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Multiple large longitudinal cohorts provide opportunities to address questions about predictors of pain and pain trajectories, even when not anticipated in design of the historical databases. This focus article uses two empirical examples to illustrate the processes of assessing the measurement properties of data from large cohort studies to answer questions about pain. In both examples, data were screened to select candidate variables that captured the impact of chronic pain on self-care activities, productivity and social activities. We describe a series of steps to select candidate items and evaluate their psychometric characteristics in relation to the measurement of pain impact proposed. In UK Biobank, a general lack of internal consistency of variables selected prevented the identification of a satisfactory measurement model, with lessons for the measurement of chronic pain impact. In the English Longitudinal Study of Ageing, a measurement model for chronic pain impact was identified, albeit limited to capturing the impact of pain on self-care and productivity but lacking coverage related to social participation. In conjunction with its supplementary material, this focus article aims to encourage exploration of these valuable prospectively collected data; to support researchers to make explicit the relationships between items in the databases and constructs of interest in pain research; and to use empirical methods to estimate the possible biases in these variables. PERSPECTIVE: This focus article outlines a theory-driven approach for fitting new measurement models to data from large cohort studies, and evaluating their psychometric properties. This aims to help researchers develop an empirical understanding of the gains and limitations connected with the process of re-purposing the data stored in these datasets.

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BACKGROUND: Frailty is an age-related syndrome characterized by loss of strength and exhaustion and associated with multi-morbidity. Early detection and prediction of the appearance of frailty could help older people age better and prevent them from needing invasive and expensive treatments. Machine learning techniques show promising results in creating a medical support tool for such a task. METHODS: This study aims to create a dataset for machine learning-based frailty studies, using Fried's Frailty Phenotype definition. Starting from a longitudinal study on aging in the UK population, we defined a frailty label for each subject. We evaluated the definition by training seven different models for detecting frailty with data that were contemporary to the ones used for the definition. We then integrated more data from two years before to obtain prediction models with a 24-month horizon. Features selection was performed using the MultiSURF algorithm, which ranks all features in order of relevance to the detection or prediction task. RESULTS: We present a new frailty dataset of 5303 subjects and more than 6500 available features. It is publicly available, provided one has access to the original English Longitudinal Study of Ageing dataset. The dataset is balanced after grouping frailty with pre-frailty, and it is suitable for multiclass or binary classification and prediction problems. The seven tested architectures performed similarly, forming a solid baseline that can be improved with future work. Linear regression achieved the best F-score and AUROC in detection and prediction tasks. CONCLUSIONS: Creating new frailty-annotated datasets of this size is necessary to develop and improve the frailty prediction techniques. We have shown that our dataset can be used to study and test machine learning models to detect and predict frailty. Future work should improve models' architecture and performance, consider explainability, and possibly enrich the dataset with older waves.

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OBJECTIVES: To investigate: (i) whether mood states associated with bipolar disorder are associated with poorer quality of life in older adults, and (ii) what are some of the predictors of quality of life in older adults with mood states associated with bipolar disorder. METHODS: The authors completed a cross-sectional multilevel analysis of panel data from seven waves of The English Longitudinal Study of Ageing dataset. The main analysis included 567 participants who reported experiencing mood states associated with bipolar disorder. Some participants reported this in more than one wave, resulting in 835 observations of mood states associated with bipolar disorder across the seven waves. Quality of life was assessed using the Control, Autonomy, Self-realization, and Pleasure-19 (CASP-19) measure. RESULTS: The presence of mood states associated with bipolar disorder was significantly associated with poorer quality of life, even after controlling for multiple covariates (age, sex, social isolation, loneliness, alcohol use, education level, and economic status). Loneliness significantly predicted poorer quality of life in older adults with mood states associated with bipolar disorder. In contrast, higher educational attainment and being female predicted better quality of life in this group. CONCLUSIONS: Older adults with mood states associated with bipolar disorder have potentially worse quality of life compared to the general population, which may be partly driven by loneliness. This has ramifications for the support offered to this population and suggests that treatments should focus on reducing loneliness to improve outcomes.

