TWINGEN - protocol for an observational clinical biobank recall and biomarker study to identify individuals with high risk of Alzheimer's disease.

Introduction: A better understanding of the earliest stages of Alzheimer's disease (AD) could expedite the development or administration of treatments. Large population biobanks hold the promise to identify individuals at an elevated risk of AD and related dementias based on health registry information. Here, we establish the protocol for an observational clinical recall and biomarker study called TWINGEN with the aim to identify individuals at high risk of AD by assessing cognition, health and AD-related biomarkers. Suitable candidates were identified and invited to participate in the new study among Finnish biobank donors according to TWINGEN study criteria. Methods and analysis: A multi-center study (n=800) to obtain blood-based biomarkers, telephone-administered and web-based memory and cognitive parameters, questionnaire information on lifestyle, health and psychological factors, and accelerometer data for measures of physical activity, sedentary behavior and sleep. A sub-cohort are being asked to participate in an in-person neuropsychological assessment (n=200) and wear an Oura ring (n=50). All participants in the TWINGEN study have genome-wide genotyping data and up to 48 years of follow-up data from the population-based older Finnish Twin Cohort (FTC) study of the University of Helsinki. TWINGEN data will be transferred to Finnish Institute of Health and Welfare (THL) biobank and we aim to further to transfer it to the FinnGen study where it will be combined with health registry data for prediction of AD. Ethics and dissemination: This recall study consists of FTC/THL/FinnGen participants whose data were acquired in accordance with the Finnish Biobank Act. The recruitment protocols followed the biobank protocols approved by Finnish Medicines Agency. The TWINGEN study plan was approved by the Ethics Committee of Hospital District of Helsinki and Uusimaa (number 16831/2022). THL Biobank approved the research plan with the permission no: THLBB2022_83.

Associations Between Engagement with the BitHabit Digital Lifestyle Intervention and Changes in Type 2 Diabetes Risk Factors.

Type 2 diabetes (T2D) can be prevented or delayed through a healthy lifestyle. Digital behavior change interventions (DBCIs) may offer cost-effective and scalable means to support lifestyle changes. This study investigated associations between user engagement with a habit-formation-based DBCI, the BitHabit app, and changes in T2D risk factors over 12 months in 963 participants at risk of T2D. User engagement was characterized by calculating use metrics from the BitHabit log data. User ratings were used as a subjective measure of engagement. The use metrics and user ratings were the strongest associated with improvements in diet quality. Weak positive associations were observed between the use metrics and changes in waist circumference and body mass index. No associations were found with changes in physical activity, fasting plasma glucose, or plasma glucose two hours after an oral glucose tolerance test. To conclude, increased use of the BitHabit app can have beneficial impacts on T2D risk factors, especially on diet quality.

Nature Ambience in a Lunch Restaurant Has the Potential to Evoke Positive Emotions, Reduce Stress, and Support Healthy Food Choices and Sustainable Behavior: A Field Experiment among Finnish Customers.

Laboratory experiments have indicated that exposure to restorative ambiences in food environments can lead to beneficial outcomes for consumers, but there is little evidence if this positive effect holds true in real-life consumption conditions. Therefore, the aim of this study was to analyze the effects of lunch restaurant ambience on customers' emotional responses, stress recovery, food choices, and generation of plate waste. The expectation was that ambience inducing positive emotional responses would lead to alleviated stress, healthier food choices, and reduced plate waste. A field experiment with a baseline and two experimental ambiences ('nature ambience' to induce positive emotions and 'fast food ambience' to induce less positive emotions) including visual and auditory stimuli was conducted in a lunch restaurant for one week per ambience. Emotional responses, and objective and subjective stress were measured from a subgroup of participants (n = 32). Food choices and plate waste were measured for all customers (n = 1610-1805 depending on the study week). During 'nature ambience' week, customers more often chose vegetarian dishes and generated less plate waste. The results on emotional responses and stress recovery were partially in line with the expectations. The study provides real-life evidence that restaurant ambience modification could lead to beneficial consequences for customers.

Urban greenspace and the indoor environment: Pathways to health via indoor particulate matter, noise, and road noise annoyance.

