The change of blood volume, of blood and plasma density (rho b, rho p) following a short ultrafiltration pulse (duration: 20 min; mean rate -35 ml/min) within the first hour of hemodialysis was analyzed in 13 hemodynamically stable patients (30 single measurements). Protein concentration of refilling volume (7 g/liter) was calculated from its density (1009.25 +/- 3.7 kg/m3, at 20 degrees C) and from the linear relationship between plasma density and protein concentration (cp) of uremic plasma samples (rho p = 1007.46 + 0.2422 x cp). The filtration coefficient (Lp,calc) determined from a relation derived from Starling's hypothesis was 5.6 +/- 1.4 ml/(min.mm Hg.50 kg lean body mass); N = 13, mean +/- SD, minimum 3.2, maximum 8.0. A model describing the dynamics of blood and plasma volume was developed. It was fit to on-line measurements of relative blood volume changes by variation of the filtration coefficient and of initial blood volume (Lp,fit, Vb,fit). The linear regression between Vb,fit and blood volume determined from anthropometry (Vb,calc) was highly significant (r = 0.79, N = 30, P < 0.001). Compared to Vb,calc, Vb,fit was typically increased by 21 +/- 11%, reflecting a fluid overload at the beginning of the treatment. Lp,fit was not different from Lp,calc. Lp,fit significantly increased with blood volume excess. Due to the small but definite protein content of refilling volume, the model accounts for increased blood volume recovery and occasional overshoot of blood and plasma volumes following ultrafiltration.

There is uncertainty regarding carotenoid intake recommendations, because positive and negative health effects have been found or are correlated with carotenoid intake and tissue levels (including blood, adipose tissue, and the macula), depending on the type of study (epidemiological vs intervention), the dose (physiological vs supraphysiological) and the matrix (foods vs supplements, isolated or used in combination). All these factors, combined with interindividual response variations (eg, depending on age, sex, disease state, genetic makeup), make the relationship between carotenoid intake and their blood/tissue concentrations often unclear and highly variable. Although blood total carotenoid concentrations &lt;1000 nmol/L have been related to increased chronic disease risk, no dietary reference intakes (DRIs) exist. Although high total plasma/serum carotenoid concentrations of up to 7500 nmol/L are achievable after supplementation, a plateauing effect for higher doses and prolonged intake is apparent. In this review and position paper, the current knowledge on carotenoids in serum/plasma and tissues and their relationship to dietary intake and health status is summarized with the aim of proposing suggestions for a “normal,” safe, and desirable range of concentrations that presumably are beneficial for health. Existing recommendations are likewise evaluated and practical dietary suggestions are included.

BackgroundUrine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration ≥5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82).Methodology/Principal FindingsProteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients.ConclusionsThese data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin.

We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY trial.

BackgroundThe pathogenesis of diabetes mellitus (DM) is variable, comprising different inflammatory and immune responses. Proteome analysis holds the promise of delivering insight into the pathophysiological changes associated with diabetes. Recently, we identified and validated urinary proteomics biomarkers for diabetes. Based on these initial findings, we aimed to further validate urinary proteomics biomarkers specific for diabetes in general, and particularity associated with either type 1 (T1D) or type 2 diabetes (T2D).Methodology/Principal FindingsTherefore, the low-molecular-weight urinary proteome of 902 subjects from 10 different centers, 315 controls and 587 patients with T1D (n = 299) or T2D (n = 288), was analyzed using capillary-electrophoresis mass-spectrometry. The 261 urinary biomarkers (100 were sequenced) previously discovered in 205 subjects were validated in an additional 697 subjects to distinguish DM subjects (n = 382) from control subjects (n = 315) with 94% (95% CI: 92–95) accuracy in this study. To identify biomarkers that differentiate T1D from T2D, a subset of normoalbuminuric patients with T1D (n = 68) and T2D (n = 42) was employed, enabling identification of 131 biomarker candidates (40 were sequenced) differentially regulated between T1D and T2D. These biomarkers distinguished T1D from T2D in an independent validation set of normoalbuminuric patients (n = 108) with 88% (95% CI: 81–94%) accuracy, and in patients with impaired renal function (n = 369) with 85% (95% CI: 81–88%) accuracy. Specific collagen fragments were associated with diabetes and type of diabetes indicating changes in collagen turnover and extracellular matrix as one hallmark of the molecular pathophysiology of diabetes. Additional biomarkers including inflammatory processes and pro-thrombotic alterations were observed.Conclusions/SignificanceThese findings, based on the largest proteomic study performed to date on subjects with DM, validate the previously described biomarkers for DM, and pinpoint differences in the urinary proteome of T1D and T2D, indicating significant differences in extracellular matrix remodeling.