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BACKGROUND: The temporal relationships across cardiometabolic diseases (CMDs) were recently conceptualized as the cardiometabolic continuum (CMC), sequence of cardiovascular events that stem from gene-environmental interactions, unhealthy lifestyle influences, and metabolic diseases such as diabetes, and hypertension. While the physiological pathways linking metabolic and cardiovascular diseases have been investigated, the study of the sex and population differences in the CMC have still not been described. METHODS: We present a machine learning approach to model the CMC and investigate sex and population differences in two distinct cohorts: the UK Biobank (17,700 participants) and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) (7162 participants). We consider the following CMDs: hypertension (Hyp), diabetes (DM), heart diseases (HD: angina, myocardial infarction, or heart failure), and stroke (STK). For the identification of the CMC patterns, individual trajectories with the time of disease occurrence were clustered using k-means. Based on clinical, sociodemographic, and lifestyle characteristics, we built multiclass random forest classifiers and used the SHAP methodology to evaluate feature importance. RESULTS: Five CMC patterns were identified across both sexes and cohorts: EarlyHyp, FirstDM, FirstHD, Healthy, and LateHyp, named according to prevalence and disease occurrence time that depicted around 95%, 78%, 75%, 88% and 99% of individuals, respectively. Within the UK Biobank, more women were classified in the Healthy cluster and more men in all others. In the EarlyHyp and LateHyp clusters, isolated hypertension occurred earlier among women. Smoking habits and education had high importance and clear directionality for both sexes. For ELSA-Brasil, more men were classified in the Healthy cluster and more women in the FirstDM. The diabetes occurrence time when followed by hypertension was lower among women. Education and ethnicity had high importance and clear directionality for women, while for men these features were smoking, alcohol, and coffee consumption. CONCLUSIONS: There are clear sex differences in the CMC that varied across the UK and Brazilian cohorts. In particular, disadvantages regarding incidence and the time to onset of diseases were more pronounced in Brazil, against woman. The results show the need to strengthen public health policies to prevent and control the time course of CMD, with an emphasis on women.

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Aim: To determine the prevalence of body image accuracy/distortion in Brazilian men and women and to investigate sociodemographic and lifestyle-related factors, and the presence of chronic diseases associated with body image distortion. Methods: Data from 6,357 men and 7,657 women participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) were collected using a multidimensional questionnaire covering sociodemographic characteristics, health behaviors, heath conditions and body image perception. Results: Most participants (53.5% of the women and 54.7% of the men) were found to have an accurate self-perception of their body. When the factors associated with the perception of being heavier than reality were investigated, adopting weight loss measures and not being hypertensive proved protective against this distortion, both in women and men. Conversely, the perception of being lighter than reality was associated, in both women and men, with better education, being black or of mixed race, adopting weight loss measures and not being hypertensive or diabetic. Additional factors associated with underestimating weight were not consuming alcohol (only in women) and belonging to a lower social class (only in men). Conclusion: These findings may contribute to the implementation of public health policies and interventions to promote health and well-being in the Brazilian population.

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BACKGROUND: Most evidence on transport use and mortality has focused on the commute to work. This study aims to fill a gap by assessing relationships between public transport use and mortality among older adults. METHODS: Data come from a cohort of 10,186 individuals aged 50 or older who participated in the English Longitudinal Study of Ageing (ELSA), with survey data linked to mortality records over 16 years (2002-2018). We assessed a binary measure of public transport use and frequency of use from 'every day or nearly every day' to 'never'. Cox proportional-hazards regression models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for associations between public transport use and mortality. Analyses were adjusted for a range of covariates including socio-demographic factors, chronic disease, and self-reported problems with daily living activities. RESULTS: Overall, 3371 participants (33.1%) died within the study period. Mortality was lower among public transport users (21.3%) compared with non-users (64.2%). Adjusted analyses found that users had 34% lower mortality than non-users (HR 0.66 (95% CI 0.61;0.71)). Adjusted analyses showed similar association sizes across frequencies of public transport use, with those using public transport every day or nearly every day having 41% lower mortality than never users (HR 0.59 (0.49;0.71)). Associations were similar among those with and without a longstanding illness. CONCLUSION: The use of public transport among older adults is linked to lower levels of mortality. Reductions in provision of public transport services could be detrimental to both transportation and population health.

