Biomarkers of Cellular Senescence and their Association with Frailty, Clinical Outcomes, and Survival in Multiple Myeloma

Biomarkers of Cellular Senescence and their Association with Frailty, Clinical Outcomes, and Survival in Multiple Myeloma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Biomarkers of Cellular Senescence and their Association with Frailty, Clinical Outcomes, and Survival in Multiple Myeloma Nadine Abdallah, Byron Smith, Thomas White, Amanda Heeren, Tanya Wildes, and 23 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8605152/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract Frailty predicts adverse outcomes in multiple myeloma (MM), yet an objective frailty biomarker is lacking. Circulating senescence-associated secretory phenotype (SASP) factors correlate with frailty and adverse outcomes in chronic diseases, but their relevance in MM is unclear. In this study, we investigated the relationship between 36 plasma SASP factors, frailty, and clinical outcomes in: (1) a historical cohort using stored specimens from 59 patients with newly diagnosed MM, and (2) a prospective cohort of 36 patients initiating new MM treatment. Several SASP factors were significantly associated with frailty after adjusting for age, sex, and body mass index; GDF-15, TNF-RI, IL-6, and IL-8 showed positive correlations with both cumulative-deficit based and physical frailty indices. Additionally, significant age-adjusted associations were observed between SASP factors and treatment outcomes, including overall survival (OS), high-grade toxicity, and healthcare utilization. We developed a 5-factor SASP model that demonstrated superior predictive performance for OS compared to chronological age, R-ISS stage, and clinical frailty, and TNF-RI alone (5-year OS AUC: 0.90). These findings highlight the association between senescence biomarkers and frailty and OS, and support further investigation of SASP factors as objective frailty biomarkers in MM. Health sciences/Medical research/Translational research Biological sciences/Cancer/Haematological cancer/Myeloma Figures Figure 1 Figure 2 Introduction Multiple myeloma (MM) is a clonal plasma cell disorder that is characterized by marked heterogeneity in disease and patient characteristics, resulting in variable treatment outcomes. One factor that has emerged as a critical determinant of treatment outcomes is frailty, 1 a multidimensional syndrome reflecting reduced physiological reserve. 2 In MM, frailty is significantly associated with increased treatment-related toxicity and decreased overall survival (OS). 1 Over the past decade, several tools have been developed to quantify frailty in MM, 3-8 among which the International Myeloma Working Group (IMWG) frailty index (FI) is the most widely recognized. 1 Despite their prognostic value, the implementation of frailty assessments in routine clinical practice remains limited, primarily due to time, resource, and workflow constraints. 9,10 While physician-rated performance scores like the Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) offer practicality, they are subjective and often discordant with the patient’s perceived physical function. 11,12 On the other hand, objective measures like hand grip strength and gait speed, which are components of the physical frailty phenotype, are resource-consuming and confounded by disease-related symptoms. Another limitation of some existing tools is their reliance on chronological age, which can overestimate frailty in robust older adults and underestimate it in younger patients who have comorbidities and/or functional decline; we previously reported that approximately 30% of newly diagnosed MM patients less than 65 years of age in our cohort were classified as frail using an age-independent definition, underscoring that chronological age is not an appropriate surrogate for biological age. 13 Together, these limitations have fueled interest in developing efficient and scalable biomarkers of frailty that can be readily integrated into both clinical care and research. The hallmarks of aging, in particular cellular senescence, have emerged as attractive candidate biomarkers of frailty, as they represent the fundamental biological drivers of physiological decline. 14-17 Cellular senescence is a state of stable growth arrest induced by cell stress. Senescent cells accumulate across tissues with aging and drive numerous aging-related pathologies, including frailty, in part through their production of proinflammatory cytokines, chemokines, growth factors, and proteases, collectively known as the senescence-associated secretory phenotype (SASP). 18 The persistent SASP factors contribute chronic inflammation, leading to tissue dysfunction, decreased physiological reserve, and increased vulnerability to stressors. 17-20 Recent work by Schafer et al. demonstrated that elevated levels of specific SASP factors in peripheral blood were strongly associated with clinical frailty, measured using a cumulative deficit FI, across multiple patient cohorts, and were predictive of adverse post-operative outcomes. 17 Notably, a SASP-factor panel outperformed chronological age and the frailty index in predictive power, highlighting the potential of SASP factors as quantifiable circulating biomarkers of frailty. 17 The utility of SASP factors as frailty and prognostic biomarkers in the context of MM remains unclear. Thus, we conducted this study to examine the association between circulating SASP factors and frailty in two separate cohorts of patients with MM, and to assess their prognostic utility significance within these cohorts. Methods Population: To evaluate the relationship between circulating SASP factor levels and frailty, we used plasma samples from two independent patient cohorts seen at Mayo Clinic in Rochester, Minnesota: Cohort 1 – Historical Cohort – included 59 patients with newly diagnosed MM who had stored plasma samples available in our institutional Biobank and sufficient clinical information to calculate the FI at diagnosis (see FI calculation section). Patients were diagnosed between February 19, 2010, and March 26, 2018. Cohort 2 - Contemporary cohort – included 36 patients with newly diagnosed or relapsed/refractory MM who were scheduled to initiate a new line of therapy for MM. Peripheral blood samples were collected and frailty assessments were performed prior to treatment initiation. Patients were diagnosed between January 22, 2009, and May 31, 2024, and blood samples were collected between October 9, 2023, and June 21, 2024; the median time from diagnosis to sample collection was 2.0 months (IQR: 1–60). For the first 4 months of treatment, data was collected on treatment-related adverse events according to National Cancer Institute CTCAE v5.0, 21,22 and on healthcare utilization (Emergency department visits/unplanned hospitalizations). FI calculation : For Cohort 1 , the FI was retrospectively calculated based on the cumulative deficit approach using 32 items abstracted from the electronic medical record, as previously described; 13 These included 14 patient-reported items on activities of daily living (ADLs), instrumental activities of daily living (IADLs), dependence on a device for normal breathing, and exercise tolerance. The other 16 items included the body mass index (BMI) and comorbidities. Individual items were scored as 0, 0.5, or 1, with 1 indicating a deficit. The FI was defined as the sum of individual scores divided by the total number of non-missing items, and patients with >2 missing items were excluded. 13 For Cohort 2 , the FI was calculated using two approaches: 1) a cumulative deficit approach adapted from the CARE-FI model, integrating patient-reported geriatric and quality of life domains. 23 To collect these data, participants completed an electronic version of the Cancer and Aging Research Group (CARG) geriatric assessment tool, which compiles validated measures of functional status, comorbidities, cognition, psychological well-being, social functioning, social support, and nutrition. In addition, patients completed the PROMIS-10 Global Health Short Form, a validated 10-item questionnaire assessing overall physical and mental health. 24,25 A total of 40 health deficits were included in the FI, each scored as 0, 0.5, or 1, with higher scores indicating greater deficit. The overall FI was calculated as the sum of all individual deficit scores divided by the total number of non-missing items. 2) The physical frailty phenotype was determined based on 5 criteria, each scored as 0 (absent) or 1 (present): unintentional weight loss ≥10 lb. in previous year, self-reported exhaustion, weakness measured by grip strength, slow walking speed, and low physical activity. 26 The total score ranges from 0 to 5, with 0 indicating robust, 1–2 indicating prefrail, and ≥3 indicating frail status. Measurement of circulating SASP factors: The plasma concentrations of 36 SASP factors previously linked to biological aging and/or morbidity were measured. 27-29 These included ADAMTS13, activin A, Eotaxin, Fas, GDF-15, GROα, ICAM-1, IFNγ, IL-15, IL-6, IL-7, IL-8, MCP-1, MDC, MMP-1, MMP-2, MMP-7, MMP-9, MPO, osteoactivin, osteopontin, PAI-1, PARC, PDGF-AA, PDGF-AB, PLA2G7, RAGE, RANTES, SOST, SPARC, TNF-RI, TNF-RII, TNFα, TRAIL, VEGF, and uPAR. SASP factor levels were quantified from EDTA plasma samples using commercially available multiplex magnetic bead–based immunoassays (R&D Systems) on the Luminex xMAP multianalyte profiling platform and analyzed with the MAGPIX System (Merck Millipore). Activin A was quantified using a Quantikine ELISA kit (R&D Systems), and PAI-2 was measured using an ELISA kit from Cloud-Clone Corp. All assays were performed according to the manufacturers’ instructions. Statistical Analysis: Baseline characteristics were summarized using descriptive statistics. Associations between individual SASP markers and the FI (treated as a continuous variable) were assessed using Spearman and partial Spearman rank correlations, with both unadjusted and age-, sex-, and BMI–adjusted estimates reported. The adjustments for age and BMI were based on known associations between chronological age and obesity respectively, and senescent cell burden. Adjustments for sex were based on known sex differences in risk profiles for age-related diseases. 