Undernutrition, cognitive decline and dementia: The collaborative PROMED-COG pooled cohorts study.

BACKGROUND & AIMS: Undernutrition may negatively impact cognitive function, but evidence of this relationship is not yet consolidated. Under the "PROtein enriched MEDiterranean diet to combat undernutrition and promote healthy neuroCOGnitive ageing" (PROMED-COG) project, we evaluated the association between undernutrition, and cognitive decline and incident dementia in older adults. METHODS: Retrospective data harmonization was performed on three Italian population-based studies: the Italian Longitudinal Study of Ageing (ILSA), the Progetto Veneto Anziani (Pro.V.A.), and the Bollate Eye Study-Follow-Up (BEST-FU). The associations between undernutrition, operationalized using the Global Leadership Initiative on Malnutrition (GLIM) criteria, and decline on the Mini-Mental State Examination (MMSE) or dementia incidence follow-up were evaluated with Cox proportional hazard regression models. RESULTS: The pooled cohort comprised 9071 individuals (52% females) aged between 42 and 101 years. The prevalence of undernutrition at the baseline was 14.3%, significantly higher among females (15.4% vs 13%) and in older age, ranging from 3.5% in those aged <60 years to 28.8% in those 85+ years. Undernutrition was associated with both cognitive decline over a median 8.3-year follow-up (Hazard Ratio (HR) 1.20, 95% Confidence Interval (CI) 1.02-1.41, p = 0.028) and incidence of dementia over a median 8.6-year follow-up (HR = 1.57, 95%CI 1.01-2.43, p = 0.046). For cognitive decline, the association with undernutrition was more marked in males than females (HR = 1.36, 95%CI 1.05-1.77, p = 0.019 vs HR = 1.10, 95% CI 0.89-1.36, p = 0.375). CONCLUSION: Undernutrition is prevalent among older people and is associated with an increased risk of experiencing cognitive decline and dementia. The prevention and early identification of undernutrition could be an important nonpharmacologic strategy to counteract neurodegeneration.

Socioeconomic Inequalities in the External Exposome in European Cohorts: The EXPANSE Project.

Socioeconomic inequalities in the exposome have been found to be complex and highly context-specific, but studies have not been conducted in large population-wide cohorts from multiple countries. This study aims to examine the external exposome, encompassing individual and environmental factors influencing health over the life course, and to perform dimension reduction to derive interpretable characterization of the external exposome for multicountry epidemiological studies. Analyzing data from over 25 million individuals across seven European countries including 12 administrative and traditional cohorts, we utilized domain-specific principal component analysis (PCA) to define the external exposome, focusing on air pollution, the built environment, and air temperature. We conducted linear regression to estimate the association between individual- and area-level socioeconomic position and each domain of the external exposome. Consistent exposure patterns were observed within countries, indicating the representativeness of traditional cohorts for air pollution and the built environment. However, cohorts with limited geographical coverage and Southern European countries displayed lower temperature variability, especially in the cold season, compared to Northern European countries and cohorts including a wide range of urban and rural areas. The individual- and area-level socioeconomic determinants (i.e., education, income, and unemployment rate) of the urban exposome exhibited significant variability across the European region, with area-level indicators showing stronger associations than individual variables. While the PCA approach facilitated common interpretations of the external exposome for air pollution and the built environment, it was less effective for air temperature. The diverse socioeconomic determinants suggest regional variations in environmental health inequities, emphasizing the need for targeted interventions across European countries.

Meta-analysis of set-based multiple phenotype association test based on GWAS summary statistics from different cohorts.

