| 1. |
- de las Heras Gala, Tonia, et al.
(author)
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Association of changes in inflammation with variation in glycaemia, insulin resistance and secretion based on the KORA study
- 2018
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In: Diabetes/Metabolism Research and Reviews. - : Wiley. - 1520-7552. ; 34:8
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Journal article (peer-reviewed)abstract
- Aims: Subclinical systemic inflammation may contribute to the development of type 2 diabetes, but its association with early progression of glycaemic deterioration in persons without diabetes has not been fully investigated. Our primary aim was to assess longitudinal associations of changes in pro-inflammatory (leukocytes, high-sensitivity C-reactive protein (hsCRP)) and anti-inflammatory (adiponectin) markers with changes in markers that assessed glycaemia, insulin resistance, and secretion (HbA1c, HOMA-IR, and HOMA-ß). Furthermore, we aimed to directly compare longitudinal with cross-sectional associations. Materials and methods: This study includes 819 initially nondiabetic individuals with repeated measurements from the Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort study (median follow-up: 7.1 years). Longitudinal and cross-sectional associations were simultaneously examined using linear mixed growth models. Changes in markers of inflammation were used as independent and changes in markers of glycaemia/insulin resistance/insulin secretion as dependent variables. Models were adjusted for age, sex, major lifestyle and metabolic risk factors for diabetes using time-varying variables in the final model. Results: Changes of leukocyte count were positively associated with changes in HbA1c and HOMA-ß while changes in adiponectin were inversely associated with changes in HbA1c. All examined cross-sectional associations were statistically significant; they were generally stronger and mostly directionally consistent to the longitudinal association estimates. Conclusions: Adverse changes in low-grade systemic inflammation go along with glycaemic deterioration and increased insulin secretion independently of changes in other risk factors, suggesting that low-grade inflammation may contribute to the development of hyperglycaemia and a compensatory increase in insulin secretion.
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| 2. |
- Klüppelholz, Birgit, et al.
(author)
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Association of subclinical inflammation with deterioration of glycaemia before the diagnosis of type 2 diabetes : the KORA S4/F4 study
- 2015
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In: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 58:10, s. 2269-2277
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Journal article (peer-reviewed)abstract
- Aims/hypothesis The role of biomarkers of subclinical inflammation in the early deterioration of glycaemia before type 2 diabetes is largely unknown. We hypothesised that increased levels of circulating proinflammatory biomarkers and decreased circulating adiponectin would be associated with 7 year increases of HbA1c in non-diabetic individuals.Methods This study was based on individuals who participated in the prospective Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999–2001) and the 7 year follow-up KORA F4 (2006–2008) survey. Individuals with type 2 diabetes at baseline or with a diagnosis of diabetes in the period between both surveys were excluded, which left a sample of 850 men and women. Multivariable linear regression analyses were performed to assess associations among baseline values of leucocyte count and levels of acute-phase proteins (high-sensitivity C-reactive protein [hsCRP], serum amyloid A [SAA] and fibrinogen), IL-6 and adiponectin with changes in HbA1c between baseline and follow-up.Results A high leucocyte count and high hsCRP, SAA and IL-6 levels were positively associated with changes in HbA1c after adjusting for age, sex, lifestyle factors and baseline HbA1c. In contrast, the adiponectin level was inversely associated with changes in HbA1c (p value between <0.0001 and 0.020). The associations of leucocyte count and levels of hsCRP and SAA with HbA1c changes remained significant after additional adjustment for waist circumference and circulating lipids at baseline and for the 7 year change in waist circumference (p value between 0.004 and 0.045).
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| 3. |
- Rauh, Simone P, et al.
(author)
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Predicting glycated hemoglobin levels in the non-diabetic general population : development and validation of the DIRECT-DETECT prediction model - a DIRECT study
- 2017
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 12:2
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Journal article (peer-reviewed)abstract
- 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.
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