Aims/hypothesis
The hyperglycaemic clamp technique and the frequently sampled IVGTT are unsuitable techniques to assess beta cell function (BCF) in large cohorts. Therefore, the aim of this study was ...to evaluate the discriminatory ability of simple OGTT-based BCF indices for prediction of prediabetes (meaning impaired fasting glucose and/or impaired glucose tolerance) and type 2 diabetes.
Methods
Glucose metabolism status was assessed by 2 h 75 g OGTT at baseline (
n
= 476, mean age 59.2 years, 38.7% women) and after 7 years of follow-up (
n
= 416) in the Cohort on Diabetes and Atherosclerosis Maastricht (CODAM) study (1999–2009). Baseline plasma glucose, insulin and C-peptide values during OGTTs were used to calculate 21 simple indices of BCF. Disposition indices (BCF index × Matsuda index), to compensate for the prevailing level of insulin resistance, were calculated for the BCF indices with the best discriminatory abilities. The discriminatory ability of the BCF indices was estimated by the area under the receiver operating characteristics curve (ROC AUC) with an outcome of incident prediabetes (
n
= 73) or type 2 diabetes (
n
= 60 and
n
= 18 cases, respectively, in individuals who were non-diabetic or had normal glucose metabolism at baseline).
Results
For incident prediabetes (
n
= 73), all ROC AUCs were less than 70%, whereas for incident type 2 diabetes, I
30
/I
0
, CP
30
/CP
0
, ΔI
30
/ΔG
30
, ΔCP
30
/ΔG
30
(where I, CP and G are the plasma concentrations of insulin, C-peptide and glucose, respectively, at the times indicated), and corrected insulin response at 30 min had ROC AUCs over 70%. In at-baseline non-diabetic individuals, disposition indices ΔI
30
/ΔG
30
, ΔCP
30
/ΔG
30
and corrected insulin response at 30 min had ROC AUCs of over 80% for incident type 2 diabetes. Moreover, these BCF disposition indices had significantly better discriminatory abilities for incident type 2 diabetes than the Matsuda index alone.
Conclusions/interpretation
BCF indices reflecting early-phase insulin secretion have the best ability to discriminate individuals who will develop prediabetes and type 2 diabetes. Of these, ΔCP
30
/ΔG
30
, often referred to as the C-peptidogenic index, performed consistently well.
Hypoxia is prevalent in atherosclerotic plaques, promoting plaque aggravation and subsequent cardiovascular disease (CVD). Transmembrane protein carbonic anhydrase IX (CAIX) is hypoxia-induced and ...can be shed into the circulation as soluble CAIX (sCAIX). As plaque macrophages are hypoxic, we hypothesized a role for CAIX in macrophage function, and as biomarker of hypoxic plaque burden and CVD. As tumor patients with probable CVD are treated with CAIX inhibitors, this study will shed light on their safety profile. CAIX co-localized with macrophages (CD68) and hypoxia (pimonidazole), and correlated with lipid core size and pro-inflammatory iNOS+ macrophages in unstable human carotid artery plaques. Although elevated pH and reduced lactate levels in culture medium of CAIX knock-out (CAIXko) macrophages confirmed its role as pH-regulator, only spare respiratory capacity of CAIXko macrophages was reduced. Proliferation, apoptosis, lipid uptake and expression of pro- and anti-inflammatory genes were not altered. Plasma sCAIX levels and plaque-resident CAIX were below the detection threshold in 50 and 90% of asymptomatic and symptomatic cases, respectively, while detectable levels did not associate with primary or secondary events, or intraplaque hemorrhage. Initial findings show that CAIX deficiency interferes with macrophage metabolism. Despite a correlation with inflammatory macrophages, plaque-resident and sCAIX expression levels are too low to serve as biomarkers of future CVD.
