Aging Phenotypes and Restoring Functional Deficits in Older Adults With Hematologic Malignancy

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  • 1 Division of Hematology,
  • | 2 College of Pharmacy,
  • | 3 Department of Biomedical Informatics,
  • | 4 Department of Molecular Genetics, Cancer Biology and Genetics, and
  • | 5 Cancer Prevention and Control, The Ohio State University, Columbus, Ohio.

Background: Gauging fitness remains a challenge among older adults with hematologic malignancies, and interventions to restore function are lacking. We pilot a structured exercise intervention and novel biologic correlates of aging using epigenetic clocks and markers of immunosenescence to evaluate changes in function and clinical outcomes. Methods: Older adults (n=30) with hematologic malignancy actively receiving treatment were screened and enrolled in a 6-month exercise intervention, the Otago Exercise Programme (OEP). The impact of the OEP on geriatric assessment metrics and health-related quality of life were captured. Clinical outcomes of overall survival and hospital utilization (inpatient length of stay and emergency department use) in relationship to geriatric deficits were analyzed. Results: Older adults (median age, 75.5 years [range, 62–83 years]) actively receiving treatment were enrolled in the OEP. Instrumental activities of daily living and physical health scores (PHS) increased significantly with the OEP intervention (median PHS: visit 1, 55 [range, 0–100]; visit 2, 70 [range, 30–100]; P<.01). Patient-reported Karnofsky performance status increased significantly, and the improvement was sustained (median [range]: visit 1, 80 [40–100]; visit 3, 90 [50–100]; P=.05). Quality of life (Patient-Reported Outcome Measurement Information System [PROMIS]) improved significantly by the end of the 6-month period (median [range]: visit 1, 32.4 [19.9–47.7]; visit 3, 36.2 [19.9–47.7]; P=.01]. Enhanced measures of gait speed and balance, using the Short Physical Performance Battery scores, were associated with a 20% decrease in risk of death (hazard ratio, 0.80; 95% CI, 0.65–0.97; P=.03) and a shorter hospital length of stay (decrease of 1.29 days; 95% CI, −2.46 to −0.13; P=.03). Peripheral blood immunosenescent markers were analyzed in relationship to clinical frailty and reports of mPhenoAge epigenetic analysis are preliminarily reported. Chronologic age had no relationship to overall survival, length of stay, or emergency department utilization. Conclusions: The OEP was effective in improving quality of life, and geriatric tools predicted survival and hospital utilization among older adults with hematologic malignancies.

Background

Functional decline is amplified in patients with cancer and is a predictor of early death.1,2 Patients with hematologic malignancy are particularly vulnerable to functional decline, resulting in increased dependence, risk of falls, and chemotherapy induced-toxicity and mortality.3 Among patients with hematologic malignancy there is inconclusive evidence that exercise improves function and quality of life in this population.4 Physical fitness is highly predictive of survival,5 yet gauging physical fitness remains a challenge for hematologists, and interventions to help this population maintain and/or regain fitness is an unmet need.

Functional decline is not an inevitable part of illness or aging, and exercise programs are proven to prevent functional decline, especially in older adults.6 The Otago Exercise Programme (OEP) has been found to be an effective exercise regimen to improve functional balance and muscle strength, prevent fall-related injury, and decrease mortality in community dwelling older adults.7 Measuring objective function using the Short Physical Performance Battery (SPPB) is a simple validated method to identify at-risk patients. The SPPB has been shown to be prognostic in predicting decline in function, rehospitalization, and mortality.8 Objective measurements of physiologic function are needed given the heterogeneity of diseases and treatments in the hematologic malignancy population.

Measures of physiologic age may complement functional assessments with use of aging biomarkers. Physicians require rapid and reliable tools to assess physiologic health in older adults with hematologic malignancy to determine treatment intensity. Aging biomarkers of interest include quantifying T-cell subsets and function, senescent cell accumulation, and epigenetic alterations. Immunosenescence has long been considered a hallmark of aging,911 with alterations in the adaptive immune system resulting in low-grade inflammation or inflamm-aging. Changes in immunosenescent molecular markers in peripheral blood T lymphocytes (PBTLs) such as p16INK4a mark biologic age,12,13 and we and others have identified that chemotherapy agents and stem cell transplant can accelerate aging of PBTLs.14,15 Epigenetic signatures can also be used to estimate biologic age and life expectancy using DNA methylation (DNAm) clocks.16 DNAm clocks are precise in predicting chronologic age.17,18 A new epigenetic clock measuring physiologic age, DNAm PhenoAge (mPhenoAge), has recently been reported, which is able to capture risk of adverse clinical outcomes across diverse tissue types19; mPhenoAge has not yet been evaluated in a population with blood cancer. In this study, we explored mPhenoAge, markers of immunosenescence, and geriatric assessments (GAs) to evaluate changes in function and clinical outcomes as a result of OEP implementation in older adults with hematologic malignancies.

