SARS-CoV-2 vaccination uptake in six ethnic groups living in Amsterdam, the Netherlands: A registry-based study within the HELIUS cohort.

OBJECTIVE: Ethnic minority groups have experienced a disproportionate burden of COVID-19, and should therefore be especially encouraged to receive SARS-CoV-2 vaccination. This study compared first-dose uptake of the primary SARS-CoV-2 vaccination series across six ethnic groups in Amsterdam, the Netherlands in 2021. METHODS: We analyzed data from participants of the population-based HELIUS cohort. We linked their data to the SARS-CoV-2 vaccination registry data of the Public Health Service of Amsterdam. We included registry data from January 6, 2021 (the start of the Dutch vaccination campaign) until September 6, 2021 (a date by which all adults in the Netherlands could have received one or two vaccine doses). SARS-CoV-2 vaccination uptake was defined as having received at least one vaccine dose of the primary vaccination series. We examined the association between ethnicity and vaccination uptake using multivariable logistic regression, while accounting for the age and sex distribution of ethnic groups in Amsterdam. RESULTS: We included 19,006 participants (median age 53 years [interquartile range 41-62], 57% female). SARS-CoV-2 vaccination uptake was highest in the South-Asian Surinamese group (60.3%, 95%CI = 58.2-62.3%), followed by the Dutch (59.6%, 95%CI = 58.0-61.1%), Ghanaian (54.1%, 95%CI = 51.7-56.5%), Turkish (47.7%, 95%CI = 45.9-49.6%), African Surinamese (43.0%, 95%CI = 41.2-44.7%), and Moroccan (35.8%, 95%CI = 34.1-37.5%) groups. After adjusting for age, sex, perceived social support, and presence of relevant comorbidities, participants of African Surinamese, Ghanaian, Turkish and Moroccan origin were significantly less likely to be vaccinated than those of Dutch origin. CONCLUSIONS: Prevention strategies should continue tailoring to specific ethnic groups to encourage vaccination uptake and reduce barriers to vaccination.

Intent to vaccinate against SARS-CoV-2 and its determinants across six ethnic groups living in Amsterdam, the Netherlands: A cross-sectional analysis of the HELIUS study.

BACKGROUND: Ethnic minority groups experience a disproportionately high burden of infections, hospitalizations and mortality due to COVID-19, and therefore should be especially encouraged to receive SARS-CoV-2 vaccination. This study aimed to investigate the intent to vaccinate against SARS-CoV-2, along with its determinants, in six ethnic groups residing in Amsterdam, the Netherlands. METHODS: We analyzed data of participants enrolled in the population-based multi-ethnic HELIUS cohort, aged 24 to 79 years, who were tested for SARS-CoV-2 antibodies and answered questions on vaccination intent from November 23, 2020 to March 31, 2021. During the study period, SARS-CoV-2 vaccination in the Netherlands became available to individuals working in healthcare or > 75 years old. Vaccination intent was measured by two statements on a 7-point Likert scale and categorized into low, medium, and high. Using ordinal logistic regression, we examined the association between ethnicity and lower vaccination intent. We also assessed determinants of lower vaccination intent per ethnic group. RESULTS: A total of 2,068 participants were included (median age 56 years, interquartile range 46-63). High intent to vaccinate was most common in the Dutch ethnic origin group (369/466, 79.2%), followed by the Ghanaian (111/213, 52.1%), South-Asian Surinamese (186/391, 47.6%), Turkish (153/325, 47.1%), African Surinamese (156/362, 43.1%), and Moroccan ethnic groups (92/311, 29.6%). Lower intent to vaccinate was more common in all groups other than the Dutch group (P < 0.001). Being female, believing that COVID-19 is exaggerated in the media, and being < 45 years of age were common determinants of lower SARS-CoV-2 vaccination intent across most ethnic groups. Other identified determinants were specific to certain ethnic groups. CONCLUSIONS: Lower intent to vaccinate against SARS-CoV-2 in the largest ethnic minority groups of Amsterdam is a major public health concern. The ethnic-specific and general determinants of lower vaccination intent observed in this study could help shape vaccination interventions and campaigns.

