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 (ß = -7.215, 95% CI -11.137 to -3.294) whereas disialylated apolipoprotein-CIII (apo-CIII2) was associated with an increased risk (ß = 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 (ß = 7.706, 95% CI 2.317 to 13.095) and lower apo-CIII0a with macrovascular complications (ß = -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.

Increased interleukin-6 is associated with higher risk of heart failure in people with type 2 diabetes.

AIMS: We aimed to determine the association between serum interleukin-6 (IL-6) concentrations and new-onset heart failure (HF) in persons with type 2 diabetes (T2D). METHODS AND RESULTS: We performed a case-control study nested in the Diabetes Care System Cohort, a prospective cohort of persons with T2D in primary care. We included 724 participants, of whom 141 developed HF during 5 years of follow-up and 583 were age- and sex-matched controls. IL-6 was measured at baseline and categorized into four groups: Group 1 was composed of participants with IL-6 below the detection limit of 1.5 pg/mL, and the remainder were divided into tertiles. We performed logistic regression analyses with categorized IL-6 or continuous IL-6 as the determinant and new-onset HF as the outcome adjusted for follow-up time, age, sex, glycated haemoglobin, estimated glomerular filtration rate, albumin/creatinine ratio, and cardiovascular disease at baseline. Effect modification by sex was tested. Participants were 70.7 ± 9.0 years, and 38% were women. In comparison with Group 1, all tertiles were associated with an increased risk of HF with odds ratios of 2.1 [95% confidence interval (CI): 1.2-2.9], 2.8 (95% CI: 2.0-3.7), and 2.1 (95% CI: 1.3-3.0), respectively, for Tertiles 1-3. Continuous IL-6 was associated with the development of HF with an odds ratio of 1.2 (95% CI: 1.0-1.5). No effect modification by sex was observed. CONCLUSIONS: Higher IL-6 levels are associated with the development of HF in persons with T2D. Further research should determine whether IL-6-lowering interventions could prevent the development of HF.

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, HbA1c 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, HbA1c, 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 HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) 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 .

Is the association between social jetlag and BMI mediated by lifestyle? A cross-sectional survey study in the Dutch general population.

OBJECTIVE: Social jetlag is a discordance between the social and biological rhythm and is associated with higher HbA1c, higher BMI, and higher odds of obesity. The pathways that could explain these associations are still debated. This study aims to assess the mediating role of several lifestyle factors in the cross-sectional association between social jetlag and BMI. METHODS: We used cross-sectional data from 1784 adults from urban areas in the Netherlands, collected in 2019. Social jetlag (difference in midpoint of sleep between week and weekend nights) was categorized as low(<1 h), moderate(1-2h), and high(>2 h). BMI(kg/m2) was calculated from self-reported height and weight. The association between social jetlag and BMI was assessed using linear regression, adjusted for sex, age, education, and sleep duration and stratified for the effect modifier stress (high vs. low). Mediation analysis was performed for self-reported smoking, physical activity, alcohol consumption, and adherence to a healthy diet. RESULTS: High social jetlag was associated with higher BMI (0.69 kg/m2,95%CI 0.05;1.33). This association was stronger in people with high stress (0.93 kg/m2,95%CI 0.09;1.76). Social jetlag was also associated with higher odds of smoking, lower physical activity, higher alcohol consumption, and lower healthy diet adherence. In people with high stress, these factors mediated 10-15% of the association between social jetlag and BMI. CONCLUSIONS: Social jetlag is associated with higher BMI and this association is stronger in people with high stress. In people with high stress, healthy diet adherence mediated 12% of this association. Other pathways involved in this association should be further investigated.

Prolongation of the QTc interval is associated with an increased risk of cardiovascular diseases: The Hoorn study.

