Apolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main... Show moreApolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main proteoforms: non-glycosylated (apo-CIII0a), and glycosylated apo-CIII with zero, one, or two sialic acids (apo-CIII0c, apo-CIII1 and apo-CIII2). Our objective is to determine how apo-CIII glycosylation affects lipid traits and type 2 diabetes prevalence, and to investigate the genetic basis of these relations with a genome-wide association study (GWAS) on apo-CIII glycosylation. We conducted GWAS on the four apo-CIII proteoforms in the DiaGene study in people with and without type 2 diabetes (n = 2318). We investigated the relations of the identified genetic loci and apo-CIII glycosylation with lipids and type 2 diabetes. The associations of the genetic variants with lipids were replicated in the Diabetes Care System (n = 5409). Rs4846913-A, in the GALNT2-gene, was associated with decreased apo-CIII0a. This variant was associated with increased high-density lipoprotein cholesterol and decreased triglycerides, while high apo-CIII0a was associated with raised high-density lipoprotein-cholesterol and triglycerides. Rs67086575-G, located in the IFT172-gene, was associated with decreased apo-CIII2 and with hypertriglyceridemia. In line, apo-CIII2 was associated with low triglycerides. On a genome-wide scale, we confirmed that the GALNT2-gene plays a major role i O-glycosylation of apolipoprotein-CIII, with subsequent associations with lipid parameters. We newly identified the IFT172/NRBP1 region, in the literature previously associated with hypertriglyceridemia, as involved in apolipoprotein-CIII sialylation and hypertriglyceridemia. These results link genomics, glycosylation, and lipid metabolism, and represent a key step towards unravelling the importance of O-glycosylation in health and disease. Show less
Nguyen, A.L.; Kezic, S.; Vermeer, M.; Quint, K.; Slieker, R.; Doorn, R. van; Rustemeyer, T. 2022
Mycosis fungoides (MF) is characterised by malignant CD4(+) T-cell infiltrates in the skin. The functional characteristics of the malignant T cells and their interaction with the tumor immune... Show moreMycosis fungoides (MF) is characterised by malignant CD4(+) T-cell infiltrates in the skin. The functional characteristics of the malignant T cells and their interaction with the tumor immune microenvironment is largely unknown. We performed tape stripping of the stratum corneum (SC), a non-invasive technique, to gain insight into the cytokine secretion patterns in MF skin lesions. In addition, we assessed whether the SC cytokine profile of MF lesions is distinct from that of atopic dermatitis (AD) lesions. We compared nine cytokine levels in 20 patients with MF, 10 patients with AD and 10 healthy controls. In patients with MF and AD, lesional SC levels of IL-8 and MMP9 were significantly higher than in non-lesional SC and in healthy controls. VEGF alpha was significantly higher in lesional MF and AD skin than in healthy controls. The SC levels of IL-1 alpha were significantly lower in MF and AD lesions than in healthy controls. There was no specific cytokine profile or inflammation pattern that could reliably distinguish MF from AD. In conclusion, in lesional SC of MF patients, pro-inflammatory cytokines can be detected. As a diagnostic method, tape stripping of lesional SC cannot discriminate MF skin from AD skin. Show less
Pagano, E.; Konings, S.R.A.; Cuonzo, D. di; Rosato, R.; Bruno, G.; Heijden, A.A. van der; ... ; Feenstra, T.L. 2021
Aim To externally validate the UK Prospective Diabetes Study Outcomes Model version 2 (UKPDS-OM2) by comparing the predicted and observed outcomes in two European population-based cohorts of people... Show moreAim To externally validate the UK Prospective Diabetes Study Outcomes Model version 2 (UKPDS-OM2) by comparing the predicted and observed outcomes in two European population-based cohorts of people with type 2 diabetes.Materials and methods We used data from the Casale Monferrato Survey (CMS; n = 1931) and a subgroup of the Hoorn Diabetes Care System (DCS) cohort (n = 5188). The following outcomes were analysed: all-cause mortality, myocardial infarction (MI), ischaemic heart disease (IHD), stroke, and congestive heart failure (CHF). Model performance was assessed by comparing predictions with observed cumulative incidences in each cohort during follow-up.Results All-cause mortality was overestimated by the UKPDS-OM2 in both the cohorts, with a bias of 0.05 in the CMS and 0.12 in the DCS at 10 years of follow-up. For MI, predictions were consistently higher than observed incidence over the entire follow-up in both cohorts (10 years bias 0.07 for CMS and 0.10 for DCS). The model performed well for stroke and IHD outcomes in both cohorts. CHF incidence was predicted well for the DCS (5 years bias -0.001), but underestimated for the CMS cohort.Conclusions The UKPDS-OM2 consistently overpredicted the risk of mortality and MI in both cohorts during follow-up. Period effects may partially explain the differences. Results indicate that transferability is not satisfactory for all outcomes, and new or adjusted risk equations may be needed before applying the model to the Italian or Dutch settings. Show less
AimSubclasses 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... Show moreAimSubclasses 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.MethodsThe 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.ResultsAt 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.ConclusionsDifferent 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. Show less
Obura, M.; Beulens, J.W.J.; Slieker, R.; Koopman, A.D.M.; Hoekstra, T.; Nijpels, G.; ... ; Rutters, F. 2020
Aim To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with... Show moreAim To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration.Methods We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months.Results At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (beta = 0.36, 95% CI 0.13-0.58), Subgroup 3 (beta = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (beta = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months.Conclusions Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified. Show less
Obura, M.; Rutters, F.; Slieker, R.; Hoekstra, T.; Nijpels, G.; Pearson, E.; ... ; DIRECT Consortium 2017
In this thesis we aimed to get insight in how the methylome is established during development and subsequently degenerates during ageing using an integrative approach to the analysis of DNA... Show moreIn this thesis we aimed to get insight in how the methylome is established during development and subsequently degenerates during ageing using an integrative approach to the analysis of DNA methylation in conjunction with other levels of genomics data. The first two empirical chapters of this thesis describe the establishment and the maintenance of the epigenome during fetal development and in later life in multiple tissues. In the subsequent two chapters we investigated the loss of control over the methylome in blood and other tissues. Show less