Genome-wide association studies (GWAS) have emerged as popular tools for identifying genetic variants that are associated with complex diseases. Standard analysis of a GWAS involves assessing the association between each variant and a disease. However, this approach suffers from limited reproducibility and difficulties in detecting multi-variant and pleiotropic effects. Although joint analysis of multiple phenotypes for GWAS can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits, most of the multiple phenotype association tests are designed for a single variant, resulting in much lower power, especially when their effect sizes are small and only their cumulative effect is associated with multiple phenotypes. To overcome these limitations, set-based multiple phenotype association tests have been developed to enhance statistical power and facilitate the identification and interpretation of pleiotropic regions. In this research, we propose a new method, named Meta-TOW-S, which conducts joint association tests between multiple phenotypes and a set of variants (such as variants in a gene) utilizing GWAS summary statistics from different cohorts. Our approach applies the set-based method that Tests for the effect of an Optimal Weighted combination of variants in a gene (TOW) and accounts for sample size differences across GWAS cohorts by employing the Cauchy combination method. Meta-TOW-S combines the advantages of set-based tests and multi-phenotype association tests, exhibiting computational efficiency and enabling analysis across multiple phenotypes while accommodating overlapping samples from different GWAS cohorts. To assess the performance of Meta-TOW-S, we develop a phenotype simulator package that encompasses a comprehensive simulation scheme capable of modeling multiple phenotypes and multiple variants, including noise structures and diverse correlation patterns among phenotypes. Simulation studies validate that Meta-TOW-S maintains a desirable Type I error rate. Further simulation under different scenarios shows that Meta-TOW-S can improve power compared with other existing meta-analysis methods. When applied to four psychiatric disorders summary data, Meta-TOW-S detects a greater number of significant genes.

Socioeconomic position during pregnancy and pre-school exposome in children from eight European birth cohort studies.

Distribution of environmental hazards and vulnerability to their effects vary across socioeconomic groups. Our objective was to analyse the relationship between child socioeconomic position (SEP) at birth and the external exposome at pre-school age (0-4 years). This study included more than 60,000 children from eight cohorts in eleven European cities (Oslo, Copenhagen, Bristol, Bradford, Rotterdam, Nancy, Poitiers, Gipuzkoa, Sabadell, Valencia and Turin). SEP was measured through maternal education and a standardised indicator of household income. Three child exposome domains were investigated: behavioral, diet and urban environment. We fitted separate logistic regression model for each exposome variable - dichotomised using the city-specific median - on SEP (medium/low vs high) adjusting for maternal age, country of birth and parity. Analyses were carried out separately in each study-area. Low-SEP children had, consistently across study-areas, lower Odds Ratios (ORs) of breastfeeding, consumption of eggs, fish, fruit, vegetables and higher ORs of TV screen time, pet ownership, exposure to second-hand smoke, consumption of dairy, potatoes, sweet beverages, savory biscuits and crisps, fats and carbohydrates. For example, maternal education-breastfeeding OR (95% Confidence Interval (CI)) ranged from 0.18 (0.14-0.24) in Bristol to 0.73 (0.58-0.90) in Oslo. SEP was also strongly associated with the urban environment with marked between-city heterogeneity. For example, income-PM2.5 OR (95%CI) ranged from 0.69 (0.47-1.02) in Sabadell to 2.44 (2.16-2.72) in Oslo. Already at pre-school age, children with lower SEP have consistently poorer diets and behaviours, which might influence their future health and wellbeing. SEP-urban environment relationships are strongly context-dependent.

Early childcare arrangements and children's internalizing and externalizing symptoms: an individual participant data meta-analysis of six prospective birth cohorts in Europe.