SARS-CoV-2 antibody prevalence and correlates of six ethnic groups living in Amsterdam, the Netherlands: a population-based cross-sectional study, June-October 2020.

OBJECTIVES: It has been suggested that ethnic minorities have been disproportionally affected by the COVID-19. We aimed to determine whether prevalence and correlates of past SARS-CoV-2 exposure varied between six ethnic groups in Amsterdam, the Netherlands. DESIGN, SETTING, PARTICIPANTS: Participants aged 25-79 years enrolled in the Healthy Life in an Urban Setting population-based prospective cohort (n=16 889) were randomly selected within ethnic groups and invited to participate in a cross-sectional COVID-19 seroprevalence substudy. OUTCOME MEASURES: We tested participants for SARS-CoV-2-specific antibodies and collected information on SARS-CoV-2 exposures. We estimated prevalence and correlates of SARS-CoV-2 exposure within ethnic groups using survey-weighted logistic regression adjusting for age, sex and calendar time. RESULTS: Between 24 June and 9 October 2020, we included 2497 participants. Adjusted SARS-CoV-2 seroprevalence was comparable between ethnic Dutch (24/498; 5.1%, 95% CI 2.8% to 7.4%), South-Asian Surinamese (22/451; 4.9%, 95% CI 2.2% to 7.7%), African Surinamese (22/400; 8.3%, 95% CI 3.1% to 13.6%), Turkish (30/408; 7.9%, 95% CI 4.4% to 11.4%) and Moroccan (32/391; 7.2%, 95% CI 4.2% to 10.1%) participants, but higher among Ghanaians (95/327; 26.3%, 95% CI 18.5% to 34.0%). 57.1% of SARS-CoV-2-positive participants did not suspect or were unsure of being infected, which was lowest in African Surinamese (18.2%) and highest in Ghanaians (90.5%). Correlates of SARS-CoV-2 exposure varied across ethnic groups, while the most common correlate was having a household member suspected of infection. In Ghanaians, seropositivity was associated with older age, larger household sizes, living with small children, leaving home to work and attending religious services. CONCLUSIONS: No remarkable differences in SARS-CoV-2 seroprevalence were observed between the largest ethnic groups in Amsterdam after the first wave of infections. The higher infection seroprevalence observed among Ghanaians, which passed mostly unnoticed, warrants wider prevention efforts and opportunities for non-symptom-based testing.

Differences in SARS-CoV-2 infections during the first and second wave of SARS-CoV-2 between six ethnic groups in Amsterdam, the Netherlands: A population-based longitudinal serological study.

BACKGROUND: Surveillance data in high-income countries have reported more frequent SARS-CoV-2 diagnoses in ethnic minority groups. We examined the cumulative incidence of SARS-CoV-2 and its determinants in six ethnic groups in Amsterdam, the Netherlands. METHODS: We analysed participants enrolled in the population-based HELIUS cohort, who were tested for SARS-CoV-2-specific antibodies and answered COVID-19-related questions between June 24-October 9, 2020 (after the first wave) and November 23, 2020-March 31, 2021 (during the second wave). We modelled SARS-CoV-2 incidence from January 1, 2020-March 31, 2021 using Markov models adjusted for age and sex. We compared incidence between ethnic groups over time and identified determinants of incident infection within ethnic groups. FINDINGS: 2,497 participants were tested after the first wave; 2,083 (83·4%) were tested during the second wave. Median age at first visit was 54 years (interquartile range=44-61); 56·6% were female. Compared to Dutch-origin participants (15·9%), cumulative SARS-CoV-2 incidence was higher in participants of South-Asian Surinamese (25·0%; adjusted hazard ratio [aHR]=1·66; 95%CI=1·16-2·40), African Surinamese (28·9%, aHR=1·97; 95%CI=1·37-2·83), Turkish (37·0%; aHR=2·67; 95%CI=1·89-3·78), Moroccan (41·9%; aHR=3·13; 95%CI=2·22-4·42), and Ghanaian (64·6%; aHR=6·00; 95%CI=4·33-8·30) origin. Compared to those of Dutch origin, differences in incidence became wider during the second versus first wave for all ethnic minority groups (all p-values for interaction<0·05), except Ghanaians. Having household members with suspected SARS-CoV-2 infection, larger household size, and low health literacy were common determinants of SARS-CoV-2 incidence across groups. INTERPRETATION: SARS-CoV-2 incidence was higher in the largest ethnic minority groups of Amsterdam, particularly during the second wave. Prevention measures, including vaccination, should be encouraged in these groups. FUNDING: ZonMw, Public Health Service of Amsterdam, Dutch Heart Foundation, European Union, European Fund for the Integration of non-EU immigrants.

