Search

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.

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.

Aims High-density lipoproteins (HDLs) are considered as anti-atherogenic. Recent experimental findings suggest that their biological properties can be modified in certain clinical conditions by accumulation of serum amyloid A (SAA). The effect of SAA on the association between HDL-cholesterol (HDL-C) and cardiovascular outcome remains unknown. Methods and results We examined the association of SAA and HDL-C with mortality in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study, which included 3310 patients undergoing coronary angiography. To validate our findings, we analysed 1255 participants of the German Diabetes and Dialysis study (4D) and 4027 participants of the Cooperative Health Research in the Region of Augsburg (KORA) S4 study. In LURIC, SAA concentrations predicted all-cause and cardiovascular mortality. In patients with low SAA, higher HDL-C was associated with lower all-cause and cardiovascular mortality. In contrast, in patients with high SAA, higher HDL-C was associated with increased all-cause and cardiovascular mortality, indicating that SAA indeed modifies the beneficial properties of HDL. We complemented these clinical observations by in vitro experiments, in which SAA impaired vascular functions of HDL. We further derived a formula for the simple calculation of the amount of biologically ‘effective' HDL-C based on measured HDL-C and SAA from the LURIC study. In 4D and KORA S4 studies, we found that measured HDL-C was not associated with clinical outcomes, whereas calculated ‘effective' HDL-C significantly predicted better outcome. Conclusion The acute-phase protein SAA modifies the biological effects of HDL-C in several clinical conditions. The concomitant measurement of SAA is a simple, useful, and clinically applicable surrogate for the vascular functionality of HDL

Aims High-density lipoproteins (HDLs) are considered as anti-atherogenic. Recent experimental findings suggest that their biological properties can be modified in certain clinical conditions by accumulation of serum amyloid A (SAA). The effect of SAA on the association between HDL-cholesterol (HDL-C) and cardiovascular outcome remains unknown. Methods and results We examined the association of SAA and HDL-C with mortality in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study, which included 3310 patients undergoing coronary angiography. To validate our findings, we analysed 1255 participants of the German Diabetes and Dialysis study (4D) and 4027 participants of the Cooperative Health Research in the Region of Augsburg (KORA) S4 study. In LURIC, SAA concentrations predicted all-cause and cardiovascular mortality. In patients with low SAA, higher HDL-C was associated with lower all-cause and cardiovascular mortality. In contrast, in patients with high SAA, higher HDL-C was associated with increased all-cause and cardiovascular mortality, indicating that SAA indeed modifies the beneficial properties of HDL. We complemented these clinical observations by in vitro experiments, in which SAA impaired vascular functions of HDL. We further derived a formula for the simple calculation of the amount of biologically ‘effective' HDL-C based on measured HDL-C and SAA from the LURIC study. In 4D and KORA S4 studies, we found that measured HDL-C was not associated with clinical outcomes, whereas calculated ‘effective' HDL-C significantly predicted better outcome. Conclusion The acute-phase protein SAA modifies the biological effects of HDL-C in several clinical conditions. The concomitant measurement of SAA is a simple, useful, and clinically applicable surrogate for the vascular functionality of HDL