Background: Early childcare attendance may be related to children's internalizing and externalizing symptoms throughout childhood and young adolescence, however evidence from Europe is limited. We aimed to assess this association across multiple population-based birth cohorts of children recruited in different European countries. Methods: Data come from six parent-offspring prospective birth cohort studies across five European countries within the EU Child Cohort Network. A total of 87,208 parent-child dyads were included in the study. To test associations between childcare attendance (centre-based or informal) anytime between ages 0 and 4 years and children's internalizing and externalizing symptoms in middle childhood and young adolescence (measured at: 5-6 years, 7-9 years, and 10-13 years) a two-stage individual participant data meta-analysis was implemented. Linear regression models were performed in each cohort separately; combined random-effects meta-analysis was then used to obtain overall association estimates. In secondary analyses, we tested interactions between childcare attendance and mother's post-partum depression, low education status, and the child's sex. Findings: Compared to children who were exclusively cared for by their parents prior to school entry, those who attended centre-based childcare had lower levels of internalizing symptoms in all age groups [5-6 years: ß: -1.78 (95% CI: -3.39, -0.16); 7-9 years: ß: -0.55 (95% CI: -0.88, -0.73); 10-13 years: ß: -0.76 (95% CI: -1.15, -0.37)]. Children who attended informal childcare appeared to have elevated levels of internalizing symptoms between 7-9 and 10-13 years, respectively [ß: 1.65 (95% CI: 1.25, 2.06); ß: 1.25 (95% CI: 0.96, 1.54)]. Informal childcare attendance was also associated with increased levels of children's externalizing symptoms between 7-9 and 10-13 years, respectively [ß: 2.84 (95% CI: 1.41, 4.26); ß: 2.19 (95% CI: 0.54, 3.84)]. Interpretation: Early centre-based childcare is associated with decreased levels of children's internalizing symptoms compared to exclusive parental care. For informal childcare, opposite associations were observed. Overall, our results suggest that centre-based childcare attendance may be associated with slight positive impacts on children's emotional development and should be encouraged by public policies. In addition, children from socioeconomically disadvantaged families require special attention, as they may not sufficiently benefit from universal early childhood education and care (ECEC). Funding: This research was funded by the ERC Consolidator grant RESEDA (Horizon Europe, 101001420).

Mapping Australia's COVID-19 quarantine cohort journeys.

BACKGROUND: Australia rapidly developed COVID-19 quarantine programs to reduce the adverse outcomes of a novel pathogen imported by visitors and returned travellers. Different quarantine pathways were utilised over the pandemic, yet no definitive cohort map exists to guide future preparedness. We created a whole-of-system cohort journey map of Australian quarantine cohorts to inform future pandemic preparedness activities. METHODS: Australian parliamentary websites and Google were searched for publicly available grey literature from 2019 to 2023. Data about quarantine cohorts, pandemic plans and documents, journey activities, viral escape events, and quarantine recommendations were extracted and plotted to produce a whole-of-system cohort journey map. RESULTS: The system mapping process identified 22 distinct quarantine cohort journeys during COVID-19, yet few of the cohorts were mentioned in pandemic and emergency plans. Viral escape events were documented 27 times, and COVID-19 reviews and inquiries produced 282 quarantine-specific recommendations. Cohorts included international and domestic travellers who experienced home, hotel, and facility quarantine iterations. Other cohorts, such as humanitarian evacuations, diplomats, airline crews, community close contacts, and people experiencing homelessness, had distinctive quarantine journeys. CONCLUSIONS: This whole-of-system quarantine cohort map furthers the case for governments and policymakers to update pandemic plans to include the 22 identified cohorts and test plans through pandemic exercises. Recommendations from inquiries should be acquitted to reduce the risk of viral escape and to strengthen national preparedness if quarantine systems are required in future pandemic responses.

Latent Class Analysis of Sexual Partnerships and Substance Use Across Generations.

Sexually transmitted infections (STIs) remain an important public health concern for people of all age groups, with older age groups experiencing a notable increase in STI burden. Historically, most research into STI risk behaviors has focused on adolescents and young adults, leaving a paucity of research on the ways STI risk factors change over the life course. Additionally, age and cohort trends in STI risk factors can be challenging to investigate with standard statistical tools as they can be collinear and are subject to sociocultural and generational influences. To help address these issues, we used multi-group latent class analysis to identify and compare risk behavior profiles defined by responses to three sexual activity and three substance use variables, across and within four age groups. We identified six behavior profiles in the unstratified dataset and five behavior profiles in each of the four age stratified groups. The five behavior profiles identified in each of the age categories appear to reflect a similar set of five underlying profile "archetypes," with the exact composition of each age category's five profiles varying in the magnitude that specific behaviors are endorsed. Interestingly, despite the similarity of profiles across the four age groups, analyses indicate that the experience of belonging to any one of these five archetypes differs by age group. This variance is likely due group specific age, period, and cohort effects, and may indicate that, when estimating one's STI risk, it is better to compare them to their peers than to the population as a whole.