Profiles of Glucose Metabolism in Different Prediabetes Phenotypes, Classified by Fasting Glycemia, 2-Hour OGTT, Glycated Hemoglobin, and 1-Hour OGTT: An IMI DIRECT Study.

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). ß-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, P < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.

Genome-Wide Association Analysis of Pancreatic Beta-Cell Glucose Sensitivity.

CONTEXT: Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity. OBJECTIVE: To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies. DESIGN: We performed a genome-wide meta-analysis for beta-cell glucose sensitivity in subjects with type 2 diabetes and nondiabetic subjects from 6 independent cohorts (n = 5706). Beta-cell glucose sensitivity was calculated from mixed meal and oral glucose tolerance tests, and its associations between known glycemia-related single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) SNPs were estimated using linear regression models. RESULTS: Beta-cell glucose sensitivity was moderately heritable (h2 ranged from 34% to 55%) using SNP and family-based analyses. GWAS meta-analysis identified multiple correlated SNPs in the CDKAL1 gene and GIPR-QPCTL gene loci that reached genome-wide significance, with SNP rs2238691 in GIPR-QPCTL (P value = 2.64 × 10-9) and rs9368219 in the CDKAL1 (P value = 3.15 × 10-9) showing the strongest association with beta-cell glucose sensitivity. These loci surpassed genome-wide significance when the GWAS meta-analysis was repeated after exclusion of the diabetic subjects. After correction for multiple testing, glycemia-associated SNPs in or near the HHEX and IGF2B2 loci were also associated with beta-cell glucose sensitivity. CONCLUSION: We show that, variation at the GIPR-QPCTL and CDKAL1 loci are key determinants of pancreatic beta-cell glucose sensitivity.

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes: An IMI-DIRECT study.

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

Prevalence of Insomnia (Symptoms) in T2D and Association With Metabolic Parameters and Glycemic Control: Meta-Analysis.

OBJECTIVE: We aimed to determine the prevalence of insomnia and insomnia symptoms and its association with metabolic parameters and glycemic control in people with type 2 diabetes (T2D) in a systematic review and meta-analysis. DATA SOURCES: A systematic literature search was conducted in PubMed/Embase until March 2018. STUDY SELECTION: Included studies described prevalence of insomnia or insomnia symptoms and/or its association with metabolic parameters or glycemic control in adults with T2D. DATA EXTRACTION: Data extraction was performed independently by 2 reviewers, on a standardized, prepiloted form. An adaptation of Quality Assessment Tool for Quantitative Studies was used to assess the methodological quality of the included studies. DATA SYNTHESIS: When possible, results were meta-analyzed using random-effects analysis and rated using Grading of Recommendations Assessment, Development and Evaluation (GRADE). RESULTS: A total of 11 329 titles/abstracts were screened and 224 were read full text in duplicate, of which 78 studies were included. The pooled prevalence of insomnia (symptoms) in people with T2D was 39% (95% confidence interval, 34-44) with I2 statistic of 100% (P < 0.00001), with a very low GRADE of evidence. Sensitivity analyses identified no clear sources of heterogeneity. Meta-analyses showed that in people with T2D, insomnia (symptoms) were associated with higher hemoglobin A1c levels (mean difference, 0.23% [0.1-0.4]) and higher fasting glucose levels (mean difference, 0.40 mmol/L [0.2-0.7]), with a low GRADE of evidence. The relative low methodological quality and high heterogeneity of the studies included in this meta-analysis complicate the interpretation of our results. CONCLUSIONS: The prevalence of insomnia (symptoms) is 39% (95% confidence interval, 34-44) in the T2D population and may be associated with deleterious glycemic control.

Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium.

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.

The association between GAD65 antibody levels and incident Type 2 Diabetes Mellitus in an adult population: A meta-analysis.

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 I2 statistic of 79% (p < 0.0001). However, excluding one study showed a decrease of I2 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.

A prospective study on glucagon responses to oral glucose and mixed meal and 7-year change in fasting glucose.

INTRODUCTION: The role of insufficient glucagon suppression after an oral load in the development of type 2 diabetes mellitus is unclear. The aim of this study was to examine the association between glucagon responses at baseline and fasting glucose levels 7 years later. METHODS: Data of the Hoorn Meal Study were used, an observational cohort study among 121 persons without diabetes with a mean age of 61.1 ± 6.7 years and 50% being female. The glucagon response to an oral glucose tolerance test and mixed meal test was expressed as early and late incremental area under the curve. The association with change in fasting glucose levels at follow-up was assessed by linear regression analysis. RESULTS: The early glucagon response following the mixed meal test was associated with an increase in fasting glucose levels of 0.18 mmol/L (95%-CI: 0.04-0.31, P = 0.01), per unit increase in the incremental area under the curve of glucagon, adjusted for confounders. No significant associations were observed for the late response after the mixed meal test or oral glucose tolerance test. CONCLUSIONS: Within a population without diabetes, relative lack of glucagon suppression early after a meal was associated with increased glucose levels over time, suggesting a role of insufficient glucagon suppression in the deterioration of glycaemic control.

The association between multiple sleep-related characteristics and the metabolic syndrome in the general population: the New Hoorn study.

BACKGROUND: Previous studies have investigated the association between sleep duration, insomnia, day-time napping and metabolic syndrome individually, but never conjointly. In addition, the association with sleep medication use has yet to be investigated. We aimed to examine the associations between these sleep-related characteristics and the metabolic syndrome, individually and conjointly, in a population-based cohort. MATERIAL AND METHODS: We used cross-sectional data of 1679 participants from the New Hoorn study, 52.6% women and age 60.8 + 6.4y. Sleep duration, insomnia, and day-time napping were measured using validated questionnaires. The use of sleep medication was documented by the registration of dispensing labels. The metabolic syndrome was defined according to ATP III. Linear and Poisson regressions were used, and all analyses were adjusted for age, sex, education level, job status, smoking, physical activity, depression and BMI. RESULTS: In our population-based cohort, 447 (26.6%) persons had the metabolic syndrome. Individual associations showed that, after correction, day-time napping for ≤30 min and >30 min was associated with a prevalence ratio for the metabolic syndrome of 1.28 (95% CI: 1.1-1.5) and 1.74 (95% CI: 1.4-2.2), respectively, compared to participants who did not nap. Sleep duration, insomnia, and sleep medication use were not associated with the metabolic syndrome individually. However, conjointly analyses showed that, after correction, having ≥2 sleep-related characteristics was associated with a PR of 1.36 (95% CI: 1.0-1.8) of having the metabolic syndrome, compared to having no sleep-related characteristics. CONCLUSION: Sleep-related characteristics were associated with a higher prevalence of the metabolic syndrome in the general population.

The Association between Eating Traits and Weight Change after a Lifestyle Intervention in People with Type 2 Diabetes Mellitus.

AIMS: To date, studies on the role of eating traits in weight loss success have only included obese people without type 2 diabetes mellitus (T2DM), thereby disregarding negative effects of T2DM-related metabolic changes. Our aim was to assess the association between eating traits and weight change after a lifestyle intervention in people with T2DM. METHODS: For the current study, we reexamined data from a six-month intervention in 120 participants. We determined eating traits at baseline, using the DEBQ, which were used to produce three groups: unsuccessful dietary restrained (high restraint, high emotional/external eating scores), successful dietary restrained (high restraint, low emotional/external eating scores), and reference (low restraint, high or low emotional/external eating scores). Linear regression was used to study the association between the eating trait groups and weight changes after six months, while correcting for possible confounders. RESULTS: On average, the weight loss success was limited, with a third of the participants being weight stable, a third losing weight > -1 kg (average loss -2.6 ± 1.9 kg), and a third gaining weight > +1 kg (average gain +3.3 ± 1.9 kg). When compared to the reference group, the unsuccessful dietary restrained gained weight during the intervention (beta = 1.2 kg, confidence interval (CI)% = 0.1; 2). No significant change was observed in the succesful dietary restrained group. CONCLUSIONS: The eating trait of being unsuccessfully dietary restrained is associated with weight-loss failure after a six-month lifestyle intervention in people with T2DM.