BACKGROUND/OBJECTIVE: Prolonged heart rate-corrected QT interval (QTc) on the electrocardiogram (ECG) is maybe associated with the occurrence of cardiovascular diseases (CVD), but the evidence is inconsistent. Therefore, we investigated whether baseline prolongation of the QTc interval is associated with CVD morbidity and mortality and its subtypes and whether glucose tolerance modifies this association in a population-based cohort study with a mean follow-up of 10.8 years. METHODS: We analyzed a glucose tolerance stratified sample (N = 487) from the longitudinal population-based Hoorn Study cohort (age 64 ± 7 years, 48% female). Cox regression was used to investigate the association between sex-specific baseline QTc quartiles and CVD morbidity and mortality. The risk was also estimated per 10 ms increase in QTc. All analyses were adjusted for age, sex, smoking status, systolic blood pressure, prevalent CVD, glucose tolerance status, hypertension and total cholesterol. In addition, stratified analyses were conducted for glucose tolerance status. RESULTS: During a mean follow-up of 10.8 years, 351 CVD events were observed. The adjusted hazard ratios (95% CI) for each 10 ms increase in QTc interval were 1.06 (95% CI: 1.02-1.10) for CVD, 1.06 (95% CI: 0.97-1.15) for acute myocardial infarction, 1.07 (95% CI: 1.01-1.13) for stroke, 1.12 (95% CI: 1.06-1.19) for heart failure, 1.04 (95% CI: 0.96-1.12) for peripheral arterial disease and 1.01 (95% CI:0.95-1.08) for coronary heart disease. Glucose tolerance status did not modify the association (P > 0.2). CONCLUSION/INTERPRETATION: Prolongation of the QTc interval is associated with morbidity and mortality due to general CVD. Glucose tolerance status did not modify these associations.

Anticholinergic burden measures, symptoms, and fall-associated risk in older adults with polypharmacy: Development and validation of a prognostic model.

BACKGROUND: Anticholinergic 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 FINDINGS: To 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. CONCLUSIONS: The 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.

The DIAbetes MANagement and Treatment (DIAMANT) Cohort.

Introduction: The increasing number of people with diabetes and the unclear long-term safety and effectiveness of newer and older blood-glucose-lowering treatments emphasize the need for more pharmaco-epidemiological studies in this field. A prospective, regularly updated cohort of people with diabetes would provide quick and up-to-date information regarding prevalence, treatment, safety and effectiveness. The current aim was to describe the design of the DIAbetes MANagement and Treatment (DIAMANT) cohort. Methods: The DIAMANT cohort is a population-based, dynamic, prospective cohort of persons with diabetes. It contains real-world data (RWD) from general practitioners (GP), including diagnoses, symptoms, examinations, communication to/from specialists and medication. Diabetes is defined as a recorded diabetes diagnosis or a prescription of drugs used in diabetes. The cohort is part of the national infrastructure of "Stichting Informatievoorziening voor Zorg en Onderzoek" (STIZON) and is linked to other data sources. Results: Currently, the cohort enables access to information of 89,883 patients in 2004 to 344,914 in 2020 (6% T1D, 84% T2D and 10% unclassified type of diabetes), with 193,931 participants still registered as being present in the GP practice (active) in 2020. The frequency of follow-up of persons with diabetes is practice dependent. The Dutch guidelines advise 2-4 contacts per year with a more extensive yearly check-up. The DIAMANT cohort is updated several times a year. Anonymised data from the DIAMANT cohort are available to researchers. Discussion: The DIAMANT cohort provides the opportunity to gain RWD insights into the treatment and outcomes among people with diabetes in daily general practice. The data can be enriched by established linkages to other data sources (eg, hospital data, the Perinatal Registry, the Cancer Registry). The DIAMANT cohort serves as a start of a national infrastructure to study, manage and provide personalised care in order to ultimately improve care and outcomes for people with diabetes.

Causal Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Peripheral Artery Disease: A Mendelian Randomization Approach.

Background We investigated the causal associations between the genetic liability to cardiovascular and lifestyle risk factors and peripheral artery disease (PAD), using a Mendelian randomization approach. Methods and Results We performed a 2-sample inverse-variance weighted Mendelian randomization analysis, multiple sensitivity analyses to assess pleiotropy and multivariate Mendelian randomization analyses to assess mediating/confounding factors. European-ancestry genomic summary data (P<5×10-8) for type 2 diabetes, lipid-fractions, smoking, alcohol and coffee consumption, physical activity, sleep, and education level were selected. Genetic associations with PAD were extracted from the Million-Veteran-Program genome-wide association studies (cases=31 307, controls=211 753, 72% European-ancestry) and the GoLEAD-SUMMIT genome-wide association studies (11 independent genome-wide association studies, European-ancestry, cases=12 086, controls=449 548). Associations were categorized as robust (Bonferroni-significant (P<0.00294), consistent over PAD-cohorts/sensitivity analyses), suggestive (P value: 0.00294-0.05, associations in 1 PAD-cohort/inconsistent sensitivity analyses) or not present. Robust evidence for genetic liability to type 2 diabetes, smoking, insomnia, and inverse associations for higher education level with PAD were found. Suggestive evidence for the genetic liability to higher low-density lipoprotein cholesterol, triglyceride-levels, alcohol consumption, and inverse associations for high-density lipoprotein cholesterol, and increased sleep duration were found. No associations were found for physical activity and coffee consumption. However, effects fully attenuated for low-density lipoprotein cholesterol and triglycerides after correcting for apoB, and for insomnia after correcting for body mass index and lipid-fractions. Nonsignificant attenuation by potential mediators was observed for education level and type 2 diabetes. Conclusions Detrimental effects of smoking and type 2 diabetes, but not of low-density lipoprotein cholesterol and triglycerides, on PAD were confirmed. Lower education level and insomnia were identified as novel risk factors for PAD; however, complete mediation for insomnia and incomplete mediation for education level by downstream risk factors was observed.

