23 Pre-surgical visceral adipose tissue may be a novel pre-surgical risk factor for acute kidney injury among clear cell renal cell cancer patients undergoing radical nephrectomy
Bibliographic record
Abstract
Abstract Background Patients with renal cancer undergoing partial (PN) or radical nephrectomy (RN) are at risk for acute kidney injury (AKI). Established risk factor for AKI include baseline comorbidities (e.g., obesity, hypertension), normal renal parenchymal loss, and ischemia-reperfusion injury. Pre-surgical body composition may be an overlooked modifiable patient characteristic that influences risk of AKI. We hypothesized that patients with higher visceral adipose tissue quantity or lower skeletal muscle quantity would have a higher risk of AKI. Methods The RESOLVE study at Memorial Sloan Kettering Cancer Center is a retrospective study of 1239 patients with stage I-III clear cell renal cell carcinoma (ccRCC) undergoing PN or RN from 2000 to 2020. We excluded from analysis patients with solitary kidney, those without preoperative serum creatinine (sCr) measurements, and those without a sCr measurement within seven days following nephrectomy, yielding a study population of 1,200 ccRCC patients with longitudinal sCr assessment (n=754 PN; n=446 PN). AKI was defined as a binary variable using the Kidney Disease – Improving Global Outcomes (KDIGO) criteria, based on a threshold of either a 1.5x relative or 0.3 mg/dl absolute increase in sCr within seven days. The cross-sectional areas and radiodensities of visceral adipose and skeletal muscle tissues were determined from pre-surgical computed tomography (CT) scans at the third lumbar vertebrae using Automatica software. We used generalized linear models with a binomial family and identity link to estimate seven-day risk differences (RD) and 95% confidence intervals in subgroups defined by surgery type. Results AKI was more frequent among patients undergoing RN (66% vs 26% among PN). Male patients, those with higher eGFR prior to surgery, those with lower stage/smaller tumors, and those with a history of hyperlipidemia more frequently experienced AKI in the post-operative period. No associations were observed with other reported comorbidities (diabetes, hypertension). While no association was observed between BMI and risk of AKI, visceral adipose and skeletal muscle variables were significantly associated with risk of AKI in univariate models. After adjustment for age, sex, comorbidities, and other body composition variables, only higher visceral adipose tissue quantity remained significantly associated with increased risk of AKI [RD per 40 unit increase (95% CI): 5.2 (1.3, 9.2)]. Muscle characteristics were not associated with AKI in multivariable models. Among patients undergoing PN, we observed a higher frequency of AKI among male patients, those with higher stage/larger tumors, and those with longer ischemia times. No significant associations were observed with comorbidity histories. In univariate models, visceral adipose tissue quantity and quality as well as skeletal muscle quantity were associated with AKI risk; however, after adjustment for age, sex, comorbidities, and other body composition variables, we observed no significant associations between any body composition feature and AKI in patients undergoing PN. Conclusions Associations between pre-surgical body composition and risk of AKI vary by surgery type. Visceral adipose tissue quantity was associated with risk of AKI among patients undergoing RN, but not PN. This finding may be relevant for patient counseling, consideration for nephron-sparing surgery, and development of interventions to lower visceral adipose tissue quantity. Future studies should evaluate the impact of both pre-surgical and post-surgical change in body composition in relation on chronic kidney disease after nephrectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".