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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.

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.

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.

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.

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/m2; 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/m2; 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. Electronic supplementary material The online version of this article (10.1007/s00125-019-4906-1) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

ObjectiveManagement 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 HbA1clevels.DesignObservational, real-life cohort study.SettingPrimary care data registry from the Hadoks (EerstelijnsZorggroepHaaglanden) care group.ExposureThe 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 measureFull monitoring of each target indicator in patients with T2DM which includes minimally one measure of HbA1clevel, systolic blood pressure, LDL, BMI, smoking behaviour and physical exercise between January and December 2014; otherwise, patients were defined as ’incompletely monitored'. HbA1clevels of 8137 fully monitored and 3958 incompletely monitored patients were compared, adjusted for the confounders diabetes duration, age and gender. Since recommended HbA1cvalues depend on age, medication use and diabetes duration, analyses were stratified into three HbA1cprofile groups. Linear multilevel analyses enabled adjustment for general practice.ResultsCompared with incompletely monitored patients, fully monitored patients had significantly lower HbA1clevels (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 HbA1cprofile group (−1.89 [−3.76 to −0.01] mmol/mol) (−0.17% [−0.34% to 0.00%]).Conclusions/interpretationThis study shows that in a care group setting, fully monitored patients had significantly lower HbA1clevels 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.

ObjectiveThe 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 methodsProspective, 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.ResultsTwo-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.ConclusionThe 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.

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.

ObjectiveWhether 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).DesignObservational, real-life cohort study.SettingPrimary care registry data from Eerstelijns Zorggroep Haaglanden (ELZHA) care group.ParticipantsAim 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.ExposureCare 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 measuresAim 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.ResultsAim 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, pAim 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).ConclusionsWe 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.

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.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 theWomen (These results suggest that intestinal metformin transporters and concomitant medications play an important role in the gastrointestinal adverse effects of metformin.

Objectives20-30% of patients with metformin treated type 2 diabetes experience gastrointestinal side effects leading to premature discontinuation in 5-10% of the cases. Gastrointestinal intolerance may reflect localised high concentrations of metformin in the gut. We hypothesized that reduced transport of metformin into the circulation via the plasma membrane monoamine transporter (PMAT) and organic cation transporter 1 (OCT1) could increase the risk of severe GI side effects.Research Design and MethodsThe study included 286 severe metformin intolerant and 1128 tolerant individuals from the IMI DIRECT consortium. We assessed the association of patient characteristics, concomitant medication and the burden of mutations in the SLC29A4 and SLC22A1, genes that encode PMAT and OCT1, respectively, on odds of metformin intolerance using a logistic regression model.ResultsWomen (p < 0.001) and older people (p < 0.001) were more likely to develop metformin intolerance. Concomitant use of metformin transporter inhibiting drugs increased the odds of intolerance by more than 70% (OR = 1.72 [1.26-2.32], p < 0.001). In a logistic regression model adjusted for age, sex, weight and population substructure, the G allele at rs3889348 (SLC29A4) was associated with GI intolerance (OR = 1.34[1.09-1.65], p = 0.005). rs3889348 is the top cis-eQTL for SLC29A4 in gut tissue where carriers of the G allele had reduced expression. Homozygous carriers of the G allele treated with metformin transporter inhibiting drugs had over three times higher odds of intolerance compared to carriers of no G allele and not treated with inhibiting drugs (OR = 3.23 [1.71-6.39], p < 0.001). Using a genetic risk score (GRS) derived from SLC29A4 (rs3889348) and previously reported SLC22A1 variants (M420del, R61C, G401S), the odds of intolerance was more than twice in individuals who carry three or more risk alleles compared with those carrying none (OR = 2.15 [1.20-4.12], p = 0.01).ConclusionsThese results suggest that intestinal metformin transporters and concomitant medications play an important role in gastrointestinal side effects of metformin.

