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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 18 F-fluorodeoxyglucose ( 18 F-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 18 F-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 18 F-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 18 F-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  18 F-FDG PET positive and negative groups, respectively., Conclusions: Patients with pre-invasive endobronchial squamous lesions and a positive baseline 18 F-FDG PET scan were at high-risk for developing lung carcinoma, highlighting that this patient group requires early radical treatment., (© 2023 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

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., (© 2021. The Author(s).)

Background: Structured primary diabetes care within a collectively supported setting is associated with better monitoring of biomedical and lifestyle-related target indicators amongst people with type 2 diabetes and with better HbA1c levels. Whether socioeconomic status affects the delivery of care in terms of monitoring and its association with HbA1c levels within this approach, is unclear. This study aims to understand whether, within a structured care approach, (1) socioeconomic categories differ concerning diabetes monitoring as recommended; (2) socioeconomic status modifies the association between monitoring as recommended and HbA1c., Methods: Observational real-life cohort study with primary care registry data from general practitioners within diverse socioeconomic areas, who are supported with the implementation of structured diabetes care. People with type 2 diabetes mellitus were offered quarterly diabetes consultations. "Monitoring as recommended" by professional guidelines implied minimally one annual registration of HbA1c, systolic blood pressure, LDL, BMI, smoking behaviour and physical activity. Regarding socioeconomic status, deprived, advantageous urban and advantageous suburban categories were compared to the intermediate category concerning (a) recommended monitoring; (b) association between recommended monitoring and HbA1c., Results: Aim 1 (n = 13 601 people): Compared to the intermediate socioeconomic category, no significant differences in odds of being monitored as recommended were found in the deprived (OR 0.45 (95% CI 0.19-1.08)), advantageous urban (OR 1.27 (95% CI 0.46-3.54)) and advantageous suburban (OR 2.32 (95% CI 0.88-6.08)) categories. Aim 2 (n = 11 164 people): People with recommended monitoring had significantly lower HbA1c levels than incompletely monitored people (-2.4 (95% CI -2.9; -1.8) mmol/mol). SES modified monitoring-related HbA1c differences, which were significantly higher in the deprived (-3.3 (95% CI -4.3; -2.4) mmol/mol) than the intermediate category (-1.3 (95% CI -2.2; -0.4) mmol/mol)., Conclusions: Within a structured diabetes care setting, socioeconomic status is not associated with recommended monitoring. Socioeconomic differences in the association between recommended monitoring and HbA1c levels advocate further exploration of practice and patient-related factors contributing to appropriate monitoring and for care adjustment to population needs., (© 2020 The Authors. International Journal of Clinical Practice published by John Wiley & Sons Ltd.)

Background: Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups., Objectives: We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands., Methods: A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77-0.92 in Dutch, 0.66-0.83 in South Asian Surinamese, 0.70-0.82 in African Surinamese, 0.61-0.81 in Ghanaian, 0.69-0.86 in Turkish, and 0.69-0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one., Conclusions: Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in., Competing Interests: The author(s) has/have no competing interests to declare., (Copyright: © 2021 The Author(s).)

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.

Objective: Whether care group participation by general practitioners improves delivery of diabetes care is unknown. Using 'monitoring of biomedical and lifestyle target indicators as recommended by professional guidelines' as an operationalisation for quality of care, we explored whether (1) in new practices monitoring as recommended improved a year after initial care group participation (aim 1); (2) new practices and experienced practices differed regarding monitoring (aim 2)., Design: Observational, real-life cohort study., Setting: Primary care registry data from Eerstelijns Zorggroep Haaglanden (ELZHA) care group., Participants: Aim 1: From six new practices (n=538 people with diabetes) that joined care group ELZHA in January 2014, two practices (n=211 people) were excluded because of missing baseline data; four practices (n=182 people) were included. Aim 2: From all six new practices (n=538 people), 295 individuals were included. From 145 experienced practices (n=21 465 people), 13 744 individuals were included., Exposure: Care group participation includes support by staff nurses on protocolised diabetes care implementation and availability of a system providing individual monitoring information. 'Monitoring as recommended' represented minimally one annual registration of each biomedical (HbA1c, systolic blood pressure, low-density lipoprotein) and lifestyle-related target indicator (body mass index, smoking behaviour, physical exercise)., Primary Outcome Measures: Aim 1: In new practices, odds of people being monitored as recommended in 2014 were compared with baseline (2013). Aim 2: Odds of monitoring as recommended in new and experienced practices in 2014 were compared., Results: Aim 1: After 1-year care group participation, odds of being monitored as recommended increased threefold (OR 3.00, 95% CI 1.84 to 4.88, p<0.001). Aim 2: Compared with new practices, no significant differences in the odds of monitoring as recommended were found in experienced practices (OR 1.21, 95% CI 0.18 to 8.37, p=0.844)., Conclusions: We observed a sharp increase concerning biomedical and lifestyle monitoring as recommended after 1-year care group participation, and subsequently no significant difference between new and experienced practices-indicating that providing diabetes care within a collective approach rapidly improves registration of care., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Aims/hypothesis: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up)., Methods: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe., Results: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m 2 ; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m 2 ; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l., Conclusions/interpretation: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.