Privacy-Preserving Workflow for the Cross-Border Federated Analysis of Clinical Data.

The motivation behind this research is to perform a privacy-preserving analysis of data located at remote sites and in different jurisdictions with no possibility of sharing individual-level information. Here, we present key findings from requirements analysis and a resulting federated data analysis workflow built using open-source research software, where patient-level information is securely stored and never exposed during the analysis process. We present additional improvements to further strengthen the security of the workflow. We emphasize and showcase the use of data harmonization in the analysis. The data analysis is done using the R language for statistical computing and DataSHIELD libraries for non-disclosive analysis of sensitive data. The workflow was validated against two data analysis scenarios, confirming the results obtained with a centralized analysis approach. The clinical datasets are part of the large Pan-European SARS-Cov-2 cohort, collected and managed by the ORCHESTRA project. We demonstrate the viability of establishing a cross-border federated data analysis framework and conducting an analysis without exposing patient-level information, achieving results equivalent to centralized non-secure analysis. However, it is vital to ensure requirements associated with data harmonization, anonymization and IT infrastructure to maintain availability, usability and data security.

The Value Proposition of Coordinated Population Cohorts Across Africa.

Building longitudinal population cohorts in Africa for coordinated research and surveillance can influence the setting of national health priorities, lead to the introduction of appropriate interventions, and provide evidence for targeted treatment, leading to better health across the continent. However, compared to cohorts from the global north, longitudinal continental African population cohorts remain scarce, are relatively small in size, and lack data complexity. As infections and noncommunicable diseases disproportionately affect Africa's approximately 1.4 billion inhabitants, African cohorts present a unique opportunity for research and surveillance. High genetic diversity in African populations and multiomic research studies, together with detailed phenotyping and clinical profiling, will be a treasure trove for discovery. The outcomes, including novel drug targets, biological pathways for disease, and gene-environment interactions, will boost precision medicine approaches, not only in Africa but across the globe.

Optimal approaches for COVID-19 control: the use of vaccines and lockdowns across societal groups.

Background: By March 2023, the COVID-19 illness had caused over 6.8 million deaths globally. Countries restricted disease spread through non-pharmaceutical interventions (NPIs; e.g. social distancing). More severe "lockdowns" were also required to manage disease spread. Although lockdowns effectively reduce virus transmission, they substantially disrupt economies and individual well-being. Fortunately, the availability of vaccines provides alternative approaches to manage disease spread. Yet, vaccination programs take several months to implement fully, require further time for individuals to develop immunity following inoculation, may not have complete coverage and/or may be imperfectly efficacious against the virus. Given these aspects of a vaccination programme, it is important to understand how NPIs (such as lockdowns) can be used in conjunction with vaccination to achieve public health goals. Methods: We use mathematical methods to, investigate optimal approaches for vaccination under varying lockdown lengths and/or severities to prevent COVID-19-related deaths exceeding critical thresholds. Results: We find that increases in vaccination rate cause a disproportionate decrease in the length and severity lockdowns to keep mortality levels below a critical threshold. With vaccination, severe lockdowns can further reduce infections by up to 89%. Notably, we include simple demographics, modelling three groups: vulnerable, front-line workers, and non-vulnerable. We investigate the sequence of vaccination. One counter-intuitive finding is that even though the vulnerable group is high risk, demographically, this is a small group and critically, per person, vaccination therefore occurs more slowly. Hence vaccinating this group first achieves limited gains in overall disease control. Discussion: Importantly, we conclude that improved disease control may be best achieved by vaccinating the non-vulnerable group coupled with longer and/or more severe NPIs.