BACKGROUND/AIM: The exposome includes urban greenspace, which may affect health via a complex set of pathways, including reducing exposure to particulate matter (PM) and noise. We assessed these pathways using indoor exposure monitoring data from the HEALS study in four European urban areas (Edinburgh, UK; Utrecht, Netherlands; Athens and Thessaloniki, Greece). METHODS: We quantified three metrics of residential greenspace at 50 m and 100 m buffers: Normalised Difference Vegetation Index (NDVI), annual tree cover density, and surrounding green land use. NDVI values were generated for both summer and the season during which the monitoring took place. Indoor PM2.5 and noise levels were measured by Dylos and Netatmo sensors, respectively, and subjective noise annoyance was collected by questionnaire on an 11-point scale. We used random-effects generalised least squares regression models to assess associations between greenspace and indoor PM2.5 and noise, and an ordinal logistic regression to model the relationship between greenspace and road noise annoyance. RESULTS: We identified a significant inverse relationship between summer NDVI and indoor PM2.5 (-1.27 µg/m3 per 0.1 unit increase [95% CI -2.38 to -0.15]) using a 100 m residential buffer. Reduced (i.e., <1.0) odds ratios (OR) of road noise annoyance were associated with increasing summer (OR = 0.55 [0.31 to 0.98]) and season-specific (OR = 0.55 [0.32 to 0.94]) NDVI levels, and tree cover density (OR = 0.54 [0.31 to 0.93] per 10 percentage point increase), also at a 100 m buffer. In contrast to these findings, we did not identify any significant associations between greenspace and indoor noise in fully adjusted models. CONCLUSIONS: We identified reduced indoor levels of PM2.5 and noise annoyance, but not overall noise, with increasing outdoor levels of certain greenspace indicators. To corroborate our findings, future research should examine the effect of enhanced temporal resolution of greenspace metrics during different seasons, characterise the configuration and composition of green areas, and explore mechanisms through mediation modelling.

Influence of residential land cover on childhood allergic and respiratory symptoms and diseases: Evidence from 9 European cohorts.

INTRODUCTION: Recent research focused on the interaction between land cover and the development of allergic and respiratory disease has provided conflicting results and the underlying mechanisms are not fully understood. In particular, green space, which confers an overall positive impact on general health, may be significantly contributing to adverse respiratory health outcomes. This study evaluates associations between surrounding residential land cover (green, grey, agricultural and blue space), including type of forest cover (deciduous, coniferous and mixed), and childhood allergic and respiratory diseases. METHODS: Data from 8063 children, aged 3-14 years, were obtained from nine European population-based studies participating in the HEALS project. Land-cover exposures within a 500 m buffer centred on each child's residential address were computed using data from the Coordination of Information on the Environment (CORINE) program. The associations of allergic and respiratory symptoms (wheeze, asthma, allergic rhinitis and eczema) with land coverage were estimated for each study using logistic regression models, adjusted for sex, age, body mass index, maternal education, parental smoking, and parental history of allergy. Finally, the pooled effects across studies were estimated using meta-analyses. RESULTS: In the pooled analyses, a 10% increase in green space coverage was significantly associated with a 5.9%-13.0% increase in the odds of wheezing, asthma, and allergic rhinitis, but not eczema. A trend of an inverse relationship between agricultural space and respiratory symptoms was observed, but did not reach statistical significance. In secondary analyses, children living in areas with surrounding coniferous forests had significantly greater odds of reporting wheezing, asthma and allergic rhinitis. CONCLUSION: Our results provide further evidence that exposure to green space is associated with increased respiratory disease in children. Additionally, our findings suggest that coniferous forests might be associated with wheezing, asthma and allergic rhinitis. Additional studies evaluating both the type of green space and its use in relation to respiratory conditions should be conducted in order to clarify the underlying mechanisms behind associated adverse impacts.

Multivariate Prediction of Hippocampal Atrophy in Alzheimer's Disease.