17 Logistic regression models were used to evaluate the association between baseline SASP factor concentrations and binary outcomes, including grade ≥3 toxicity and ≥1 ED visit/unplanned hospitalization within the first 4 months of treatment ( Cohort 2 only). Univariable Cox proportional hazards models were used to evaluate the association between each biomarker and time dependent outcomes ( Cohort 1 only): Progression-free survival (PFS), TTNT (time to next treatment), and overall survival (OS). Separate bivariate models adjusting for age were also constructed. TTNT was defined as the interval from diagnosis to the initiation of the next line of therapy. Patients who had not started a new treatment at the time of last follow-up were censored at that date. PFS was defined as the time from diagnosis to documented disease progression or death from any cause, whichever occurred first. Patients alive and without evidence of progression at last follow-up were censored at that date. OS was defined as the time from diagnosis to death from any cause, with patients still alive at last follow-up censored on that date. For OS, time-dependent receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated for each predictor at 3 and 5 years. For time-dependent models, SASP factor concentrations were log-transformed [log(x + 1)] to reduce skewness, stabilize variance, and better satisfy proportional hazards assumptions. Parsimonious models were developed to predict OS ( cohort 1 ) and FI (both cohorts) using the least absolute shrinkage and selection operator (LASSO) applied to log-transformed biomarker concentrations. LASSO was chosen to accommodate the modest sample size and address multicollinearity among predictors. The optimal penalty parameter was determined through five-fold cross-validation, and variables with non-zero coefficients at this value were retained in the final model. For the FI, a LASSO regression was fitted in cohort 1 , with FI as the dependent variable. Model performance was assessed using R², F-statistic, and residual standard error. The resulting model was externally validated in cohort 2 by correlating LASSO-predicted frailty scores with both cumulative-deficit and physical frailty indices. For OS, a penalized Cox proportional hazards model with LASSO regularization was applied in cohort 1 . Model discrimination was evaluated using Harrell’s concordance index (C-index) and time-dependent AUC at 3 and 5 years. An OS model was not developed for cohort 2 due to limited follow-up. For all analyses, two-sided p values were reported, with p < 0.05 considered statistically significant. Statistical analyses were conducted using R software version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). This study was approved by the Mayo Clinic Institutional Review Board. All participants provided written informed consent for the use of their biological samples and clinical data for research purposes. Results Patient demographic and clinical characteristics Cohort 1 (newly diagnosed MM) : The median age was 60 years (IQR, 55–68), and 29% were female. Patients received proteasome inhibitor (PI)–based (27%), immunomodulatory drug (IMiD)–based (37%), or combined PI + IMiD–based (36%) induction therapy, and 41 (69%) underwent autologous stem-cell transplantation (ASCT) within one year of diagnosis. The median FI was 0.10 (IQR, 0.05–0.20). Cohort 2 (Newly diagnosed and relapsed/refractory) : The median age at sample collection was 70 years (IQR: 67–74), and 16 patients (44%) were female. Twenty-three patients (64%) had newly diagnosed MM, including 3 who underwent ASCT consolidation following induction therapy; the remaining patients had relapsed or refractory disease and were initiating a new line of treatment. Seventeen patients (47%) received quadruplet-based and 19 (53%) received triplet-based regimens; half of the cohort (18 patients) received a daratumumab-containing regimen. The median FI was 0.22 (IQR, 0.13–0.37). For the physical frailty phenotype, over half of patients (53%) had a score of 0 (not frail), while 19% and 17% had scores of 1 and 2 (pre-frail), and 8% and 3% had scores 3 and 4 (frail). Circulating SASP factors correlate with frailty in patients with MM SASP and frailty in Cohort 1: We first evaluated the association between plasma SASP factor concentrations and the FI in Cohort 1 . In unadjusted analyses, 10 SASP factors showed significant positive correlations with the FI: Activin A, Fas, GDF-15, IL-6, IL-8, osteoactivin, osteopontin, TNF-RI, TNF-RII, and uPAR. After adjustment for age, sex, and BMI, all associations remained statistically significant except for osteoactivin (p = 0.06). Three additional factors—GROα, RAGE, and TNF-α—became significantly correlated with frailty in adjusted models. The strongest associations were observed for TNF-RI (r = 0.501, p < 0.001), TNF-RII (r = 0.501, p < 0.001), GDF-15 (r = 0.463, p < 0.001), uPAR (r = 0.423, p = 0.001), IL-8 (r = 0.387, p = 0.003), IL-6 (r = 0.382, p = 0.004), osteopontin (r = 0.345, p = 0.009), and RAGE (r = 0.344, p = 0.009) (Table 1) . SASP and frailty in Cohort 2: We next examined associations between plasma SASP factor concentrations and frailty in Cohort 2 using two distinct frailty measures: a cumulative deficit FI and the physical frailty phenotype. SASP and cumulative deficit index in cohort 2: In unadjusted analyses, factors identified in cohort 1 including : activin A, Fas, GDF-15, IL-6, IL-8, TNF-RI, and RAGE, in addition to IFNγ, MCP-1, and MMP-7, showed significant positive correlations with the FI. After adjustment for age, sex, and BMI, all associations remained statistically significant. The strongest correlations observed were with MMP-7 (r = 0.52, p = 0.002), GDF-15 (r = 0.51, p = 0.002), IL-6 (r = 0.46, p = 0.007), IL-8 (r = 0.44, p = 0.010), IFNγ (r = 0.41, p = 0.019), Fas (r = 0.39, p = 0.027), MCP-1 (r = 0.39, p = 0.025), and TNF-RI (r = 0.39, p = 0.025) (Table 2) . SASP and the physical frailty phenotype in cohort 2: In unadjusted analyses, and consistent with the FI results, Fas, GDF-15, IL-6, IL-8, MMP-7, osteoactivin, and TNF-RI, along with IL-15, showed significant positive correlations with physical frailty. After adjustment for age, sex, and BMI, all associations remained statistically significant except for MMP-7 and osteoactivin. The strongest associations were observed for IL-6 (r = 0.522, p = 0.002), GDF-15 (r = 0.490, p = 0.004), TNF-RI (r = 0.437, p = 0.011), IL-15 (r = 0.412, p = 0.017), Fas (r = 0.351, p = 0.045), and IL-8 (r = 0.373, p = 0.033) (Table 3) . A multi-factor SASP model correlates with frailty in both cohorts: A LASSO regression model was fitted in cohort 1 to predict the FI using SASP factors as predictors. The model performance was then assessed in cohort 2 . In cohort 1 , the model retained GDF-15, TNF-RI, and uPAR, yielding an R² of 0.31 and an F-statistic of 8.37 ( p < 0.001) with a residual standard error of 0.11. When this 3-biomarker model was applied to cohort 2 , the predicted frailty scores demonstrated moderate to strong correlations with both the FI (r = 0.44, p = 0.007; Figure 1A ) and the physical frailty phenotype ( r = 0.41, p = 0.013; Figure 1B ). Select SASP factors are associated with non-hematologic toxicity and healthcare utilization in MM We examined the association between plasma SASP factor concentrations and treatment toxicity in cohort 2 . SASP and Grade 3–4 non-hematologic toxicity: Five of 36 patients (14%) had grade 3–4 non-hematologic toxicity during the first 4 months of treatment. In age-adjusted analyses, higher baseline concentrations of several SASP factors were significantly associated with an increased likelihood of grade 3–4 non-hematologic toxicity. These included GDF-15 (odds ratio [OR]: 4.8, 95% CI: 1.5–29.9, p = 0.006; AUC: 0.85), Fas (OR: 3.3, 95% CI: 1.0–16.7, p = 0.04; AUC: 0.80), PAI-1 (OR: 6.3, 95% CI: 1.7–78.3, p = 0.004; AUC: 0.90), SPARC (OR: 2.7, 95% CI: 1.1–8.5, p = 0.03; AUC: 0.83), and IL-6 (OR: 3.1, 95% CI: 1.1–28.1, p = 0.03; AUC: 0.89). SASP and healthcare utilization : At least 1 ED visit and/or unplanned hospitalization(s) occurred in 15 of 36 patients (42%). In age-adjusted analyses, higher baseline concentrations of eotaxin (OR: 2.7, 95% CI: 1.2–7.4, p = 0.01; AUC: 0.76), TNF-RI (OR: 2.7, 95% CI: 1.2–8.0, p = 0.01; AUC: 0.81), TNF-RII (OR: 2.7, 95% CI: 1.2–7.3, p = 0.01; AUC: 0.79), and IFNγ (OR: 4.1, 95% CI: 1.4–21.9, p = 0.006; AUC: 0.77) were significantly associated with increased healthcare utilization. In contrast, higher concentrations of PDGF-AA (OR: 0.37, 95% CI: 0.07–0.95, p = 0.04; AUC: 0.72) and PDGF-AB (OR: 0.36,95% CI: 0.06-0.92, p = 0.03; AUC: 0.73) were significantly associated with decreased healthcare utilization SASP factors are associated with inferior PFS and TTNT in MM We examined the association between plasma SASP factor concentrations and PFS and TTNT in cohort 1 . Median follow-up was 5.0 (95% CI: 3.6–7.1) years. Elevated levels of several SASP factors were significantly associated with shorter PFS after adjusting for chronological age: Eotaxin (HR: 2.4, 95% CI: 1.1–4.9; P = 0.02), VEGF (HR: 1.9, 95% CI: 1.2–3.1; P = 0.01), RAGE (HR: 2.4, 95% CI: 1.1–5.2; P = 0.04), TNF-RII (HR = 4.4, 95% CI: 1.9–10.3; P < 0.001), GDF-15 (HR: 1.8, 95% CI 1.1–2.9; P = 0.02), TNF-RI (HR: 3.5, 95% CI: 1.6–7.7; P = 0.002), uPAR (HR: 3.7, 95% CI: 1.7–8.4; P = 0.002), Activin A (HR: 2.1, 95% CI: 1.1–3.7; P = 0.02), and IL-8 (HR: 2.7, 95% CI: 1.3–5.7; P = 0.008). Among these factors, RAGE (HR: 2.4, 95% CI: 1.0-5.8, P=0.046), TNF-RII (HR: 16.7, 95%CI: 4.9-64.5, P<0.001), TNF-R1 (HR: 10.0, 95% CI: 3.3-30.9, P<0.001), and uPAR (HR: 5.7, 95% CI: 1.7-21.4, P=0.007) were significant associated with shorter TTNT. SASP factors are associated with inferior OS in MM Finally, we examined the association between plasma SASP factor concentrations and OS in cohort 1 . In Cox models using log-transformed biomarker concentrations, several SASP factors were significantly associated with inferior OS after adjusting for age, including activin A, ICAM-1, GDF-15, IL-8, MMP-7, osteopontin, RAGE, SOST, TNF-RI, TNF-RII, uPAR, and VEGF. The strongest predictors of OS were TNF-RI (HR: 10.5, C-index = 0.813, p < 0.001), TNF-RII (HR: 11.9, C-index = 0.789, p < 0.001), uPAR (HR: 7.8, C-index = 0.781, p < 0.001), IL-8 (HR: 5.3, C-index = 0.744, p < 0.001), and GDF-15 (HR: 2.9, C-index = 0.786, p < 0.001) (Table 4) . A LASSO-Cox model was developed to predict OS using SASP factors as predictors. The model for OS retained IL-8, osteopontin, SOST, TNF-RI, and uPAR. This 5-factor model demonstrated superior predictive performance for OS compared with chronological age, R-ISS stage, the FI, and TNF-RI alone. The 5-year AUC values were chronological age: 0.57, R-ISS stage III (vs I/II): 0.61, FI: 0.75, TNF-RI: 0.78, and the SASP panel: 0.90 (Figure 2B) . The corresponding AUC values for 3-year OS were 0.58, 0.58, 0.67, 0.80, and 0.89, respectively (Figure 2A) . Discussion The SASP is increasingly recognized as a key mediator of aging-related conditions, including frailty, and is linked to adverse clinical outcomes across diverse populations. 17-20,30-33 Although some differences were observed, GDF-15, TNF-RI, IL-6, and IL-8 consistently correlated with frailty across both patient cohorts, as measured by both clinical and phenotypic frailty indices. These findings partially align with Schafer et al., who reported associations between TNF-R1, GDF-15, and osteopontin with frailty index scores across three independent cohorts (older adults undergoing aortic stenosis surgery, older adults undergoing ovarian cancer surgery, and community biobank participants), while activin A and IL-6 were associated with frailty primarily in nonsurgical populations. 17 Our analysis also revealed that several SASP factors were associated with reduced PFS and OS in newly diagnosed MM patients, suggesting that increased SASP levels may reflect an interplay between disease burden and biological aging. We hypothesize that MM-related inflammation may accelerate cellular senescence and physiological decline, manifesting as disease-related frailty in some patients. This is supported by reports showing that frailty status improvements in a subset of patients following effective MM therapy. 34,35 We have previously demonstrated that cellular senescence influences disease evolution from monoclonal gammopathy of undetermined significance to symptomatic MM. 36 Moreover, individual SASP factors may exert distinct effects at the intersection of aging and disease progression. For example, in our previous study, GDF-15 and TNF-R1 were enriched in patients with stable smoldering MM, suggesting these markers may be independent of MM pathogenesis. In active MM, Corre et al. reported GDF-15 overexpression by bone marrow mesenchymal stromal cells enhanced stromal support, promoting aggressive disease features, treatment resistance, and inferior clinical outcomes. 