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BACKGROUND: Thyroid dysfunction has been associated with cognitive decline and dementia. However, the role of subtle thyroid hormone alterations in cognitive function is still debatable. METHODS: Participants without overt thyroid dysfunction aged 35-74 years at baseline were evaluated in 3 study waves (2008-2010, 2012-2014, and 2017-2019). We assessed baseline thyroid-stimulating hormone (TSH), free thyroxine (FT4), and free triiodothyronine (FT3). Cognitive performance was evaluated every 4 years in each wave using 10-word immediate and late recall, word recognition, semantic (animals category) and phonemic (letter f) verbal fluency, and the trail-making B-version tests. A global composite z-score was derived from these tests. The associations of TSH, FT4, and FT3 levels with cognitive decline over time were evaluated using linear mixed-effect models adjusted for sociodemographic, clinical, and lifestyle variables. RESULTS: In 9 524 participants (mean age 51.2 ±â€…8.9 years old, 51% women, 52% White), there was no association between baseline TSH, FT4, and FT3 levels and cognitive decline during the follow-up. However, increase in FT4 levels over time was associated with faster memory (ß = -0.004, 95% CI = -0.007; -0.001, p = .014), verbal fluency (ß = -0.003, 95% CI = -0.007; -0.0005, p = .021), executive function (ß = -0.004, 95% CI = -0.011; -0.003, p < .001), and global cognition decline (ß = -0.003, 95% CI = -0.006; -0.001, p = .001). Decrease in FT4 levels over time was associated with faster verbal fluency (ß = -0.003, 95% CI = -0.007; -0.0004, p = .025) and executive function (ß = -0.004, 95% CI = -0.007; -0.0003, p = .031) decline. CONCLUSIONS: An increase or decrease in FT4 levels over time was associated with faster cognitive decline in middle-aged and older adults without overt thyroid dysfunction during 8 years of follow-up.

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BACKGROUND: Mild cognitive impairment (MCI) and sarcopenia are two common conditions in older people. It is not widely known if MCI could predict the onset of sarcopenia. Therefore, we aimed to investigate whether MCI could predict the occurrence of sarcopenia in a population of older adults. METHODS: In the ELSA (English Longitudinal Study on Ageing), MCI was defined as the absence of dementia, preserved functional capacity and low performance in three objective cognitive tests. Sarcopenia was diagnosed as having low handgrip strength and low skeletal muscle mass index during follow-up. The longitudinal association between MCI at the baseline and incident sarcopenia was assessed using a multivariable logistic regression model, reporting the data as adjusted odds ratios (OR) and 95% confidence intervals (95%CI). RESULTS: 3,106 participants (mean age of 63.1 years; 55.3% males) were included. People with MCI reported significantly lower mean handgrip strength values and Skeletal Mass Index (SMI), as well as a higher prevalence of obesity at baseline. At baseline, 729 people had MCI and during the ten years follow-up period, 12.1% of the initial population included had sarcopenia. On multivariate analysis, adjusted for 18 potential confounders, the presence of MCI (OR = 1.236; 95%CI: 1.090-1.596, p = 0.01) significantly predicted the onset of sarcopenia during follow-up. CONCLUSION: The presence of MCI at baseline was associated with a higher incidence of sarcopenia at ten-years follow-up, demonstrating a likely role of MCI as a predictor of the onset of sarcopenia in older people.

