]) have largely ignored inference regarding the network structure and either assumed a particular structureor sought the most likely structure without considering the relative probabilities of all possible structures. The latter is especially problematic when there are many near equally likely structures, as is inevitably the case within complex networks of interacting variables such as for chronic disease.
], to place probabilities on all possible network structures rather than selecting a single most likely network structure.Data for the analyses came from ‘Growing Up in Australia: The Longitudinal Study of Australian Children’ [], Australia’s nationally representative children’s longitudinal study, focusing on social, economic, physical, and cultural impacts on health, learning, social and cognitive development.
A selection of ~25 variables was chosen from the questionnaires for inclusion in Bayesian network models, informed by the existing literature on childhood obesity; e.g. the literature indicates that parental body mass index , socio-economic status, birthweight score and screen time are causally associated with childhood BMI.We analysed 12 of the cross-sectional datasets . For each wave and cohort, a Bayesian network [] was used to model the factors surrounding childhood BMI.
To investigate the causal factors of childhood BMI in different genders, we further split each data set into boys and girls and made inferences on the corresponding Bayesian networks separately.When aiming to infer causality, graph structures are sought which do not contain any cycles/loops . These structures are called directed acyclic graphs .
BMCMedicine I disagree. If the world adopted the Korean diet (Subway & Pizza Hut commercials on Kdramas I'm looking at you) the obesity rate across all classes would be dramatically reduced. Primal cultures WAY healthier before exposed to SAD (Standard American Diet.) This goes for 🇦🇺as well
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Water intake, hydration status and 2-year changes in cognitive performance: a prospective cohort study - BMC MedicineBackground Water intake and hydration status have been suggested to impact cognition; however, longitudinal evidence is limited and often inconsistent. This study aimed to longitudinally assess the association between hydration status and water intake based on current recommendations, with changes in cognition in an older Spanish population at high cardiovascular disease risk. Methods A prospective analysis was conducted of a cohort of 1957 adults (aged 55–75) with overweight/obesity (BMI between ≥ 27 and | 40 kg/m2) and metabolic syndrome from the PREDIMED-Plus study. Participants had completed bloodwork and validated, semiquantitative beverage and food frequency questionnaires at baseline, as well as an extensive neuropsychological battery of 8 validated tests at baseline and 2 years of follow-up. Hydration status was determined by serum osmolarity calculation and categorized as | 295 mmol/L (hydrated), 295–299.9 mmol/L (impending dehydration), and ≥ 300 mmol/L (dehydrated). Water intake was assessed as total drinking water intake and total water intake from food and beverages and according to EFSA recommendations. Global cognitive function was determined as a composite z-score summarizing individual participant results from all neuropsychological tests. Multivariable linear regression models were fitted to assess the associations between baseline hydration status and fluid intake, continuously and categorically, with 2-year changes in cognitive performance. Results The mean baseline daily total water intake was 2871 ± 676 mL/day (2889 ± 677 mL/day in men; 2854 ± 674 mL/day in women), and 80.2% of participants met the ESFA reference values for an adequate intake. Serum osmolarity (mean 298 ± 24 mmol/L, range 263 to 347 mmol/L) indicated that 56% of participants were physiologically dehydrated. Lower physiological hydration status (i.e., greater serum osmolarity) was associated with a greater decline in global cognitive function z-score over a 2-year period (β: − 0
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Egg consumption reduces the risk of depressive symptoms in the elderly: findings from a 6-year cohort study - BMC PsychiatryBackground Accumulating evidence showed that dietary habits might modify the risk of depression. This study aimed to evaluate the longitudinal association of egg consumption with depressive symptoms in the Chinese elderly. Methods We analyzed the data from Zhejiang Ageing and Health Cohort Study including 8289 participants. The Patient Health Questionnaire-9 scale (PHQ-9) was used to assess depressive symptoms at baseline and three waves of follow-up (2015, 2016, and 2019–2020). A PHQ-9 cut-off score ≥ 5 was used to define depressive symptoms. The participants with depressive symptoms at baseline were excluded. Egg consumption was evaluated through the diet habits section of the baseline questionnaire. Self-reported egg consumption was measured as the number of eggs per week and categorized into three categories. Log-binomial regression models with Generalized Estimating Equations were utilized to evaluate the association of egg consumption with depressive symptoms and estimate relative risks (RRs). Results The mean age of included participants was 68.6 years. After 6 years of follow-up, 1385 (16.7%) participants were indicated with depressive symptoms by PHQ-9 at least once. Compared with non-consumers or less-than-weekly consumers, participants consuming | 3 eggs/week and ≥ 3 eggs/week had 30% (RR = 0.70, 95%CI 0.62–0.80) and 38% (RR = 0.62, 95%CI 0.54–0.71) lower risks of depressive symptoms, respectively. A linear association was confirmed (P for trend | 0.01), and each egg increment per week was associated with a 4% lower risk of depressive symptoms (RR = 0.96, 95%CI 0.93–0.99). Sensitivity analyses yielded consistent results to the main analyses. Conclusions Egg consumption is prospectively related to a lower risk of depressive symptoms in the Chinese elderly. More prospective studies are needed to verify the association.
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Unravelling the potential of social prescribing in individual-level type 2 diabetes prevention: a mixed-methods realist evaluation - BMC MedicineBackground Social prescribing (SP) usually involves linking patients in primary care with services provided by the voluntary and community sector. Preliminary evidence suggests that SP may offer a means of connecting patients with community-based health promotion activities, potentially contributing to the prevention of long-term conditions, such as type 2 diabetes (T2D). Methods Using mixed-methods realist evaluation, we explored the possible contribution of SP to individual-level prevention of T2D in a multi-ethnic, socio-economically deprived population in London, UK. We made comparisons with an existing prevention programme (NHS Diabetes Prevention Programme (NDPP)) where relevant and possible. Anonymised primary care electronic health record data of 447,360 people 18+ with an active GP registration between December 2016 and February 2022 were analysed using quantitative methods. Qualitative data (interviews with 11 primary care clinicians, 11 social prescribers, 13 community organisations and 8 SP users at high risk of T2D; 36 hours of ethnographic observations of SP and NDPP sessions; and relevant documents) were analysed thematically. Data were integrated using visual means and realist methods. Results People at high risk of T2D were four times more likely to be referred into SP than the eligible general population (RR 4.31 (95% CI 4.17–4.46)), with adjustment for socio-demographic variables resulting in attenuation (RR 1.33 (95% CI 1.27–1.39)). More people at risk of T2D were referred to SP than to NDPP, which could be explained by the broad referral criteria for SP and highly supportive (proactive, welcoming) environments. Holistic and sustained SP allowed acknowledgement of patients’ wider socio-economic constraints and provision of long-term personalised care. The fact that SP was embedded within the local community and primary care infrastructure facilitated the timely exchange of information and cross-referrals across providers, resulting in enhanced se
Source: BioMedCentral - 🏆 22. / 71 Read more »