37 Consistent with this, we found that elevated GDF-15 in MM was significantly associated with shorter PFS and OS. Other SASP factors, including eotaxin, SOST, and ICAM-1, were linked to clinical outcomes but not frailty. Notably, only GDF-15, Fas, PAI-1, SPARC, and IL-6 were significantly associated with high grade non-hematologic toxicity after adjusting for age. However, the overall incidence of high-grade toxicity in cohort 2 was low, possibly reflecting improved tolerability of contemporary treatment regimens and/or the limited sensitivity of CTCAE grading in capturing patient symptoms. Additionally, only TNF-R1, TNF-RII, eotaxin, and IFN-γ were significantly associated with ED visits/unplanned hospitalizations, likely due to disease-related complications (e.g., bone pain, pathological fracture) and/or patient-specific factors comorbidities. The consistent associations of GDF-15, TNF-R1, and IL-6 with both frailty and adverse outcomes across oncologic and non-oncologic settings suggest that these factors contribute to frailty through mechanisms extending beyond MM biology. Furthermore, their superior prognostic performance compared to disease stage and chronological age further indicates that these associations with clinical vulnerability are not solely driven by disease burden. Consistent with the findings of Schafer et al., our findings support the use of a multi-marker SASP panel for improved prognostic accuracy compared to a single biomarker. 17 Beyond their prognostic potential, SASP factors could serve as modifiable therapeutic targets through both pharmacologic and non-pharmacologic strategies. These include senolytics, which selectively induce apoptosis in senescent cells, and senomorphics, which modulate senescent cell activity without causing cell death. The senolytic combination of dasatinib plus quercetin (D+Q), has been shown to lower circulating SASP factors and reduce senescence-associated dysfunction in preclinical 38,39 and early clinical studies. 40 JAK inhibition also suppressed SASP production and alleviated age-related frailty in a preclinical study. 41 Similarly, NF-κB pathway inhibition was shown to mitigate chemotherapy-induced cachexia and sarcopenia in a preclinical study by reducing senescence-mediated signaling including SASP secretion. 42 SASP modulation has also been achieved with non-pharmacologic interventions: in a study by Englund et al., 12 week structured exercise interventions were associated with significant reductions in SASP factor levels and improvement in physical function in older adults, with baseline SASP levels predicting functional improvements. 28 Our study has several limitations including modest sample size, single institution design, and predominantly white population, which may limit the generalizability of our findings. Due to small sample size, our analyses were not sufficiently powered to assess subgroup effects or to adjust for all potential confounders. Additionally, the relatively short follow-up period in cohort 2 precluded evaluation of survival outcomes in this group. We also acknowledge that while senescent cells are a plausible source of the cytokines, chemokines, tissue remodeling, growth factor, and other proteins measured, they are not the only source. Despite these limitations, to our knowledge, this study represents the first systematic evaluation of circulating SASP factors as correlates of frailty in MM. The replication of key findings across two independent cohorts supports the internal consistency and robustness of our results. Notably, although cohort 2 was small, it was prospectively designed and included comprehensive and prospectively collected data on toxicity and healthcare utilization. Our observations and reported associations need to be validated in larger studies with more diverse populations. Beyond SASP factors, other molecular markers of biological aging—such as p16^INK4a^ expression and epigenetic clocks—may also serve as objective measures of frailty in patients with MM, as suggested by recent work from Rosko et al., and warrant further investigation. 14 Conclusion In conclusion, we found that specific SASP proteins are associated with frailty in patients with MM and are predictive of inferior OS. These findings highlight the potential for SASP factors as objective biomarkers of physiological reserve, complementing traditional clinical assessments and warrant further validation in larger prospective studies. Declarations Acknowledgements: This work was supported by the American Society of Hematology (ASH) through the Research Training Award for Fellows (RTAF) granted to Nadine Abdallah. This project was also supported by the Glenn Foundation for Medical Research and grants from the National Institute on Aging, including R01 AG55529 and R01 AG089150 to NKL. Competing Interests: T.M.W: served as a consultant for Janssen and Pfizer and has received research funding from Janssen. A.D. received research funding from Celgene, Millennium Pharmaceuticals, Pfizer, and Janssen, and a travel grant from Pfizer. M.G. served as a consultant for Millennium Pharmaceuticals and received honoraria from Celgene, Millennium Pharmaceuticals, Onyx Pharmaceuticals, Novartis, GlaxoSmithKline, Prothena, Ionis Pharmaceuticals, and Amgen. S.V.R. has received grants from the National Institutes of Health and research funding from Celgene for clinical trials. S.K. served as a consultant for Celgene, Millennium Pharmaceuticals, Onyx Pharmaceuticals, Janssen, and Bristol Myers Squibb, and received research funding from Celgene, Millennium Pharmaceuticals, Novartis, Onyx Pharmaceuticals, AbbVie, Janssen, and Bristol Myers Squibb. The remaining authors declare no competing financial interests. Rest of the authors do not have any relevant disclosures. Author Contributions: Conceptualization of the project: NA, NL, SKK, and MMW; Data collection and analysis: NA, BS, ThW, AH, BK, RM, CY, AB, NL MWW; Discussion and interpretation of results: NA, BS, NL, SKK, MMW; Drafting of manuscript: NA, MMW; All authors reviewed the final manuscript. Data Availability Statement: The datasets analyzed for this study are provided upon reasonable request from the corresponding author. References Palumbo A, Bringhen S, Mateos M-V, et al. Geriatric assessment predicts survival and toxicities in elderly myeloma patients: an International Myeloma Working Group report. Blood 2015;125(13):2068-2074. DOI: 10.1182/blood-2014-12-615187. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet 2013;381(9868):752-62. DOI: 10.1016/S0140-6736(12)62167-9. Engelhardt M, Domm AS, Dold SM, et al. A concise revised Myeloma Comorbidity Index as a valid prognostic instrument in a large cohort of 801 multiple myeloma patients. Haematologica 2017;102(5):910-921. (In eng). DOI: 10.3324/haematol.2016.162693. Sandra Maria D, Mandy-Deborah M, Gabriele I, et al. Validation of the revised myeloma comorbidity index and other comorbidity scores in a multicenter German study group multiple myeloma trial. Haematologica 2020;106(3):875-880. DOI: 10.3324/haematol.2020.254235. Cook G, Royle KL, Pawlyn C, et al. A clinical prediction model for outcome and therapy delivery in transplant-ineligible patients with myeloma (UK Myeloma Research Alliance Risk Profile): a development and validation study. Lancet Haematol 2019;6(3):e154-e166. (In eng). DOI: 10.1016/s2352-3026(18)30220-5. Milani P, Vincent Rajkumar S, Merlini G, et al. N-terminal fragment of the type-B natriuretic peptide (NT-proBNP) contributes to a simple new frailty score in patients with newly diagnosed multiple myeloma. Am J Hematol 2016;91(11):1129-1134. (In eng). DOI: 10.1002/ajh.24532. Facon T, Dimopoulos MA, Meuleman N, et al. A simplified frailty scale predicts outcomes in transplant-ineligible patients with newly diagnosed multiple myeloma treated in the FIRST (MM-020) trial. Leukemia 2020;34(1):224-233. (In eng). DOI: 10.1038/s41375-019-0539-0. Stege CAM, van der Holt B, Dinmohamed AG, et al. Validation of the FIRST simplified frailty scale using the ECOG performance status instead of patient-reported activities. Leukemia 2020;34(7):1964-1966. DOI: 10.1038/s41375-020-0713-4. Walston J, Buta B, Xue QL. Frailty Screening and Interventions: Considerations for Clinical Practice. Clin Geriatr Med 2018;34(1):25-38. DOI: 10.1016/j.cger.2017.09.004. Buta BJ, Walston JD, Godino JG, et al. Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments. Ageing Res Rev 2016;26:53-61. (In eng). DOI: 10.1016/j.arr.2015.12.003. Broderick JM, Hussey J, Kennedy MJ, DM OD. Patients over 65 years are assigned lower ECOG PS scores than younger patients, although objectively measured physical activity is no different. J Geriatr Oncol 2014;5(1):49-56. DOI: 10.1016/j.jgo.2013.07.010. Schnadig ID, Fromme EK, Loprinzi CL, et al. Patient-physician disagreement regarding performance status is associated with worse survivorship in patients with advanced cancer. Cancer 2008;113(8):2205-14. DOI: 10.1002/cncr.23856. Abdallah N, Dizona P, Kumar A, et al. Cumulative deficits frailty index and relationship status predict survival in multiple myeloma. Blood Adv 2025;9(5):1137-1146. DOI: 10.1182/bloodadvances.2024014624. Rosko AE, Elsaid MI, Woyach J, et al. Determining the relationship of p16(INK4a) and additional molecular markers of aging with clinical frailty in hematologic malignancy. J Cancer Surviv 2024;18(4):1168-1178. (In eng). DOI: 10.1007/s11764-024-01591-6. Smitherman AB, Wood WA, Mitin N, et al. Accelerated aging among childhood, adolescent, and young adult cancer survivors is evidenced by increased expression of p16INK4a and frailty. Cancer 2020;126(22):4975-4983. DOI: https://doi.org/10.1002/cncr.33112. Sanoff HK, Deal AM, Krishnamurthy J, et al. Effect of cytotoxic chemotherapy on markers of molecular age in patients with breast cancer. J Natl Cancer Inst 2014;106(4):dju057. (In eng). DOI: 10.1093/jnci/dju057. Schafer MJ, Zhang X, Kumar A, et al. The senescence-associated secretome as an indicator of age and medical risk. JCI Insight 2020;5(12):e133668. (In eng). DOI: 10.1172/jci.insight.133668. Cuollo L, Antonangeli F, Santoni A, Soriani A. The Senescence-Associated Secretory Phenotype (SASP) in the Challenging Future of Cancer Therapy and Age-Related Diseases. Biology 2020;9(12):485. (https://www.mdpi.com/2079-7737/9/12/485). He Y, Xie W, Li H, Jin H, Zhang Y, Li Y. Cellular Senescence in Sarcopenia: Possible Mechanisms and Therapeutic Potential. Front Cell Dev Biol 2021;9:793088. DOI: 10.3389/fcell.2021.793088. Fielding RA, Atkinson EJ, Aversa Z, et al. Associations between biomarkers of cellular senescence and physical function in humans: observations from the lifestyle interventions for elders (LIFE) study. Geroscience 2022 (In English). DOI: 10.1007/s11357-022-00685-2. Common Terminology Criteria for Adverse Events (CTCAE) v5.0. (https://ctep.cancer.gov/protocoldevelopment/electronic_applications/docs/CTCAE_v5_Quick_Reference_5x7.pdf). Dueck AC, Mendoza TR, Mitchell SA, et al. Validity and Reliability of the US National Cancer Institute’s Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). JAMA Oncology 2015;1(8):1051-1059. DOI: 10.1001/jamaoncol.2015.2639. Giri S, Al-Obaidi M, Harmon C, et al. Patient-reported geriatric assessment-based frailty index among older adults with gastrointestinal malignancies. J Am Geriatr Soc 2023;71(1):136-144. (In eng). DOI: 10.1111/jgs.18054. Hurria A, Cirrincione CT, Muss HB, et al. Implementing a geriatric assessment in cooperative group clinical cancer trials: CALGB 360401. J Clin Oncol 2011;29(10):1290-6. DOI: 10.1200/JCO.2010.30.6985. Hurria A, Gupta S, Zauderer M, et al. Developing a cancer-specific geriatric assessment: a feasibility study. Cancer 2005;104(9):1998-2005. DOI: 10.1002/cncr.21422. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56(3):M146-56. (In eng). DOI: 10.1093/gerona/56.3.m146. Schafer MJ, Zhang X, Kumar A, et al. The senescence-associated secretome as an indicator of age and medical risk. JCI Insight 2020;5(12) (In English). DOI: ARTN e13366810.1172/jci.insight.133668. Englund DA, Sakamoto AE, Fritsche CM, et al. Exercise reduces circulating biomarkers of cellular senescence in humans. Aging Cell 2021;20(7):e13415. DOI: https://doi.org/10.1111/acel.13415. Englund DA, Jolliffe A, Aversa Z, et al. p21 induces a senescence program and skeletal muscle dysfunction. Mol Metab 2022:101652. DOI: 10.1016/j.molmet.2022.101652. Tylutka A, Walas L, Zembron-Lacny A. Level of IL-6, TNF, and IL-1beta and age-related diseases: a systematic review and meta-analysis. Front Immunol 2024;15:1330386. DOI: 10.3389/fimmu.2024.1330386. Kim M, Walston JD, Won CW. Associations Between Elevated Growth Differentiation Factor-15 and Sarcopenia Among Community-dwelling Older Adults. J Gerontol A Biol Sci Med Sci 2022;77(4):770-780. DOI: 10.1093/gerona/glab201. Ying L, Zhang Q, Yang YM, Zhou JY. A Combination of Serum Biomarkers in Elderly Patients with Sarcopenia: A Cross-Sectional Observational Study. Int J Endocrinol 2022;2022:4026940. DOI: 10.1155/2022/4026940. Evans DS, Young D, Tanaka T, et al. Proteomic Analysis of the Senescence-Associated Secretory Phenotype: GDF-15, IGFBP-2, and Cystatin-C Are Associated With Multiple Aging Traits. J Gerontol A Biol Sci Med Sci 2024;79(3). DOI: 10.1093/gerona/glad265. Mian H, Wildes TM, Vij R, Pianko MJ, Major A, Fiala MA. Dynamic frailty risk assessment among older adults with multiple myeloma: A population-based cohort study. Blood Cancer J 2023;13(1):76. (In eng). DOI: 10.1038/s41408-023-00843-5. Abdallah N, Dizona P, Kumar A, et al. Cumulative deficits frailty index and relationship status predict survival in multiple myeloma. Blood Advances 2025;9(5):1137-1146. DOI: 10.1182/bloodadvances.2024014624. Borges GA, Diaz-delCastillo M, Guilatco AJ, et al. Senescence profiling of monoclonal gammopathies reveals paracrine senescence as a crucial defense against disease progression. Leukemia 2025;39(5):1206-1217. DOI: 10.1038/s41375-025-02572-z. Corre J, Labat E, Espagnolle N, et al. Bioactivity and Prognostic Significance of Growth Differentiation Factor GDF15 Secreted by Bone Marrow Mesenchymal Stem Cells in Multiple Myeloma. Cancer Research 2012;72(6):1395-1406. DOI: 10.1158/0008-5472.Can-11-0188. Xu M, Pirtskhalava T, Farr JN, et al. Senolytics improve physical function and increase lifespan in old age. Nature Medicine 2018;24(8):1246-1256. DOI: 10.1038/s41591-018-0092-9. Hickson LJ, Langhi Prata LGP, Bobart SA, et al. Senolytics decrease senescent cells in humans: Preliminary report from a clinical trial of Dasatinib plus Quercetin in individuals with diabetic kidney disease. Ebiomedicine 2019;47:446-456. DOI: 10.1016/j.ebiom.2019.08.069. Justice JN, Nambiar AM, Tchkonia T, et al. Senolytics in idiopathic pulmonary fibrosis: Results from a first-in-human, open-label, pilot study. Ebiomedicine 2019;40:554-563. DOI: 10.1016/j.ebiom.2018.12.052. Xu M, Tchkonia T, Ding H, et al. JAK inhibition alleviates the cellular senescence-associated secretory phenotype and frailty in old age. Proc Natl Acad Sci U S A 2015;112(46):E6301-10. (In eng). DOI: 10.1073/pnas.1515386112. Englund DA, Jolliffe AM, Hanson GJ, et al. Senotherapeutic drug treatment ameliorates chemotherapy-induced cachexia. JCI Insight 2024;9(2). DOI: 10.1172/jci.insight.169512. Tables Tables are available in the Supplementary Files section. Additional Declarations Yes there is potential conflict of interest. Supplementary Files Tables.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: revise 12 Feb, 2026 Editor assigned by journal 16 Jan, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 14 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8605152","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590359647,"identity":"3bf68c71-4343-4d41-a64e-cdef094e9912","order_by":0,"name":"Nadine Abdallah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACNgkGA4YHBQwJDBJgxCAHlWDGqYUfpCUBiBhkIFqMCWqRnAHTIgfUAhRIbCCkxeB288YPCQZ2eQxyyQ9vPNxhk762/YzhA4YKa5heTC13jhVLJBgkFzNIpxlbJJ5Jy912JsfYgOFMOm4tN3IMgFqYExukE8wkEtsO5247kGMmwdh2GKcW+xs5xj8SDOoTGySffwNq+Z9udv4NUMs/3FqAtpgBbQEqkHgDsuVAghlIhLEBj5Y7x8osEgyOJ7ZJvCm2SGxLNtx241mxQcKxdGOcWm43b77xoaI6sV8ifePNn2128mbnkzc++FBjLYtLCxywIZgcoGgiDbA/IFHDKBgFo2AUDHMAAF3GYrrv7tmbAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-9195-1589","institution":"Mayo clinic","correspondingAuthor":true,"prefix":"","firstName":"Nadine","middleName":"","lastName":"Abdallah","suffix":""},{"id":590359648,"identity":"dee909cc-6157-40b4-a2bf-46db9e1c266b","order_by":1,"name":"Byron Smith","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Byron","middleName":"","lastName":"Smith","suffix":""},{"id":590359649,"identity":"e536aeb6-3062-4f42-9765-5cb9b5fd9abc","order_by":2,"name":"Thomas White","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"White","suffix":""},{"id":590359650,"identity":"bfb5fd58-89d0-458b-bcce-cc0149386984","order_by":3,"name":"Amanda Heeren","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"","lastName":"Heeren","suffix":""},{"id":590359651,"identity":"43ecbc1a-dcc1-42dd-ba6f-f3b3e1e4b220","order_by":4,"name":"Tanya Wildes","email":"","orcid":"","institution":"Division of Oncology \u0026 Hematology, Department of Internal Medicine, University of Nebraska Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Tanya","middleName":"","lastName":"Wildes","suffix":""},{"id":590359652,"identity":"8b763325-df38-48b2-90ab-6adc6ebfab0f","order_by":5,"name":"Rosalyn Marar","email":"","orcid":"https://orcid.org/0000-0002-6912-6547","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Rosalyn","middleName":"","lastName":"Marar","suffix":""},{"id":590359653,"identity":"9b56de39-5a8e-433e-8e51-8577d249b3b6","order_by":6,"name":"Claire Yee","email":"","orcid":"https://orcid.org/0000-0003-1601-9163","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Yee","suffix":""},{"id":590359654,"identity":"e4a07d21-8b41-4b56-9175-0b760f3bcfc3","order_by":7,"name":"Anna Bodily","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Bodily","suffix":""},{"id":590359655,"identity":"65003f0b-5e39-4c9b-9599-11a2d322fe37","order_by":8,"name":"Brian Kotajarvi","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Kotajarvi","suffix":""},{"id":590359656,"identity":"be3d2960-b1c7-452d-8e79-67532c335187","order_by":9,"name":"Sarah Aug","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Aug","suffix":""},{"id":590359657,"identity":"f6d6bd07-9749-4c16-b4eb-214d36bb5104","order_by":10,"name":"Francis Buadi","email":"","orcid":"https://orcid.org/0000-0003-3214-0203","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Buadi","suffix":""},{"id":590359658,"identity":"64c831ab-f60b-4b27-ab23-452950323f4e","order_by":11,"name":"Prashant Kapoor","email":"","orcid":"https://orcid.org/0000-0002-4342-364X","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Prashant","middleName":"","lastName":"Kapoor","suffix":""},{"id":590359659,"identity":"74744c4d-060d-4259-8c87-a7b6e7201ef8","order_by":12,"name":"Angela Dispenzieri","email":"","orcid":"https://orcid.org/0000-0001-8780-9512","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"","lastName":"Dispenzieri","suffix":""},{"id":590359660,"identity":"a862b610-e381-4d34-864a-bb42ec961ea8","order_by":13,"name":"Suzanne Hayman","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Suzanne","middleName":"","lastName":"Hayman","suffix":""},{"id":590359661,"identity":"5f99e05e-5dc6-4643-aeea-fd82b0aaa84f","order_by":14,"name":"David Dingli","email":"","orcid":"https://orcid.org/0000-0001-7477-3004","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Dingli","suffix":""},{"id":590359662,"identity":"e3ac0355-e9e9-4d20-8654-0b2444583027","order_by":15,"name":"Wilson Gonsalves","email":"","orcid":"https://orcid.org/0000-0001-6890-969X","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Wilson","middleName":"","lastName":"Gonsalves","suffix":""},{"id":590359663,"identity":"20040909-3bda-4707-b75a-4ddd3c123ef0","order_by":16,"name":"Joselle Cook","email":"","orcid":"https://orcid.org/0000-0001-5335-9533","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Joselle","middleName":"","lastName":"Cook","suffix":""},{"id":590359664,"identity":"1d9dc75b-cb6b-455d-a4a5-b3eecdb0a646","order_by":17,"name":"Saurabh Zanwar","email":"","orcid":"https://orcid.org/0000-0001-5074-8453","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Saurabh","middleName":"","lastName":"Zanwar","suffix":""},{"id":590359665,"identity":"5089c082-c591-42aa-8ba2-fd1890580d6f","order_by":18,"name":"Moritz Binder","email":"","orcid":"https://orcid.org/0000-0001-9014-9658","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Moritz","middleName":"","lastName":"Binder","suffix":""},{"id":590359666,"identity":"9eed27f3-c261-495d-abe0-137c3fd8bd91","order_by":19,"name":"Yi Lin","email":"","orcid":"https://orcid.org/0000-0002-1556-6416","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Lin","suffix":""},{"id":590359667,"identity":"ee62f9ba-2f35-4d93-980a-06e79ee3d0aa","order_by":20,"name":"Taxiarchis Kourelis","email":"","orcid":"https://orcid.org/0000-0001-8573-9434","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Taxiarchis","middleName":"","lastName":"Kourelis","suffix":""},{"id":590359668,"identity":"16a6ac47-ff75-4adc-aa77-7f7f80cae72c","order_by":21,"name":"Morie Gertz","email":"","orcid":"https://orcid.org/0000-0002-3853-5196","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Morie","middleName":"","lastName":"Gertz","suffix":""},{"id":590359669,"identity":"d409dd23-cf76-4a18-bfe4-02107aac6545","order_by":22,"name":"Rahma Warsame","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Rahma","middleName":"","lastName":"Warsame","suffix":""},{"id":590359670,"identity":"5f44862c-d651-4647-a316-46b836f184e4","order_by":23,"name":"S Rajkumar","email":"","orcid":"https://orcid.org/0000-0002-5862-1833","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"","lastName":"Rajkumar","suffix":""},{"id":590359671,"identity":"555ba468-4ebf-405f-a928-907515fe9703","order_by":24,"name":"Jad Sfeir","email":"","orcid":"https://orcid.org/0000-0003-1576-1534","institution":"Mayo Clinic College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jad","middleName":"","lastName":"Sfeir","suffix":""},{"id":590359672,"identity":"d483e756-1042-4f7d-8ae1-3f6d824fd57f","order_by":25,"name":"Nathan LeBrasseur","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"","lastName":"LeBrasseur","suffix":""},{"id":590359673,"identity":"7ec5e20f-082a-4e13-a72a-9a5d84049fd2","order_by":26,"name":"Shaji Kumar","email":"","orcid":"https://orcid.org/0000-0001-5392-9284","institution":"Mayo clinic","correspondingAuthor":false,"prefix":"","firstName":"Shaji","middleName":"","lastName":"Kumar","suffix":""},{"id":590359674,"identity":"1c0c46aa-8f4d-4dd4-8c50-7fa40291eacb","order_by":27,"name":"Megan Weivoda","email":"","orcid":"https://orcid.org/0000-0003-0240-0123","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Megan","middleName":"","lastName":"Weivoda","suffix":""}],"badges":[],"createdAt":"2026-01-14 21:35:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8605152/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8605152/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104996476,"identity":"d46e54ce-3b84-401d-9f60-340dc2b71e47","added_by":"auto","created_at":"2026-03-19 16:14:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between SASP-based frailty model and observed frailty in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ecohort 2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. \u003c/strong\u003eScatterplots depict the relationship between predicted frailty scores (x-axis) generated from the 3-factor SASP LASSO model developed in cohort 1 (GDF-15, TNF-RI, and uPAR) and observed frailty indices (y-axis) in \u003cem\u003ecohort 2\u003c/em\u003e: (A) Cumulative deficit Frailty Index and (B) Physical Frailty Index. Blue lines represent fitted linear regressions with 95% confidence intervals (gray shading). Pearson correlation coefficients (R) and p values are shown in each panel. \u003cem\u003eAbbreviations: SASP: senescence-associated secretory phenotype.