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BackgroundAnticholinergic burden has been associated with adverse outcomes such as falls. To date, no gold standard measure has been identified to assess anticholinergic burden, and no conclusion has been drawn on which of the different measure algorithms best predicts falls in older patients from general practice. This study compared the ability of five measures of anticholinergic burden to predict falls. To account for patients' individual susceptibility to medications, the added predictive value of typical anticholinergic symptoms was further quantified in this context.Methods and findingsTo predict falls, models were developed and validated based on logistic regression models created using data from two German cluster-randomized controlled trials. The outcome was defined as "≥ 1 fall" vs. "no fall" within a 6-month follow-up period. Data from the RIME study (n = 1,197) were used in model development, and from PRIMUM (n = 502) for external validation. The models were developed step-wise in order to quantify the predictive ability of anticholinergic burden measures, and anticholinergic symptoms. In the development set, 1,015 patients had complete data and 188 (18.5%) experienced ≥ 1 fall within the 6-month follow-up period. The overall predictive value of the five anticholinergic measures was limited, with neither the employed anticholinergic variable (binary / count / burden), nor dose-dependent or dose-independent measures differing significantly in their ability to predict falls. The highest c-statistic was obtained using the German Anticholinergic Burden Score (0.73), whereby the optimism-corrected c-statistic was 0.71 after interval validation using bootstrapping and 0.63 in the external validation. Previous falls and dizziness / vertigo had the strongest prognostic value in all models.ConclusionsThe ability of anticholinergic burden measures to predict falls does not appear to differ significantly, and the added value they contribute to risk classification in fall-prediction models is limited. Previous falls and dizziness / vertigo contributed most to model performance.

BackgroundMany Muslims with diabetes choose to fast against medical advice during Ramadan, potentially increasing their risk of acute complications. Patients are often reluctant to disclose fasting to their health care providers, and their needs regarding Ramadan are not met in consultations. For healthcare professionals to provide patient-centred care, it is important to gain more insight into patients' decision-making process. This study therefore aims to explore how Muslims with diabetes decide whether to fast during Ramadan.MethodsA qualitative study was conducted consisting of 15 focus groups with Muslims with diabetes within a constructivist paradigm. Convenience sampling was used. All focus groups were transcribed verbatim and analyzed using Braun and Clarke's reflexive thematic analysis.ResultsFour themes were found to be important in the decision on whether to fast: (1) values and beliefs concerning Ramadan, (2) experiences and emotions concerning Ramadan, (3) the perception of illness, and (4) advice from health care professionals, imams and family. Many participants indicated fasting against medical advice and trusting their subjective assessments on whether they could fast. Moreover, three main stages in the decision-making process for eventually refraining from fasting were identified: (1) the stage where positive experiences with fasting dominate, (2) the stage where one encounters challenges but their determination to fast prevails and (3) the stage where one decides to refrain from fasting after experiencing too many physical difficulties with fasting.ConclusionsMuslims with diabetes experience autonomy in their decisions on Ramadan fasting. The decision to refrain from fasting often resulted from a difficult and dynamic decision-making process and was often made after participants reached their physical limits. These findings highlight the importance of not only shared decision-making to empower patients to make well-informed decisions on Ramadan fasting but also pre-Ramadan diabetes education to help people with diabetes have a safe Ramadan.

BackgroundThis study aimed to determine the within-person and between-persons associations of low-grade inflammation (LGI) and endothelial dysfunction (ED) with echocardiographic measures related to diastolic dysfunction (DD) in two general populations and whether these associations differed by sex.MethodsBiomarkers and echocardiographic measures were measured at both baseline and follow-up in the Hoorn Study (n = 383) and FLEMENGHO (n = 491). Individual biomarker levels were combined into either a Z-score of LGI (CRP, SAA, IL-6, IL-8, TNF-α and sICAM-1) or ED (sICAM-1, sVCAM-1, sE-selectin and sTM). Mixed models were used to determine within-person and between-persons associations of biomarker Z-scores with left ventricular ejection fraction (LVEF), left ventricular mass index (LVMI) and left atrial volume index (LAVI). These associations were adjusted for a-priori selected confounders.ResultsOverall Z-scores for LGI or ED were not associated with echocardiographic measures. Effect modification by sex was apparent for ED with LVEF in both cohorts (P-for interaction = 0.08 and 0.06), but stratified results were not consistent. Effect modification by sex was apparent for TNF-α in the Hoorn Study and E-selectin in FLEMENGHO with LVEF (P-for interaction≤0.05). In the Hoorn Study, women whose TNF-α levels increased with 1-SD over time had a decrease in LVEF of 2.2 (-4.5;0.01) %. In FLEMENGHO, men whose E-selectin levels increased with 1-SD over time had a decrease in LVEF of 1.6 (-2.7;-0.5) %.ConclusionOur study did not show consistent associations of LGI and ED with echocardiographic measures. Some evidence of effect modification by sex was present for ED and specific biomarkers.