BACKGROUND: Hippocampal atrophy (HA) is one of the biomarkers for Alzheimer's disease (AD). OBJECTIVE: To identify the best biomarkers and develop models for prediction of HA over 24 months using baseline data. METHODS: The study included healthy elderly controls, subjects with mild cognitive impairment, and subjects with AD, obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) databases. Predictor variables included cognitive and neuropsychological tests, amyloid-ß, tau, and p-tau from cerebrospinal fluid samples, apolipoprotein E, and features extracted from magnetic resonance images (MRI). Least-mean-squares regression with elastic net regularization and least absolute deviation regression models were tested using cross-validation in ADNI 1. The generalizability of the models including only MRI features was evaluated by training the models with ADNI 1 and testing them with AIBL. The models including the full set of variables were not evaluated with AIBL because not all needed variables were available in it. RESULTS: The models including the full set of variables performed better than the models including only MRI features (root-mean-square error (RMSE) 1.76-1.82 versus 1.93-2.08). The MRI-only models performed well when applied to the independent validation cohort (RMSE 1.66-1.71). In the prediction of dichotomized HA (fast versus slow), the models achieved a reasonable prediction accuracy (0.79-0.87). CONCLUSIONS: These models can potentially help identifying subjects predicted to have a faster HA rate. This can help in selection of suitable patients into clinical trials testing disease-modifying drugs for AD.

A Decision Support System for Diagnostics and Treatment Planning in Traumatic Brain Injury.

Traumatic brain injury (TBI) occurs when an external force causes functional or structural alterations in the brain. Clinical characteristics of TBI vary greatly from patient to patient, and a large amount of data is gathered during various phases of clinical care in these patients. It is hard for clinicians to efficiently integrate and interpret all of these data and plan interventions in a timely manner. This paper describes the technical architecture and functionality of a web-based decision support system (DSS), which not only provides advanced support for visualizing complex TBI data but also predicts a possible outcome by using a state-of-the-art Disease State Index machine-learning algorithm. The DSS is developed by using a three-layered architecture and by employing modern programming principles, software design patterns, and using robust technologies (C#, ASP.NET MVC, HTML5, JavaScript, Entity Framework, etc.). The DSS is comprised of a patient overview module, a disease-state prediction module, and an imaging module. After deploying it on a web-server, the DSS was made available to two hospitals in U.K. and Finland. Afterwards, we conducted a validation study to evaluate its usability in clinical settings. Initial results of the study indicate that especially less experience clinicians may benefit from this type of decision support software tool.

Disease-related determinants are associated with mortality in dementia due to Alzheimer's disease.

BACKGROUND: Survival after dementia diagnosis varies considerably. Previous studies were focused mainly on factors related to demographics and comorbidity rather than on Alzheimer's disease (AD)-related determinants. We set out to answer the question whether markers with proven diagnostic value also have prognostic value. We aimed to identify disease-related determinants associated with mortality in patients with AD. METHODS: We included 616 patients (50% female; age 67 ± 8 years; mean Mini Mental State Examination score 22 ± 3) with dementia due to AD from the Amsterdam Dementia Cohort. Information on mortality was obtained from the Dutch Municipal Register. We used age- and sex-adjusted Cox proportional hazards analysis to study associations of baseline demographics, comorbidity, neuropsychology, magnetic resonance imaging (MRI) (medial temporal lobe, global cortical and parietal atrophy, and measures of small vessel disease), and cerebrospinal fluid (CSF) (ß-amyloid 1-42, total tau, and tau phosphorylated at threonine 181 [p-tau]) with mortality (outcome). In addition, we built a multivariate model using forward selection. RESULTS: After an average of 4.9 ± 2.0 years, 213 (35%) patients had died. Age- and sex-adjusted Cox models showed that older age (HR 1.29 [95% CI 1.12-1.48]), male sex (HR 1.60 [95% CI 1.22-2.11]), worse scores on cognitive functioning (HR 1.14 [95% CI 1.01-1.30] to 1.31 [95% CI 1.13-1.52]), and more global and hippocampal atrophy on MRI (HR 1.18 [95% CI 1.01-1.37] and HR 1.18 [95% CI 1.02-1.37]) were associated with increased risk of mortality. There were no associations with comorbidity, level of activities of daily living, apolipoprotein E (APOE) ε4 status, or duration of disease. Using forward selection, the multivariate model included a panel of age, sex, cognitive tests, atrophy of the medial temporal lobe, and CSF p-tau. CONCLUSIONS: In this relatively young sample of patients with AD, disease-related determinants were associated with an increased risk of mortality, whereas neither comorbidity nor APOE genotype had any prognostic value.

Computer-assisted prediction of clinical progression in the earliest stages of AD.