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1SASP.png","url":"https://assets-eu.researchsquare.com/files/rs-8605152/v1/17e433bdb87dd7da7cd07bd5.png"},{"id":104996477,"identity":"da4f4b61-2e39-4c14-b04f-9a9e88af9bf3","added_by":"auto","created_at":"2026-03-19 16:14:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":114276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive performance of a 5-factor SASP models for overall survival. \u003c/strong\u003eReceiver operating characteristic (ROC) curves showing the performance of different models—SASP 5-factor panel (black), chronological age (red), frailty score (blue), and TNF-RII (green)—for predicting (A) 3-year and (B) 5-year overall survival in patients with newly diagnosed multiple myeloma (\u003cem\u003eCohort 1\u003c/em\u003e). The SASP 5-factor panel includes IL-8, Osteopontin, SOST, TNF-RI, and uPAR.\u003c/p\u003e","description":"","filename":"Figure2SASP.png","url":"https://assets-eu.researchsquare.com/files/rs-8605152/v1/4a3216e7bd3145ef625cff9f.png"},{"id":104996502,"identity":"a2fb1e29-7f80-46f0-ac84-f6b0e2a4cdab","added_by":"auto","created_at":"2026-03-19 16:14:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":995593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8605152/v1/fb680557-5c00-4c1a-a26f-c703ffe04494.pdf"},{"id":104996475,"identity":"17866d32-7f54-42df-bd13-7d289e1ea1a6","added_by":"auto","created_at":"2026-03-19 16:14:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33100,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8605152/v1/2e8934aac7f33548563129c3.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Biomarkers of Cellular Senescence and their Association with Frailty, Clinical Outcomes, and Survival in Multiple Myeloma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple myeloma (MM) is a clonal plasma cell disorder that is characterized by marked heterogeneity in disease and patient characteristics, resulting in variable treatment outcomes. One factor that has emerged as a critical determinant of treatment outcomes is frailty,\u003csup\u003e1\u003c/sup\u003e a multidimensional syndrome reflecting reduced physiological reserve.\u003csup\u003e2\u003c/sup\u003e In MM, frailty is significantly associated with increased treatment-related toxicity and decreased overall survival (OS).\u003csup\u003e1\u003c/sup\u003e Over the past decade, several tools have been developed to quantify frailty in MM,\u003csup\u003e3-8\u003c/sup\u003e among which the International Myeloma Working Group (IMWG) frailty index (FI) is the most widely recognized.\u003csup\u003e1\u003c/sup\u003e Despite their prognostic value, the implementation of frailty assessments in routine clinical practice remains limited, primarily due to time, resource, and workflow constraints.\u003csup\u003e9,10\u003c/sup\u003e While physician-rated performance scores like the Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) offer practicality, they are subjective and often discordant with the patient\u0026rsquo;s perceived physical function.\u003csup\u003e11,12\u003c/sup\u003e On the other hand, objective measures like hand grip strength and gait speed, which are components of the physical frailty phenotype, are resource-consuming and confounded by disease-related symptoms. Another limitation of some existing tools is their reliance on chronological age, which can overestimate frailty in robust older adults and underestimate it in younger patients who have comorbidities and/or functional decline; we previously reported that approximately 30% of newly diagnosed MM patients less than 65 years of age in our cohort were classified as frail using an age-independent definition, underscoring that chronological age is not an appropriate surrogate for biological age.\u003csup\u003e13\u003c/sup\u003e Together, these limitations have fueled interest in developing efficient and scalable biomarkers of frailty that can be readily integrated into both clinical care and research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe hallmarks of aging, in particular cellular senescence, have emerged as attractive candidate biomarkers of frailty, as they represent the fundamental biological drivers of physiological decline.\u003csup\u003e14-17\u003c/sup\u003e Cellular senescence is a state of stable growth arrest induced by cell stress. Senescent cells accumulate across tissues with aging and drive numerous aging-related pathologies, including frailty, in part through their production of proinflammatory cytokines, chemokines, growth factors, and proteases, collectively known as the senescence-associated secretory phenotype (SASP).\u003csup\u003e18\u003c/sup\u003e The persistent SASP factors contribute chronic inflammation, leading to tissue dysfunction, decreased physiological reserve, and increased vulnerability to stressors.\u003csup\u003e17-20\u003c/sup\u003e Recent work by Schafer et al. demonstrated that elevated levels of specific SASP factors in peripheral blood were strongly associated with clinical frailty, measured using a cumulative deficit FI, \u0026nbsp;across multiple patient cohorts, and were predictive of adverse post-operative outcomes.\u003csup\u003e17\u003c/sup\u003e Notably, a SASP-factor panel outperformed chronological age and the frailty index in predictive power, highlighting the potential of SASP factors as quantifiable circulating biomarkers of frailty.\u003csup\u003e17\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe utility of SASP factors as frailty and prognostic biomarkers in the context of MM remains unclear. Thus, we conducted this study to examine the association between circulating SASP factors and frailty in two separate cohorts of patients with MM, and to assess their prognostic utility significance within these cohorts.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePopulation:\u003c/strong\u003e To evaluate the relationship between circulating SASP factor levels and frailty, we used plasma samples from two independent patient cohorts seen at Mayo Clinic in Rochester, Minnesota: \u003cem\u003eCohort 1\u003c/em\u003e \u0026ndash; Historical Cohort \u0026ndash; included 59 patients with newly diagnosed MM who had stored plasma samples available in our institutional Biobank and sufficient clinical information to calculate the FI at diagnosis (see FI calculation section). Patients were diagnosed between February 19, 2010, and March 26, 2018. \u0026nbsp;\u003cem\u003eCohort 2\u003c/em\u003e - Contemporary cohort \u0026ndash; included 36 patients with newly diagnosed or relapsed/refractory MM who were scheduled to initiate a new line of therapy for MM. Peripheral blood samples were collected and frailty assessments were performed prior to treatment initiation. Patients were diagnosed between January 22, 2009, and May 31, 2024, and blood samples were collected between October 9, 2023, and June 21, 2024; the median time from diagnosis to sample collection was 2.0 months (IQR: 1\u0026ndash;60). For the first 4 months of treatment, data was collected on treatment-related adverse events according to National Cancer Institute CTCAE v5.0,\u003csup\u003e21,22\u003c/sup\u003e and on healthcare utilization (Emergency department visits/unplanned hospitalizations).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFI calculation\u003c/strong\u003e: For\u0026nbsp;\u003cem\u003eCohort 1\u003c/em\u003e, the FI was retrospectively calculated based on the cumulative deficit approach using 32 items abstracted from the electronic medical record, as previously described;\u003csup\u003e13\u003c/sup\u003e These included 14 patient-reported items on activities of daily living (ADLs), instrumental activities of daily living (IADLs), dependence on a device for normal breathing, and exercise tolerance. The other 16 items included the body mass index (BMI) and comorbidities. Individual items were scored as 0, 0.5, or 1, with 1 indicating a deficit. The FI was defined as the sum of individual scores divided by the total number of non-missing items, and patients with \u0026gt;2 missing items were excluded.\u003csup\u003e13\u003c/sup\u003e For\u0026nbsp;\u003cem\u003eCohort 2\u003c/em\u003e, the FI was calculated using two approaches: 1) a cumulative deficit approach adapted from the CARE-FI model, integrating patient-reported geriatric and quality of life domains.\u003csup\u003e23\u003c/sup\u003e To collect these data, participants completed an electronic version of the Cancer and Aging Research Group (CARG) geriatric assessment tool, which compiles validated measures of functional status, comorbidities, cognition, psychological well-being, social functioning, social support, and nutrition. In addition, patients completed the PROMIS-10 Global Health Short Form, a validated 10-item questionnaire assessing overall physical and mental health.\u003csup\u003e24,25\u003c/sup\u003e \u0026nbsp;A total of 40 health deficits were included in the FI, each scored as 0, 0.5, or 1, with higher scores indicating greater deficit. The overall FI was calculated as the sum of all individual deficit scores divided by the total number of non-missing items. 2) The physical frailty phenotype was determined based on 5 criteria, each scored as 0 (absent) or 1 (present):\u0026nbsp;unintentional weight loss\u0026nbsp;\u0026ge;10 lb. in previous year, self-reported exhaustion, weakness measured by grip strength, slow walking speed, and low physical activity.\u003csup\u003e26\u003c/sup\u003e The total score ranges from 0 to 5, with 0 indicating robust, 1\u0026ndash;2 indicating prefrail, and \u0026ge;3 indicating frail status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of circulating SASP factors:\u0026nbsp;\u003c/strong\u003eThe plasma concentrations of 36 SASP factors previously linked to biological aging and/or morbidity were measured.\u003csup\u003e27-29\u003c/sup\u003e These included ADAMTS13, activin A, Eotaxin, Fas, GDF-15, GRO\u0026alpha;, ICAM-1, IFN\u0026gamma;, IL-15, IL-6, IL-7, IL-8, MCP-1, MDC, MMP-1, MMP-2, MMP-7, MMP-9, MPO, osteoactivin, osteopontin, PAI-1, PARC, PDGF-AA, PDGF-AB, PLA2G7, RAGE, RANTES, SOST, SPARC, TNF-RI, TNF-RII, TNF\u0026alpha;, TRAIL, VEGF, and uPAR. SASP factor levels were quantified from EDTA plasma samples using commercially available multiplex magnetic bead\u0026ndash;based immunoassays (R\u0026amp;D Systems) on the Luminex xMAP multianalyte profiling platform and analyzed with the MAGPIX System (Merck Millipore). Activin A was quantified using a Quantikine ELISA kit (R\u0026amp;D Systems), and PAI-2 was measured using an ELISA kit from Cloud-Clone Corp. All assays were performed according to the manufacturers\u0026rsquo; instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis:\u0026nbsp;\u003c/strong\u003eBaseline characteristics were summarized using descriptive statistics. Associations between individual SASP markers and the FI (treated as a continuous variable) were assessed using Spearman and partial Spearman rank correlations, with both unadjusted and age-, sex-, and BMI\u0026ndash;adjusted estimates reported. The adjustments for age and BMI were based on known associations between chronological age and obesity respectively, and senescent cell burden. Adjustments for sex were based on known sex differences in risk profiles for age-related diseases.\u003csup\u003e17\u003c/sup\u003e Logistic regression models were used to evaluate the association between baseline SASP factor concentrations and binary outcomes, including grade \u0026ge;3 toxicity and \u0026ge;1 ED visit/unplanned hospitalization within the first 4 months of treatment (\u003cem\u003eCohort 2\u003c/em\u003e only).