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Introduction: Hair cortisol level has recently been identified as a promising marker for detecting long-term cortisol levels and a marker of hypothalamic-pituitary-adrenal cortex (HPA) axis activity. However, research on the association between obesity and an altered cortisol metabolism remains controversial. Objective: This study aimed to investigate the relationship between hair cortisol levels and overweight and obesity in participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Methods: This was a cross-sectional study involving 2,499 participants from the second follow-up (visit 3, 2017-2019) attending research centers in Rio de Janeiro and Rio Grande do Sul states. Hair samples were collected, and cortisol levels were analyzed using enzyme-linked immunosorbent assay (ELISA) kits. Cortisol levels were classified as low (< 40 pg/mg), medium (40-128 pg/mg), or high (> 128 pg/mg). The participants were classified as eutrophic, overweight, or obese according to their weight (kg) and height (m2). Odds ratios (ORs) with 95% confidence intervals (95%CI) were estimated. Results: Of the 2499 individuals, 30% had eutrophic weight, 40% were overweight, and 30% were obese. Notably, cortisol levels gradually increased with increasing body weight. Among participants with high hair cortisol levels, 41.2% were classified as overweight and 34.2% as obese. Multinomial logistic regression analysis indicated that participants with high cortisol levels were 43% (OR =1.43; 95%CI: 1.02-2.03) more likely to be overweight and 72% (OR =1.72; 95%CI:1.20-2.47) more likely to be obese than participants with low hair cortisol levels. After adjustment for all covariates, high cortisol levels remained associated with obesity (OR = 1.54; 95%CI:1.02-2.31) and overweight (OR =1.33; 95%CI:0.91-1.94). Conclusion: In the ELSA-Brazil cohort, hair stress were positively associated with overweight and obesity. These results underscore the importance of considering stress and cortisol as potential factors in obesity prevention and intervention efforts, and highlight a novel aspect of the complex relationship between stress and obesity in the Brazilian population.

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Atherosclerosis is an inflammatory disease. Coronary artery calcium (CAC) is a marker of atherosclerotic disease events and mortality risk. Increased GlycA, an emerging marker of inflammation, is associated with a higher risk for coronary artery disease (CAD). However, there is conflicting evidence on whether GlycA predicts subclinical CAD progression. We hypothesized that GlycA can predict subclinical CAC incidence/progression in healthy participants. We included 2,690 ELSA-Brasil cohort participants without cardiovascular/chronic inflammatory disease not receiving statin therapy who had GlycA levels measured and 2 interval CAC assessments between 2010 and 2018. Multivariable logistic and linear regression models were computed to evaluate GlycA as a predictor of CAC incidence and progression. CAC incidence required a baseline CAC of 0. CAC progression required a baseline CAC >0. The mean age of participants was 48.6 ± 7.7 years, 56.7% were women, and 54.6% and 16.1% (429 of 2,690) were White and Black, respectively. The mean CAC interscan period was 5.1 ± 0.9 years, the mean GlycA level was 414.7 ± 65 µmol/L, and the incidence of CAC was 13.1% (280 of 2,129). The GlycA level odds ratio for CAC incidence was 1.002 (95% confidence interval 1.0005 to 1.005, p = 0.016), adjusted for demographics, lifestyle, a family history of early CAD (≤60 years), lipids, and co-morbidities. The GlycA (≤p25 vs ≥p75) odds ratio for CAC progression (Berry definition) was 1.77 (95% confidence interval 1.07 to 2.96, p = 0.03) in a similar multivariable-adjusted model. Higher GlycA levels were associated with CAC incidence and progression in a healthy Brazilian cohort.