Methods

This is single-institution prospective study evaluating the feasibility and impact of an exercise program (the OEP) for older adults with hematologic malignancy (ClinicalTrials.gov identifier: NCT02791737). Patients had baseline GA metrics, health-related quality of life (HRQoL; Patient-Reported Outcomes Measurement Information System [PROMIS]), and biologic markers of aging captured at baseline and during the OEP program. Clinical outcomes of overall survival (OS) and hospital utilization (hospital length of stay [LoS] and emergency department [ED] visits) were captured. Potential participants were recruited from the hematology clinics at The Ohio State University Comprehensive Cancer Center. Potential participants were eligible if they (1) were aged ≥60 years; (2) had a hematologic malignancy; (3) were actively receiving treatment (eg, any chemotherapy, immunotherapy, targeted agents); (4) scored ≤9 on the SPPB, denoting mild/moderate physical impairment at time of screening entry only; (5) attained medical clearance to participate in a moderate-intensity physical activity program; (6) were willing to provide blood samples; (7) were English-speaking; and (8) were able to understand and provide written informed consent. Potential participants with medical conditions (eg, congestive heart failure, cardiac arrhythmia) that would limit compliance with study procedures were excluded from the study. At baseline, a peripheral blood sample was also collected for a custom Nanostring Codeset (OSU_Senescence) and mPhenoAge epigenetic clock analysis. The study was approved by The Ohio State University Institutional Review Board and all patients provided written informed consent in accordance with the Declaration of Helsinki. Enrolled patients underwent a GA, SPPB battery, and PROMIS global quality of life assessment at baseline (visit 1), after a 4-month structured OEP (visit 2), and at 6 months (visit 3). Most assessments were self-administered (GA, PROMIS) electronically.

Exercise Intervention: OEP

The structured OEP focuses on strengthening, balance retraining, and walking. It is a combination of physical therapist–prescribed and home-based exercise that is known to improve balance and functional decline.20 The OEP structured intervention was administered by a certified physical therapist. The same certified physical therapist performed all assessments. The OEP comprises 8 physical therapy visits over 4 months, with required walking twice weekly at home. The physical therapist personalized the OEP based on individual patient needs (eg, weekly, biweekly, physical strength). Patients were assessed on their progress with the program at their twice-monthly appointments.

PROMIS

The PROMIS Global Health Scale Short Form v1.1 HRQoL measures provide information to the clinician regarding the patient experience of treatment or intervention. The PROMIS global health scale has been rigorously tested for reliability and validity and can be applied to all populations, with higher scores reflecting improved quality of life.21 It consists of 10 questions and was given to the patients for self-administration at 3 intervals.

GA Metrics

The Cancer and Aging Research Group GA,22 also known as the Hurria GA, was used to assess patients’ medical history, as well as functional, cognitive, and psychosocial status. The subscales involved include the following: (1) The Instrumental Activities of Daily Living (iADLs),23,24 which measures the level of independence a patient experiences in community activities, as well as activities required to maintain that independence. The iADL contains 7 items, with scores ranging from 0 to 14. A higher score is indicative of more independence. (2) The Medical Outcomes Study-Physical Health Scale25 (MOS-PHS) measures the level of physical function limitation with basic activities. The MOS-PHS consists of 10 questions, with scores ranging from 0 to 100; a higher score indicates a higher level of physical ability. (3) Patient-reported Karnofsky performance status (KPS)26 measures the patient’s self-reported level of physical function. Scoring ranges from 30 (severely disabled) to 100 (normal), with a higher score indicating a higher level of physical function. (4) The Older Americans Resources and Services (OARS) Questionnaire Physical Health Section23 was used to assess 13 common comorbid conditions and other medical conditions. Scores range from 0 to 14 and are derived by summing the number of comorbidities present. (5) The Mental Health Inventory-1727 (MHI-17) is a 17-item tool that assesses the psychologic state of patients in terms of feelings over the past 2 weeks. The measure combines subscores of psychologic distress and well-being. In this study, we used the total score, which ranges from 0% to 100%. A higher score on the MHI-17 signifies better mental health. (6) The MOS-Social Activity Limitations Measure28 (SAL) evaluates the extent to which physical and emotional problems interfere with social activities. It consists of 4 questions about social functioning, with scores ranging from 0 to 100. A higher score specifies a better level of social activity. (7) MOS-Social Support Survey29 (SSS) considers an individual’s perception of available social support in several domains. In this study, we included emotional/informational support and tangible support. Subscale scores for these domains range from 0 to 100, with higher scores indicating greater perception of available social support. The SPPB was used to measure balance, strength, and gait speed (scores range from 0 to 12, with higher scores representing higher physical function).8 GA scores were calculated (median, range, and percent impairment) and compiled. Cumulative geriatric deficits were summarized and clustered to create a frailty phenotype.

