Determinants of Left Ventricular Characteristics Assessed by Cardiac Magnetic Resonance Imaging and Cardiovascular Biomarkers Related to Kidney Transplantation
Bibliographic record
Abstract
Background: Cardiac magnetic resonance (CMR) imaging accurately and precisely measures left ventricular (LV) mass and function. Identifying mechanisms by which LV mass change and functional improvement occur in some end-stage kidney disease (ESKD) patients may help to appropriately target kidney transplant (KT) recipients for further investigation and intervention. The concentration of serum adiponectin, a cardiovascular biomarker, increases in cardiac failure, its production being enhanced by B-type natriuretic peptide (BNP), and both serum adiponectin and BNP concentrations decline posttransplantation. Objective: We tested the hypothesis that kidney transplantation alters LV characteristics that relate to serum adiponectin concentrations. Design: Prospective and observational cohort study. Setting: The study was performed at 3 adult kidney transplant and dialysis centers in Ontario, Canada. Patients: A total of 82 KT candidate subjects were recruited (39 to the KT group and 43 to the dialysis group). Predialysis patients were excluded. Measurements: Subjects underwent CMR with a 1.5-tesla whole-body magnetic resonance scanner using a phased-array cardiac coil and retrospective vectorographic gating. LV mass, LV ejection fraction (LVEF), LV end-systolic volume (LVESV), and LV end-diastolic volume (LVEDV) were measured by CMR pre-KT and again 12 months post-KT (N = 39), or 12 months later if still receiving dialysis (N = 43). LV mass, LVESV, and LVEDV were indexed for height (m 2.7 ) to calculate left ventricular mass index (LVMI), left ventricular end-systolic volume index (LVESVI), and left ventricular end-diastolic volume index (LVEDVI), respectively. Serum total adiponectin and N-terminal proBNP (NT-proBNP) concentrations were measured at baseline, 3 months, and 12 months. Methods: We performed a prospective 1:1 observational study comparing KT candidates with ESKD either receiving a living donor organ (KT group) or waiting for a deceased donor organ (dialysis group). Results: Left ventricular mass index change was −1.98 ± 5.5 and −0.36 ± 5.7 g/m 2.7 for KT versus dialysis subjects ( P = .44). Left ventricular mass change was associated with systolic blood pressure (SBP) ( P = .0008) and average LV mass ( P = .0001). Left ventricular ejection fraction did not improve (2.9 ± 6.6 vs 0.7 ± 4.9 %, P = .09), while LVESVI and LVEDVI decreased more post-KT than with continued dialysis (−3.36 ± 5.6 vs −0.22 ± 4.4 mL/m 2.7 , P < .01 and −4.9 ± 8.5 vs −0.3 ± 9.2 mL/m 2.7 , P = .02). Both adiponectin (−7.1 ± 11.3 vs −0.11 ± 7.9 µg/mL, P < .0001) and NT-proBNP (−3811 ± 8130 vs 1665 ± 20013 pg/mL, P < .0001) declined post-KT. Post-KT adiponectin correlated with NT-proBNP ( P = .001), but not estimated glomerular filtration rate (eGFR) ( P = .13). Change in adiponectin did not correlate with change in LVEF in the KT group (Spearman ρ = 0.16, P = .31) or dialysis group (Spearman ρ = 0.19, P = .21). Limitations: Few biomarkers of cardiac function were measured to fully contextualize their role during changing kidney function. Limited intrapatient biomarker sampling and CMR measurements precluded constructing dose-response curves of biomarkers to LV mass and function. The CMR timing in relation to dialysis was not standardized. Conclusions: The LVESVI and LVEDVI but not LVMI or LVEF improve post-KT. LVMI and LVEF change is independent of renal function and adiponectin. As adiponectin correlates with NT-proBNP post-KT, improved renal function through KT restores the normal heart-endocrine axis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".