ObjectivesTo assess the interobserver agreement in categories of electrocardiogram (ECG) abnormalities using the Minnesota Code criteria.MethodsWe used a random sample of 180 ECGs from people with type 2 diabetes. ECG abnormalities were classified and coded using the Minnesota ECG Classification. Each ECG was independently rated on several abnormalities by an experienced rater (rater 1) and by two cardiologists (raters 2 and 3) trained to apply the Minnesota codes on four Minnesota codes; 1-codes as an indication for myocardial infarction, 4 en 5-codes as an indication for ischemic abnormalities, 3-codes as an indication for left ventricle hypertrophy, 7-1-codes as an indication for ventricular conduction abnormalities, and 8-3-codes as an indication for atrial fibrillation / atrial flutter. After all pairwise tables were summed, the overall agreement, the specific positive and negative agreement were calculated with a 95% confidence interval (CI) for each abnormality. Also, Kappa's with a 95% CI were calculated.ResultsThe overall agreement (with 95% CI) were for myocardial infarction, ischemic abnormalities, left ventricle hypertrophy, conduction abnormalities and atrial fibrillation/atrial flutter respectively: 0.87 (0.84-0.91), 0.79 (0.74-0.84), 0.81 (0.76-0.85), 0.93 (0.90-0.95), 0.96 (0.93-0.97).ConclusionThis study shows that the overall agreement of the Minnesota code is good to excellent.

AIM:To investigate the relative contribution of phenotypic and lifestyle factors to HbA1c, independent of fasting plasma glucose (FPG) and 2h post-load glucose (2hPG), in the general population. METHODS:The study populations included 2309 participants without known diabetes from the first wave of the Hoorn Study (1989) and 2619 from the second wave (2006). Multivariate linear regression models were used to analyze the relationship between potential determinants and HbA1c in addition to FPG and 2hPG. The multivariate model was derived in the first wave of the Hoorn Study, and replicated in the second wave. RESULTS:In both cohorts, independent of FPG and 2hPG, higher age, female sex, larger waist circumference, and smoking were associated with a higher HbA1c level. Larger hip circumference, higher BMI, higher alcohol consumption and vitamin C intake were associated with a lower HbA1c level. FPG and 2hPG together explained 41.0% (first wave) and 53.0% (second wave) of the total variance in HbA1c. The combination of phenotypic and lifestyle determinants additionally explained 5.7% (first wave) and 3.9% (second wave). CONCLUSIONS:This study suggests that, independent of glucose, phenotypic and lifestyle factors are associated with HbA1c, but the contribution is relatively small. These findings contribute to a better understanding of the low correlation between glucose levels and HbA1c in the general population.

AimSubclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D.MethodsThe study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders.ResultsAt baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose.ConclusionsDifferent glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk.

Falls can have devastating consequences for older people. We determined the relationship between the likelihood of fall incidents and daily-life behavior. We used wearable sensors to assess habitual physical activity and daily-life gait quality (in terms of e.g. stability, variability, smoothness and symmetry), and determined their predictive ability for time-to-first-and-second-falls. 319 older people wore a trunk accelerometer (Dynaport MoveMonitor, McRoberts) during one week. Participants further completed questionnaires and performed grip strength and trail making tests to identify risk factors for falls. Their prospective fall incidence was followed up for six to twelve months. We determined interrelations between commonly used gait characteristics to gain insight in their interpretation and determined their association with time-to-falls. For all data -including questionnaires and tests- we determined the corresponding principal components and studied their predictive ability for falls. We showed that gait characteristics of walking speed, stride length, stride frequency, intensity, variability, smoothness, symmetry and complexity were often moderately to highly correlated (r > 0.4). We further showed that these characteristics were predictive of falls. Principal components dominated by history of falls, alcohol consumption, gait quality and muscle strength proved predictive for time-to-fall. The cross-validated prediction models had adequate to high accuracy (time dependent AUC of 0.66-0.72 for time-to-first-fall and 0.69-0.76 for -second-fall). Daily-life gait quality obtained from a single accelerometer on the trunk is predictive for falls. These findings confirm that ambulant measurements of daily behavior contribute substantially to the identification of elderly at (high) risk of falling.

Physical function is a crucial factor in the prevention and treatment of health conditions in older adults and is usually measured objectively with physical performance tests and/or physical activity monitoring.To examine whether 1) physical performance (PP) and physical activity (PA) constitute separate domains of physical function; 2) differentiation of PA classes is more informative than overall PA.Cross-sectional study to explore the relationships within and among PP and PA measures.In 49 older participants (83 ± 7 years; M ± SD), performance-based tests were conducted and PA was measured for one week. Activity monitor data were reduced in terms of duration, periods, and mean duration of periods of lying, sitting, standing and locomotion. The relation between and within PP scores and PA outcomes were analysed using rank order correlation and factor analysis.Factor structure after varimax rotation revealed two orthogonal factors explaining 78% of the variance in the data: one comprising all PA variables and one comprising all PP variables. PP scores correlated moderately with PA in daily life. Differentiation of activity types and quantification of their duration, intensity and frequency of occurrence provided stronger associations with PP, as compared to a single measure of acceleration expressing overall PA.For independent validation, the conclusions about the validity of the presented conceptual framework and its clinical implications need to be confirmed in other studies.PP and PA represent associated but separate domains of physical function, suggesting that an improvement of PP does not automatically imply an increase of PA, i.e. a change to a more active lifestyle. Differentiation of activity classes in the analysis of PA provides more insights into PA and its association with PP than using a single overall measure of acceleration.