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Harmful algal blooms (HABs) and their associated phycotoxins are increasing globally, posing great threats to local coastal ecosystems and human health. Nutrients have been carried by the freshwater Yangtze River and have entered the estuary, which was reported to be a biodiversity-rich but HAB-frequent region. Here, in situ solid phase adsorption toxin tracking (SPATT) was used to monitor lipophilic shellfish toxins (LSTs) in seawaters, and extended local similarity analysis (eLSA) was conducted to trace the temporal and special regions of those LSTs in a one-year trail in a mussel culture ranch in the Yangtze River Estuary. Nine analogs of LSTs, including okadaic acid (OA), dinophysistoxin-1 (DTX1), yessotoxin (YTX), homoyessotoxin (homoYTX), 45-OH-homoYTX, pectenotoxin-2 (PTX2), 7-epi-PTX2 seco acid (7-epi-PTX2sa), gymnodimine (GYM) and azaspiracids-3 (AZA3), were detected in seawater (SPATT) or rope farmed mussels. The concentrations of OA + DTX1 and homoYTX in mussels were positively correlated with those in SPATT samplers (Pearson test, p < 0.05), indicating that SPATT (with resin HP20) would be a good monitoring tool and potential indicator for OA + DTX1 and homoYTX in mussel Mytilus coruscus. The eLSA results indicated that late summer and early autumn were the most phycotoxin-contaminated seasons in the Yangtze River Estuary. OA + DTX1, homoYTX, PTX2 and GYM were most likely driven by the local growing HAB species in spring and summer, while Yangtze River diluted water may impact the accumulation of HAB species, causing potential phycotoxin contamination in the Yangtze River Estuary in autumn and winter. Together, the results showed that the mussel harvesting season, late summer and early autumn, would be the season with the greatest phycotoxin risk and would be the most contaminated by local growing toxic algae. Routine monitoring sites should be set up close to the local seawaters.

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PURPOSE: Fully convolutional neural networks architectures have proven to be useful for brain tumor segmentation tasks. However, their performance in learning long-range dependencies is limited to their localized receptive fields. On the other hand, vision transformers (ViTs), essentially based on a multi-head self-attention mechanism, which generates attention maps to aggregate spatial information dynamically, have outperformed convolutional neural networks (CNNs). Inspired by the recent success of ViT models for the medical images segmentation, we propose in this paper a new network based on Swin transformer for semantic brain tumor segmentation. METHODS: The proposed method for brain tumor segmentation combines Transformer and CNN modules as an encoder-decoder structure. The encoder incorporates ELSA transformer blocks used to enhance local detailed feature extraction. The extracted feature representations are fed to the decoder part via skip connections. The encoder part includes channel squeeze and spatial excitation blocks, which enable the extracted features to be more informative both spatially and channel-wise. RESULTS: The method is evaluated on the public BraTS 2021 datasets containing 1251 cases of brain images, each with four 3D MRI modalities. Our proposed approach achieved excellent segmentation results with an average Dice score of 89.77% and an average Hausdorff distance of 8.90 mm. CONCLUSION: We developed an automated framework for brain tumor segmentation using Swin transformer and enhanced local self-attention. Experimental results show that our method outperforms state-of-th-art 3D algorithms for brain tumor segmentation.

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BACKGROUND: Life's Simple 7, a lifestyle and cardiovascular index associated with cognition, has been updated to Life's Essential 8 (LE8) to include sleep. LE8 has been related to cardiovascular outcomes but its association with cognition is unclear. METHODS: In this longitudinal analysis of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), LE8 score was based on health behaviors (diet, physical activity, nicotine exposure, and sleep health) as well as health-related factors (body mass index, blood lipids, blood glucose, and blood pressure). Cognition was assessed in three waves, 4 years apart, using the Consortium to Establish a Registry for Alzheimer's Disease - Word List, semantic and phonemic verbal fluency, the Trail-Making Test B (TMT-B), and a global composite score. We used linear mixed-model analysis, inverse probability weighting, and interaction analysis. RESULTS: At baseline, the mean age of the study cohort was 51.4 ± 8.9 years, 56% were women, and 53% were White. Higher baseline LE8 scores were associated with slower decline in global cognition (ß = 0.001, 95% confidence interval [CI] 0.001, 0.002; p < 0.001), memory (ß = 0.001, 95% CI 0.000, 0.002; p = 0.013), verbal fluency (ß = 0.001, 95% CI 0.000, 0.002; p = 0.003), and TMT-B (ß = 0.004, 95% CI 0.003, 0.005; p < 0.001). This association was mainly driven by LE8 health factors, particularly blood glucose and blood pressure. Age, sex, and race were modifiers of the association between LE8 and global cognitive decline (p < 0.001), suggesting it was more pronounced in older, male, and Black participants. CONCLUSIONS: Higher baseline LE8 scores were associated with slower global and domain-specific cognitive decline during 8 years of follow-up, mainly due to health factors such as blood glucose and blood pressure. Sociodemographic factors were modifiers of this association.