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.

BACKGROUND: Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults. METHODS: Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively. RESULTS: We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination. CONCLUSIONS: Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.

Assessing the lack of diversity in genetics research across neurodegenerative diseases: A systematic review of the GWAS Catalog and literature.

The under-representation of non-European cohorts in neurodegenerative disease genome-wide association studies (GWAS) hampers precision medicine efforts. Despite the inherent genetic and phenotypic diversity in these diseases, GWAS research consistently exhibits a disproportionate emphasis on participants of European ancestry. This study reviews GWAS up to 2022, focusing on non-European or multi-ancestry neurodegeneration studies. We conducted a systematic review of GWAS results and publications up to 2022, focusing on non-European or multi-ancestry neurodegeneration studies. Rigorous article inclusion and quality assessment methods were employed. Of 123 neurodegenerative disease (NDD) GWAS reviewed, 82% predominantly featured European ancestry participants. A single European study identified over 90 risk loci, compared to a total of 50 novel loci in identified in all non-European or multi-ancestry studies. Notably, only six of the loci have been replicated. The significant under-representation of non-European ancestries in NDD GWAS hinders comprehensive genetic understanding. Prioritizing genomic diversity in future research is crucial for advancing NDD therapies and understanding. HIGHLIGHTS: Eighty-two percent of neurodegenerative genome-wide association studies (GWAS) focus on Europeans. Only 6 of 50 novel neurodegenerative disease (NDD) genetic loci have been replicated. Lack of diversity significantly hampers understanding of NDDs. Increasing diversity in NDD genetic research is urgently required. New initiatives are aiming to enhance diversity in NDD research.

Characteristics, consent patterns, and challenges of randomized trials using the Trials within Cohorts (TwiCs) design - A scoping review.

OBJECTIVES: Trials within Cohorts (TwiCs) is a pragmatic design approach that may overcome frequent challenges of traditional randomized trials such as slow recruitment, burdensome consent procedures, or limited external validity. This scoping review aims to identify all randomized controlled trials using the TwiCs design and to summarize their design characteristics, ways to obtain informed consent, output, reported challenges and mitigation strategies. STUDY DESIGN AND SETTING: Systematic search of Medline, Embase, Cochrane, trial registries and citation tracking up to December 2022. TwiCs were defined as randomized trials embedded in a cohort with postrandomization consent for the intervention group and no specific postrandomization consent for the usual care control group. Information from identified TwiCs was extracted in duplicate from protocols, publications, and registry entries. We analyzed the information descriptively and qualitatively to highlight methodological challenges and solutions related to nonuptake of interventions and informed consent procedure. RESULTS: We identified a total of 46 TwiCs conducted between 2005 and 2022 in 14 different countries by a handful of research groups. The most common medical fields were oncology (11/46; 24%), infectious diseases (8/46; 17%), and mental health (7/46; 15%). A typical TwiCs was investigator-initiated (46/46; 100%), publicly funded (36/46; 78%), and recruited outpatients (27/46; 59%). Excluding eight pilot trials, only 16/38 (42%) TwiCs adjusted their calculated sample size for nonuptake of the intervention, anticipating a median nonuptake of 25% (interquartile range 10%-32%) in the experimental arm. Seventeen TwiCs (45%) planned analyses to adjust effect estimates for nonuptake. Regarding informed consent, we observed three patterns: 1) three separate consents for cohort participation, randomization, and intervention (17/46; 37%); 2) combined consent for cohort participation and randomization and a separate intervention consent (10/46; 22%); and 3) consent only for cohort participation and intervention (randomization consent not mentioned; 19/46; 41%). CONCLUSION: Existing TwiCs are globally scattered across a few research groups covering a wide range of medical fields and interventions. Despite the potential advantages, the number of TwiCs remains small. The variability in consent procedures and the possibility of substantial nonuptake of the intervention warrants further research to guide the planning, implementation, and analysis of TwiCs.