The longitudinal association between chronic stress and (visceral) obesity over seven years in the general population: The Hoorn Studies.

BACKGROUND: We aimed to study the mediating role of diet quality, physical activity, smoking, and alcohol intake in the association of stressful life events with visceral obesity over a seven-year period and assessed effect modification by sex and SES. METHODS: In total, 2416 participants with a mean age of 56.1 (±7.3) years, of which 51.4% were women, and 12.5% had a lower educational level from the Hoorn studies were followed for seven years. Stress was measured with a 'Serious Life Events' questionnaire, which was summed into a total score (range zero to ten events) and stratified to account for nonlinearity. Changes in visceral obesity were assessed by changes in BMI (kg/m2) and waist circumference (cm) in seven years. We used the product of coefficient approach to assess mediation of the following lifestyle factors: diet, physical activity, smoking, and alcohol intake. We analyzed associations between stressful life events and change in BMI and waist circumference with linear regression models. RESULTS: Within the low education group, we observed a significant association between ≥3 stressful life events and a change in BMI (0.60 kg/m2 (CI: 0.05, 1.14)) and waist circumference (2.23 cm (CI: 0.19, 4.48)), compared to experiencing no events. For both BMI and waist circumference, no significant associations were observed when experiencing 1 or 2 events. In the moderate to high education group, we observed only statistically significant associations for waist circumference when experiencing ≥3 stressful life events (0.86 cm (CI: 0.05, 1.41)) and not for the other event groups. Our mediation analyses showed that the proportion mediated by smoking was 13.2%, while the other lifestyle factors showed no mediating effect. CONCLUSIONS: Multiple stressful life events are associated with an increase in waist circumference and BMI in those with lower education. Smoking might play a mediating role in this association.

Sex differences in the longitudinal relationship of low-grade inflammation and echocardiographic measures in the Hoorn and FLEMENGHO Study.

BACKGROUND: This 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. METHODS: Biomarkers 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. RESULTS: Overall 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) %. CONCLUSION: Our 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.

Predicting negative health outcomes in older general practice patients with chronic illness: Rationale and development of the PROPERmed harmonized individual participant data database.

The prevalence of multimorbidity and polypharmacy increases significantly with age and are associated with negative health consequences. However, most current interventions to optimize medication have failed to show significant effects on patient-relevant outcomes. This may be due to ineffectiveness of interventions themselves but may also reflect other factors: insufficient sample sizes, heterogeneity of population. To address this issue, the international PROPERmed collaboration was set up to obtain/synthesize individual participant data (IPD) from five cluster-randomized trials. The trials took place in Germany and The Netherlands and aimed to optimize medication in older general practice patients with chronic illness. PROPERmed is the first database of IPD to be drawn from multiple trials in this patient population and setting. It offers the opportunity to derive prognostic models with increased statistical power for prediction of patient-relevant outcomes resulting from the interplay of multimorbidity and polypharmacy. This may help patients from this heterogeneous group to be stratified according to risk and enable clinicians to identify patients that are likely to benefit most from resource/time-intensive interventions. The aim of this manuscript is to describe the rationale behind PROPERmed collaboration, characteristics of the included studies/participants, development of the harmonized IPD database and challenges faced during this process.

A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy.