\u0026nbsp;Univariable Cox proportional hazards models were used to evaluate the association between each biomarker and time dependent outcomes (\u003cem\u003eCohort 1\u003c/em\u003e only): Progression-free survival (PFS), TTNT (time to next treatment), and overall survival (OS). Separate bivariate models adjusting for age were also constructed. TTNT was defined as the interval from diagnosis to the initiation of the next line of therapy. Patients who had not started a new treatment at the time of last follow-up were censored at that date. PFS was defined as the time from diagnosis to documented disease progression or death from any cause, whichever occurred first. Patients alive and without evidence of progression at last follow-up were censored at that date. OS was defined as the time from diagnosis to death from any cause, with patients still alive at last follow-up censored on that date. For OS, time-dependent receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated for each predictor at 3 and 5 years.\u0026nbsp;For time-dependent models, SASP factor concentrations were log-transformed [log(x + 1)] to reduce skewness, stabilize variance, and better satisfy proportional hazards assumptions.\u003c/p\u003e\n\u003cp\u003eParsimonious models were developed to predict OS (\u003cem\u003ecohort 1\u003c/em\u003e) and FI (both cohorts) using the least absolute shrinkage and selection operator (LASSO) applied to log-transformed biomarker concentrations. LASSO was chosen to accommodate the modest sample size and address multicollinearity among predictors. The optimal penalty parameter was determined through five-fold cross-validation, and variables with non-zero coefficients at this value were retained in the final model. For the FI, a LASSO regression was fitted in \u003cem\u003ecohort 1\u003c/em\u003e, with FI as the dependent variable. Model performance was assessed using R\u0026sup2;, F-statistic, and residual standard error. The resulting model was externally validated in \u003cem\u003ecohort 2\u003c/em\u003e by correlating LASSO-predicted frailty scores with both cumulative-deficit and physical frailty indices. For OS, a penalized Cox proportional hazards model with LASSO regularization was applied in \u003cem\u003ecohort 1\u003c/em\u003e. Model discrimination was evaluated using Harrell\u0026rsquo;s concordance index (C-index) and time-dependent AUC at 3 and 5 years. An OS model was not developed for cohort 2 due to limited follow-up.\u003c/p\u003e\n\u003cp\u003eFor all analyses, two-sided \u003cem\u003ep\u003c/em\u003e values were reported, with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 considered statistically significant. Statistical analyses were conducted using R software version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). This study was approved by the Mayo Clinic Institutional Review Board. All participants provided written informed consent for the use of their biological samples and clinical data for research purposes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient demographic and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCohort 1 (newly diagnosed MM)\u003c/em\u003e: The median age was 60 years (IQR, 55\u0026ndash;68), and 29% were female. Patients received proteasome inhibitor (PI)\u0026ndash;based (27%), immunomodulatory drug (IMiD)\u0026ndash;based (37%), or combined PI + IMiD\u0026ndash;based (36%) induction therapy, and 41 (69%) underwent autologous stem-cell transplantation (ASCT) within one year of diagnosis. The median FI was 0.10 (IQR, 0.05\u0026ndash;0.20).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCohort 2 (Newly diagnosed and relapsed/refractory)\u003c/em\u003e: The median age at sample collection was 70 years (IQR: 67\u0026ndash;74), and 16 patients (44%) were female. Twenty-three patients (64%) had newly diagnosed MM, including 3 who underwent ASCT consolidation following induction therapy; the remaining patients had relapsed or refractory disease and were initiating a new line of treatment. Seventeen patients (47%) received quadruplet-based and 19 (53%) received triplet-based regimens; half of the cohort (18 patients) received a daratumumab-containing regimen. The median FI was 0.22 (IQR, 0.13\u0026ndash;0.37). For the physical frailty phenotype, over half of patients (53%) had a score of 0 (not frail), while 19% and 17% had scores of 1 and 2 (pre-frail), and 8% and 3% had scores 3 and 4 (frail).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCirculating SASP factors correlate with frailty in patients with MM\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSASP and frailty in Cohort 1:\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWe first evaluated the association between plasma SASP factor concentrations and the FI in \u003cem\u003eCohort 1\u003c/em\u003e. In unadjusted analyses, 10 SASP factors showed significant positive correlations with the FI: Activin A, Fas, GDF-15, IL-6, IL-8, osteoactivin, osteopontin, TNF-RI, TNF-RII, and uPAR. After adjustment for age, sex, and BMI, all associations remained statistically significant except for osteoactivin (p = 0.06). Three additional factors\u0026mdash;GRO\u0026alpha;, RAGE, and TNF-\u0026alpha;\u0026mdash;became significantly correlated with frailty in adjusted models. The strongest associations were observed for TNF-RI (r = 0.501, p \u0026lt; 0.001), TNF-RII (r = 0.501, p \u0026lt; 0.001), GDF-15 (r = 0.463, p \u0026lt; 0.001), uPAR (r = 0.423, p = 0.001), IL-8 (r = 0.387, p = 0.003), IL-6 (r = 0.382, p = 0.004), osteopontin (r = 0.345, p = 0.009), and RAGE (r = 0.344, p = 0.009) \u003cstrong\u003e(Table 1)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSASP and frailty in Cohort 2:\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWe next examined associations between plasma SASP factor concentrations and frailty in \u003cem\u003eCohort 2\u003c/em\u003e using two distinct frailty measures: a cumulative deficit FI and the physical frailty phenotype.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSASP and cumulative deficit index in cohort 2:\u003c/em\u003e In unadjusted analyses, factors identified in\u003cem\u003e\u0026nbsp;cohort 1\u003c/em\u003e including : activin A, Fas, GDF-15, IL-6, IL-8, TNF-RI, and RAGE, in addition to IFN\u0026gamma;, MCP-1, and MMP-7, showed significant positive correlations with the FI. After adjustment for age, sex, and BMI, all associations remained statistically significant. The strongest correlations observed were with MMP-7 (r = 0.52, p = 0.002), GDF-15 (r = 0.51, p = 0.002), IL-6 (r = 0.46, p = 0.007), IL-8 (r = 0.44, p = 0.010), IFN\u0026gamma; (r = 0.41, p = 0.019), Fas (r = 0.39, p = 0.027), MCP-1 (r = 0.39, p = 0.025), and TNF-RI (r = 0.39, p = 0.025) \u003cstrong\u003e(Table 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSASP and the physical frailty phenotype in cohort 2:\u003c/em\u003e In unadjusted analyses, and consistent with the FI results, Fas, GDF-15, IL-6, IL-8, MMP-7, osteoactivin, and TNF-RI, along with IL-15, showed significant positive correlations with physical frailty. After adjustment for age, sex, and BMI, all associations remained statistically significant except for MMP-7 and osteoactivin. The strongest associations were observed for IL-6 (r = 0.522, p = 0.002), GDF-15 (r = 0.490, p = 0.004), TNF-RI (r = 0.437, p = 0.011), IL-15 (r = 0.412, p = 0.017), Fas (r = 0.351, p = 0.045), and IL-8 (r = 0.373, p = 0.033) \u003cstrong\u003e(Table 3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA multi-factor SASP model correlates with frailty in both cohorts:\u0026nbsp;\u003c/em\u003eA LASSO regression model was fitted \u003cem\u003ein cohort 1\u003c/em\u003e to predict the FI using SASP factors as predictors. The model performance was then assessed in \u003cem\u003ecohort 2\u003c/em\u003e. In \u003cem\u003ecohort 1\u003c/em\u003e, the model retained GDF-15, TNF-RI, and uPAR, yielding an R\u0026sup2; of 0.31 and an F-statistic of 8.37 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) with a residual standard error of 0.11. When this 3-biomarker model was applied to \u003cem\u003ecohort 2\u003c/em\u003e, the predicted frailty scores demonstrated moderate to strong correlations with both the FI (r = 0.44, p = 0.007; \u003cstrong\u003eFigure 1A\u003c/strong\u003e) and the physical frailty phenotype (\u003cem\u003er\u003c/em\u003e = 0.41, \u003cem\u003ep\u003c/em\u003e = 0.013; \u003cstrong\u003eFigure 1B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelect SASP factors are associated with non-hematologic toxicity and healthcare utilization in MM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the association between plasma SASP factor concentrations and treatment toxicity in \u003cem\u003ecohort 2\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSASP and Grade 3\u0026ndash;4 non-hematologic toxicity:\u003c/em\u003e Five of 36 patients (14%) had grade 3\u0026ndash;4 non-hematologic toxicity during the first 4 months of treatment. In age-adjusted analyses, higher baseline concentrations of several SASP factors were significantly associated with an increased likelihood of grade 3\u0026ndash;4 non-hematologic toxicity. These included GDF-15 (odds ratio [OR]: 4.8, 95% CI: 1.5\u0026ndash;29.9, p = 0.006; AUC: 0.85), Fas (OR: 3.3, 95% CI: 1.0\u0026ndash;16.7, p = 0.04; AUC: 0.80), \u0026nbsp;PAI-1 (OR: 6.3, 95% CI: 1.7\u0026ndash;78.3, p = 0.004; AUC: 0.90), SPARC (OR: 2.7, 95% CI: 1.1\u0026ndash;8.5, p = 0.03; AUC: 0.83), and IL-6 (OR: 3.1, 95% CI: 1.1\u0026ndash;28.1, p = 0.03; AUC: 0.89).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSASP and healthcare utilization\u003c/em\u003e: At least 1 ED visit and/or unplanned hospitalization(s) occurred in 15 of 36 patients (42%). In age-adjusted analyses, higher baseline concentrations of eotaxin (OR: 2.7, 95% CI: 1.2\u0026ndash;7.4, p = 0.01; AUC: 0.76), TNF-RI (OR: 2.7, 95% CI: 1.2\u0026ndash;8.0, p = 0.01; AUC: 0.81), TNF-RII (OR: 2.7, 95% CI: 1.2\u0026ndash;7.3, p = 0.01; AUC: 0.79), and IFN\u0026gamma; (OR: 4.1, 95% CI: 1.4\u0026ndash;21.9, p = 0.006; AUC: 0.77) were significantly associated with increased healthcare utilization. In contrast, higher concentrations of PDGF-AA (OR: 0.37, 95% CI: 0.07\u0026ndash;0.95, p = 0.04; AUC: 0.72) and PDGF-AB (OR: 0.36,95% CI: 0.06-0.92, p = 0.03; AUC: 0.73) were significantly associated with decreased healthcare utilization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSASP factors are associated with inferior PFS and TTNT in MM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the association between plasma SASP factor concentrations and PFS and TTNT in \u003cem\u003ecohort 1\u003c/em\u003e. Median follow-up was 5.0 (95% CI: 3.6\u0026ndash;7.1) years. Elevated levels of several SASP factors were significantly associated with shorter PFS after adjusting for chronological age: Eotaxin (HR: 2.4, 95% CI: 1.1\u0026ndash;4.9; P = 0.02), VEGF (HR: 1.9, 95% CI: 1.2\u0026ndash;3.1; P = 0.01), RAGE (HR: 2.4, 95% CI: 1.1\u0026ndash;5.2; P = 0.04), TNF-RII (HR = 4.4, 95% CI: 1.9\u0026ndash;10.3; P \u0026lt; 0.001), GDF-15 (HR: 1.8, 95% CI 1.1\u0026ndash;2.9; P = 0.02), TNF-RI (HR: 3.5, 95% CI: 1.6\u0026ndash;7.7; P = 0.002), uPAR (HR: 3.7, 95% CI: 1.7\u0026ndash;8.4; P = 0.002), Activin A (HR: 2.1, 95% CI: 1.1\u0026ndash;3.7; P = 0.02), and IL-8 (HR: 2.7, 95% CI: 1.3\u0026ndash;5.7; P = 0.008). Among these factors, RAGE (HR: 2.4, 95% CI: 1.0-5.8, P=0.046), TNF-RII (HR: 16.7, 95%CI: 4.9-64.5, P\u0026lt;0.001), TNF-R1 (HR: 10.0, 95% CI: 3.3-30.9, P\u0026lt;0.001), and uPAR (HR: 5.7, 95% CI: 1.7-21.4, P=0.007) were significant associated with shorter TTNT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSASP factors are associated with inferior OS in MM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we examined the association between plasma SASP factor concentrations and OS in \u003cem\u003ecohort 1\u003c/em\u003e. In Cox models using log-transformed biomarker concentrations, several SASP factors were significantly associated with inferior OS after adjusting for age, including activin A, ICAM-1, GDF-15, IL-8, MMP-7, osteopontin, RAGE, SOST, TNF-RI, TNF-RII, uPAR, and VEGF. The strongest predictors of OS were TNF-RI (HR: 10.5, C-index = 0.813, p \u0026lt; 0.001), TNF-RII (HR: 11.9, C-index = 0.789, p \u0026lt; 0.001), uPAR (HR: 7.8, C-index = 0.781, p \u0026lt; 0.001), IL-8 (HR: 5.3, C-index = 0.744, p \u0026lt; 0.001), and GDF-15 (HR: 2.9, C-index = 0.786, p \u0026lt; 0.001) \u003cstrong\u003e(Table 4)\u003c/strong\u003e. A LASSO-Cox model was developed to predict OS using SASP factors as predictors. The model for OS retained IL-8, osteopontin, SOST, TNF-RI, and uPAR. This 5-factor model demonstrated superior predictive performance for OS compared with chronological age, R-ISS stage, the FI, and TNF-RI alone. The 5-year AUC values were chronological age: 0.57, R-ISS stage III (vs I/II): 0.61, FI: 0.75, TNF-RI: 0.78, and the SASP panel: 0.90 \u003cstrong\u003e(Figure 2B)\u003c/strong\u003e. The corresponding AUC values for 3-year OS were 0.58, 0.58, 0.67, \u0026nbsp;0.80, and 0.89, respectively \u003cstrong\u003e(Figure 2A)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe \u0026nbsp;SASP is increasingly recognized as a key mediator of aging-related conditions, including frailty, and is linked to adverse clinical outcomes across diverse populations.