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Background: We aimed to find the best predictive model for 14-day mortality in antibiotic-treated nursing home residents with dementia and pneumonia by first applying an existing model to the recent PneuMonitor study. Second, we evaluated whether model performance improved by revising variables or adding variables related to recent changes in the care for older people.Methods: The original prognostic model included gender, respiratory rate, respiratory difficulty, pulse rate, decreased alertness, fluid intake, eating dependency, and pressure sores. This model was applied to 380 recent pneumonia episodes in nursing home residents with dementia, updated by considering revising and/or adding variables, internally validated using bootstrapping, and transformed into a simplified risk score that can be used in clinical practice. Model performance was evaluated by Hosmer-Lemeshow statistics and calibration graphs to assess calibration; and area under the receiver operating characteristic curve (AUC) to assess discrimination.Results: The newer cohort had lower 14-day mortality and was less often dehydrated or malnourished. Median AUC of the original model over the imputed datasets was 0.76 (interquartile range: 0.76-0.77), compared to 0.80 in the old cohort. Extending the model with dehydration, bowel incontinence, increase in eating dependency and cardiovascular history, while removing pressure sores, improved AUC: 0.80 (interquartile range: 0.80-0.81) after internal validation. Calibration remained adequate (Hosmer-Lemeshow statistic: p = .67).Conclusions: In the newer cohort with less severe illness, model performance of the existing model was adequate, but a new extended model distinguished better between residents at low and high mortality risk. [ABSTRACT FROM AUTHOR]

Background: Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), share many modifiable risk factors and can be prevented using combined prevention programs. Valid risk prediction tools are needed to accurately identify individuals at risk.Objective: We aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases.Design: The previously developed tool is stratified for sex and contains the predictors age, BMI, waist circumference, use of antihypertensives, smoking, family history of myocardial infarction/stroke, and family history of diabetes. This tool was externally validated, evaluating model performance using area under the receiver operating characteristic curve (AUC)-assessing discrimination-and Hosmer-Lemeshow goodness-of-fit (HL) statistics-assessing calibration. The intercept was recalibrated to improve calibration performance.Participants: The risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab).Key Results: Discrimination was acceptable, with an AUC of 0.78 (95% CI 0.75-0.81) in men and 0.78 (95% CI 0.74-0.81) in women. Calibration was poor (HL statistic: p < 0.001), but improved considerably after intercept recalibration. Examination of individual outcomes showed that in men, AUC was highest for CKD (0.85 [95% CI 0.78-0.91]) and lowest for T2D (0.69 [95% CI 0.65-0.74]). In women, AUC was highest for CVD (0.88 [95% CI 0.83-0.94)]) and lowest for T2D (0.71 [95% CI 0.66-0.75]).Conclusions: Validation of our previously developed tool showed robust discriminative performance across populations. Model recalibration is recommended to account for different disease rates. Our risk prediction tool can be useful in large-scale prevention programs for identifying those in need of further risk profiling because of their increased risk for chronic cardiometabolic diseases. [ABSTRACT FROM AUTHOR]

Only a few studies have investigated the metabolic consequences of social jetlag. Therefore, we examined the association of social jetlag with the metabolic syndrome and type 2 diabetes mellitus in a population-based cohort. We used cross-sectional data from the New Hoorn Study cohort (n = 1585, 47% men, age 60.8 ± 6 years). Social jetlag was calculated as the difference in midpoint sleep (in hours) between weekdays and weekend days. Poisson and linear regression models were used to study the associations, and age was regarded as a possible effect modifier. We adjusted for sex, employment status, education, smoking, physical activity, sleep duration, and body mass index. In the total population, we only observed an association between social jetlag and the metabolic syndrome, with prevalence ratios adjusted for sex, employment status, and educational levels of 1.64 (95% CI 1.1-2.4), for participants with >2 h social jetlag, compared with participants with <1 h social jetlag. However, we observed an interaction effect of median age (<61 years). In older participants (≥61 years), no significant associations were observed between social jetlag status, the metabolic syndrome, and diabetes or prediabetes. In the younger group (<61 years), the adjusted prevalence ratios were 1.29 (95% CI 0.9-1.9) and 2.13 (95% CI 1.3-3.4) for the metabolic syndrome and 1.39 (95% CI 1.1-1.9) and 1.75 (95% CI 1.2-2.5) for diabetes/prediabetes, for participants with 1-2 h and >2 h social jetlag, compared with participants with <1 h social jetlag. In conclusion, in our population-based cohort, social jetlag was associated with a 2-fold increased risk of the metabolic syndrome and diabetes/prediabetes, especially in younger (<61 years) participants. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. Methods: Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA S4/F4) were combined to predict HbA1c levels at six year follow-up. Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. Explained variance, discrimination and calibration were assessed using R2, classification tables (comparing highest/lowest 50% HbA1c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM. Results: At baseline, mean HbA1c level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbA1c level was 5.7% (39 mmol/mol). Calibration graphs showed that predicted HbA1c levels were somewhat underestimated in the Inter99 cohort and overestimated in the Hoorn and KORA cohorts, indicating that the model’s intercept should be adjusted for each cohort to improve predictions. Sensitivity and specificity (95% CI) were 55.7% (53.9, 57.5) and 56.9% (55.1, 58.7) respectively, for women, and 54.6% (52.7, 56.5) and 54.3% (52.4, 56.2) for men. External validation showed similar performance in the METSIM cohort. Conclusions/interpretation: In the non-diabetic population, our DIRECT-DETECT prediction model, including readily available predictors, has a relatively low explained variance and moderate discriminative performance, but can help to distinguish between future highest and lowest HbA1c levels. Absolute HbA1c values are cohort-dependent. [ABSTRACT FROM AUTHOR]