INTRODUCTION: Individuals with subjective cognitive decline (SCD) are at increased risk for clinical progression. We studied how combining different diagnostic tests can help to identify individuals who are likely to show clinical progression. METHODS: We included 674 patients with SCD (46% female, 64 ± 9 years, Mini-Mental State Examination 28 ± 2) from three memory clinic cohorts. A multivariate model based on the Disease State Index classifier incorporated the available baseline tests to predict progression to MCI or dementia over time. We developed and internally validated the model in one cohort and externally validated it in the other cohorts. RESULTS: After 2.9 ± 2.0 years, 151(22%) patients showed clinical progression. Overall performance of the classifier when combining cognitive tests, magnetic resonance imagining, and cerebrospinal fluid showed a balanced accuracy of 74.0 ± 5.5, with high negative predictive value (93.3 ± 2.8). DISCUSSION: We found that a combination of diagnostic tests helps to identify individuals at risk of progression. The classifier had particularly good accuracy in identifying patients who remained stable.

An m-Health system for education and motivation in cardiac rehabilitation: the experience of HeartCycle guided exercise.

Introduction Home-based programmes for cardiac rehabilitation play a key role in the recovery of patients with coronary artery disease. However, their necessary educational and motivational components have been rarely implemented with the help of modern mobile technologies. We developed a mobile health system designed for motivating patients to adhere to their rehabilitation programme by providing exercise monitoring, guidance, motivational feedback, and educational content. Methods Our multi-disciplinary approach is based on mapping "desired behaviours" into specific system's specifications, borrowing concepts from Fogg's Persuasive Systems Design principles. A randomised controlled trial was conducted to compare mobile-based rehabilitation (55 patients) versus standard care (63 patients). Results Some technical issues related to connectivity, usability and exercise sessions interrupted by safety algorithms affected the trial. For those who completed the rehabilitation (19 of 55), results show high levels of both user acceptance and perceived usefulness. Adherence in terms of started exercise sessions was high, but not in terms of total time of performed exercise or drop-outs. Educational level about heart-related health improved more in the intervention group than the control. Exercise habits at 6 months follow-up also improved, although without statistical significance. Discussion Results indicate that the adopted design methodology is promising for creating applications that help improve education and foster better exercise habits, but further studies would be needed to confirm these indications.

Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase-L1 Are Not Specific Biomarkers for Mild CT-Negative Traumatic Brain Injury.

Glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1) have been studied as potential biomarkers of mild traumatic brain injury (mTBI). We report the levels of GFAP and UCH-L1 in patients with acute orthopedic injuries without central nervous system involvement, and relate them to the type of extracranial injury, head magnetic resonance imaging (MRI) findings, and levels of GFAP and UCH-L1 in patients with CT-negative mTBI. Serum UCH-L1 and GFAP were longitudinally measured from 73 patients with acute orthopedic injury on arrival and on days 1, 2, 3, 7 after admission, and on the follow-up visit 3-10 months after the injury. The injury types were recorded, and 71% patients underwent also head MRI. The results were compared with those found in patients with CT-negative mTBI (n = 93). The levels of GFAP were higher in patients with acute orthopedic trauma than in patients with CT-negative mTBI (p = 0.026) on arrival; however, no differences were found on the following days. The levels of UCH-L1 were not significantly different between these two groups at any measured point of time. Levels of GFAP and UCH-L1 were not able to distinguish patients with CT-negative mTBI from patients with orthopedic trauma. Patients with orthopedic trauma and high levels of UCH-L1 or GFAP values may be falsely diagnosed as having a concomitant mTBI, predisposing them to unwarranted diagnostics and unnecessary brain imaging. This casts a significant doubt on the diagnostic value of GFAP and UCH-L1 in cases with mTBI.

Prediction of Outcome after Traumatic Brain Injury: Comparison of Disease State Index and IMPACT Calculator.

Traumatic brain injury (TBI) is a major cause of death and disability, especially in young adults. A reliable prediction of outcome after TBI is of great importance in clinical practice and research. We aimed to compare performance of the well-established IMPACT calculator and an alternative method, Disease State Index (DSI), in the prediction of six-month outcome after TBI. Performance of the models was evaluated using 2036 patients with moderate or severe TBI from the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) database. Prediction performance of both models was similar. The models with more variables provided better performance than the simpler models. This study showed that the DSI is a valid tool with efficient visualizations that can help clinicians with their decision making process in clinical practice.