Incidence rates of non-hip major osteoporotic fractures (MOF) remain poorly characterized in the Netherlands. The Dutch FRAX® algorithm, which predicts 10-year probabilities of hip fracture and MOF (first of hip, humerus, forearm, clinical vertebral), therefore incorporates imputed MOF rates. Swedish incidence rate ratios for hip fracture to MOF (Malmo 1987-1996) were used to perform this imputation. However, equality of these ratios between countries is uncertain and recent evidence is scarce. Aims were to estimate incidence rates of hip fracture and MOF and to compare observed MOF rates to those predicted by the imputation method for the Netherlands.Using hospitalisation and general practitioner records from the Dutch PHARMO Database Network (2002-2011) we calculated age-and-sex-specific and age-standardized incidence rates (IRs) of hip and other MOFs (humerus, forearm, clinical vertebral) and as used in FRAX®. Observed MOF rates were compared to those predicted among community-dwelling individuals ≥50 years by the standardized incidence ratio (SIR; 95% CI).Age-standardized IRs (per 10,000 person-years) of MOF among men and women ≥50 years were 25.9 and 77.0, respectively. These numbers were 9.3 and 24.0 for hip fracture. Among women 55-84 years, observed MOF rates were significantly higher than predicted (SIR ranged between 1.12-1.50, depending on age). In men, the imputation method performed reasonable.Observed MOF incidence was higher than predicted for community-dwelling women over a wide age-range, while it agreed reasonable for men. As miscalibration may influence treatment decisions, there is a need for confirmation of results in another data source. Until then, the Dutch FRAX® output should be interpreted with caution.

OBJECTIVES:The objective of this study was to determine the prevalence of pAmpC beta-lactamases in community-acquired Gram negative bacteria in the Netherlands, and to identify possible risk factors for carriage of these strains. METHODS:Fecal samples were obtained from community-dwelling volunteers. Participants also returned a questionnaire for analysis of risk factors. Screening for pAmpC was performed with selective enrichment broth and a selective screening agar. Confirmation of AmpC-production was performed with two double disc combination tests: cefotaxime and ceftazidime with either boronic acid or cloxacillin as inhibitor. Multiplex PCR was used as gold standard for detection of pAmpC. 16S rRNA PCR and AFLP were performed as required, plasmids were identified by PCR-based replicon typing. Questionnaire results were analyzed with SPSS, version 20.0. RESULTS:Fecal samples were obtained from 550 volunteers; mean age 51 years (range: 18-91), 61% were females. pAmpC was present in seven E. coli isolates (7/550, 1.3%, 0.6-2.7 95% CI): six CMY-2-like pAmpC and one DHA. ESBL-encoding genes were found in 52/550 (9.5%, 7.3-12.2 95% CI) isolates; these were predominantly blaCTX-M genes. Two isolates had both ESBL and pAmpC. Admission to a hospital in the previous year was the only risk factor we identified. CONCLUSIONS:Our data indicate that the prevalence of pAmpC in the community seems still low. However, since pAmpC-producing isolates were not identified as ESBL producers by routine algorithms, there is consistent risk that further increase of their prevalence might go undetected.

Aim: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. Methods: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. Results: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1–3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18–1.92) for subgroup 2 and 1.88 (-0.08–3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. Conclusions: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk. [ABSTRACT FROM AUTHOR]

Aim: To investigate the relative contribution of phenotypic and lifestyle factors to HbA1c, independent of fasting plasma glucose (FPG) and 2h post-load glucose (2hPG), in the general population. Methods: The study populations included 2309 participants without known diabetes from the first wave of the Hoorn Study (1989) and 2619 from the second wave (2006). Multivariate linear regression models were used to analyze the relationship between potential determinants and HbA1c in addition to FPG and 2hPG. The multivariate model was derived in the first wave of the Hoorn Study, and replicated in the second wave. Results: In both cohorts, independent of FPG and 2hPG, higher age, female sex, larger waist circumference, and smoking were associated with a higher HbA1c level. Larger hip circumference, higher BMI, higher alcohol consumption and vitamin C intake were associated with a lower HbA1c level. FPG and 2hPG together explained 41.0% (first wave) and 53.0% (second wave) of the total variance in HbA1c. The combination of phenotypic and lifestyle determinants additionally explained 5.7% (first wave) and 3.9% (second wave). Conclusions: This study suggests that, independent of glucose, phenotypic and lifestyle factors are associated with HbA1c, but the contribution is relatively small. These findings contribute to a better understanding of the low correlation between glucose levels and HbA1c in the general population. [ABSTRACT FROM AUTHOR]