Background: Dutch standard diabetes care is generally protocol-driven. However, considering that general practices wish to tailor diabetes care to individual patients and encourage self-management, particularly in light of current COVID-19 related constraints, protocols and other barriers may hinder implementation. The impact of dispensing with protocol and implementation of self-management interventions on patient monitoring and experiences are not known. This study aims to evaluate tailoring of care by understanding experiences of well-organised practices 1) when dispensing with protocol; 2) determining the key conditions for successful implementation of self-management interventions; and furthermore exploring patients' experiences regarding dispensing with protocol and self-management interventions.Methods: in this mixed-methods prospective study, practices (n = 49) were invited to participate if they met protocol-related quality targets, and their adult patients with well-controlled type 2 diabetes were invited if they had received protocol-based diabetes care for a minimum of 1 year. For practices, study participation consisted of the opportunity to deliver protocol-free diabetes care, with selection and implementation of self-management interventions. For patients, study participation provided exposure to protocol-free diabetes care and self-management interventions. Qualitative outcomes (practices: 5 focus groups, 2 individual interviews) included experiences of dispensing with protocol and the implementation process of self-management interventions, operationalised as implementation fidelity. Quantitative outcomes (patients: routine registry data, surveys) consisted of diabetes monitoring completeness, satisfaction, wellbeing and health status at baseline and follow-up (24 months).Results: Qualitative: In participating practices (n = 4), dispensing with protocol encouraged reflection on tailored care and selection of various self-management interventions A focus on patient preferences, team collaboration and intervention feasibility was associated with high implementation fidelity Quantitative: In patients (n = 126), likelihood of complete monitoring decreased significantly after two years (OR 0.2 (95% CI 0.1-0.5), p < 0.001) Satisfaction decreased slightly (- 1.6 (95% CI -2.6;-0.6), p = 0.001) Non-significant declines were found in wellbeing (- 1.3 (95% CI -5.4; 2.9), p = 0.55) and health status (- 3.0 (95% CI -7.1; 1.2), p = 0.16).Conclusions: To tailor diabetes care to individual patients within well-organised practices, we recommend dispensing with protocol while maintaining one structural annual monitoring consultation, combined with the well-supported implementation of feasible self-management interventions. Interventions should be selected and delivered with the involvement of patients and should involve population preferences and solid team collaborations. [ABSTRACT FROM AUTHOR]

Introduction: Pre‐invasive squamous lesions of the central airways can progress into invasive lung cancers. Identifying these high‐risk patients could enable detection of invasive lung cancers at an early stage. In this study, we investigated the value of 18F‐fluorodeoxyglucose (18F‐FDG) positron emission tomography (PET) scans in predicting progression in patients with pre‐invasive squamous endobronchial lesions. Methods: In this retrospective study, patients with pre‐invasive endobronchial lesions, who underwent an 18F‐FDG PET scan at the VU University Medical Center Amsterdam, between January 2000 and December 2016, were included. Autofluorescence bronchoscopy (AFB) was used for tissue sampling and was repeated every 3 months. The minimum and median follow‐up was 3 and 46.5 months. Study endpoints were the occurrence of biopsy proven invasive carcinoma, time‐to‐progression and overall survival (OS). Results: A total number of 40 of 225 patients met the inclusion criteria of which 17 (42.5%) patients had a positive baseline 18F‐FDG PET scan. A total of 13 of 17 (76.5%) developed invasive lung carcinoma during follow‐up, with a median time to progression of 5.0 months (range, 3.0–25.0). In 23 (57.5%) patients with a negative 18F‐FDG PET scan at baseline, 6 (26%) developed lung cancer, with a median time to progression of 34.0 months (range, 14.0–42.0 months, p < 0.002). With a median OS of 56.0 months (range, 9.0–60.0 months) versus 49.0 months (range, 6.0–60.0 months) (p = 0.876) for the 18F‐FDG PET positive and negative groups, respectively. Conclusions: Patients with pre‐invasive endobronchial squamous lesions and a positive baseline 18F‐FDG PET scan were at high‐risk for developing lung carcinoma, highlighting that this patient group requires early radical treatment. [ABSTRACT FROM AUTHOR]