\u003csup\u003e17-20,30-33\u003c/sup\u003e Although some differences were observed, GDF-15, TNF-RI, IL-6, and IL-8 consistently correlated with frailty across both patient cohorts, as measured by both clinical and phenotypic frailty indices. These findings partially align with Schafer et al., who reported associations between TNF-R1, GDF-15, and osteopontin with frailty index scores across three independent cohorts (older adults undergoing aortic stenosis surgery, older adults undergoing ovarian cancer surgery, and community biobank participants), while activin A and IL-6 were associated with frailty primarily in nonsurgical populations.\u003csup\u003e17\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur analysis also revealed that several SASP factors were associated with reduced PFS and OS in newly diagnosed MM patients, suggesting that increased SASP levels may reflect an interplay between disease burden and biological aging. We hypothesize that MM-related inflammation may accelerate cellular senescence and physiological decline, manifesting as disease-related frailty in some patients. This is supported by reports showing that frailty status improvements in a subset of patients following effective MM therapy.\u003csup\u003e34,35\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWe have previously demonstrated that cellular senescence influences disease evolution from monoclonal gammopathy of undetermined significance to symptomatic MM.\u003csup\u003e36\u003c/sup\u003e Moreover, individual SASP factors may exert distinct effects at the intersection of aging and disease progression. For example, in our previous study, GDF-15 and TNF-R1 were enriched in patients with stable smoldering MM, suggesting these markers may be independent of MM pathogenesis. In active MM, Corre et al. reported GDF-15 overexpression by bone marrow mesenchymal stromal cells enhanced stromal support, promoting aggressive disease features, treatment resistance, and inferior clinical outcomes.\u003csup\u003e37\u003c/sup\u003e Consistent with this, we found that elevated GDF-15 in MM was significantly associated with shorter PFS and OS. Other SASP factors, including eotaxin, SOST, and ICAM-1, were linked to clinical outcomes but not frailty.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, only GDF-15, Fas, PAI-1, SPARC, and IL-6 were significantly associated with high grade non-hematologic toxicity after adjusting for age. However, the overall incidence of high-grade toxicity in \u003cem\u003ecohort 2\u003c/em\u003e was low, possibly reflecting improved tolerability of contemporary treatment regimens and/or the limited sensitivity of CTCAE grading in capturing patient symptoms. Additionally, only TNF-R1, TNF-RII, eotaxin, and\u0026nbsp;IFN-\u0026gamma; were significantly associated with ED visits/unplanned hospitalizations, likely due to disease-related complications (e.g., bone pain, pathological fracture) and/or patient-specific factors comorbidities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe consistent associations of GDF-15, TNF-R1, and IL-6 with both frailty and adverse outcomes across oncologic and non-oncologic settings suggest that these factors contribute to frailty through mechanisms extending beyond MM biology. Furthermore, their superior prognostic performance compared to disease stage and chronological age further indicates that these associations with clinical vulnerability are not solely driven by disease burden. Consistent with the findings of Schafer et al., our findings support the use of a multi-marker SASP panel for improved prognostic accuracy compared to a single biomarker.\u003csup\u003e17\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond their prognostic potential, SASP factors could serve as modifiable therapeutic targets through both pharmacologic and non-pharmacologic strategies. These include senolytics, which selectively induce apoptosis in senescent cells, and senomorphics, which modulate senescent cell activity without causing cell death. The senolytic combination of dasatinib plus quercetin (D+Q), has been shown to lower circulating SASP factors and reduce senescence-associated dysfunction in preclinical\u003csup\u003e38,39\u003c/sup\u003e and early clinical studies.\u003csup\u003e40\u003c/sup\u003e JAK inhibition also suppressed SASP production and alleviated age-related frailty in a preclinical study.\u003csup\u003e41\u003c/sup\u003e Similarly, NF-\u0026kappa;B pathway inhibition was shown to mitigate chemotherapy-induced cachexia and sarcopenia in a preclinical study by reducing senescence-mediated signaling including SASP secretion.\u003csup\u003e42\u003c/sup\u003e SASP modulation has also been achieved with non-pharmacologic interventions: in a study by Englund et al., 12 week structured exercise interventions were associated with significant reductions in SASP factor levels and improvement in physical function in older adults, with baseline SASP levels predicting functional improvements.\u003csup\u003e28\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study has several limitations including modest sample size, single institution design, and predominantly white population, which may limit the generalizability of our findings. Due to small sample size, our analyses were not sufficiently powered to assess subgroup effects or to adjust for all potential confounders. Additionally, the relatively short follow-up period in \u003cem\u003ecohort 2\u003c/em\u003e precluded evaluation of survival outcomes in this group. We also acknowledge that while senescent cells are a plausible source of the cytokines, chemokines, tissue remodeling, growth factor, and other proteins measured, they are not the only source. Despite these limitations, to our knowledge, this study represents the first systematic evaluation of circulating SASP factors as correlates of frailty in MM. The replication of key findings across two independent cohorts supports the internal consistency and robustness of our results. Notably, although cohort 2 was small, it was prospectively designed and included comprehensive and prospectively collected data on toxicity and healthcare utilization. Our observations and reported associations need to be validated in larger studies with more diverse populations. Beyond SASP factors, other molecular markers of biological aging\u0026mdash;such as p16^INK4a^ expression and epigenetic clocks\u0026mdash;may also serve as objective measures of frailty in patients with MM, as suggested by recent work from Rosko et al., and warrant further investigation.\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we found that specific SASP proteins are associated with frailty in patients with MM and are predictive of inferior OS. These findings highlight the potential for SASP factors as objective biomarkers of physiological reserve, complementing traditional clinical assessments and warrant further validation in larger prospective studies.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThis work was supported by the American Society of Hematology (ASH) through the Research Training Award for Fellows (RTAF) granted to Nadine Abdallah.\u0026nbsp;This project was also supported by the Glenn Foundation for Medical Research and grants from the National Institute on Aging, including R01 AG55529 and R01 AG089150 to NKL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;T.M.W:\u003c/strong\u003e served as a consultant for Janssen and Pfizer and has received research funding from Janssen.\u003cstrong\u003e\u0026nbsp;A.D.\u0026nbsp;\u003c/strong\u003ereceived research funding from Celgene, Millennium Pharmaceuticals, Pfizer, and Janssen, and a travel grant from Pfizer.\u003cstrong\u003e\u0026nbsp;M.G.\u0026nbsp;\u003c/strong\u003eserved as a consultant for Millennium Pharmaceuticals and received honoraria from Celgene, Millennium Pharmaceuticals, Onyx Pharmaceuticals, Novartis, GlaxoSmithKline, Prothena, Ionis Pharmaceuticals, and Amgen.\u003cstrong\u003e\u0026nbsp;S.V.R.\u0026nbsp;\u003c/strong\u003ehas received grants from the National Institutes of Health and research funding from Celgene for clinical trials.\u003cstrong\u003e\u0026nbsp;S.K.\u0026nbsp;\u003c/strong\u003eserved as a consultant for Celgene, Millennium Pharmaceuticals, Onyx Pharmaceuticals, Janssen, and Bristol Myers Squibb, and received research funding from Celgene, Millennium Pharmaceuticals, Novartis, Onyx Pharmaceuticals, AbbVie, Janssen, and Bristol Myers Squibb. The remaining authors declare no competing financial interests. Rest of the authors do not have any relevant disclosures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization of the project: NA, NL, SKK, and MMW; Data collection and analysis: NA, BS, ThW, AH, BK, RM, CY, AB, NL MWW; Discussion and interpretation of results: NA, BS, NL, SKK, MMW; Drafting of manuscript: NA, MMW; All authors reviewed the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe datasets analyzed for this study are provided upon reasonable request from the corresponding author.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePalumbo A, Bringhen S, Mateos M-V, et al. Geriatric assessment predicts survival and toxicities in elderly myeloma patients: an International Myeloma Working Group report. Blood 2015;125(13):2068-2074. DOI: 10.1182/blood-2014-12-615187.\u003c/li\u003e\n\u003cli\u003eClegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet 2013;381(9868):752-62. DOI: 10.1016/S0140-6736(12)62167-9.\u003c/li\u003e\n\u003cli\u003eEngelhardt M, Domm AS, Dold SM, et al. A concise revised Myeloma Comorbidity Index as a valid prognostic instrument in a large cohort of 801 multiple myeloma patients. Haematologica 2017;102(5):910-921. (In eng). DOI: 10.3324/haematol.2016.162693.\u003c/li\u003e\n\u003cli\u003eSandra Maria D, Mandy-Deborah M, Gabriele I, et al. Validation of the revised myeloma comorbidity index and other comorbidity scores in a multicenter German study group multiple myeloma trial. Haematologica 2020;106(3):875-880. DOI: 10.3324/haematol.2020.254235.\u003c/li\u003e\n\u003cli\u003eCook G, Royle KL, Pawlyn C, et al. A clinical prediction model for outcome and therapy delivery in transplant-ineligible patients with myeloma (UK Myeloma Research Alliance Risk Profile): a development and validation study. Lancet Haematol 2019;6(3):e154-e166. (In eng). DOI: 10.1016/s2352-3026(18)30220-5.\u003c/li\u003e\n\u003cli\u003eMilani P, Vincent Rajkumar S, Merlini G, et al. N-terminal fragment of the type-B natriuretic peptide (NT-proBNP) contributes to a simple new frailty score in patients with newly diagnosed multiple myeloma. Am J Hematol 2016;91(11):1129-1134. (In eng). DOI: 10.1002/ajh.24532.\u003c/li\u003e\n\u003cli\u003eFacon T, Dimopoulos MA, Meuleman N, et al. A simplified frailty scale predicts outcomes in transplant-ineligible patients with newly diagnosed multiple myeloma treated in the FIRST (MM-020) trial. Leukemia 2020;34(1):224-233. (In eng). DOI: 10.1038/s41375-019-0539-0.