OBJECTIVES: To develop and validate a prognostic model to predict deterioration in health-related quality of life (dHRQoL) in older general practice patients with at least one chronic condition and one chronic prescription. STUDY DESIGN AND SETTING: We used individual participant data from five cluster-randomized trials conducted in the Netherlands and Germany to predict dHRQoL, defined as a decrease in EQ-5D-3 L index score of ≥5% after 6-month follow-up in logistic regression models with stratified intercepts to account for between-study heterogeneity. The model was validated internally and by using internal-external cross-validation (IECV). RESULTS: In 3,582 patients with complete data, of whom 1,046 (29.2%) showed deterioration in HRQoL, and 12/87 variables were selected that were related to single (chronic) conditions, inappropriate medication, medication underuse, functional status, well-being, and HRQoL. Bootstrap internal validation showed a C-statistic of 0.71 (0.69 to 0.72) and a calibration slope of 0.88 (0.78 to 0.98). In the IECV loop, the model provided a pooled C-statistic of 0.68 (0.65 to 0.70) and calibration-in-the-large of 0 (-0.13 to 0.13). HRQoL/functionality had the strongest prognostic value. CONCLUSION: The model performed well in terms of discrimination, calibration, and generalizability and might help clinicians identify older patients at high risk of dHRQoL. REGISTRATION: PROSPERO ID: CRD42018088129.

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

Phenotypic and lifestyle determinants of HbA1c in the general population-The Hoorn Study.

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.

Are Anticholinergic Symptoms a Risk Factor for Falls in Older General Practice Patients With Polypharmacy? Study Protocol for the Development and Validation of a Prognostic Model.

Background: Cumulative anticholinergic exposure, also known as anticholinergic burden, is associated with a variety of adverse outcomes. However, studies show that anticholinergic effects tend to be underestimated by prescribers, and anticholinergics are the most frequently prescribed potentially inappropriate medication in older patients. The grading systems and drugs included in existing scales to quantify anticholinergic burden differ considerably and do not adequately account for patients' susceptibility to medications. Furthermore, their ability to link anticholinergic burden with adverse outcomes such as falls is unclear. This study aims to develop a prognostic model that predicts falls in older general practice patients, to assess the performance of several anticholinergic burden scales, and to quantify the added predictive value of anticholinergic symptoms in this context. Methods: Data from two cluster-randomized controlled trials investigating medication optimization in older general practice patients in Germany will be used. One trial (RIME, n = 1,197) will be used for the model development and the other trial (PRIMUM, n = 502) will be used to externally validate the model. A priori, candidate predictors will be selected based on a literature search, predictor availability, and clinical reasoning. Candidate predictors will include socio-demographics (e.g. age, sex), morbidity (e.g. single conditions), medication (e.g. polypharmacy, anticholinergic burden as defined by scales), and well-being (e.g. quality of life, physical function). A prognostic model including sociodemographic and lifestyle-related factors, as well as variables on morbidity, medication, health status, and well-being, will be developed, whereby the prognostic value of extending the model to include additional patient-reported symptoms will be also assessed. Logistic regression will be used for the binary outcome, which will be defined as "no falls" vs. "≥1 fall" within six months of baseline, as reported in patient interviews. Discussion: As the ability of different anticholinergic burden scales to predict falls in older patients is unclear, this study may provide insights into their relative importance as well as into the overall contribution of anticholinergic symptoms and other patient characteristics. The results may support general practitioners in their clinical decision-making and in prescribing fewer medications with anticholinergic properties.

High Diabetes Distress Among Ethnic Minorities Is Not Explained by Metabolic, Cardiovascular, or Lifestyle Factors: Findings From the Dutch Diabetes Pearl Cohort.

OBJECTIVE: Diabetes distress among patients from ethnic minorities is still poorly understood. We investigated the association between ethnicity and diabetes distress among ethnic minority groups of people with type 2 diabetes in the Netherlands, focusing on the possible effects of glycemic control, lifestyle factors, cardiovascular risk factors, and diabetes complications. RESEARCH DESIGN AND METHODS: Cross-sectional data from the Dutch Diabetes Pearl cohort included people with type 2 diabetes from primary, secondary, and tertiary diabetes care programs. We used the 20-item Problem Areas in Diabetes Survey (PAID) scale to assess diabetes distress; a score ≥40 is considered to represent high distress. Ethnicity was estimated on the basis of country of birth. Sociodemographic and lifestyle data were self-reported; cardiovascular and metabolic data were retrieved from medical charts. Logistic regression analysis determined the association between ethnicity and diabetes distress, with Caucasians as the reference group. RESULTS: Diabetes distress scores and ethnicity were available for 4,191 people with type 2 diabetes: 3,684 were Caucasian, 83 were Asian, 51 were Moroccan, 92 were African, 134 were Latin American, 46 were Turkish, and 101 were Hindustani-Surinamese. Overall, participants in minority groups had worse health outcomes than those of Caucasian descent, and diabetes distress was more prevalent (ranging from 9.6 to 31.7%, compared with 5.8% among Caucasians), even after adjusting for age, sex, education level, alcohol use, smoking, BMI, lipid profile, HbA1c, medication use, and the presence of diabetes complications. CONCLUSIONS: Among people with type 2 diabetes in the Netherlands, ethnicity is independently associated with high diabetes distress. Further research is warranted to explain the higher prevalence of diabetes distress in minority groups and to develop effective interventions.