Purpose: Reducing postprandial hyperglycemia has beneficial effects on diabetes-related risk factors, but the magnitude of the reduction needed to achieve such an effect is unknown. The purpose of the study was to quantify the relationship of acute glucose and insulin postprandial responses with longer-term effects on diabetes-related risk factors by performing a systematic review and meta-analysis of dietary intervention studies. Methods: We systematically searched EMBASE and MEDLINE. Dietary intervention studies among any human population aiming to reduce postprandial glycemia, with actual measures of postprandial glucose (PPG) and/or insulin (PPI) as acute exposures (incremental area under the curve, iAUC) as well as markers of glucose metabolism (fasting glucose, HbA1c) and insulin sensitivity (fasting insulin, HOMA-IR) after at least 4 weeks of diet intervention as outcomes were included. Meta-analyses were performed for the effects on acute exposures and on diabetes-related risk factors. The relationship between changes in acute exposures and changes in risk factor outcomes was estimated by meta-regression analyses. Results: Out of the 13,004 screened papers, 13 papers with 14 comparisons were included in the quantitative analysis. The dietary interventions acutely reduced mean PPG [mean difference (MD), − 0.27 mmol/l; 95% CI − 0.41 to − 0.14], but not mean PPI (MD − 7.47 pmol/l; 95% CI − 16.79 to 1.86). There were no significant overall effects on fasting glucose and insulin. HbA1c was reduced by − 0.20% (95% CI − 0.35 to − 0.05). Changes in acute PPG were significantly associated with changes in fasting plasma glucose (FPG) [per 10% change in PPG: β = 0.085 (95% CI 0.003, 0.167), k = 14], but not with fasting insulin [β = 1.20 (95% CI − 0.32, 2.71), k = 12]. Changes in acute PPI were not associated with changes in FPG [per 10% change in PPI: β = − 0.017 (95% CI − 0.056, 0.022), k = 11]. Conclusions: Only a limited number of postprandial glucose-lowering dietary intervention studies measured acute postprandial exposures to PPG/PPI during the interventions. In this small heterogeneous set of studies, an association was found between the magnitude of the acute postprandial responses and the change in fasting glucose, but no other outcomes. More studies are needed to quantify the relationship between acute postprandial changes and long-term effects on risk factors. [ABSTRACT FROM AUTHOR]

Objective: Gastrointestinal adverse effects occur in 20-30% of patients with metformin-treated type 2 diabetes, leading to premature discontinuation in 5-10% of the cases. Gastrointestinal intolerance may reflect localized high concentrations of metformin in the gut. We hypothesized that reduced transport of metformin via the plasma membrane monoamine transporter (PMAT) and organic cation transporter 1 (OCT1) could increase the risk of severe gastrointestinal adverse effects.Research Design and Methods: The study included 286 severe metformin-intolerant and 1,128 metformin-tolerant individuals from the IMI DIRECT (Innovative Medicines Initiative: DIabetes REsearCh on patient straTification) consortium. We assessed the association of patient characteristics, concomitant medication, and the burden of mutations in the SLC29A4 and SLC22A1 genes on odds of intolerance.Results: Women (P < 0.001) and older people (P < 0.001) were more likely to develop metformin intolerance. Concomitant use of transporter-inhibiting drugs increased the odds of intolerance (odds ratio [OR] 1.72, P < 0.001). In an adjusted logistic regression model, the G allele at rs3889348 (SLC29A4) was associated with gastrointestinal intolerance (OR 1.34, P = 0.005). rs3889348 is the top cis-expression quantitative trait locus for SLC29A4 in gut tissue where carriers of the G allele had reduced expression. Homozygous carriers of the G allele treated with transporter-inhibiting drugs had more than three times higher odds of intolerance compared with carriers of no G allele and not treated with inhibiting drugs (OR 3.23, P < 0.001). Use of a genetic risk score derived from rs3889348 and SLC22A1 variants found that the odds of intolerance were more than twice as high in individuals who carry three or more risk alleles compared with those carrying none (OR 2.15, P = 0.01).Conclusions: These results suggest that intestinal metformin transporters and concomitant medications play an important role in the gastrointestinal adverse effects of metformin. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to assess the time to insulin initiation in type 2 diabetes mellitus (T2DM) patients treated with oral glucose-lowering agents and to determine the baseline characteristics associated with time to insulin initiation. This was evaluated in T2DM patients with HbA1c levels consistently ≥7.0% during total follow up and in those with fluctuating HbA1c levels around 7.0%. Design and methods: Prospective, observational study was performed, comprising 2418 persons with T2DM aged ≥40 years who entered the Diabetes Care System between 1998 and 2012 with a minimum follow up of at least 3 years, following the first HbA1c level ≥7.0%. Cox regression analyses were performed to assess the determinants of time to insulin initiation. Data related to long-term effects ofinsulin initiation were studied at baseline and at the end of follow up using descriptive summary statistics. Results: Two-thirds of the patients initiated insulin during follow up. The time to insulin varied from 1.2 years (range 0.3-3.1) in patients with HbA1c levels consistently≥7.0% to 5.4 years (range 3.0-7.5) in patients with fluctuating HbA1c levels around 7.0%. Longer diabetes duration (hazard ratio (HR) 1.04 95% CI 1.03-1.05) and lower age (HR 1.00 95% CI 0.99-1.00) at baseline were associated with a shorter time to initiation. More insulin initiators had retinopathy compared with patients that remained on oral glucose-lowering agents during follow up. Conclusion: The time to insulin initiation was short, and most of the patients with HbA1c levels consistently ≥7.0% were initiating insulin. Longer diabetes duration and younger age shortened the time to insulin. [ABSTRACT FROM AUTHOR]