Context:
Advanced glycation end-products (AGEs) are thought to be involved in the pathogenesis of Alzheimer's disease. AGEs are products resulting from nonenzymatic chemical reactions between reduced ...sugars and proteins, which accumulate during natural aging, and their accumulation is accelerated in hyperglycemic conditions such as type 2 diabetes mellitus.
Objective:
The objective of the study was to examine associations between AGEs and cognitive functions.
Design, Setting, and Participants:
This study was performed as part of the Maastricht Study, a population-based cohort study in which, by design, 215 participants (28.1%) had type 2 diabetes mellitus.
Main Outcome Measures:
We examined associations of skin autofluorescence (SAF) (n = 764), an overall estimate of skin AGEs, and specific plasma protein-bound AGEs (n = 781) with performance on tests for global cognitive functioning, information processing speed, verbal memory (immediate and delayed word recall), and response inhibition.
Results:
After adjustment for demographics, diabetes, smoking, alcohol, waist circumference, total cholesterol/high-density lipoprotein cholesterol ratio, triglycerides, and lipid-lowering medication use, higher SAF was significantly associated with worse delayed word recall (regression coefficient, b = −0.44; P = .04), and response inhibition (b = 0.03; P = .04). After further adjustment for systolic blood pressure, cardiovascular disease, estimated glomerular filtration rate, and depression, associations were attenuated (delayed word recall, b = −0.38, P = .07; response inhibition, b = 0.02, P = .07). Higher pentosidine levels were associated with worse global cognitive functioning (b = −0.61; P = .04) after full adjustment, but other plasma AGEs were not. Associations did not differ between individuals with and without diabetes.
Conclusion:
We found inverse associations of SAF (a noninvasive marker for tissue AGEs) with cognitive performance, which were attenuated after adjustment for vascular risk factors and depression.
Aims/hypotheses
Our aim was to examine the independent and combined (cross-sectional) associations of sedentary time (ST), higher intensity physical activity (HPA) and cardiorespiratory fitness (CRF) ...with metabolic syndrome and diabetes status.
Methods
In 1933 adults (aged 40–75 years) ST and HPA (surrogate measure for moderate to vigorous physical activity) were measured with the activPAL3. CRF was assessed by submaximal cycle–ergometer testing. Metabolic syndrome was defined according to the Adult Treatment Panel (ATP) III guidelines. Diabetes status (normal, prediabetes i.e. impaired glucose tolerance and/or impaired fasting glucose or type 2 diabetes) was determined from OGTT. (Multinomial) logistic regression analyses were used to calculate likelihood for the metabolic syndrome, prediabetes and type 2 diabetes according to ST, HPA and CRF separately and combinations of ST–CRF and HPA–CRF.
Results
Higher ST, lower HPA and lower CRF were associated with greater odds for the metabolic syndrome and type 2 diabetes independently of each other. Compared with individuals with high CRF and high HPA (CRF
high
–HPA
high
), odds for the metabolic syndrome and type 2 diabetes were higher in groups with a lower CRF regardless of HPA. Individuals with low CRF and low HPA (CRF
low
–HPA
low
) had a particularly high odds for the metabolic syndrome (OR 5.73 95% CI 3.84, 8.56) and type 2 diabetes (OR 6.42 95% CI 3.95, 10.45). Similarly, compared with those with high CRF and low ST (CRF
high
–ST
low
), those with medium or low CRF had higher odds for the metabolic syndrome, prediabetes and type 2 diabetes, irrespective of ST. In those with high CRF, high ST was associated with significantly high odds for the metabolic syndrome (OR 2.93 95% CI 1.72, 4.99) and type 2 diabetes (OR 2.21 95% CI 1.17, 4.17). The highest odds for the metabolic syndrome and type 2 diabetes were observed in individuals with low CRF and high ST (CRF
low
–ST
high
) (OR 95% CI: the metabolic syndrome, 9.22 5.74, 14.80; type 2 diabetes, 8.38 4.83, 14.55).