\u003c/li\u003e\n\u003cli\u003eStege CAM, van der Holt B, Dinmohamed AG, et al. Validation of the FIRST simplified frailty scale using the ECOG performance status instead of patient-reported activities. Leukemia 2020;34(7):1964-1966. DOI: 10.1038/s41375-020-0713-4.\u003c/li\u003e\n\u003cli\u003eWalston J, Buta B, Xue QL. Frailty Screening and Interventions: Considerations for Clinical Practice. Clin Geriatr Med 2018;34(1):25-38. DOI: 10.1016/j.cger.2017.09.004.\u003c/li\u003e\n\u003cli\u003eButa BJ, Walston JD, Godino JG, et al. Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments. Ageing Res Rev 2016;26:53-61. (In eng). DOI: 10.1016/j.arr.2015.12.003.\u003c/li\u003e\n\u003cli\u003eBroderick JM, Hussey J, Kennedy MJ, DM OD. Patients over 65 years are assigned lower ECOG PS scores than younger patients, although objectively measured physical activity is no different. J Geriatr Oncol 2014;5(1):49-56. DOI: 10.1016/j.jgo.2013.07.010.\u003c/li\u003e\n\u003cli\u003eSchnadig ID, Fromme EK, Loprinzi CL, et al. Patient-physician disagreement regarding performance status is associated with worse survivorship in patients with advanced cancer. Cancer 2008;113(8):2205-14. DOI: 10.1002/cncr.23856.\u003c/li\u003e\n\u003cli\u003eAbdallah N, Dizona P, Kumar A, et al. Cumulative deficits frailty index and relationship status predict survival in multiple myeloma. Blood Adv 2025;9(5):1137-1146. DOI: 10.1182/bloodadvances.2024014624.\u003c/li\u003e\n\u003cli\u003eRosko AE, Elsaid MI, Woyach J, et al. Determining the relationship of p16(INK4a) and additional molecular markers of aging with clinical frailty in hematologic malignancy. J Cancer Surviv 2024;18(4):1168-1178. (In eng). DOI: 10.1007/s11764-024-01591-6.\u003c/li\u003e\n\u003cli\u003eSmitherman AB, Wood WA, Mitin N, et al. Accelerated aging among childhood, adolescent, and young adult cancer survivors is evidenced by increased expression of p16INK4a and frailty. Cancer 2020;126(22):4975-4983. DOI: https://doi.org/10.1002/cncr.33112.\u003c/li\u003e\n\u003cli\u003eSanoff HK, Deal AM, Krishnamurthy J, et al. Effect of cytotoxic chemotherapy on markers of molecular age in patients with breast cancer. J Natl Cancer Inst 2014;106(4):dju057. (In eng). DOI: 10.1093/jnci/dju057.\u003c/li\u003e\n\u003cli\u003eSchafer MJ, Zhang X, Kumar A, et al. The senescence-associated secretome as an indicator of age and medical risk. JCI Insight 2020;5(12):e133668. (In eng). DOI: 10.1172/jci.insight.133668.\u003c/li\u003e\n\u003cli\u003eCuollo L, Antonangeli F, Santoni A, Soriani A. The Senescence-Associated Secretory Phenotype (SASP) in the Challenging Future of Cancer Therapy and Age-Related Diseases. Biology 2020;9(12):485. (https://www.mdpi.com/2079-7737/9/12/485).\u003c/li\u003e\n\u003cli\u003eHe Y, Xie W, Li H, Jin H, Zhang Y, Li Y. Cellular Senescence in Sarcopenia: Possible Mechanisms and Therapeutic Potential. Front Cell Dev Biol 2021;9:793088. DOI: 10.3389/fcell.2021.793088.\u003c/li\u003e\n\u003cli\u003eFielding RA, Atkinson EJ, Aversa Z, et al. Associations between biomarkers of cellular senescence and physical function in humans: observations from the lifestyle interventions for elders (LIFE) study. Geroscience 2022 (In English). DOI: 10.1007/s11357-022-00685-2.\u003c/li\u003e\n\u003cli\u003eCommon Terminology Criteria for Adverse Events (CTCAE) v5.0. (https://ctep.cancer.gov/protocoldevelopment/electronic_applications/docs/CTCAE_v5_Quick_Reference_5x7.pdf).\u003c/li\u003e\n\u003cli\u003eDueck AC, Mendoza TR, Mitchell SA, et al. Validity and Reliability of the US National Cancer Institute\u0026rsquo;s Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). JAMA Oncology 2015;1(8):1051-1059. DOI: 10.1001/jamaoncol.2015.2639.\u003c/li\u003e\n\u003cli\u003eGiri S, Al-Obaidi M, Harmon C, et al. Patient-reported geriatric assessment-based frailty index among older adults with gastrointestinal malignancies. J Am Geriatr Soc 2023;71(1):136-144. (In eng). DOI: 10.1111/jgs.18054.\u003c/li\u003e\n\u003cli\u003eHurria A, Cirrincione CT, Muss HB, et al. Implementing a geriatric assessment in cooperative group clinical cancer trials: CALGB 360401. J Clin Oncol 2011;29(10):1290-6. DOI: 10.1200/JCO.2010.30.6985.\u003c/li\u003e\n\u003cli\u003eHurria A, Gupta S, Zauderer M, et al. Developing a cancer-specific geriatric assessment: a feasibility study. Cancer 2005;104(9):1998-2005. DOI: 10.1002/cncr.21422.\u003c/li\u003e\n\u003cli\u003eFried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56(3):M146-56. (In eng). DOI: 10.1093/gerona/56.3.m146.\u003c/li\u003e\n\u003cli\u003eSchafer MJ, Zhang X, Kumar A, et al. The senescence-associated secretome as an indicator of age and medical risk. JCI Insight 2020;5(12) (In English). DOI: ARTN e13366810.1172/jci.insight.133668.\u003c/li\u003e\n\u003cli\u003eEnglund DA, Sakamoto AE, Fritsche CM, et al. Exercise reduces circulating biomarkers of cellular senescence in humans. Aging Cell 2021;20(7):e13415. DOI: https://doi.org/10.1111/acel.13415.\u003c/li\u003e\n\u003cli\u003eEnglund DA, Jolliffe A, Aversa Z, et al. p21 induces a senescence program and skeletal muscle dysfunction. Mol Metab 2022:101652. DOI: 10.1016/j.molmet.2022.101652.\u003c/li\u003e\n\u003cli\u003eTylutka A, Walas L, Zembron-Lacny A. Level of IL-6, TNF, and IL-1beta and age-related diseases: a systematic review and meta-analysis. Front Immunol 2024;15:1330386. DOI: 10.3389/fimmu.2024.1330386.\u003c/li\u003e\n\u003cli\u003eKim M, Walston JD, Won CW. Associations Between Elevated Growth Differentiation Factor-15 and Sarcopenia Among Community-dwelling Older Adults. J Gerontol A Biol Sci Med Sci 2022;77(4):770-780. DOI: 10.1093/gerona/glab201.\u003c/li\u003e\n\u003cli\u003eYing L, Zhang Q, Yang YM, Zhou JY. A Combination of Serum Biomarkers in Elderly Patients with Sarcopenia: A Cross-Sectional Observational Study. Int J Endocrinol 2022;2022:4026940. DOI: 10.1155/2022/4026940.\u003c/li\u003e\n\u003cli\u003eEvans DS, Young D, Tanaka T, et al. Proteomic Analysis of the Senescence-Associated Secretory Phenotype: GDF-15, IGFBP-2, and Cystatin-C Are Associated With Multiple Aging Traits. J Gerontol A Biol Sci Med Sci 2024;79(3). DOI: 10.1093/gerona/glad265.\u003c/li\u003e\n\u003cli\u003eMian H, Wildes TM, Vij R, Pianko MJ, Major A, Fiala MA. Dynamic frailty risk assessment among older adults with multiple myeloma: A population-based cohort study. Blood Cancer J 2023;13(1):76. (In eng). DOI: 10.1038/s41408-023-00843-5.\u003c/li\u003e\n\u003cli\u003eAbdallah N, Dizona P, Kumar A, et al. Cumulative deficits frailty index and relationship status predict survival in multiple myeloma. Blood Advances 2025;9(5):1137-1146. DOI: 10.1182/bloodadvances.2024014624.\u003c/li\u003e\n\u003cli\u003eBorges GA, Diaz-delCastillo M, Guilatco AJ, et al. Senescence profiling of monoclonal gammopathies reveals paracrine senescence as a crucial defense against disease progression. Leukemia 2025;39(5):1206-1217. DOI: 10.1038/s41375-025-02572-z.\u003c/li\u003e\n\u003cli\u003eCorre J, Labat E, Espagnolle N, et al. Bioactivity and Prognostic Significance of Growth Differentiation Factor GDF15 Secreted by Bone Marrow Mesenchymal Stem Cells in Multiple Myeloma. Cancer Research 2012;72(6):1395-1406. DOI: 10.1158/0008-5472.Can-11-0188.\u003c/li\u003e\n\u003cli\u003eXu M, Pirtskhalava T, Farr JN, et al. Senolytics improve physical function and increase lifespan in old age. Nature Medicine 2018;24(8):1246-1256. DOI: 10.1038/s41591-018-0092-9.\u003c/li\u003e\n\u003cli\u003eHickson LJ, Langhi Prata LGP, Bobart SA, et al. Senolytics decrease senescent cells in humans: Preliminary report from a clinical trial of Dasatinib plus Quercetin in individuals with diabetic kidney disease. Ebiomedicine 2019;47:446-456. DOI: 10.1016/j.ebiom.2019.08.069.\u003c/li\u003e\n\u003cli\u003eJustice JN, Nambiar AM, Tchkonia T, et al. Senolytics in idiopathic pulmonary fibrosis: Results from a first-in-human, open-label, pilot study. Ebiomedicine 2019;40:554-563. DOI: 10.1016/j.ebiom.2018.12.052.\u003c/li\u003e\n\u003cli\u003eXu M, Tchkonia T, Ding H, et al. JAK inhibition alleviates the cellular senescence-associated secretory phenotype and frailty in old age. Proc Natl Acad Sci U S A 2015;112(46):E6301-10. (In eng). DOI: 10.1073/pnas.1515386112.\u003c/li\u003e\n\u003cli\u003eEnglund DA, Jolliffe AM, Hanson GJ, et al. Senotherapeutic drug treatment ameliorates chemotherapy-induced cachexia. JCI Insight 2024;9(2). DOI: 10.1172/jci.insight.169512.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"blood-cancer-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bcj","sideBox":"Learn more about [Blood Cancer Journal](http://www.nature.com/bcj/)","snPcode":"41408","submissionUrl":"https://mts-bcj.nature.com/cgi-bin/main.plex","title":"Blood Cancer Journal","twitterHandle":"@bloodcancerjnl","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8605152/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8605152/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Frailty predicts adverse outcomes in multiple myeloma (MM), yet an objective frailty biomarker is lacking. Circulating senescence-associated secretory phenotype (SASP) factors correlate with frailty and adverse outcomes in chronic diseases, but their relevance in MM is unclear. In this study, we investigated the relationship between 36 plasma SASP factors, frailty, and clinical outcomes in: (1) a historical cohort using stored specimens from 59 patients with newly diagnosed MM, and (2) a prospective cohort of 36 patients initiating new MM treatment. Several SASP factors were significantly associated with frailty after adjusting for age, sex, and body mass index; GDF-15, TNF-RI, IL-6, and IL-8 showed positive correlations with both cumulative-deficit based and physical frailty indices. Additionally, significant age-adjusted associations were observed between SASP factors and treatment outcomes, including overall survival (OS), high-grade toxicity, and healthcare utilization. We developed a 5-factor SASP model that demonstrated superior predictive performance for OS compared to chronological age, R-ISS stage, and clinical frailty, and TNF-RI alone (5-year OS AUC: 0.90). These findings highlight the association between senescence biomarkers and frailty and OS, and support further investigation of SASP factors as objective frailty biomarkers in MM.","manuscriptTitle":"Biomarkers of Cellular Senescence and their Association with Frailty, Clinical Outcomes, and Survival in Multiple Myeloma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 16:14:42","doi":"10.21203/rs.3.rs-8605152/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-02-12T15:01:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-16T15:49:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T15:49:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Blood Cancer Journal","date":"2026-01-14T21:32:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"blood-cancer-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bcj","sideBox":"Learn more about [Blood Cancer Journal](http://www.nature.com/bcj/)","snPcode":"41408","submissionUrl":"https://mts-bcj.nature.com/cgi-bin/main.plex","title":"Blood Cancer Journal","twitterHandle":"@bloodcancerjnl","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8903a3dd-1fc5-429d-ad77-6496b0dae5fb","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":62823017,"name":"Health sciences/Medical research/Translational research"},{"id":62823018,"name":"Biological sciences/Cancer/Haematological cancer/Myeloma"}],"tags":[],"updatedAt":"2026-04-09T08:58:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 16:14:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8605152","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8605152","identity":"rs-8605152","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}