Stressful life events are associated with the metabolic syndrome in cross-sectional studies, but prospective studies addressing this issue are rare and limited. We therefore evaluated whether the number of stressful life events is associated with incident metabolic syndrome. We assessed the association between the number of stressful life events experienced in the 5 years up until baseline and incident metabolic syndrome after 6.5 years at follow-up in the Hoorn study, a middle-aged and elderly population-based cohort. Participants with prevalent metabolic syndrome at baseline were excluded. Metabolic syndrome was defined according to the Adult Treatment Panel III, including fasting plasma glucose levels, HDL-C levels, triglyceride levels, waist circumference and hypertension. We included 1099 participants (47% male; age 60 ± 7 years). During 6.5 years of follow-up, 238 participants (22%) developed the metabolic syndrome. Logistic regression adjusted for age, sex, education level and follow-up duration showed a positive association between the number of stressful life events at baseline and incident metabolic syndrome [OR 1.13 (1.01–1.27) per event,p = 0.049]. In addition, a Poisson model showed a significant positive association between the number of stressful life events at baseline and the number of metabolic syndrome factors at follow-up [OR 1.05 (1.01–1.11) per event,p = 0.018]. Finally, we observed a significant association between the number of stressful life events at baseline and waist circumference at follow-up [adjusted for confoundersβ0.86 (0.39–1.34) cm per event,p < 0.001]. Overall, we concluded that persons who reported more stressful life events at baseline had a significantly increased risk for developing metabolic syndrome during 6.5 years of follow-up, in a middle-aged and elderly population-based cohort. [ABSTRACT FROM PUBLISHER]

Psychosocial stress is associated with chronic disease. We evaluated whether in the general population the number of stressful life events is associated with risk of mortality and whether this association is mediated by behavioral factors and morbidities. We conducted this study in the Hoorn cohort; a population-based cohort study among older men and women. Our main variable of interest was the number of stressful life events experienced during the previous 5 years, which were assessed by questionnaire. We calculated Cox proportional hazard ratios (HRs) for all-cause and cause-specific mortality during follow-up for those who experienced stressful life events compared to those who did not. We included 2385 participants (46% male; 62 ± 7 years). During 20 years of follow-up 834 (35%) participants died, of whom 239 (28.6%) died of cardiovascular disease. Compared to the group with no stressful life events, the age, sex and socioeconomic status adjusted HRs (with 95% confidence intervals) for all-cause mortality, for the groups who had 1 event, 2 events, 3 events and ≥4 events were 0.89 (0.72–1.09), 1.01 (0.81–1.24), 1.29 (1.00–1.66) and 1.44 (1.08–1.92), respectively. Similar results were observed for cardiovascular mortality. Mediation analysis showed that smoking, prevalent type 2 diabetes and cardiovascular disease were statistically significant mediators of the association between the number of stressful life events and mortality. Having 3 or more stressful life events is associated with a significantly increased risk for mortality in an elderly population-based cohort. This association is mediated by smoking, type 2 diabetes and cardiovascular disease. [ABSTRACT FROM AUTHOR]