Conclusions/interpretation
These data suggest that ST, HPA and CRF should all be targeted in order to optimally reduce the risk for the metabolic syndrome and type 2 diabetes.
Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ...ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.
We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).
Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).
Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.
Abstract Objective Type 2 diabetes mellitus (T2DM) is associated with elevated plasma apolipoprotein B and triglycerides levels, reduced HDL cholesterol and the presence of small-dense LDL particles. ...The present study was conducted to investigate the role of plasma proprotein convertase subtilisin kexin type 9 (PCSK9) levels, a regulator of LDL-receptor expression, in the occurrence of diabetic dyslipidemia. Methods Plasma PCSK9 was measured in a cohort of subjects with normal glucose metabolism (NGM; n = 288), impaired glucose metabolism (IGM; n = 121) and type 2 diabetes mellitus (T2DM; n = 139) to study whether its relation with plasma apolipoprotein B, triglycerides, total cholesterol, non-HDL cholesterol, LDL cholesterol and HDL cholesterol differed by levels of glucose metabolism status. Results Plasma PCSK9 levels were not different between the three groups (82, 82 and 80 ng/mL in NGM, IGM and T2DM, respectively). PCSK9 was positively associated with total cholesterol, non-HDL cholesterol, LDL cholesterol, apolipoprotein B and triglycerides levels in all subgroups. The regression slopes for the associations with non-HDL cholesterol were steeper among individuals with T2DM than with NGM ( β = 0.016 versus β = 0.009, p -interaction = 0.05). Similar results were obtained for the relation with apolipoprotein B ( β = 0.004 versus β = 0.002, p -interaction = 0.09). Conclusions Although glucose metabolism status per se is not associated with plasma PCSK9 levels, the presence of T2DM may modify the relation between plasma PCSK9 and non-HDL cholesterol and apolipoprotein B. These observations should be regarded as hypothesis generating for further studies aimed at elucidating the role of PCSK9 in the pathogenesis and treatment of diabetic dyslipidemia.
In response to a study previously published in PLOS Biology, this Formal Comment thoroughly examines the concept of 'glucotypes' with regard to its generalisability, interpretability and relationship ...to more traditional measures used to describe data from continuous glucose monitoring.
Epidemiological evidence regarding the relationship between fructose intake and intrahepatic lipid (IHL) content is inconclusive. We, therefore, assessed the relationship between different sources of ...fructose and IHL at the population level.
We used cross-sectional data from The Maastricht Study, a population-based cohort study (n = 3,981; mean ± SD age: 60 ± 9 years; 50% women). We assessed the relationship between fructose intake (assessed with a food-frequency questionnaire)-total and derived from fruit, fruit juice, and sugar-sweetened beverages (SSB)-and IHL (quantified with 3T Dixon MRI) with adjustment for age, sex, type 2 diabetes, education, smoking status, physical activity, and intakes of total energy, alcohol, saturated fat, protein, vitamin E, and dietary fiber.
Energy-adjusted total fructose intake and energy-adjusted fructose from fruit were not associated with IHL in the fully adjusted models (P = 0.647 and P = 0.767). In contrast, energy-adjusted intake of fructose from fruit juice and SSB was associated with higher IHL in the fully adjusted models (P = 0.019 and P = 0.009). Individuals in the highest tertile of energy-adjusted intake of fructose from fruit juice and SSB had a 1.04-fold (95% CI 0.99; 1.11) and 1.09-fold (95% CI 1.03; 1.16) higher IHL, respectively, in comparison with the lowest tertile in the fully adjusted models. Finally, the association for fructose from fruit juice was stronger in individuals with type 2 diabetes (P for interaction = 0.071).
Fructose from fruit juice and SSB is independently associated with higher IHL. These cross-sectional findings contribute to current knowledge in support of measures to reduce the intake of fructose-containing beverages as a means to prevent nonalcoholic fatty liver disease at the population level.
