Multifactorial intervention to reduce falls in older people at high risk of recurrent falls a randomized controlled trial

Background: Falls occur frequently in older people and strongly affect quality of life. Guidelines recommend multifactorial, targeted fall prevention. We evaluated the effectiveness of a multifactorial intervention in older persons with a high risk of recurrent falls. Methods: A randomized controlled trial was conducted from April 3, 2005, to July 21, 2008, at the geriatric outpatient clinic of a university hospital and regional general practices in the Netherlands. Of 2015 persons identified, 217 persons aged 65 years or older were selected to participate. They had a high risk of recurrent falls and no cognitive impairment and had visited the emergency department or their family physician after a fall. The geriatric assessment and intervention were aimed at reduction of fall risk factors. Primary outcome measures were time to first and second falls after randomization. Secondary outcome measures were fractures, activities of daily living, quality of life, and physical performance. Results: Within 1 year, 55 (51.9%) of the 106 intervention participants and 62 (55.9%) of the 111 usual care (control) participants fell at least once. No significant treatment effect was demonstrated for the time to first fall (hazard ratio, 0.96; 95% confidence interval, 0.67-1.37) or the time to second fall (1.13; 0.71-1.80). Similar results were obtained for secondary outcome measures and for perprotocol analysis. One intervention participant died vs 7 in the control group (hazard ratio, 0.15; 95% confidence interval, 0.02-1.21). Conclusion: This multifactorial fall-prevention program does not reduce falls in high-risk, cognitively intact older persons. Trial Registration: isrctn.org Identifier: ISRCTN11546541

A personalised screening strategy for diabetic retinopathy:a cost-effectiveness perspective

Aims/hypothesis: In this study we examined the cost-effectiveness of three different screening strategies for diabetic retinopathy: using a personalised adaptive model, annual screening (fixed intervals), and the current Dutch guideline (stratified based on previous retinopathy grade). Methods: For each individual, optimal diabetic retinopathy screening intervals were determined, using a validated risk prediction model. Observational data (1998–2017) from the Hoorn Diabetes Care System cohort of people with type 2 diabetes were used (n = 5514). The missing values of retinopathy grades were imputed using two scenarios of slow and fast sight-threatening retinopathy (STR) progression. By comparing the model-based screening intervals to observed time to develop STR, the number of delayed STR diagnoses was determined. Costs were calculated using the healthcare perspective and the societal perspective. Finally, outcomes and costs were compared for the different screening strategies. Results: For the fast STR progression scenario, personalised screening resulted in 11.6% more delayed STR diagnoses and €11.4 less costs per patient compared to annual screening from a healthcare perspective. The personalised screening model performed better in terms of timely diagnosis of STR (8.8% less delayed STR diagnosis) but it was slightly more expensive (€1.8 per patient from a healthcare perspective) than the Dutch guideline strategy. Conclusions/interpretation: The personalised diabetic retinopathy screening model is more cost-effective than the Dutch guideline screening strategy. Although the personalised screening strategy was less effective, in terms of timely diagnosis of STR patients, than annual screening, the number of delayed STR diagnoses is low and the cost saving is considerable. With around one million people with type 2 diabetes in the Netherlands, implementing this personalised model could save €11.4 million per year compared with annual screening, at the cost of 658 delayed STR diagnoses with a maximum delayed time to diagnosis of 48 months. Graphical abstract: [Figure not available: see fulltext.]

Does environment influence childhood BMI? A longitudinal analysis of children aged 3 to 11

Background: Childhood overweight/obesity has been associated with environmental context, such as green space, gardens, crime and deprivation. This paper assesses the longitudinal association between environment and body mass index (BMI) for children across the ages of 3-11 years. It also investigates the relationship between environment and child overweight/obesity Methods: 6001 Children from the UK Millennium Cohort Study living in England were analysed. We estimated fixed effects linear and logistic regression models of the association between environment (levels of green space, gardens, crime and deprivation) and BMI/overweight of children at four time points between the ages of 3 and 11. Models were adjusted for age-related changes in weight, child sex, and education level of the main carer. Results: Statistically significant associations were found between environmental measures of both more gardens and lower levels of crime and lower BMI (effect size [95% confidence interval (CI)] respectively: -0.02 [-0.04–0.00], -0.04 [-0.07– -0.02]). Areas with less crime were associated with a slightly lower odds of overweight among children with a higher educated parent (odds ratio [OR] 0,93 [0,87 – 0,99]) Conclusions: By exploiting longitudinal measures of environment and BMI this study is able to establish a more causal association between environment and BMI. Environments with more gardens and lower crime tend to result in slightly lower BMI. However, the effect sizes are small and non-significant odds of changing weight status do not support environmental factors as a key determinant of cohort changes in childhood overweight/obesity

