Only a few studies have investigated the metabolic consequences of social jetlag. Therefore, we examined the association of social jetlag with the metabolic syndrome and type 2 diabetes mellitus in a ...population-based cohort. We used cross-sectional data from the New Hoorn Study cohort (n = 1585, 47% men, age 60.8 ± 6 years). Social jetlag was calculated as the difference in midpoint sleep (in hours) between weekdays and weekend days. Poisson and linear regression models were used to study the associations, and age was regarded as a possible effect modifier. We adjusted for sex, employment status, education, smoking, physical activity, sleep duration, and body mass index. In the total population, we only observed an association between social jetlag and the metabolic syndrome, with prevalence ratios adjusted for sex, employment status, and educational levels of 1.64 (95% CI 1.1-2.4), for participants with >2 h social jetlag, compared with participants with <1 h social jetlag. However, we observed an interaction effect of median age (<61 years). In older participants (≥61 years), no significant associations were observed between social jetlag status, the metabolic syndrome, and diabetes or prediabetes. In the younger group (<61 years), the adjusted prevalence ratios were 1.29 (95% CI 0.9-1.9) and 2.13 (95% CI 1.3-3.4) for the metabolic syndrome and 1.39 (95% CI 1.1-1.9) and 1.75 (95% CI 1.2-2.5) for diabetes/prediabetes, for participants with 1-2 h and >2 h social jetlag, compared with participants with <1 h social jetlag. In conclusion, in our population-based cohort, social jetlag was associated with a 2-fold increased risk of the metabolic syndrome and diabetes/prediabetes, especially in younger (<61 years) participants.
Abstract
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.
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.
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.
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.
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.
•SARS-CoV-2 vaccination intent was remarkably lower in ethnic minority groups.•Females and those believing COVID-19 was exaggerated in the media commonly had lower intent.•Other determinants of lower ...intent were specific to certain ethnic groups.•Low intent could exacerbate existing inequalities of COVID-19 between ethnic groups.•Targeted strategies are warranted to address the needs of specific ethnic groups.
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.
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.
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.
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.
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.
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.
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.
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.
Prevention strategies should continue tailoring to specific ethnic groups to encourage vaccination uptake and reduce barriers to vaccination.
Abstract
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.
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). β-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.
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.
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.
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.
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.
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.
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.
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.
Sleep-related characteristics were associated with a higher prevalence of the metabolic syndrome in the general population.
•Day-time napping was associated with a 28–74% higher chance of having the metabolic syndrome.•Sleep duration, insomnia, and sleep medication use were not associated with the metabolic syndrome individually.•Having 2 or more sleep-related characteristics was associated 36% higher chance of having the metabolic syndrome.
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