies during the COVID-19 pandemic - a longitudinal mixed-methods study.

Introduction: Older age is a main risk factor for severe COVID-19. In 2020, a broad political debate was initiated as to what extent older adults need special protection and isolation to minimize their risk for SARS-CoV-2 infection. However, isolation might also have indirect negative psychological (e.g., loneliness, stress, fear, anxiety, depression) or physical (e.g., lack of exercise, missing medical visits) consequences depending on individual strategies and personality traits to cope longitudinally with this crisis. Methods: To examine the impact of individuals' coping with the pandemic on mental health, a large sample of 880 older adults of the prospective longitudinal cohort TREND study were surveyed six times about their individual coping strategies in the COVID-19 pandemic between May 2020 (05/2020: Mage = 72.1, SDage = 6.4, Range: 58-91 years) and November 2022 in an open response format. The relevant survey question was: "What was helpful for you to get through the last months despite the COVID-19 pandemic? E.g., phone calls, going for a walk, or others." Results and Discussion: In total, we obtained 4,561 records containing 20,578 text passages that were coded and assigned to 427 distinct categories on seven levels based on qualitative content analysis using MAXQDA. The results allow new insights into the impact of personal prerequisites (e.g., value beliefs, living conditions), the general evaluation of the pandemic (e.g., positive, irrelevant, stressful) as well as the applied coping strategies (e.g., cognitive, emotional- or problem-focused) to deal with the COVID-19 pandemic by using an adapted Lazarus stress model. Throughout the pandemic emotional-focused as well as problem-focused strategies were the main coping strategies, whereas general beliefs, general living conditions and the evaluation were mentioned less frequently.

Longitudinal dynamics of depression in risk groups of older individuals during the COVID-19 pandemic.

Background: Older individuals are most at risk of severe COVID-19 and particularly require protection causing (self)restriction of psychosocial interaction in daily living. So far, the impact of psychosocial withdrawal on mental health seems less pronounced in community-dwelling older individuals compared to younger individuals. However, dynamics and adverse long-term effects of the pandemic, such as increases in depression, are still mostly unclear, especially for vulnerable subgroups. Methods: Pre-pandemic and 3-, 8-, 14-, 20-month peri-pandemic data were analyzed in 877 older participants (age at 3-month peri-pandemic: mean ± SD: 72.3 ± 6.3, range: 58-91 years) of the observational prospective TREND study in Germany. Severity of depression (Beck's Depression Inventory-II scores) and key factors of (mental) health were investigated for cross-sectional associations using path modeling. Risk groups defined by resilience, loneliness, history of depression, stress, health status and fear of COVID-19 were investigated for differences in depression between timepoints. Findings: The early pandemic (3-month) severity of depression was most strongly associated with history of depression, stress and resilience. Overall increases in clinically relevant depression (mild-severe) from pre- to 3-month peri-pandemic were small (% with depression at pre-/3-month peri-pandemic: 8.3%/11.5%). Changes were most pronounced in risk groups with low resilience (27.2%/41.8%), loneliness (19.0%/28.9%), fear of COVID-19 (17.6%/31.4%), high stress (24.4%/34.2%), a history of depression (27.7%/36.9%), and low health status (21.8%/31.4%). Changes in depression were largely observed from pre- to 3-month and were sustained to the 20-month peri-pandemic timepoint, overall and in stratified risk groups defined by single and cumulative risk factors. Changes between timepoints were heterogenous as indicated by alluvial diagrams. Conclusion: Only specific risk groups of older individuals showed a large increase in depression during the COVID-19 pandemic. Since these increases occurred early in the pandemic and were sustained over 20 months, these vulnerable risk groups need to be prioritized for counselling and risk mitigation of depression.

A blind spot in mental healthcare? Psychotherapists lack education and expertise for the support of adults on the autism spectrum.

LAY ABSTRACT: Most autistic adults experience mental health problems. There is a great demand for psychotherapeutic support that addresses the specific needs of autistic individuals. However, people with autism encounter difficulties trying to access diagnostic and therapeutic services. This study was conducted by a participatory autism research group: a group in which autistic individuals and scientists collaborate. The group developed a questionnaire for psychotherapists in Germany to assess their knowledge about autism. Psychotherapists also rated their ability to diagnose and treat autistic patients without intellectual disability, and patients with other psychological diagnoses. Many of the 498 psychotherapists that responded reported little knowledge and outdated beliefs about autism, as well as little training on treating patients with autism. Their expertise about other psychological conditions was more comprehensive. However, many psychotherapists were interested in professional training on autism. Those with more knowledge were also more open to treating autistic patients. In conclusion, psychotherapists' lack of knowledge and expertise seem to be a major barrier for adults with autism to receiving helpful psychotherapeutic support. The results demonstrate the need for an advancement in autism education during psychotherapists' training and in continuous education.