/SummaryBackground and aims:Understanding the aetiology, clinical presentation and prognosis of type 2 diabetes (T2D) and optimizing its treatment might be facilitated by biomarkers that help predict a person’s susceptibility to the risk factors that cause diabetes or its complications, or response to treatment. The IMI DIRECT (Diabetes Research on Patient Stratification) Study is a European Union (EU) Innovative Medicines Initiative (IMI) project that seeks to test these hypotheses in two recently established epidemiological cohorts. Here, we describe the characteristics of these cohorts at baseline and at the first main follow-up examination (18-months).Materials and methods:From a sampling-frame of 24,682 European-ancestry adults in whom detailed health information was available, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm and enrolled into a prospective cohort study (n=2127) undertaken at four study centres across Europe (Cohort 1: prediabetes). We also recruited people from clinical registries with recently diagnosed T2D (n=789) into a second cohort study (Cohort 2: diabetes). The two cohorts were studied in parallel with matched protocols. Endogenous insulin secretion and insulin sensitivity were modelled from frequently sampled 75g oral glucose tolerance (OGTT) in Cohort 1 and with mixed-meal tolerance tests (MMTT) in Cohort 2. Additional metabolic biochemistry was determined using blood samples taken when fasted and during the tolerance tests. Body composition was assessed using MRI and lifestyle measures through self-report and objective methods.Results:Using ADA-2011 glycaemic categories, 33% (n=693) of Cohort 1 (prediabetes) had normal glucose regulation (NGR), and 67% (n=1419) had impaired glucose regulation (IGR). 76% of the cohort was male, age=62(6.2) years; BMI=27.9(4.0) kg/m2; fasting glucose=5.7(0.6) mmol/l; 2-hr glucose=5.9(1.6) mmol/l [mean(SD)]. At follow-up, 18.6(1.4) months after baseline, fasting glucose=5.8(0.6) mmol/l; 2-hr OGTT glucose=6.1(1.7) mmol/l [mean(SD)]. In Cohort 2 (diabetes): 65% (n=508) were lifestyle treated (LS) and 35% (n=271) were lifestyle + metformin treated (LS+MET). 58% of the cohort was male, age=62(8.1) years; BMI=30.5(5.0) kg/m2; fasting glucose=7.2(1.4)mmol/l; 2-hr glucose=8.6(2.8) mmol/l [mean(SD)]. At follow-up, 18.2(0.6) months after baseline, fasting glucose=7.8(1.8) mmol/l; 2-hr MMTT glucose=9.5(3.3) mmol/l [mean(SD)].Conclusion:The epidemiological IMI DIRECT cohorts are the most intensely characterised prospective studies of glycaemic deterioration to date. Data from these cohorts help illustrate the heterogeneous characteristics of people at risk of or with T2D, highlighting the rationale for biomarker stratification of the disease - the primary objective of the IMI DIRECT consortium.Abbreviations:ASATAbdominal subcutaneous adipose tissueDIRECTDiabetes Research on Patient StratificationEUEuropean UnionMMTTMixed-meal tolerance testMRIMagnetic resonance imaginghpfVMHigh-pass filtered vector magnitudeIAATIntra-abdominal adipose tissueIGRImpaired glucose regulationIMIInnovative Medicines InitiativeMEmultiechoNGRNormal glucose regulationOGTTOral glucose tolerance testPAPhysical activityTAATTotal abdominal adipose tissueT2DType 2 Diabetes

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 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.

We conducted the first prospective observational study in which we examined the association between incretin responses to an oral glucose tolerance test (OGTT) and mixed meal test (MMT) at baseline and changes in fasting glucose levels 7 years later, in individuals who were non-diabetic at baseline. We used data from the Hoorn Meal Study; a population-based cohort study among 121 subjects, aged 61.0±6.7y. GIP and GLP-1 responses were determined at baseline and expressed as total and incremental area under the curve (tAUC and iAUC). The association between incretin response at baseline and changes in fasting glucose levels was assessed using linear regression. The average change in glucose over 7 years was 0.43 ± 0.5 mmol/l. For GIP, no significant associations were observed with changes in fasting glucose levels. In contrast, participants within the middle and highest tertile of GLP-1 iAUC responses to OGTT had significantly smaller increases (actually decreases) in fasting glucose levels; -0.28 (95% confidence interval: -0.54;-0.01) mmol/l and -0.39 (-0.67;-0.10) mmol/l, respectively, compared to those in the lowest tertile. The same trend was observed for tAUC GLP-1 following OGTT (highest tertile: -0.32 (0.61;-0.04) mmol/l as compared to the lowest tertile). No significant associations were observed for GLP-1 responses following MMT. In conclusion, within our non-diabetic population-based cohort, a low GLP-1 response to OGTT was associated with a steeper increase in fasting glucose levels during 7 years of follow-up. This suggests that a reduced GLP-1 response precedes glucose deterioration and may play a role in the etiology of type 2 diabetes mellitus.

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

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 2 h social jetlag, compared with participants with

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

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.

