Arterial stiffness is associated with visceral fat mass in kidney transplanted patients-A nationwide cohort study.

Arterial stiffness, visceral fat, and hyperglycemia are acknowledged risk factors for adverse outcomes after transplantation, but whether arterial stiffness is associated with visceral adipose tissue and hyperglycemia is unknown. We studied 162 non-diabetic kidney transplant recipients 8-10 weeks after transplantation. Arterial stiffness was measured as pulse wave velocity (PWV) by SphygmoCor and visceral fat using a validated software applied on DXA scans. Also a standard oral glucose tolerance test was performed. Median PWV was 8.6 m/s (IQR 7.3-10.4 m/s). Patients in the upper quartile of PWV had 31%-106% higher visceral fat percentage (P < 0.001), they were older (P < 0.001) and had a fasting plasma glucose of 5.8 mmol/L that was higher than in the other quartiles (P = 0.006). In univariate analysis, visceral fat percentage and age were the parameters strongest associated with PWV (P < 0.001), but cholesterol and glucose were also significant (P < 0.05). In multivariate analysis, visceral fat was the only significant predictor of PWV along with age (P < 0.001). In conclusion, arterial stiffness is significantly associated with visceral fat but not hyperglycemia in non-diabetic kidney transplant patients. We identified age and VAT as risk variables for arterial stiffness. A potential reversibility of arterial wall stiffness with reduction in VAT needs further study.

Visceral fat is strongly associated with post-transplant diabetes mellitus and glucose metabolism 1 year after kidney transplantation.

Body composition after kidney transplantation is linked to glucose metabolism, and impaired glucose metabolism is associated with increased risk of cardiovascular events and death. One year after transplantation, we examined 150 patients for post-transplant diabetes performing oral glucose tolerance tests and body composition measurements including visceral adipose tissue (VAT) content from dual-energy X-ray absorptiometry scans. We found that glucose metabolism was generally improved over the first year post-transplant, and that the levels of VAT and percentage VAT of total body fat mass (VAT%totBFM ) were lowest in those with normal glucose tolerance and highest in those with post-transplant diabetes mellitus. In a multivariable linear regression analysis, 87.4% of the variability in fasting glucose concentration was explained by insulin resistance (P<.001, HOMA-IR index), beta cell function (P<.001, HOMA-beta), VAT%totBFM (P=.007), and body mass index (BMI; P=.015; total model P<.001), while insulin resistance (P<.001) and beta cell function (P<.001) explained 31.9% of the variability in 2-hour glucose concentration in a multivariable model (total model P<.001). VAT was associated with glucose metabolism to a larger degree than BMI. In conclusion, VAT is associated with hyperglycemia one year after kidney transplantation, and insulin resistance and beta cell function estimates are the most robust markers of glucose metabolism.

Prediction of Fat-Free Mass in Kidney Transplant Recipients.

BACKGROUND: Individualization of drug doses is essential in kidney transplant recipients. For many drugs, the individual dose is better predicted when using fat-free mass (FFM) as a scaling factor. Multiple equations have been developed to predict FFM based on healthy subjects. These equations have not been evaluated in kidney transplant recipients. The objectives of this study were to develop a kidney transplant specific equation for FFM prediction and to evaluate its predictive performance compared with previously published equations. METHODS: Ten weeks after transplantation, FFM was measured by dual-energy X-ray absorptiometry. Data from a consecutive cohort of 369 kidney transplant recipients were randomly assigned to an equation development data set (n = 245) or an evaluation data set (n = 124). Prediction equations were developed using linear and nonlinear regression analysis. The predictive performance of the developed equation and previously published equations in the evaluation data set was assessed. RESULTS: The following equation was developed: FFM (kg) = {FFMmax × body weight (kg)/[81.3 + body weight (kg)]} × [1 + height (cm) × 0.052] × [1-age (years) × 0.0007], where FFMmax was estimated to be 11.4 in males and 10.2 in females. This equation provided an unbiased, precise prediction of FFM in the evaluation data set: mean error (ME) (95% CI), -0.71 kg (-1.60 to 0.19 kg) in males and -0.36 kg (-1.52 to 0.80 kg) in females, root mean squared error 4.21 kg (1.65-6.77 kg) in males and 3.49 kg (1.15-5.84 kg) in females. Using previously published equations, FFM was systematically overpredicted in kidney-transplanted males [ME +1.33 kg (0.40-2.25 kg) to +5.01 kg (4.06-5.95 kg)], but not in females [ME -2.99 kg (-4.07 to -1.90 kg) to +3.45 kg (2.29-4.61) kg]. CONCLUSIONS: A new equation for FFM prediction in kidney transplant recipients has been developed. The equation may be used for population pharmacokinetic modeling and clinical dose selection in kidney transplant recipients.

Visceral fat is better related to impaired glucose metabolism than body mass index after kidney transplantation.

The role of visceral adipose tissue (VAT) in post-transplant hyperglycaemia is not known. We evaluated 167 patients without diabetes 8-10 weeks after kidney transplantation, performing oral glucose tolerance tests and measuring VAT content from dual-energy X-ray absorptiometry scans. Median VAT weight in normal glucose tolerance patients was 0.9 kg, impaired fasting glucose patients 1.0 kg, impaired glucose tolerance patients 1.3 kg and patients with post-transplant diabetes (PTDM) 2.1 kg (P = 0.004, indicating a difference between groups). Percentage VAT of total body fat was associated with fasting (R(2) = 0.094, P < 0.001) and 2-h glucose concentration (R(2) = 0.062, P = 0.001), while BMI was only associated with 2-h glucose concentration (R(2) = 0.029, P = 0.028). An association between BMI and 2-h glucose concentration was lost in adjusted models, as opposed to the associations between VAT as percentage of total body fat and glucose concentrations (R(2) = 0.132, P < 0.001 and R(2) = 0.097, P = 0.001, respectively for fasting and 2-h glucose concentration). In conclusion, VAT is more closely related to impaired glucose metabolism than BMI after kidney transplantation. The association with central obesity should encourage additional studies on lifestyle interventions to prevent PTDM.