Effects of aging on functional connectivity in a neurodegenerative risk cohort: resting state versus task measurement using near-infrared spectroscopy.

Changes in functional brain organization are considered to be particularly sensitive to age-related effects and may precede structural cognitive decline. Recent research focuses on aging processes determined by resting state (RS) functional connectivity (FC), but little is known about differences in FC during RS and cognitive task conditions in elderly participants. The purpose of this study is to compare FC within and between the cognitive control (CCN) and dorsal attention network (DAN) at RS and during a cognitive task using functional near-infrared spectroscopy (fNIRS). In a matched, neurodegenerative high-risk cohort comprising early (n = 98; 50-65 y) and late (n = 98; 65-85 y) elder subjects, FC was measured at RS and during performance of the Trail Making Test (TMT) via fNIRS. Both, under RS and task conditions our results revealed a main effect for age, characterized by reduced FC for late elder subjects within the left inferior frontal gyrus. During performance of the TMT, negative correlations of age and FC were confirmed in various regions of the CCN and DAN. For the whole sample, FC of within-region connections was elevated, while FC between regions was decreased at RS. The results confirm a reorganization of functional brain connectivity with increasing age and cognitive demands.

Age-related deterioration of performance and increase of cortex activity comparing time- versus item-controlled fNIRS measurement.

In our aging society, research into neurodegenerative processes is of great interest. Thereby, cortical activation under different neurocognitive conditions is considered to be a promising predictor. Against this background, the executive functions of a total of 250 healthy older adults (53-84 years) have been investigated using the Trail Making Test (TMT) and functional near-infrared spectroscopy in a block design. We investigated effects of age on the performance and cortical blood oxygenation during the TMT. Since it is assumed that older people may compensate for cognitive deficits by slowing their processing speed, we additionally analyzed the cortical blood oxygenation per solved item. Our results showed a significant decrease in processing speed in older participants compared to middle-aged individuals, however, also lower error rates during TMT part A. On a neurophysiological level, we observed increased cortical blood oxygenation in the older participants when completing the TMT. Finally, with respect to the combined measurement (O2Hb/item), no significantly higher hemodynamic cortical response per item was found within the older participants. The results confirm a deterioration of cognitive performance and an increase of cortical activity with increasing age. The findings are discussed in the light of current research.

Impaired Executive Function and Depression as Independent Risk Factors for Reported Delirium Symptoms: An Observational Cohort Study Over 8 Years.

BACKGROUND: Acute medical illnesses, surgical interventions, or admissions to hospital in older individuals are frequently associated with a delirium. In this cohort study, we investigated the impact of specific cognitive domains and depression before the occurrence of delirium symptoms in an 8-year observation of older non-hospitalized individuals. METHODS: In total, we included 807 participants (48-83 years). Deficits in specific cognitive domains were measured using the CERAD test battery, and depressive symptoms were measured using Beck Depression Inventory and the Geriatric Depression Scale (GDS) before the onset of a delirium. Delirium symptoms were retrospectively assessed by a questionnaire based on the established Nursing Delirium Screening Scale. RESULTS: Fifty-eight of eight hundred seven participants (7.2%) reported delirium symptoms over the 8-year course of the study. Sixty-nine percent (n = 40) of reported delirium symptoms were related to surgeries. In multivariate regression analysis, impaired executive function was an independent risk factor (p = 0.034) for the occurrence of delirium symptoms. Furthermore, age (p = 0.014), comorbidities [captured by the Charlson Comorbidity Index (CCI)] (p < 0.001), and depression (p = 0.012) were significantly associated with reported delirium symptoms. CONCLUSION: Especially prior to elective surgery or medical interventions, screening for impaired executive function and depression could be helpful to identify patients who are at risk to develop delirium symptoms.

Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients.