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BACKGROUND: Increased serum urate (SU) and hyperuricemia (HU) are associated with chronic noncommunicable diseases and mortality. SU concentrations are affected by several factors, including diet, and are expected to rise with age. We investigated whether the Dietary Approaches to Stop Hypertension (DASH) diet alter this trend. OBJECTIVE: The objective was to assess whether adherence to the DASH diet predicts a longitudinal change in SU concentrations and risk of HU in 8 y of follow-up. METHODS: Longitudinal analyses using baseline (2008-2010, aged 35-74 y), second (2012-2014), and third (2016-2018) visits data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The inclusion criteria were having complete food frequency questionnaire (FFQ) and urinary sodium measurement, in addition to having SU measurement at the 1st visit and at least 1 of the 2 follow-up visits. For the HU incidence analyses, participants had also to be free from HU at baseline. The final samples included 12575 individuals for the SU change analyses and 10549 for the HU incidence analyses. Adherence to DASH diet was assessed as continuous value. HU was defined as SU>6.8 mg/dL and/or urate-lowering therapy use. Mixed-effect linear and Poisson regressions (incidence rate ratio [IRR] and 95% confidence interval [CI]) were used in the analyses, adjusted for confounders. RESULTS: The mean age was 51.4 (8.7) y, and 55.4% were females. SU means (standard deviation) were 5.4 (1.4) at 1st visit, 5.2 (1.4) at 2nd visit, and 5.1(1.3) mg/dL at 3rd visit. The HU incidence rate was 8.87 per 1000 person-y. Each additional point in adherence to the DASH diet accelerated SU decline (P< 0.01) and lowered the incidence of HU by 4.3% (IRR: 0.957; 95% CI: 0.938,0.977) in adjusted model. CONCLUSION: The present study findings reinforce the importance of encouraging the DASH diet as a healthy dietary pattern to control and reduce the SU concentrations and risk of HU.

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BACKGROUND: Literature on the association between sarcopenia and cognitive impairment is largely unclear and mainly limited to non-European populations. Therefore, the aim of this study is to explore if the presence of sarcopenia at the baseline could increase the risk of cognitive impairment in a large cohort of older people participating to the English Longitudinal Study of Ageing (ELSA), over ten years of follow-up. METHODS: Sarcopenia was diagnosed as having low handgrip strength and low skeletal muscle mass index at the baseline, using a muscle mass prediction model; cognitive function was evaluated in the ELSA through several tests. The results are reported in the whole sample adjusted for potential baseline confounders and after matching sarcopenic and non-sarcopenic participants with a propensity score. RESULTS: 2738 people (mean age: 68.7 years, 54.4% males) were included. During the ten years of follow-up, sarcopenia was associated with significantly lower scores in memory (p < 0.001), verbal fluency (p < 0.001), immediate word recall (p <0.001), delayed word recall (p = 0.018), and in recall summary score (p < 0.001). After adjusting for eight potential confounders, the presence of sarcopenia was significantly associated with poor verbal fluency (odds ratio, OR= 1.417, 95% confidence intervals, CI= 1.181-1.700) and in propensity-score matched analyses (OR=1.272, 95%CI= 1.071- 1.511). CONCLUSIONS AND IMPLICATIONS: Sarcopenia was found to be associated with a significantly higher incidence of poor cognitive status in a large population of elderly people followed up for 10 years, suggesting it may be an important potential risk factor for dementia.