Context: Antibodies to the 65 kD isoform of glutamic acid decarboxylase (GAD65) have been associated with incident Type 2 Diabetes Mellitus, however results are inconsistent., Objective: To assess the association between GAD65 antibody positivity and incident Type 2 Diabetes Mellitus in a non-diabetic adult (≥18 years) population, in a systematic review and meta-analysis., Data Sources: A systematic literature search was conducted in Pubmed (MEDLINE) and Embase until January 14th, 2019., Study Selection: Included studies were 1) prospective studies on the association between GAD65 antibodies and incident Type 2 Diabetes Mellitus; 2) in a non-diabetic adult (≥18 years) population. To strengthen the review, unpublished data from 1302 Hoorn Study participants were included., Data Extraction: Data extraction and quality assessment were performed independently by two observers. Ten studies were rated for methodological quality and seven were pooled using a random-effects meta-analysis, of which 2 strong, 2 moderate and 3 of low methodological quality., Data Synthesis: The pooled risk estimate of incident Type 2 Diabetes Mellitus for GAD65 antibody positivity, compared to GAD65 antibody negativity was 3.36 (95% CI: 1.9-5.9). This result was robust to sensitivity analyses. Heterogeneity between studies was significant with I 2 statistic of 79% (p < 0.0001). However, excluding one study showed a decrease of I 2 to 19% (p < 0.0001), explaining a large part of the heterogeneity., Conclusion: GAD65 antibody positivity was associated with an increased risk of future Type 2 Diabetes Mellitus in adults., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (© 2019 by the American Diabetes Association.)

Objective: Management of type 2 diabetes mellitus (T2DM) requires frequent monitoring of patients. Within a collective care group setting, doubts on the clinical effects of registration are a barrier for full adoption of T2DM registration in general practice. We explored whether full monitoring of biomedical and lifestyle-related target indicators within a care group approach is associated with lower HbA 1c levels., Design: Observational, real-life cohort study., Setting: Primary care data registry from the Hadoks (EerstelijnsZorggroepHaaglanden) care group., Exposure: The care group provides general practitioners collectively with organisational support to facilitate structured T2DM primary care. Patients are offered quarterly medical and lifestyle-related consultation., Main Outcome Measure: Full monitoring of each target indicator in patients with T2DM which includes minimally one measure of HbA 1c level, systolic blood pressure, LDL, BMI, smoking behaviour and physical exercise between January and December 2014; otherwise, patients were defined as 'incompletely monitored'. HbA 1c levels of 8137 fully monitored and 3958 incompletely monitored patients were compared, adjusted for the confounders diabetes duration, age and gender. Since recommended HbA 1c values depend on age, medication use and diabetes duration, analyses were stratified into three HbA 1c profile groups. Linear multilevel analyses enabled adjustment for general practice., Results: Compared with incompletely monitored patients, fully monitored patients had significantly lower HbA 1c levels (95% CI) in the first (-2.03 [-2.53 to -1.52] mmol/mol) (-0.19% [-0.23% to -0.14%]), second (-3.36 [-5.28 to -1.43] mmol/mol) (-0.31% [-0.48% to -0.13%]) and third HbA 1c profile group (-1.89 [-3.76 to -0.01] mmol/mol) (-0.17% [-0.34% to 0.00%])., Conclusions/interpretation: This study shows that in a care group setting, fully monitored patients had significantly lower HbA 1c levels compared with incompletely monitored patients. Since this difference might have considerable clinical impact in terms of T2DM-related risks, this might help general practices in care group settings to overcome barriers on adequate registration and thus improve structured T2DM primary care. From population health management perspective, we recommend a systematic approach to adjust the structured care protocol for incompletely monitored subgroups., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives/hypothesis: To develop a prediction model that can accurately predict the chance of success following revision stapes surgery in patients with recurrent or persistent otosclerosis at 2- to 6-months follow-up and to validate this model internally., Study Design: A retrospective cohort study of prospectively gathered data in a tertiary referral center., Methods: The associations of 11 prognostic factors with treatment success were tested in 705 cases using multivariable logistic regression analysis with backward selection. Success was defined as a mean air-bone gap closure to 10 dB or less. The most relevant predictors were used to derive a clinical prediction rule to determine the probability of success. Internal validation by means of bootstrapping was performed. Model performance indices, including the Hosmer-Lemeshow test, the area under the receiver operating characteristics curve (AUC), and the explained variance were calculated., Results: Success was achieved in 57.7% of cases at 2- to 6-months follow-up. Certain previous surgical techniques, primary causes of failure leading up to revision stapes surgery, and positions of the prosthesis placed during revision surgery were associated with higher success percentages. The clinical prediction rule performed moderately well in the original dataset (Hosmer-Lemeshow P = .78; AUC = 0.73; explained variance = 22%), which slightly decreased following internal validation by means of bootstrapping (AUC = 0.69; explained variance = 13%)., Conclusions: Our study established the importance of previous surgical technique, primary cause of failure, and type of the prosthesis placed during the revision surgery in predicting the probability of success following stapes surgery at 2- to 6-months follow-up., Level of Evidence: 2b. Laryngoscope, 128:2390-2396, 2018., (© 2018 The American Laryngological, Rhinological and Otological Society, Inc.)

