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

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

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.]

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Aims/hypothesis: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist’s novel diabetes subgroups and previously analysed by Slieker et al. Methods: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. Results: Subgroups’ risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. Conclusions/interpretation: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators. Graphical Abstract

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

Apolipoprotein-CIII O-Glycosylation, a Link between GALNT2 and Plasma Lipids

Apolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main proteoforms: non-glycosylated (apo-CIII0a), and glycosylated apo-CIII with zero, one, or two sialic acids (apo-CIII0c, apo-CIII1 and apo-CIII2). Our objective is to determine how apo-CIII glycosylation affects lipid traits and type 2 diabetes prevalence, and to investigate the genetic basis of these relations with a genome-wide association study (GWAS) on apo-CIII glycosylation. We conducted GWAS on the four apo-CIII proteoforms in the DiaGene study in people with and without type 2 diabetes (n = 2318). We investigated the relations of the identified genetic loci and apo-CIII glycosylation with lipids and type 2 diabetes. The associations of the genetic variants with lipids were replicated in the Diabetes Care System (n = 5409). Rs4846913-A, in the GALNT2-gene, was associated with decreased apo-CIII0a. This variant was associated with increased high-density lipoprotein cholesterol and decreased triglycerides, while high apo-CIII0a was associated with raised high-density lipoprotein-cholesterol and triglycerides. Rs67086575-G, located in the IFT172-gene, was associated with decreased apo-CIII2 and with hypertriglyceridemia. In line, apo-CIII2 was associated with low triglycerides. On a genome-wide scale, we confirmed that the GALNT2-gene plays a major role i O-glycosylation of apolipoprotein-CIII, with subsequent associations with lipid parameters. We newly identified the IFT172/NRBP1 region, in the literature previously associated with hypertriglyceridemia, as involved in apolipoprotein-CIII sialylation and hypertriglyceridemia. These results link genomics, glycosylation, and lipid metabolism, and represent a key step towards unravelling the importance of O-glycosylation in health and disease.</p

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

Apolipoprotein-CIII O-Glycosylation Is Associated with Micro- and Macrovascular Complications of Type 2 Diabetes

Apolipoprotein-CIII (apo-CIII) inhibits the clearance of triglycerides from circulation and is associated with an increased risk of diabetes complications. It exists in four main proteoforms: O-glycosylated variants containing either zero, one, or two sialic acids and a non-glycosylated variant. O-glycosylation may affect the metabolic functions of apo-CIII. We investigated the associations of apo-CIII glycosylation in blood plasma, measured by mass spectrometry of the intact protein, and genetic variants with micro- and macrovascular complications (retinopathy, nephropathy, neuropathy, cardiovascular disease) of type 2 diabetes in a DiaGene study (n = 1571) and the Hoorn DCS cohort (n = 5409). Mono-sialylated apolipoprotein-CIII (apo-CIII1) was associated with a reduced risk of retinopathy (β = −7.215, 95% CI −11.137 to −3.294) whereas disialylated apolipoprotein-CIII (apo-CIII2) was associated with an increased risk (β = 5.309, 95% CI 2.279 to 8.339). A variant of the GALNT2-gene (rs4846913), previously linked to lower apo-CIII0a, was associated with a decreased prevalence of retinopathy (OR = 0.739, 95% CI 0.575 to 0.951). Higher apo-CIII1 levels were associated with neuropathy (β = 7.706, 95% CI 2.317 to 13.095) and lower apo-CIII0a with macrovascular complications (β = −9.195, 95% CI −15.847 to −2.543). In conclusion, apo-CIII glycosylation was associated with the prevalence of micro- and macrovascular complications of diabetes. Moreover, a variant in the GALNT2-gene was associated with apo-CIII glycosylation and retinopathy, suggesting a causal effect. The findings facilitate a molecular understanding of the pathophysiology of diabetes complications and warrant consideration of apo-CIII glycosylation as a potential target in the prevention of diabetes complications.</p