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OBJECTIVES: To analyse whether dynapenic abdominal obesity is a risk factor for Metabolic syndrome (MetS) and its components in individuals 50 years of age or older. DESIGN: A longitudinal study was conducted with an eight-year follow-up. SETTING: Representative sample of community-dwelling participants of the English Longitudinal Study of Ageing (ELSA). PARTICIPANTS: 3,952 individuals free of MetS at baseline. MEASUREMENTS: Dynapenic abdominal obesity was defined based on waist circumference (> 102 cm for men and > 88 cm for women) and grip strength (< 26 kg for men and < 16 kg for women). The participants were classified as non-abdominally obese/non-dynapenic (NAO/ND - reference group), abdominally obese/non-dynapenic (AO/ND), non-abdominally obese/dynapenic (NAO/D) and abdominally obese/dynapenic (AO/D). The outcome was the incidence of MetS based on the presence of three or more of the following criteria: hypertriglyceridemia, hyperglycaemia, low HDL cholesterol, arterial hypertension or body mass index ≥ 30 kg/m2 throughout eight-year follow-up. Additionally, the incidence of each component of MetS was also analyzed. Poisson regression models were run and controlled for sociodemographic, behavioural and clinical variables. RESULTS: The mean age of the participants was 65 years and 55% were women. The prevalence of AO/ND, NAO/D and AO/D were 35.3, 4.3 and 2.2%, respectively. At the end of follow-up 558 incident cases of MetS were recorded. The adjusted model demonstrated that although abdominal obesity was a risk factor for MetS (IRR: 2.26; 95% CI: 1.87 - 2.73), the IRR was greater in AO/D individuals (IRR: 3.34; 95% CI: 2.03 - 5.50) compared with ND/NAO group. Furthermore, ND/AO was a risk factor for incidence of hypertriglyceridemia (IRR: 1.27; 95% CI: 1.06 - 1.52), hyperglycaemia (IRR: 1.41; 95% CI: 1.18 - 1.69), low HDL cholesterol (IRR: 1.70; 95% CI: 1.32 - 2.19) and BMI ≥ 30 kg/m2 (IRR: 2.58; 95% CI: 2.04 - 3.26) while D/AO was a risk factor for hyperglycaemia (IRR: 1.78; 95% CI: 1.02 - 3.10), low HDL cholesterol (IRR: 2.36; 95% CI: 1.10 - 5.08), and BMI ≥ 30 kg/m2 (IRR: 2.79; 95% CI: 1.38 - 5.62). CONCLUSIONS: Dynapenic abdominal obesity increases the risk of MetS, with a higher IRR compared to obesity alone. The understanding of this synergic action could guide specific clinical strategies, enabling the prevention of metabolic changes that can lead to cardiovascular disease, disability and death.

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Human cerebral organoids (HCOs) are model systems that enable researchers to investigate the human brain in ways that had previously been impossible. The emergence of HCOs was accompanied by both expert and layperson discussions concerning the possibility of these novel entities developing sentience or consciousness. Such concerns are reflected in deliberations about how to handle and regulate their use. This perspective article resulted from an international and interdisciplinary research retreat "Ethical, Legal and Social Aspects of Human Cerebral Organoids and their Governance in Germany, the United Kingdom and the United States", which took place in Tübingen, Germany, in August 2022. The retreat focused on whether HCO research requires new ethical and regulatory approaches. It addressed epistemic issues around the detection and theorisation of consciousness, ethical concerns around moral status and research conduct, difficulties for legislation and guidelines managing these entities, and public engagement.

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OBJECTIVES: This paper models cognitive aging, across mid and late life, and estimates birth cohort and sex differences in both initial levels and aging trajectories over time in a sample with multiple cohorts and a wide span of ages. METHODS: The data used in this study came from the first 9 waves of the English Longitudinal Study of Ageing, spanning 2002-2019. There were n = 76,014 observations (proportion male 45%). Dependent measures were verbal fluency, immediate recall, delayed recall, and orientation. Data were modeled using a Bayesian logistic growth curve model. RESULTS: Cognitive aging was substantial in 3 of the 4 variables examined. For verbal fluency and immediate recall, males and females could expect to lose about 30% of their initial ability between the ages of 52 and 89. Delayed recall showed a steeper decline, with males losing 40% and females losing 50% of their delayed recall ability between ages 52 and 89 (although females had a higher initial level of delayed recall). Orientation alone was not particularly affected by aging, with less than a 10% change for either males or females. Furthermore, we found cohort effects for initial ability level, with particularly steep increases for cohorts born between approximately 1930 and 1950. DISCUSSION: These cohort effects generally favored later-born cohorts. Implications and future directions are discussed.

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