Urban environment exposures, energy balance-related behaviors and their combination in preschoolers from three European countries.

BACKGROUND: Urban environments are characterized by many factors that may influence children's energy balance-related behaviors (EBRBs), but there is limited research on the impact of prospective exposure to multiple urban factors in preschoolers. We evaluated prospective associations between various urban exposures and EBRBs in preschoolers across Europe, with EBRBs considered both individually and combined into lifestyle patterns. METHODS: We used data from 4,073 preschoolers (aged 3-4 years) participating in three European cohorts from the EU Child Cohort Network: BiB (United Kingdom), EDEN (France), and INMA (Spain). Eighteen built and food environment, green spaces, road traffic and ambient air pollution exposures were characterized at residential addresses. Various EBRBs were considered as the outcomes including screen time, sleep duration and diet (fruit, vegetables, discretionary sweet foods, sweet beverages) individually and combined into unhealthy lifestyle patterns, using principal components analysis. Associations between urban exposures and outcomes were estimated using a single-exposure analysis and the deletion-substitution-addition algorithm was used to construct multi-exposure models. RESULTS: In multi-exposure models, greater walkability and smaller distance to the nearest road were associated with higher scores on the unhealthy lifestyle patterns. Likewise, greater walkability was associated with higher screen time and more frequent discretionary sweet food consumption. A smaller distance to the nearest road was also associated with lower sleep duration and more frequent sweet beverages consumption. On the other hand, higher levels of street connectivity showed an inverse association with the unhealthy lifestyle patterns. In the same vein, greater street connectivity was associated with decreased screen time. CONCLUSION: This comprehensive examination of multiple urban exposures indicates that residing in walkable environments and in close proximity to roads in densely-populated areas may not be advantageous for children EBRBs, while residing in neighborhoods with higher street connectivity appears to supposedly be beneficial.

Levodopa-induced dyskinesia in Parkinson's disease: Insights from cross-cohort prognostic analysis using machine learning.

BACKGROUND: Prolonged levodopa treatment in Parkinson's disease (PD) often leads to motor complications, including levodopa-induced dyskinesia (LID). Despite continuous levodopa treatment, some patients do not develop LID symptoms, even in later stages of the disease. OBJECTIVE: This study explores machine learning (ML) methods using baseline clinical characteristics to predict the development of LID in PD patients over four years, across multiple cohorts. METHODS: Using interpretable ML approaches, we analyzed clinical data from three independent longitudinal PD cohorts (LuxPARK, n = 356; PPMI, n = 484; ICEBERG, n = 113) to develop cross-cohort prognostic models and identify potential predictors for the development of LID. We examined cohort-specific and shared predictive factors, assessing model performance and stability through cross-validation analyses. RESULTS: Consistent cross-validation results for single and multiple cohort analyses highlighted the effectiveness of the ML models and identified baseline clinical characteristics with significant predictive value for the LID prognosis in PD. Predictors positively correlated with LID include axial symptoms, freezing of gait, and rigidity in the lower extremities. Conversely, the risk of developing LID was inversely associated with the occurrence of resting tremors, higher body weight, later onset of PD, and visuospatial abilities. CONCLUSIONS: This study presents interpretable ML models for dyskinesia prognosis with significant predictive power in cross-cohort analyses. The models may pave the way for proactive interventions against dyskinesia in PD by optimizing levodopa dosing regimens and adjunct treatments with dopamine agonists or MAO-B inhibitors, and by employing non-pharmacological interventions such as dietary adjustments affecting levodopa absorption for high-risk LID patients.

