Variability of glomerular filtration rate estimation using single injection methods in Type 1 diabetic patients

Decreased eGFR as a Risk Factor for Heart Failure in 13 781 Individuals With Type 1 Diabetes

BACKGROUND

Impaired renal function is a well-known risk factor of cardiovascular disease, but its relation to heart failure in individuals with type 1 diabetes has been sparsely studied. The aim of our study was to quantify the risk increase for development of heart failure with decreasing kidney function in individuals with type 1 diabetes.

METHODS

Three equations were used to calculate eGFR (estimated glomerular filtration rate) for individuals with T1D and no known heart failure in the Swedish National Diabetes Registry. Proportional hazards regression models were constructed to evaluate the association between eGFR and hospitalization for heart failure (HF).

RESULTS

Among 13 781 individuals (mean age 41.1 [SD 13.3] years at baseline), 330 (2.4%) were hospitalized for HF over median follow-up of 7.0 years. Renal function was normal (eGFR > 90 mL/min/1.73 m(2)) in 67% of individuals according to the Cockcroft-Gault formula, compared to 51% and 41% according to the Chronic Kidney Disease Epidemiology (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) formulas. For eGFR 45-60 ml/min/1.73 m(2), hazard ratios (HRs) for hospitalization (reference >90 mL/min/1.73 m(2)) were 3.18 (95% CI 2.17, 4.65), 2.12 (1.16, 3.08), and 2.44 (1.69, 3.55) using the Cockcroft-Gault, MDRD, and CKD-EPI formulas. With eGFR <30 ml/min/1.73 m(2) there was a HR of 3.78 (2.15, 5.91), 3.44 (2.14, 5.51), and 3.51 (2.21, 5.51) compared to normal kidney function (>90 mL/min/1.73 m(2)).

CONCLUSIONS

In individuals with T1D, risk of hospitalization for heart failure was over 2 times greater at eGFR 45-60 ml/min/1.73 m(2) and more than 3 times greater at eGFR <30 ml/min/1.73 m(2) when compared to normal eGFR.

Variability in estimated glomerular filtration rate is an independent risk factor for death among patients with stage 3 chronic kidney disease

Associations between variability of glomerular filtration rate (GFR), death, and cardiovascular events have not been reported among patients with chronic kidney disease (CKD). In order to evaluate this, we retrospectively analyzed the risk of death and de novo heart failure as a function of variability in estimated GFR among a cohort of 3361 patients with stage 3 CKD. At baseline, patients with greater variability were younger, more likely to have diabetes, hypertension, and other comorbid conditions, and were more likely to have proteinuria and higher estimated GFR. In multivariate-adjusted Cox proportional hazard models over a median follow-up of 3.9 years, the risk of death associated with the highest relative to the lowest quartile of variability was 1.40 (95% confidence interval 1.05-1.87); there was no association with new-onset heart failure. The mortality association was independent of serum albumin, proteinuria, baseline estimated GFR, and the slope of the estimated GFR. Thus, variability in estimated GFR predicts death among patients with stage 3 CKD independent of previously reported risk factors. The prognostic utility of complementing existing risk stratification metrics with dynamic changes in GFR among patients with CKD warrants investigation.

Tikhonov adaptively regularized gamma variate fitting to assess plasma clearance of inert renal markers

The Tk-GV model fits Gamma Variates (GV) to data by Tikhonov regularization (Tk) with shrinkage constant, λ, chosen to minimize the relative error in plasma clearance, CL (ml/min). Using (169)Yb-DTPA and (99m)Tc-DTPA (n = 46, 8-9 samples, 5-240 min) bolus-dilution curves, results were obtained for fit methods: (1) Ordinary Least Squares (OLS) one and two exponential term (E₁ and E₂), (2) OLS-GV and (3) Tk-GV. Four tests examined the fit results for: (1) physicality of ranges of model parameters, (2) effects on parameter values when different data subsets are fit, (3) characterization of residuals, and (4) extrapolative error and agreement with published correction factors. Test 1 showed physical Tk-GV results, where OLS-GV fits sometimes-produced nonphysical CL. Test 2 showed the Tk-GV model produced good results with 4 or more samples drawn between 10 and 240 min. Test 3 showed that E₁ and E₂ failed goodness-of-fit testing whereas GV fits for t > 20 min were acceptably good. Test 4 showed CL(Tk-GV) clearance values agreed with published CL corrections with the general result that CL(E1) > CL(E2) > CL(Tk-GV) and finally that CL(Tk-GV) were considerably more robust, precise and accurate than CL(E2), and should replace the use of CL(E2) for these renal markers.

Physiological hyperinsulinemia has no detectable effect on access of macromolecules to insulin-sensitive tissues in healthy humans

OBJECTIVE

Physiologically elevated insulin concentrations promote access of macromolecules to skeletal muscle in dogs. We investigated whether insulin has a stimulating effect on the access of macromolecules to insulin-sensitive tissues in humans as well.

RESEARCH DESIGN AND METHODS

In a randomized, controlled trial, euglycemic-hyperinsulinemic clamp (1.2 mU x kg(-1) x min(-1) insulin) and saline control experiments were performed in 10 healthy volunteers (aged 27.5 +/- 4 years, BMI 22.6 +/- 1.6 kg/m(2)). Distribution and clearance parameters of inulin were determined in a whole-body approach, combining primed intravenous infusion of inulin with compartment modeling. Inulin kinetics were measured in serum using open-flow microperfusion in interstitial fluid of femoral skeletal muscle and subcutaneous adipose tissue.

RESULTS

Inulin kinetics in serum were best described using a three-compartment model incorporating a serum and a fast and a slow equilibrating compartment. Inulin kinetics in interstitial fluid of peripheral insulin-sensitive tissues were best represented by the slow equilibrating compartment. Serum and interstitial fluid inulin kinetics were comparable between the insulin and saline groups. Qualitative analysis of inulin kinetics was confirmed by model-derived distribution and clearance parameters of inulin. Physiological hyperinsulinemia (473 +/- 6 vs. 18 +/- 2 pmol/l for the insulin and saline group, respectively; P < 0.001) indicated no effect on distribution volume (98.2 +/- 6.2 vs. 102.5 +/- 5.7 ml/kg; NS) or exchange parameter (217.6 +/- 34.2 vs. 243.1 +/- 28.6 ml/min; NS) of inulin to peripheral insulin-sensitive tissues. All other parameters identified by the model were also comparable between the groups.

CONCLUSIONS

Our data suggest that in contrast to studies performed in dogs, insulin at physiological concentrations does not augment recruitment of insulin-sensitive tissues in healthy humans.