Association of body mass index change and insulin resistance with survival during induction therapy in newly diagnosed multiple myeloma.
Bibliographic record
Abstract
e24076 Background: Elevated BMI is a known risk factor in newly diagnosed multiple myeloma (NDMM), with extremes (underweight and obese) linked to worse survival. However, the impact of BMI changes and adiponectin leptin (AL) ratio (a marker of insulin resistance and adipose tissue dysfunction) during induction therapy remains unknown. We aimed to identify the risk factors and significance of BMI changes and AL ratio during induction in NDMM. Methods: We retrospectively analyzed 389 NDMM patients (pts) treated with either KRd (N = 191) or VRd (N = 198) from 01/2016 - 12/2022. Data on BMI, age, gender, RISS, cytogenetics, cardiac history, diabetes history. Pts were classified by BMI into underweight (BMI < 18.5), normal (BMI 18.5-24.9), and overweight/obese (BMI ≥25). BMI changes during induction therapy were categorized as weight stable (BMI change < 5%), weight loss (BMI decrease ≥5%), and weight gain (BMI increase ≥5%). We also measured markers of metabolic health (adiponectin, leptin, and adiponectin/leptin (AL) ratio) using biobank specimens from 128/389 pts at baseline, 57 of whom had paired post-induction samples. Associations between baseline BMI, BMI change, and AL ratio with progression-free survival (PFS) and overall survival (OS) were assessed using multivariable Cox regression and landmark analysis. Results: At baseline, 1% of pts were underweight, 22% had normal BMI, 73% were overweight/obese, and 4% had missing data. During induction, 65% were weight stable, 19% experienced weight gain, and 16% experienced weight loss. Older pts were more likely to lose weight during induction (-0.03kg/m 2 per year increase in age, p = 0.0005), and pts with RISS 2-3 were more likely to experience weight changes compared to RISS 1 pts (weight loss: 20% vs 10%; weight gain: 20% vs 15%; p = 0.0075). Compared to weight loss, weight gain during induction was linked to higher progression risk (HR 2.12, p = 0.028), while weight stability did not significantly impact PFS. Elevated BMI (as a continuous variable) correlated with worse OS at baseline (HR 1.05, p = 0.02) and post-induction (HR 1.05, p = 0.03). Consistent with prior evidence, RISS3, and high-risk cytogenetics predicted worse outcomes. A high baseline BMI was associated with a high blood leptin (p < 0.001), low adiponectin (p = 0.0002), and a low AL ratio (p < 0.0001), an association which persisted for post-induction BMI. Higher AL ratio following induction was associated with improved OS (HR = 0.02, p = 0.04). Conclusions: Although most myeloma patients undergoing induction maintained a stable weight, many experienced extreme weight loss or weight gain. Weight gain was linked to worse outcomes during frontline therapy, while a higher AL ratio at end of induction correlated with better survival. These findings emphasize the importance of dietary and metabolic health in myeloma supportive care to improve outcomes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".