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.

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.

Purpose: People with type 2 diabetes (T2D) have a doubled morbidity and mortality risk compared with persons with normal glucose tolerance. Despite treatment, clinical targets for cardiovascular risk factors are not achieved. The Hoorn Diabetes Care System cohort (DCS) is a prospective cohort representing a comprehensive dataset on the natural course of T2D, with repeated clinical measures and outcomes. In this paper, we describe the design of the DCS cohort., Participants: The DCS consists of persons with T2D in primary care from the West-Friesland region of the Netherlands. Enrolment in the cohort started in 1998 and this prospective dynamic cohort currently holds 12 673 persons with T2D., Findings to Date: Clinical measures are collected annually, with a high internal validity due to the centrally organised standardised examinations. Microvascular complications are assessed by measuring kidney function, and screening feet and eyes. Information on cardiovascular disease is obtained by 1) self-report, 2) electrocardiography and 3) electronic patient records. In subgroups of the cohort, biobanking and additional measurements were performed to obtain information on, for example, lifestyle, depression and genomics. Finally, the DCS cohort is linked to national cancer and all-cause mortality registers. A selection of published findings from the DCS includes identification of subgroups with distinct development of haemoglobin A1c, blood pressure and retinopathy, and their predictors; validation of a prediction model for personalised retinopathy screening; the assessment of the role of genetics in development and treatment of T2D, providing options for personalised medicine., Future Plans: We will continue with the inclusion of persons with newly diagnosed T2D, follow-up of persons in the cohort and linkage to morbidity and mortality registries. Currently, we are involved in (inter)national projects on, among others, biomarkers and prediction models for T2D and complications and we are interested in collaborations with external researchers., Trial Registration: ISRCTN26257579., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

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., Competing Interests: The Novo Nordisk funding did not influence the scientific work, or have any influence on design, data collection, analyses or interpretation of the results. We can confirm that this does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Our aim was to study the prevalence of self-reported hypoglycaemic sensations and its association with mortality in patients with type 2 diabetes (T2D) treated with insulin in usual care., Methods: Demographics, clinical characteristics and mortality data were obtained from 1667 patients with T2D treated with insulin in the Hoorn Diabetes Care System Cohort (DCS), a prospective cohort study using clinical care data. Self-reported hypoglycaemic sensations were defined as either mild: events not requiring help; or severe: events requiring help from others (either medical assistance or assistance of others). The association between hypoglycaemic sensations and mortality was analysed using logistic regression analysis., Results: At baseline, 981 patients (59%) reported no hypoglycaemic sensations in the past year, 612 (37%) reported only mild sensations and 74 (4%) reported severe hypoglycaemic sensations. During a median follow-up of 1.9 years, 98 patients (5.9%) died. Reporting only mild hypoglycaemic sensations was associated with a lower mortality risk (OR 0.48, 95% CI 0.28 to 0.80), while reporting severe sensations was not significantly associated with mortality (OR 0.76, 95% CI 0.33 to 1.80), compared with reporting no hypoglycaemic sensations, and adjusting for demographic and clinical characteristics. Sensitivity analyses showed an OR of 1.38 (95% CI 0.31 to 6.11) for patients reporting severe hypoglycaemic sensations requiring medical assistance., Conclusions: Self-reported hypoglycaemic sensations are highly prevalent in our insulin-treated T2D population. Patients reporting hypoglycaemic sensations not requiring medical assistance did not have an increased risk of mortality, suggesting that these sensations are not an indicator of increased short-term mortality risk in patients with T2D., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Objective: To evaluate whether a model that was previously developed to predict 14-day mortality for nursing home residents with dementia and lower respiratory tract infection who received antibiotics could be applied to residents who were not treated with antibiotics. Specifically, in this same data set, to update the model using recalibration methods; and subsequently examine the historical, geographical, methodological and spectrum transportability through external validation of the updated model., Design: 1 cohort study was used to develop the prediction model, and 4 cohort studies from 2 countries were used for the external validation of the model., Setting: Nursing homes in the Netherlands and the USA., Participants: 157 untreated residents were included in the development of the model; 239 untreated residents were included in the external validation cohorts., Outcome: Model performance was evaluated by assessing discrimination: area under the receiver operating characteristic curves; and calibration: Hosmer and Lemeshow goodness-of-fit statistics and calibration graphs. Further, reclassification tables allowed for a comparison of patient classifications between models., Results: The original prediction model applied to the untreated residents, who were sicker, showed excellent discrimination but poor calibration, underestimating mortality. Adjusting the intercept improved calibration. Recalibrating the slope did not substantially improve the performance of the model. Applying the updated model to the other 4 data sets resulted in acceptable discrimination. Calibration was inadequate only in one data set that differed substantially from the other data sets in case-mix. Adjusting the intercept for this population again improved calibration., Conclusions: The discriminative performance of the model seems robust for differences between settings. To improve calibration, we recommend adjusting the intercept when applying the model in settings where different mortality rates are expected. An impact study may evaluate the usefulness of the two prediction models for treated and untreated residents and whether it supports decision-making in clinical practice., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