Chronic kidney disease, which is defined as having a reduced kidney function (estimated glomerular filtration rate (eGFR)) and/or signs of kidney damage (albuminuria), is highly prevalent in Western ...society and is associated with adverse health outcomes, such as cardiovascular disease. This warrants a search for risk factors of lower eGFR and higher albuminuria. Physical activity and sedentary behavior may be such risk factors.
To examine associations of physical activity (total, high, low), sedentary time and sedentary behavior patterns (breaks, prolonged bouts, average bout duration) with eGFR and albuminuria.
We examined these associations in 2,258 participants of the Maastricht Study (average age 60.1±8.1 years; 51.3% men), who wore an accelerometer 24h/day on 7 consecutive days. Associations with continuous eGFR and categories of urinary albumin excretion (UAE; <15 reference category, 15-<30, ≥30 mg/24h) were evaluated with linear regression analyses and multinomial logistic regression analyses, respectively.
After adjustment for potential confounders, each extra hour of total physical activity was associated with a more favorable kidney function (betaeGFR = 2.30 (95%CI = 1.46; 3.14)), whereas each extra hour of sedentary behavior was associated with a more adverse kidney function (betaeGFR = -0.71 (-1.08; -0.35)). Also, compared to individuals with the lowest levels of total physical activity, individuals with the highest levels had less kidney damage (OR15-<30mg/24h = 0.63 (0.41; 0.96), OR≥30mg/24h = 0.84 (0.53; 1.35). An extra hour of sedentary behavior was associated with more kidney damage (OR15-<30 mg/24h = 1.11 (1.01; 1.22), OR≥30 mg/24h = 1.10 (0.99; 1.22)). Further, a highly sedentary pattern was associated with a more adverse kidney function, but no association was seen with kidney damage.
Physical activity and sedentary behavior were associated with kidney function and kidney damage. Additionally, sedentary behavior patterns were associated with kidney function. Causal studies are required to examine whether this indeed implicates that prevention strategies should focus not only on increasing physical activity, but on reducing sedentary behavior as well.
Objectives
To assess the psychometric properties and identify the best cutoff value of the Patient Health Questionnaire‐9 (PHQ‐9) for depression screening in individuals with type 2 diabetes mellitus ...(T2DM).
Design
Observational population‐based cohort study.
Setting
The Maastricht Study.
Participants
Individuals with and without T2DM (mean age 58.6 ± 8.1, 44.6% male) according to an oral glucose tolerance test (N = 2,997).
Measurements
Depressive disorder and depressive symptoms were measured using the Mini‐International Neuropsychiatric Interview (MINI) as the reference and the PHQ‐9. Cronbach alpha, Cohen's kappa and receiver operating characteristic (ROC) analyses were used. Differences in factorial structure between participants with and without T2DM were tested using multigroup confirmatory factor analysis.
Results
Based on the traditional PHQ‐9 cutoff value, 133 (4.4%) participants had depressive symptoms (PHQ‐9 score ≥10). Internal consistency of the PHQ‐9 was good (Cronbach α = 0.87 with T2DM, 0.82 without T2DM), the kappa of agreement between the PHQ‐9 and the MINI was moderate (0.40 with T2DM, 0.43 without T2DM). Area under the ROC curve for the PHQ‐9 was 0.87 in participants with T2DM and 0.88 in those without. A PHQ‐9 cutoff score of 5 provided the best sensitivity (92.3%), with acceptable specificity (70.4%), for T2DM, similar to sensitivity and specificity in individuals without T2DM. Factor analysis suggested a similar two‐factor structure in both groups (affective and somatic symptoms).
Conclusion
Patient Health Questionnaire‐9 performs well as a screening tool for depressive symptoms in individuals with and without T2DM based on the cutoff value of 5, indicating that the PHQ‐9 can be used in two‐stage screening in primary care to select individuals with T2DM for further psychological evaluation.
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