BACKGROUND: Social inequalities in bodyweight start early in life and track into adulthood. Dietary patterns are an important determinant of weight development in children, towards both overweight and underweight. Therefore, we aimed to examine weight development between age 5 and 10 years by ethnicity, SES and thereafter by BMI category at age 5, to explore its association with dietary patterns at age 5.METHODS: Participants were 1765 children from the Amsterdam Born Children and their Development (ABCD) cohort that had valid data on BMI at age 5 and 10 and diet at age 5. Linear mixed model analysis was used to examine weight development between age 5 and 10 years and to assess if four previously identified dietary patterns at age 5 (snacking, full-fat, meat and healthy) were associated with weight development. Analyses were adjusted for relevant confounders, stratified by ethnicity and SES and thereafter stratified per BMI category at age 5.RESULTS: Overall, weight decreased in Dutch and high SES children and increased in non-Dutch and low/middle SES children. Across the range of bodyweight categories at age 5, we observed a conversion to normal weight, which was stronger in Dutch and high SES children but less pronounced in non-Dutch and low/middle SES children. Overall, the observed associations between weight development and dietary patterns were mixed with some unexpected findings: a healthy dietary pattern was positively associated with weight development in most groups, regardless of ethnicity and SES (e.g. Dutch B 0.084, 95% CI 0.038;0.130 and high SES B 0.096, 95% CI 0.047;0.143) whereas the full-fat pattern was negatively associated with weight development (e.g. Dutch B -0.069, 95% CI -0.114;-0.024 and high SES B -0.072, 95% CI -0.119;-0.026).CONCLUSIONS: We observed differential weight development per ethnic and SES group. Our results indicate that each ethnic and SES group follows its own path of weight development. Associations between dietary patterns and weight development showed some unexpected findings; follow-up research is needed to understand the association between dietary patterns and weight development.
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Abstract: Hypertension is both a health problem and a financial one globally. It affects nearly 30 % of the general population. Elderly people, aged ≥65 years, are a special group of hypertensive patients. In this group, the overall prevalence of the disease reaches 60 %, rising to 70 % in those aged ≥80 years. In the elderly population, isolated systolic hypertension is quite common. High systolic blood pressure is associated with an increased risk of cardiovascular disease, cerebrovascular disease, peripheral artery disease, cognitive impairment and kidney disease. Considering the physiological changes resulting from ageing alongside multiple comorbidities, treatment of hypertension in elderly patients poses a significant challenge to treatment teams. Progressive disability with regard to the activities of daily life, more frequent hospitalisations and low quality of life are often seen in elderly patients. There is discussion in the literature regarding frailty syndrome associated with old age. Frailty is understood to involve decreased resistance to stressors, depleted adaptive and physiological reserves of a number of organs, endocrine dysregulation and immune dysfunction. The primary dilemma concerning frailty is whether it should only be defined on the basis of physical factors, or whether psychological and social factors should also be included. Proper nutrition and motor rehabilitation should be prioritised in care for frail patients. The risk of orthostatic hypotension is a significant issue in elderly patients. It results from an autonomic nervous system dysfunction and involves maladjustment of the cardiovascular system to sudden changes in the position of the body. Other significant issues in elderly patients include polypharmacy, increased risk of falls and cognitive impairment. Chronic diseases, including hypertension, deteriorate baroreceptor function and result in irreversible changes in cerebral and coronary circulation. Concurrent frailty or other components of geriatric syndrome in elderly patients are associated with a worse perception of health, an increased number of comorbidities and social isolation of the patient. It may also interfere with treatment adherence. Identifying causes of non-adherence to pharmaceutical treatment is a key factor in planning therapeutic interventions aimed at increasing control, preventing complications, and improving long-term outcomes and any adverse effects of treatment. Diagnosis of frailty and awareness of the associated difficulties in adhering to treatment may allow targeting of those elderly patients who have a poorer prognosis or may be at risk of complications from untreated or undertreated hypertension, and for the planning of interventions to improve hypertension control.
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To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
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