Importance: The overall low survival rate of patients with lung cancer calls for improved detection tools to enable better treatment options and improved patient outcomes. Multivariable molecular signatures, such as blood-borne microRNA (miRNA) signatures, may have high rates of sensitivity and specificity but require additional studies with large cohorts and standardized measurements to confirm the generalizability of miRNA signatures. Objective: To investigate the use of blood-borne miRNAs as potential circulating markers for detecting lung cancer in an extended cohort of symptomatic patients and control participants. Design, Setting, and Participants: This multicenter, cohort study included patients from case-control and cohort studies (TREND and COSYCONET) with 3102 patients being enrolled by convenience sampling between March 3, 2009, and March 19, 2018. For the cohort study TREND, population sampling was performed. Clinical diagnoses were obtained for 3046 patients (606 patients with non-small cell and small cell lung cancer, 593 patients with nontumor lung diseases, 883 patients with diseases not affecting the lung, and 964 unaffected control participants). No samples were removed because of experimental issues. The collected data were analyzed between April 2018 and November 2019. Main Outcomes and Measures: Sensitivity and specificity of liquid biopsy using miRNA signatures for detection of lung cancer. Results: A total of 3102 patients with a mean (SD) age of 61.1 (16.2) years were enrolled. Data on the sex of the participants were available for 2856 participants; 1727 (60.5%) were men. Genome-wide miRNA profiles of blood samples from 3046 individuals were evaluated by machine-learning methods. Three classification scenarios were investigated by splitting the samples equally into training and validation sets. First, a 15-miRNA signature from the training set was used to distinguish patients diagnosed with lung cancer from all other individuals in the validation set with an accuracy of 91.4% (95% CI, 91.0%-91.9%), a sensitivity of 82.8% (95% CI, 81.5%-84.1%), and a specificity of 93.5% (95% CI, 93.2%-93.8%). Second, a 14-miRNA signature from the training set was used to distinguish patients with lung cancer from patients with nontumor lung diseases in the validation set with an accuracy of 92.5% (95% CI, 92.1%-92.9%), sensitivity of 96.4% (95% CI, 95.9%-96.9%), and specificity of 88.6% (95% CI, 88.1%-89.2%). Third, a 14-miRNA signature from the training set was used to distinguish patients with early-stage lung cancer from all individuals without lung cancer in the validation set with an accuracy of 95.9% (95% CI, 95.7%-96.2%), sensitivity of 76.3% (95% CI, 74.5%-78.0%), and specificity of 97.5% (95% CI, 97.2%-97.7%). Conclusions and Relevance: The findings of the study suggest that the identified patterns of miRNAs may be used as a component of a minimally invasive lung cancer test, complementing imaging, sputum cytology, and biopsy tests.

Outpatient Psychotherapy for Adults with High-Functioning Autism Spectrum Condition: Utilization, Treatment Satisfaction, and Preferred Modifications.

Many adults with autism spectrum condition (ASC) desire outpatient psychotherapy (PT). This study compared reasons for seeking PT, experiences with PT, and modifications preferred with respect to PT of individuals with ASC to non-autistic individuals with depression (MDD). Furthermore, factors predictive for treatment satisfaction were identified in individuals with ASC. A total of 262 adults with ASC without intellectual impairment and 304 non-autistic controls with MDD were surveyed. In this pilot study both groups predominantly sought treatment for depressiveness. A low level of expertise with autism was the main reason for being declined by therapists and a contributing factor to the overall treatment dissatisfaction of patients with ASC. ASC patients desire adjustments such as written communication, and clearly structured sessions.

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by adequate statistical learning approaches. Previously, we published high-throughput sequencing based microRNA (miRNA) signatures in Alzheimer's disease (AD) patients in the United States (US) and Germany. Here, we determined abundance levels of 21 known circulating miRNAs in 465 individuals encompassing AD patients and controls by RT-qPCR. We computed models to assess the relation between miRNA expression and phenotypes, gender, age, or disease severity (Mini-Mental State Examination; MMSE). Of the 21 miRNAs, expression levels of 20 miRNAs were consistently de-regulated in the US and German cohorts. 18 miRNAs were significantly correlated with neurodegeneration (Benjamini-Hochberg adjusted P < 0.05) with highest significance for miR-532-5p (Benjamini-Hochberg adjusted P = 4.8 × 10-30). Machine learning models reached an area under the curve (AUC) value of 87.6% in differentiating AD patients from controls. Further, ten miRNAs were significantly correlated with MMSE, in particular miR-26a/26b-5p (adjusted P = 0.0002). Interestingly, the miRNAs with lower abundance in AD were enriched in monocytes and T-helper cells, while those up-regulated in AD were enriched in serum, exosomes, cytotoxic t-cells, and B-cells. Our study represents the next important step in translational research for a miRNA-based AD test.