Anemia in chronic kidney disease (CKD) is an almost universal complication of this condition. Fibroblast growth factor 23 (FGF23), a key-player in mineral metabolism, is reportedly associated with anemia and hemoglobin levels in non-dialysis CKD patients. Here, we sought to further characterize this association while taking into account the biologically active, intact fraction of FGF23, iron metabolism, and erythropoietin (EPO). Hemoglobin, EPO, iron, and mineral metabolism parameters, including both intact and c-terminal-FGF23 (iFGF23 and cFGF23, respectively) were measured cross-sectionally in 225 non-dialysis CKD patients (stage 1–5, median eGFR: 30 mL/min./1.73m2) not on erythropoiesis stimulating agents or intravenous iron therapy. Statistical analysis was performed by multiple linear regression. After adjustment for eGFR and other important confounders, only cFGF23 but not iFGF23 was significantly associated with hemoglobin levels and this association was largely accounted for by iron metabolism parameters. cFGF23 but not iFGF23 was also associated with mean corpuscular hemoglobin (MCH) and mean corpuscular volume (MCV), again in dependence on iron metabolism parameters. Similarly, EPO concentrations were associated with cFGF23 but not iFGF23, but their contribution to the association of cFGF23 with hemoglobin levels was marginal. In pre-dialysis CKD patients, the observed association of FGF23 with hemoglobin seems to be restricted to cFGF23 and largely explained by the iron status.
The accurate segmentation of in vivo magnetic resonance imaging (MRI) data is a crucial prerequisite for the reliable assessment of disease progression, patient stratification or the establishment of putative imaging biomarkers. This is especially important for the hippocampal formation, a brain area involved in memory formation and often affected by neurodegenerative or psychiatric diseases. FreeSurfer, a widely used automated segmentation software, offers hippocampal subfield delineation with multiple input options. While a single T1-weighted (T1) sequence is regularly used by most studies, it is also possible and advised to use a high-resolution T2-weighted (T2H) sequence or multispectral information. In this investigation it was determined whether there are differences in volume estimations depending on the input images and which combination of these deliver the most reliable results in each hippocampal subfield. 41 healthy participants (age = 25.2 years ± 4.2 SD) underwent two structural MRIs at three Tesla (time between scans: 23 days ± 11 SD) using three different structural MRI sequences, to test five different input configurations (T1, T2, T2H, T1 and T2, and T1 and T2H). We compared the different processing pipelines in a cross-sectional manner and assessed reliability using test-retest variability (%TRV) and the dice coefficient. Our analyses showed pronounced significant differences and large effect sizes between the processing pipelines in several subfields, such as the molecular layer (head), CA1 (head), hippocampal fissure, CA3 (head and body), fimbria and CA4 (head). The longitudinal analysis revealed that T1 and multispectral analysis (T1 and T2H) showed overall higher reliability across all subfields than T2H alone. However, the specific subfields had a substantial influence on the performance of segmentation results, regardless of the processing pipeline. Although T1 showed good test-retest metrics, results must be interpreted with caution, as a standard T1 sequence relies heavily on prior information of the atlas and does not take the actual fine structures of the hippocampus into account. For the most accurate segmentation, we advise the use of multispectral information byusing a combination of T1 and high-resolution T2-weighted sequences or a T2 high-resolution sequence alone.
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