Does environment influence childhood BMI? A longitudinal analysis of children aged 3 to 11

Background: Childhood overweight/obesity has been associated with environmental context, such as green space, gardens, crime and deprivation. This paper assesses the longitudinal association between environment and body mass index (BMI) for children across the ages of 3-11 years. It also investigates the relationship between environment and child overweight/obesity Methods: 6001 Children from the UK Millennium Cohort Study living in England were analysed. We estimated fixed effects linear and logistic regression models of the association between environment (levels of green space, gardens, crime and deprivation) and BMI/overweight of children at four time points between the ages of 3 and 11. Models were adjusted for age-related changes in weight, child sex, and education level of the main carer. Results: Statistically significant associations were found between environmental measures of both more gardens and lower levels of crime and lower BMI (effect size [95% confidence interval (CI)] respectively: -0.02 [-0.04–0.00], -0.04 [-0.07– -0.02]). Areas with less crime were associated with a slightly lower odds of overweight among children with a higher educated parent (odds ratio [OR] 0,93 [0,87 – 0,99]) Conclusions: By exploiting longitudinal measures of environment and BMI this study is able to establish a more causal association between environment and BMI. Environments with more gardens and lower crime tend to result in slightly lower BMI. However, the effect sizes are small and non-significant odds of changing weight status do not support environmental factors as a key determinant of cohort changes in childhood overweight/obesity

The impact of greenspace and condition of the neighbourhood on child overweight

Background: Childhood overweight/obesity has been associated with environmental, parenting and socioeconomic status (SES) factors. This paper assesses the influence of the amount of green space, accessibility to a garden and neighbourhood condition on being overweight/obese. It investigates whether parental behaviours moderate or mediate this influence and evaluates the interaction of SES with environmental context. Methods: 6467 children from the UK Millennium Cohort Study living in England were analysed. We estimated logistic regressions to examine the initial association between environment and overweight. Subsequently, parenting determinants comprising: food consumption, physical activity, rules and regularity were evaluated as moderators or mediators. Lastly SES related variables were tested as moderators or mediators of the associations. Results: Statistically significant associations were found between low levels of green space, no access to a garden, run down area and childhood overweight/obesity [odds ratio (OR) [95% confidence interval (CI)] respectively: 1.14 (1.02–1.27), 1.35 (1.16–1.58), 1.22 (1.05–1.42)]. None of the parental constructs mediated or moderated the relationships between environment and childhood overweight/obesity. Including SES, parental education moderated the effect of environmental context. Specifically, among lower educated households lack of garden access and less green space was associated with overweight/obesity; and among higher educated households poor neighbourhood condition influenced the probability of overweight/obesity respectively: 1.38 (1.12–1.70) OR 1.38, 95% CI (1.21–1.70). Conclusions: This study suggests that limits on access to outdoor space are associated with future childhood overweight/obesity although the ways in which this occurs are moderated by parental education leve

Effectiveness of a Patient-Tailored, Pharmacist-Led Intervention Program to Enhance Adherence to Antihypertensive Medication: The CATI Study

Introduction: Non-adherence to medication is a complex health care problem. In spite of substantial efforts, up till now little progress has been made to effectively tackle the problem with adherence-enhancing interventions. The aim of this study was to investigate the effectiveness of a patient-tailored, pharmacist-led and theory-driven intervention program aimed to enhance self-reported adherence to antihypertensive medication.Materials and Methods: A parallel-group randomized controlled trial in 20 community pharmacies with nine months follow-up was conducted. Patients (45–75 years) using antihypertensive medication and considered non-adherent based on both pharmacy dispensing data and a self-report questionnaire were eligible to participate. The intervention program consisted of two consultations with the pharmacist to identify participants’ barriers to adhere to medication and to counsel participants in overcoming these barriers. The primary outcome was self-reported medication adherence. Secondary outcomes were beliefs about medicines, illness perceptions, quality of life and blood pressure. Mixed-model and generalized estimating equation (GEE) analyses were used to assess overall effects of the intervention program and effects per time point.Results: 170 patients were included. No significant differences between intervention and control groups were found in self-reported adherence, quality of life, illness perceptions, beliefs about medicines (concern scale), and blood pressure. After nine months, intervention participants had significantly stronger beliefs about the necessity of using their medicines as compared to control participants (mean difference 1.25 [95% CI: 0.27 to 2.24], p = 0.012).Discussion: We do not recommend to implement the intervention program in the current form for this study population. Future studies should focus on how to select eligible patient groups with appropriate measures in order to effectively target adherence-enhancing interventions.Trial Register: NTR5017 http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5017