ImportanceEvidence for an acute effect of physical activity on cognitive performance within the school setting is limited. The purpose of this study was to gain insight into acute effects of a short physical activity bout on selective attention in primary school children, specifically in the school setting.MethodsHundred and twenty three 10–11 years old children, 49.6% girls, engaged in four experimental breaks in random order: 1 h of regular cognitive school tasks followed by a 15 min episode with the following conditions 1) ‘no break’ (continuing a cognitive task), 2) passive break (listening to a story), 3) moderate intensity physical activity break (jogging, passing, dribbling) and 4) vigorous intensity physical activity break (running, jumping, skipping). Selective attention in the classroom was assessed by the TEA-Ch test before and after the 15 min break in each condition.ResultsAfter the passive break, the moderate intensity physical activity break and the vigorous intensity physical activity break attention scores were significantly better (p ConclusionThe results show a significant positive effect of both a passive break as well as a physical activity break on selective attention, with the largest effect of a moderate intensity physical activity break. This suggests that schools could implement a moderate intensity physical activity break during the school day to optimize attention levels and thereby improve school performance.Trial registrationNTR2386.

Aims/hypothesisTo develop a prediction model that can predict HbA1 c levels after six years in the non-diabetic general population, including previously used readily available predictors.MethodsData from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA 54/F4) were combined to predict HbAl c 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 R-2, classification tables (comparing highest/lowest 50% HbA1 c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM.ResultsAt baseline, mean HbAlc level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbAlc 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/interpretationIn 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.

Aims: Individual indicators of socio-economic status have been associated with glycaemic control in people with Type 2 diabetes, but little is known about the association between partner’s socio-economic status and HbA1c levels. We therefore examined the cross-sectional association between individual and partner’s level of occupation on HbA1c levels in people with Type 2 diabetes in the Netherlands. Methods: We included people with Type 2 diabetes with a partner who were treated in primary, secondary and tertiary care in the Diabetes Pearl cohort. Occupational level was classified according to International Standard Classification of Occupations (ISCO)-08 skill levels. Linear regression analyses were performed stratified for sex, and corrected for age, recruitment centre and diabetes medication. Results: In total, 3257 participants (59.8% men, mean 62.2±9.4 years) were included. For men, having a partner with an intermediate level of occupation was associated with lower HbA1c levels [e.g. ISCO level 3: –2 mmol/mol (95% CI –4;–1) or -0.2% (95% CI –0.4;–0.1)], compared with having a partner of the highest occupational level (ISCO level 4). In women, having an unemployed partner was associated with higher HbA1c levels [14 mmol/mol (95% CI 6; 22) or 1.3% (95% CI 0.6; 2.0)], compared with having a partner of the highest occupational level. Conclusions: Partner's occupational status provided additional information on the association between socio-economic status and HbA1c levels in people with Type 2 diabetes. Women seemed to benefit from a partner with a higher occupational status, while men seemed to benefit from a partner with a lower status. Because of the cross-sectional nature of the present study, more research is necessary to explore this association.

Objective. With depression being present in approximately 20% of people with type 2 diabetes mellitus (T2DM), we expect equally frequent prescription of antidepressants, anxiolytics, and hypnotics. Nevertheless, prescription data in people with T2DM is missing and the effect of depression on glycaemic control is contradictory. The aim of this study was to assess the prevalence of antidepressants, anxiolytics, and/or hypnotics use in a large, managed, primary care system cohort of people with T2DM and to determine the sociodemographic characteristics, comorbidities, T2DM medication, and metabolic control associated with its use. Method. The prevalence of antidepressants, anxiolytics, and/or hypnotics use in the years 2007–2012 was assessed in the Hoorn Diabetes Care System Cohort from the Netherlands. Results. From the 7016 people with T2DM, 500 people (7.1%) used antidepressants only, 456 people (6.5%) used anxiolytics and/or hypnotics only, and 254 people (3.6%) used a combination. Conclusion. We conclude that in our managed, primary care system 17% of all people with T2DM used antidepressants, anxiolytics, and/or hypnotics. Users of antidepressants, anxiolytics, and/or hypnotics were more often female, non-Caucasian, lower educated, and more often treated with insulin.

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.

Contains fulltext : 171230.pdf (Publisher’s version ) (Open Access) 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.

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.

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 140mmHg) 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.

Low socio-economic status (SES) is associated with increased risk of type 2 diabetes. Most studies however only include 1 indicator of SES: education, income or occupation. The aim of our study is to evaluate the strength of the association between education, income, occupation or the combination of all 3 SES indicators and diabetes risk in the general population.

The DETECT-2 risk score is a simple screening tool for prediction of incident type 2 diabetes (based on fasting and/or Oral Glucose Tolerance Test glucose levels). Recently, glycated haemoglobin (HbA1c) levels have been added to diabetes diagnostic criteria. Our study evaluates whether predictors from the DETECT-2 score could be used to predict HbA1c levels after 6 years.

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.