Screening for sexual dissatisfaction among people with type 2 diabetes in primary care.

AIMS: The identification and discussion of sexual care needs in people with type 2 diabetes mellitus (T2DM) in primary care is currently insufficient. The objective of this study was to determine the prevalence of sexual dissatisfaction, sexual problems and need for help by using a screening instrument among people with T2DM in primary care. METHODS: Data were collected in 45 general practices in the Netherlands from January 2015 to February 2016. The Brief Sexual Symptom Checklist (BSSC) was used to screen among 40-75 year old men and women. RESULTS: In total, 786 people with T2DM (66.5% men) were screened. The prevalence of sexual dissatisfaction was 36.6%, significantly higher among men than among women (41.1% vs. 27.8%). Sexually dissatisfied men most often reported erectile dysfunction (71.6%); for sexually dissatisfied women, low sexual desire (52.8%) and lubrication problems (45.8%) were most common. More than half of all dissatisfied people had a need for care (61.8%), significantly more men than women (66.8% vs. 47.2%). CONCLUSIONS: One third of people with T2DM is sexually dissatisfied and more than half of these people report a need for help. The BSSC could be used a tool to proactively identify sexually dissatisfied people in primary care.

The effectiveness of optimised clinical medication reviews for geriatric patients: Opti-Med a cluster randomised controlled trial.

Background: Inappropriate drug use is a frequent problem in older patients and associated with adverse clinical outcomes and an important determinant of geriatric problems. Clinical medication reviews (CMR) may reduce inappropriate drug use. Objective: The aim of this study is to investigate the effectiveness of CMR on quality of life (QoL) and geriatric problems in comparison with usual care in older patients with geriatric problems in the general practice. Methods: We performed a cluster randomised controlled trial in 22 Dutch general practices. Patients of ≥65 years were eligible if they newly presented with pre-specified geriatric symptoms in general practice and the chronic use of ≥1 prescribed drug. The intervention consisted of CMRs which were prepared by an independent expert team and discussed with the patient by the general practitioner. Primary outcomes: QoL and the presence of self-reported geriatric problems after a follow-up period of 6 months. Results: 518 patients were included. No significant differences between the intervention and control group and over time were found for QoL, geriatric problems, satisfaction with medication and self-reported medication adherence. After 6 months the percentage of solved Drug Related Problems (DRPs) was significantly higher in the intervention group compared to the control group [B 22.6 (95%CI 14.1-31.1), P < 0.001]. Conclusion: The study intervention did not influence QoL and geriatric problems. The higher percentage of solved DRPs in the intervention group did not result in effects on the patient's health. CMRs on a large scale seem not meaningful and should be reconsidered.

Information on actual medication use and drug-related problems in older patients: questionnaire or interview?

BACKGROUND: Information on medication use and drug-related problems is important in the preparation of clinical medication reviews. Critical information can only be provided by patients themselves, but interviewing patients is time-consuming. Alternatively, patient information could be obtained with a questionnaire. OBJECTIVE: In this study the agreement between patient information on medication use and drug-related problems in older patients obtained with a questionnaire was compared with information obtained during an interview. SETTING: General practice in The Netherlands. METHOD: A questionnaire was developed to obtain information on actual medication use and drug-related problems. Two patient groups ≥65 years were selected based on general practitioner electronic medical records in nine practices; I. polypharmacy and II. ≥1 predefined general geriatric problems. Eligible patients were asked to complete the questionnaire and were interviewed afterwards. MAIN OUTCOME MEASURE: Agreement on information on medication use and drug-related problems collected with the questionnaire and interview was calculated. RESULTS: Ninety-seven patients participated. Of all medications used, 87.6 % (95 % CI 84.7-90.5) was reported identically in the questionnaire and interview. Agreement for the complete medication list was found for 45.4 % (95 % CI 35.8-55.3) of the patients. On drug-related problem level, agreement between questionnaire and interview was 75 %. Agreement tended to be lower in vulnerable patients characterized by ≥4 chronic diseases, ≥10 medications used and low health literacy. CONCLUSION: Information from a questionnaire showed reasonable agreement compared with interviewing. The patients reported more medications and drug-related problems in the interview than the questionnaire. Taking the limitations into account, a questionnaire seems a suitable tool for medication reviews that may replace an interview for most patients.