Association of body mass index change and insulin resistance with survival during induction therapy in newly diagnosed multiple myeloma.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».