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 of insulin 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., (© 2016 European Society of Endocrinology.)

Aims: In order to eventually improve blood pressure (BP) management, the aim of this study was to identify subgroups of type 2 diabetes mellitus (T2DM) patients with distinct trajectories of SBP levels. Identifying subgroups with distinct SBP trajectories helps to better understand the course of SBP levels in T2DM patients and its associated consequences. Subgroup characteristics were determined and the prevalence of complications and mortality rates over time in the different subgroups was investigated., Methods: Five thousand, seven hundred and eleven T2DM patients with at least two SBP follow-up measurements were selected from a prospective T2DM cohort of 9849 T2DM patients. The mean follow-up period was 5.7 years (range 2-9 years). Latent Class Growth Modeling, as currently the most flexible cluster analysis available, was performed to identify subgroups of patients with distinct SBP trajectories. Subgroup characteristics were determined by multinomial logistic regression analyses., Results: Four subgroups with distinct SBP trajectories were identified. The largest subgroup (85.6%) showed adequate SBP control (at or around 140 mmHg) over time. The second subgroup (5.6%) were hypertensive in the first years, responded slowly to BP management and eventually reached SBP control. The third subgroup (3.4%) showed deteriorating hypertension during the first 4 years, then showed insufficient response to BP management. The fourth subgroup (5.4%) showed deteriorating hypertension over time. Patients within subgroups 2-4 were significantly older, comprised more women, used more antihypertensive medication and had a higher prevalence of retinopathy, microalbuminuria and cardiovascular disease (CVD) mortality., Conclusion: More than 85% reached and maintained adequate SBP control. Subgroups with a more unfavourable course of SBP control also showed higher rates of microvascular complications and CVD mortality over time. This study identified important subgroups to target in order to improve BP management in T2DM patients.

Aims: The aim of this study was to identify subgroups of type 2 diabetes mellitus patients with distinct hemoglobin A1c (HbA1c) trajectories. Subgroup characteristics were determined and the prevalence of microvascular complications over time was investigated., Study Design and Setting: Data from a cohort of 5,423 type 2 diabetes patients from a managed primary care system were used [mean follow-up 5.7 years (range 2-9 years)]. Latent class growth modeling was used to identify subgroups of patients with distinct HbA1c trajectories. Multinomial logistic regression analyses were conducted to determine which characteristics were associated with different classes., Results: Four subgroups were identified. The first and largest subgroup (83 %) maintained good glycemic control over time (HbA1c ≤53 mmol/mol), the second subgroup (8 %) initially showed severe hyperglycemia, but reached the recommended HbA1c target within 2 years. Patients within this subgroup had significantly higher baseline HbA1c levels but were otherwise similar to the good glycemic control group. The third subgroup (5 %) showed hyperglycemia and a delayed response without reaching the recommended HbA1c target. The fourth subgroup (3.0 %) showed deteriorating hyperglycemia over time. Patients within the last two subgroups were significantly younger, had higher HbA1c levels and a longer diabetes duration at baseline. These subgroups also showed a higher prevalence of retinopathy and microalbuminuria., Conclusion: Four subgroups with distinct HbA1c trajectories were identified. More than 90 % reached and maintained good glycemic control (subgroup one and two). Patients within the two subgroups that showed a more unfavorable course of glycemic control were younger, had higher HbA1c levels and a longer diabetes duration at baseline.

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 confounders β 0.86 (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.

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., (Copyright © 2014 Elsevier Ltd. All rights reserved.)