Core data set para la generación de datos de la vida real en esclerosis múltiple: adaptación de una iniciativa global para América Latina / Core data set for real world data in multiple sclerosis: customization for latin america from a global task force recommendation

Introducción: Los objetivos primarios del core data set son reducir la heterogeneidad y promover la armonización entre las fuentes de datos en la esclerosis múltiple (EM), reduciendo así el tiempo necesario para ejecutar esfuerzos en la recolección de datos de vida real. Recientemente, un grupo liderado por la Multiple Sclerosis Data Alliance ha desarrollado un core data set para la recolección de datos del mundo real en EM a nivel global. Nuestro objetivo ha sido adaptar y consensuar este conjunto de datos globales a las necesidades de América Latina para que pueda ser implementado por los registros ya desarrollados y en proceso de desarrollo en la región. Material y métodos. Se conformó un grupo de trabajo regionalmente y se adaptó el core data set creado globalmente (proceso de traducción al español, incorporación de variables regionales y consenso sobre variables que se iban a utilizar). El consenso se obtuvo a través de la metodología Delphi remoto de ronda de cuestionarios y discusión a distancia de las variables del core data set. Resultados: Veinticinco profesionales de América Latina llevaron adelante el proceso de adaptación entre noviembre de 2022 y julio de 2023. Se estableció un acuerdo sobre un core data set de nueve categorías y 45 variables, versión 2023, con la sugerencia de implementarlo en registros desarrollados o en vías de desarrollo y cohortes de EM en la región. Conclusión: El core data set busca armonizar las variables recolectadas por los registros y las cohortes de EM en América Latina con el fin de facilitar dicha recolección y permitir una colaboración entre fuentes. Su implementación facilitará la recolección de datos de vida real y la colaboración en la región.(AU)

Introduction: The primary objective of the core data set is to reduce heterogeneity and promote harmonization among data sources in EM, thereby reducing the time needed to execute real life data collection efforts. Recently, a group led by the Multiple Sclerosis Data Alliance has developed a core data set for collecting real-world data on multiple sclerosis (MS) globally. Our objective was to adapt this global data set to the needs of Latin America, so that it can be implemented by the registries already developed and in the process of development in the region. Material and methods: A working group was formed regionally, the core data set created globally was adapted (translation process into Spanish, incorporation of regional variables and consensus on variables to be used). Consensus was obtained through the remote Delphi methodology of a round of questionnaires and remote discussion of the core data set variables. Results: A total of 25 professionals from Latin America carried out the adaptation process between November 2022 and July 2023. Agreement was established on a core data set of nine categories and 45 variables, version 2023 to suggest its implementation in developed or developing registries, and MS cohorts in the region. Conclusion: The core data set seeks to harmonize the variables collected by registries and cohorts in MS in Latin America in order to facilitate said collection and allow collaboration between sources. Its implementation will facilitate real life data collection and collaboration in the region.(AU)

Divergence in Chinese Sexual Attitudes: An Age-Period-Cohort Analysis.

A crucial component of comprehending societal change is understanding how sexual attitudes have evolved over time. The substantial and typical changes in China have created an ideal quasi-experimental design and a wealth of empirical data for tracking the evolution of sexual attitudes. However, existing research has failed to adequately analyze the temporal trends in Chinese sexual attitudes. This study employed an age-period-cohort framework to investigate changes in public sexual attitudes, including premarital sex, extramarital sex, and homosexuality. And it further delved into these attitudes in light of two unique aspects of Chinese society: urban-rural divide and political status. It explored the contributing elements and potential processes of changing public sexual attitudes in China using data from seven waves of national social survey conducted from 2010 to 2021. The findings indicated that public sexual attitudes became more conservative with age; the period effect exhibited a fluctuating upward trend, indicating a general increase in acceptance of the three sexual attitudes; notable differences in sexual attitudes among cohorts were identified. The divergence in sexual attitudes was significantly influenced by urban-rural divide and political status.

Towards an understanding of multi-generational higher education cohorts in gamified entrepreneurship education.

