DTL Conference 2018 - Program
Workshop Modelling human metabolism
We studied in people with type 2 diabetes whether blood metabolomic measures are associated with insufficient glycemic control and if this association is influenced differentially by various diabetes drugs. We then tested whether the same metabolomic profiles associate with initiation of insulin therapy.
One-hundred-and-sixty-two metabolomic measures were analyzed using a NMR-based method in people with type 2 diabetes from four cohort studies (n=2641) and one replication cohort (n=395). Linear and logistic regression with adjustment for potential confounders followed by meta-analyses was done to analyze associations with HbA1c levels, six glucose-lowering drug categories, and insulin initiation during seven year follow-up (n=698).
After Bonferroni correction twenty-six measures were associated with insufficient glycemic control (HbA1c>53 mmol/mol). The strongest association was with glutamine (OR=0.66 (95%CI 0.61;0.73), P=7.6×10-19). In addition when compared to treatment naïve patients thirty-one metabolomic measures were associated with glucose-lowering drugs use (representing various metabolite categories, all P≤3.1×10-4). In drug-stratified analyses, associations with insufficient glycemic control were only mildly affected by different glucose-lowering drugs. Five of the 26 metabolomic measures (ApoA1 and M-HDL subclasses) were also associated with insulin initiation during follow-up in both discovery and replication. With the strongest association observed for M-HDL-CE (OR=0.54 (95%CI=0.42;0.71); P=4.5×10-6).
In conclusion blood metabolomic measures were associated with present and future glycemic control and may thus provide relevant cues to identify those at increased risk of treatment failure.
OBJECTIVE Accumulation of reactive oxygen species by increased uric acid production has been suggested as a possible underlying mechanism for the association between uric acid and high blood pressure (BP). We, therefore, investigated the association between serum uric acid concentration and 24-h urinary uric acid excretion, as proxy for uric acid production, with ambulatory 24-h blood pressure and hypertension.
METHODS Cross-sectional analyses were conducted among 2555 individuals [52% men, mean age 60.0 ± 8.2 years; 27% type 2 diabetes (by design)] from The Maastricht Study. Multivariable regression analyses were performed to investigate the association of serum uric acid and 24-h urinary uric acid excretion with 24-h pulse pressure, 24-h mean arterial pressure (MAP), and hypertension.
RESULTS After adjustment for traditional hypertension risk factors, serum uric acid concentration (per SD of 81 μmol/l) was associated with higher 24-h MAP [β 0.63 mmHg; confidence interval (CI) 0.27-1.00] and positively associated with hypertension (odds ratio 1.43; CI 1.27-1.61). Urinary uric acid excretion (per SD of 140 mg/day/1.73 m) was associated with higher 24-h MAP (β 0.79 mmHg; CI 0.46-1.12) and with hypertension (odds ratio 1.13; CI 1.02-1.25). There was no significant association between serum and 24-h urinary uric acid excretion with 24-h pulse pressure. There was no interaction with sex or age for the aforementioned associations.
CONCLUSION Higher serum and urinary uric acid concentrations were associated with higher 24-h MAP and hypertension. These results suggest that serum and 24-urinary uric acid concentrations, the latter as proxy for uric acid production are, independent of each other, associated with BP and hypertension. more
a common model underlying apparently different symptoms and markers
serum glutamine hypoxia
The serum levels of choline, succinate, taurine, alanine, and glutamine also increased and phosphocholine decreased in the acute hypoxia group.
Hydrocortisone produced a simultaneous increase in GS translation, GS level, and activity.
glutamine purine synthesis
Glutamine, glycine and 10-formyl tetrahydrofolate are consumed during de novo purine biosynthesis.
Type 2 diabetes mellitus (T2DM) is associated with an increased risk of colorectal cancer (CRC); however, studies differentiating between subsites of CRC are limited. We investigated how diabetes mellitus (DM) was associated with subsite-specific CRC risk in men and women.
The Netherlands Cohort Study on diet and cancer is a prospective study among 120 852 men and women aged 55-69 years old at baseline in 1986. Information on DM, anthropometric, dietary and lifestyle factors was self-reported at baseline. T2DM was defined as the diagnosis of DM after 30 years of age. Incident CRC cases were identified by record linkage with the Netherlands cancer registry and the Dutch pathology registry. After 17.3 years of follow-up, 1735 incident male CRC cases and 1321 female CRC cases were available for analyses. Subsite-specific hazard ratios (HRs) for CRC were estimated in case-cohort analyses using Cox regression.
At baseline, 3.1% of subcohort members reported T2DM, of whom 80% were diagnosed after 50 years of age. Multivariable-adjusted models showed that the risk of proximal colon cancer was significantly increased in women with T2DM versus women without T2DM (HR=1.80, 95% confidence interval: 1.10-2.94). There was no association between T2DM and the risk of overall CRC, distal colon cancer and rectal cancer in women. In men, T2DM was not associated with overall CRC (HR=0.98, 95% confidence interval: 0.64-1.50), or with risk at any subsite.
This prospective study showed an increased risk of proximal colon cancer in women with T2DM compared with non-T2DM women.
Natal van Riel
A physiology-based model describing heterogeneity in glucose metabolism: the core of the Eindhoven Diabetes Education Simulator (E-DES).2015
Current diabetes education methods are costly, time-consuming, and do not actively engage the patient. Here, we describe the development and verification of the physiological model for healthy subjects that forms the basis of the Eindhoven Diabetes Education Simulator (E-DES). E-DES shall provide diabetes patients with an individualized virtual practice environment incorporating the main factors that influence glycemic control: food, exercise, and medication. The physiological model consists of 4 compartments for which the inflow and outflow of glucose and insulin are calculated using 6 nonlinear coupled differential equations and 14 parameters. These parameters are estimated on 12 sets of oral glucose tolerance test (OGTT) data ( 226 healthy subjects ) obtained from literature. The resulting parameter set is verified on 8 separate literature OGTT data sets ( 229 subjects ). The model is considered verified if 95% of the glucose data points lie within an acceptance range of ±20% of the corresponding model value. All glucose data points of the verification data sets lie within the predefined acceptance range. Physiological processes represented in the model include insulin resistance and β-cell function. Adjusting the corresponding parameters allows to describe heterogeneity in the data and shows the capabilities of this model for individualization. We have verified the physiological model of the E-DES for healthy subjects. Heterogeneity of the data has successfully been modeled by adjusting the 4 parameters describing insulin resistance and β-cell function. Our model will form the basis of a simulator providing individualized education on glucose control.
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