Prediction models for development of retinopathy in people with type 2 diabetes:systematic review and external validation in a Dutch primary care setting

Aims/hypothesis: The aims of this study were to identify all published prognostic models predicting retinopathy risk applicable to people with type 2 diabetes, to assess their quality and accuracy, and to validate their predictive accuracy in a head-to-head comparison using an independent type 2 diabetes cohort. Methods: A systematic search was performed in PubMed and Embase in December 2019. Studies that met the following criteria were included: (1) the model was applicable in type 2 diabetes; (2) the outcome was retinopathy; and (3) follow-up was more than 1 year. Screening, data extraction (using the checklist for critical appraisal and data extraction for systemic reviews of prediction modelling studies [CHARMS]) and risk of bias assessment (by prediction model risk of bias assessment tool [PROBAST]) were performed independently by two reviewers. Selected models were externally validated in the large Hoorn Diabetes Care System (DCS) cohort in the Netherlands. Retinopathy risk was calculated using baseline data and compared with retinopathy incidence over 5 years. Calibration after intercept adjustment and discrimination (Harrell’s C statistic) were assessed. Results: Twelve studies were included in the systematic review, reporting on 16 models. Outcomes ranged from referable retinopathy to blindness. Discrimination was reported in seven studies with C statistics ranging from 0.55 (95% CI 0.54, 0.56) to 0.84 (95% CI 0.78, 0.88). Five studies reported on calibration. Eight models could be compared head-to-head in the DCS cohort (N = 10,715). Most of the models underestimated retinopathy risk. Validating the models against different severities of retinopathy, C statistics ranged from 0.51 (95% CI 0.49, 0.53) to 0.89 (95% CI 0.88, 0.91). Conclusions/interpretation: Several prognostic models can accurately predict retinopathy risk in a population-based type 2 diabetes cohort. Most of the models include easy-to-measure predictors enhancing their applicability. Tailoring retinopathy screening frequency based on accurate risk predictions may increase the efficiency and cost-effectiveness of diabetic retinopathy care. Registration: PROSPERO registration ID CRD42018089122

Magnesium intake and vascular structure and function:the Hoorn Study

PURPOSE: Circulating and dietary magnesium have been shown to be inversely associated with the prevalence of cardiovascular disease (CVD) and mortality in both high and low-risk populations. We aimed to examine the association between dietary magnesium intake and several measures of vascular structure and function in a prospective cohort. METHODS: We included 789 participants who participated in the vascular screening sub-cohort of the Hoorn Study, a population-based, prospective cohort study. Baseline dietary magnesium intake was estimated with a validated food frequency questionnaire and categorised in energy-adjusted magnesium intake tertiles. Several measurements of vascular structure and function were performed at baseline and most measurements were repeated after 8 years of follow-up (n = 432). Multivariable linear and logistic regression was performed to study the cross-sectional and longitudinal associations of magnesium intake and intima-media thickness (IMT), augmentation index (Aix), pulse wave velocity (PWV), flow-mediated dilatation (FMD), and peripheral arterial disease (PAD). RESULTS: Mean absolute magnesium intake was 328 ± 83 mg/day and prior CVD and DM2 was present in 55 and 41% of the participants, respectively. Multivariable regression analyses did not demonstrate associations between magnesium intake and any of the vascular outcomes. Participants in the highest compared to the lowest magnesium intake tertile demonstrated in fully adjusted cross-sectional analyses a PWV of −0.21 m/s (95% confidence interval −1.95, 1.52), a FMD of −0.03% (−0.89, 0.83) and in longitudinal analyses an IMT of 0.01 mm (−0.03, 0.06), an Aix of 0.70% (−1.69, 3.07) and an odds ratio of 0.84 (0.23, 3.11) for PAD CONCLUSION: We did not find associations between dietary magnesium intake and multiple markers of vascular structure and function, in either cross-sectional or longitudinal analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00394-021-02667-0

Osteoporosis and increased risk of fractures

Osteoporosis is a common condition in older people. This condition leads to increased risk of fractures and is associated with morbidity and mortality. The number of patients with osteoporosis will increase significantly in the years to come due to the increasing numbers of older people and increasing life expectancy. This will be accompanied by increasing demand for care and clinical practice will be faced with questions about therapeutic options and the optimal treatment duration for patients with osteoporosis or increased risk of fractures. In this educational article, we are using practical questions to provide an overview of pathophysiology, diagnostics and treatment of osteoporosis and increased risk of fractures

An omics-based machine learning approach to predict diabetes progression:a RHAPSODY study

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA 1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA 1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA 1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA 1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. Data availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch. Graphical Abstract: (Figure presented.).</p