The increasing inclusion of gamified courses in entrepreneurship programmes in higher education have left gaps in understanding the critical essentials of the multi-generational student cohort undertaking these programmes. In this paper, we interrogate the educational experiences of multi-generational higher education students in a core gamified entrepreneurship course in an undergraduate business school programme. The research analyzed 392 course feedback responses from three generations (X, Y and Z) of a multi-generational cohort. The study developed and validated a behaviour-results model for gamified entrepreneurship courses leading to student entrepreneurial intention and entrepreneurial orientation, and disaggregated student engagement into it's multiple dimensions of cognitive, behavioural and emotional. The model also validated six dimensions of individual entrepreneurship orientation. Using the model, the study found differences in the component variables based on student Generations X, Y, and Z. Also, student cognitive and behavioural engagement led to entrepreneurial intention which also influenced student entrepreneurial orientation. There were marked differences in student grit, cognitive engagement, and emotional engagement between Generations X and Z. Furthermore, generational differences existed amongst Generation Z and Y, and also for Generation Z and X in student entrepreneurial intention. The study also confirmed the difference in entrepreneurial orientation between Generations X and Z. Additionally, the study found that there is a need to contextualize student engagement facilitators such as results demonstrability of the business simulation platform, student grit and user characteristics as they have selective effects on student cognition, behavioural and emotional engagements in a multi-generational student cohort of Generation X, Y and Z.

Validation of the Health Assessment Tool (HAT) based on four aging cohorts from the Swedish National study on Aging and Care.

BACKGROUND: As global aging accelerates, routinely assessing the functional status and morbidity burden of older patients becomes paramount. The aim of this study is to assess the validity of the comprehensive clinical and functional Health Assessment Tool (HAT) based on four cohorts of older adults (60 + years) from the Swedish National study on Aging and Care (SNAC) spanning urban, suburban, and rural areas. METHODS: The HAT integrates five health indicators (gait speed, global cognition, number of chronic diseases, and basic and instrumental activities of daily living), providing an individual-level score between 0 and 10. The tool was constructed using nominal response models, first separately for each cohort and then in a harmonized dataset. Outcomes included all-cause mortality over a maximum follow-up of 16 years and unplanned hospital admissions over a maximum of 3 years of follow-up. The predictive capacity was assessed through the area under the curve (AUC) using logistic regressions. For time to death, Cox regressions were performed, and Harrell's C-indices were reported. Results from the four cohorts were pooled using individual participant data meta-analysis and compared with those from the harmonized dataset. RESULTS: The HAT demonstrated high predictive capacity across all cohorts as well as in the harmonized dataset. In the harmonized dataset, the AUC was 0.84 (95% CI 0.81-0.87) for 1-year mortality, 0.81 (95% CI 0.80-0.83) for 3-year mortality, 0.80 (95% CI 0.79-0.82) for 5-year mortality, 0.69 (95% CI 0.67-0.70) for 1-year unplanned admissions, and 0.69 (95% CI 0.68-0.70) for 3-year unplanned admissions. The Harrell's C for time-to-death throughout 16 years of follow-up was 0.75 (95% CI 0.74-0.75). CONCLUSIONS: The HAT is a highly predictive, clinically intuitive, and externally valid instrument with potential for better addressing older adults' health needs and optimizing risk stratification at the population level.

The Importance of Large-Scale Genomic Studies to Unravel Genetic Risk Factors for Autism.

Autism spectrum disorder (ASD) is a common and highly heritable neurodevelopmental disorder. During the last 15 years, advances in genomic technologies and the availability of increasingly large patient cohorts have greatly expanded our knowledge of the genetic architecture of ASD and its neurobiological mechanisms. Over two hundred risk regions and genes carrying rare de novo and transmitted high-impact variants have been identified. Additionally, common variants with small individual effect size are also important, and a number of loci are now being uncovered. At the same time, these new insights have highlighted ongoing challenges. In this perspective article, we summarize developments in ASD genetic research and address the enormous impact of large-scale genomic initiatives on ASD gene discovery.