The Association between Social Jetlag, the Metabolic Syndrome, and Type 2 Diabetes Mellitus in the General Population: The New Hoorn Study.

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.

Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study.

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.

Heterogeneity in glucose response curves during an oral glucose tolerance test and associated cardiometabolic risk.

We aimed to examine heterogeneity in glucose response curves during an oral glucose tolerance test with multiple measurements and to compare cardiometabolic risk profiles between identified glucose response curve groups. We analyzed data from 1,267 individuals without diabetes from five studies in Denmark, the Netherlands and the USA. Each study included between 5 and 11 measurements at different time points during a 2-h oral glucose tolerance test, resulting in 9,602 plasma glucose measurements. Latent class trajectories with a cubic specification for time were fitted to identify different patterns of plasma glucose change during the oral glucose tolerance test. Cardiometabolic risk factor profiles were compared between the identified groups. Using latent class trajectory analysis, five glucose response curves were identified. Despite similar fasting and 2-h values, glucose peaks and peak times varied greatly between groups, ranging from 7-12 mmol/L, and 35-70 min. The group with the lowest and earliest plasma glucose peak had the lowest estimated cardiovascular risk, while the group with the most delayed plasma glucose peak and the highest 2-h value had the highest estimated risk. One group, with normal fasting and 2-h values, exhibited an unusual profile, with the highest glucose peak and the highest proportion of smokers and men. The heterogeneity in glucose response curves and the distinct cardiometabolic risk profiles may reflect different underlying physiologies. Our results warrant more detailed studies to identify the source of the heterogeneity across the different phenotypes and whether these differences play a role in the development of type 2 diabetes and cardiovascular disease.

Stressful life events and incident metabolic syndrome: the Hoorn study.

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 ß 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.

Does clinical rehabilitation impose sufficient cardiorespiratory strain to improve aerobic fitness?

OBJECTIVE: To investigate the cardiorespiratory strain experienced by patients over a day and during different types of rehabilitation therapies during a clinical rehabilitation programme. In addition, to investigate the use of the Borg scale as an instrument to monitor exercise intensity. DESIGN: An observational, cross-sectional study. SETTING: Rehabilitation centre in the Netherlands. PARTICIPANTS: Eleven people after stroke (age range 20-71 years), 9 people with a lower limb amputation (age range 21-66 years) and 11 people with a spinal cord injury (age range 28-65 years). All participants were inpatients undergoing clinical rehabilitation. MAIN OUTCOME MEASURES: Frequency distribution of percentage heart rate reserve (%HRR) and length of time heart rate (HR) > 40%HRR over one day, and mean %HRR, length of time HR > 40%HRR and HR > 70%HRR during different types of rehabilitation therapies were compared with the American College of Sports Medicine guidelines for achieving an aerobic training effect. The correlation coefficient between the Borg scale score and %HRR was assessed. RESULTS: Patients' mean HR was 114 min/day (standard deviation 92) > 40%HRR, of which 1 h was spent in therapy. In 5 out of 10 rehabilitation therapies (fitness, hydrotherapy, walking group, wheelchair group and cycling/handbike group) a mean HR > 40%HRR was reached and more than half of the time was spent > 40%HRR. A moderate correlation (R = 0.56) was found between Borg scale score and %HRR. All outcome measures showed large variation between and within patients. CONCLUSION: In general, patients in a clinical rehabilitation programme experience adequate cardiorespiratory strain to potentially induce an aerobic training effect. The large variation in cardiorespiratory strain, however, necessitates individual monitoring to ensure proper exercise intensity. The Borg scale was shown to be of limited value for this monitoring, and therefore the use of HR monitors during rehabilitation should be considered.