Statistical Analysis

For the entire cohort of patients, we used descriptive statistics to summarize the Hurria GA, SPPB, and PROMIS HRQoL scores at all 3 time points: baseline, visit 2 after OEP completion, and visit 3 at 6 months. Linear mixed models with repeated measures were fitted to test the difference from baseline. OS was calculated from the date of the first visit to the date of death, censoring patients who were still alive at the time of last known follow-up. The correlation between the baseline GA metrics and clinical outcomes was assessed using various generalized linear models, with the proportional hazards model used for OS, a logistic model used for institutional ED visits, and a generalized linear model with identity link function used for unplanned hospitalizations calculating inpatient LoS. For the clustering analysis, all visits were included in the GA scores. The first step was that centered and standardized values (z scores) were calculated for each geriatric metric. Then an unsupervised hierarchical clustering was applied with Ward linkage and Pearson correlation-based dissimilarity measure to create a heatmap of cumulative geriatric deficits.

Correlative Analysis

Patients enrolled had PBTL collected for mRNA analysis at baseline, visit 2, and visit 3. CD3-positive T cells were isolated using RosetteSep reagents (STEMCELL Technologies). RNA was extracted using the RNeasy Plus Mini Kit (QIAGEN) and analyzed on a custom nanostring codeset (OSU_Senescence) that includes detectors for cellular senescence (CDKN2A/p16INK4a) and standard housekeeping genes (GUSB, HPRT1, PGK1, UBC, YWAZ). Housekeeping genes of varying overall expression levels were selected based on their relative stability in T cells. OSU_Senescence genes were correlated with clinical factors (age, performance status, GA profiles, SPPB scores). OSU_Senescence gene expression profiles were age-corrected. Spearman rank correlation coefficients were used in relation to continuous measures (eg, age). Peripheral blood mononuclear cells (PBMCs) methylation age was calculated using the mPhenoAge19 epigenetic clock. PBTL DNA was analyzed on the Illumina Methylation EPIC Array chip with 850K methylation sites. All DNA samples were tested to ensure integrity and purity prior to CpG conversion. Resulting data were analyzed for indicators of poor CpG conversion and library preparation. The raw intensity of the Illumina Bead Chip was scanned, extracted, and summarized and processed using the R/Bioconductor package minfi.30 Data are normalized using Horvath algorithms for methylation age estimation, and corrected for batch and chip effects via an empirical Bayes batch-correction method (ComBat)31 in the sva package.32 Missing probe data were imputed using the K nearest neighbor method.33 Rigorous quality-control criteria was used for filtering loci and samples, and 2 replicates were included for quality control.

Results

Assessment and Intervention

Older adults (median age, 75.5 years; range, 62–83 years) actively receiving treatment for hematologic malignancy were enrolled (n=30). Patients were predominately White (n=24; 80%) and female (n=17; 57%). Most patients had plasma cell dyscrasia (n=19; 63%), others had non-Hodgkin lymphoma (n=6; 20%) or leukemia (n=5; 17%). Most patients were married or partnered (n=19; 63%) and retired (n=21; 70%). GA variables are presented in Table 1 at baseline (visit 1), after a 4-month structured OEP (visit 2), and at 6 months (visit 3). iADLs and physical health scores, as measured by the MOS-PHS, increased significantly with a structured exercise intervention (Figure 1) (median [range]: visit 1, 55 [0–100]; visit 2, 70 [30–100], P=.001; visit 3, 57.5 [0–90], P=.36). Patient-reported KPS increased significantly and the improvement was sustained at visit 3 (median [range]: visit 1, 80 [40–100]; visit 2, 90 [60–100], P=.03; visit 3, 90 [50–100], P=.05). Objective measures of physical function (SPPB) improved to normal scores by visit 2 and were sustained at visit 3 (median [range]: visit 1, 7 [0–11]; visit 2, 11 [4–12], P<.001; visit 3, 9 [2–12], P=.003). Physician-reported KPS was unchanged throughout the exercise study. Most patients did not endorse depression or anxiety, although their mental health status (MHI-17) improved significantly with a structured exercise intervention (median [range]: visit 1, 78.8 [55.3–100]; visit 2, 87.1 [60–100], P=.02; visit 3, 79.4 [49.4–96.5], P=.39). Scores for social support (emotional, tangible) were higher than social activities scores, and did not change with exercise. Quality of life, using the physical health PROMIS measure, improved significantly by the end of the 6-month exercise intervention period (median [range]: visit 1, 32.4 [19.9–47.7]; visit 2, 34.9 [26.7–47.7], P=.09; visit 3, 36.2 [19.9–47.7], P=.01).

Table 1.

Longitudinal Geriatric Assessment Scores

Table 1.
Figure 1.
Figure 1.

(A) SPPB measure of physical function improved to normal scores by visit 2 and were sustained. (B) Patient-reported KPS increased significantly and the improvement was sustained across all time points, whereas no significant change was found across study visits in physician-reported KPS. (C) Physical health scores, as measured by the MOS-PHS, increased significantly at the second. (D) PROMIS physical health T-score improved significantly at the third and final visit.

Abbreviations: KPS, Karnofsky performance status; MOS-PHS, Medical Outcomes Study-Physical Health Scale; PROMIS, Patient-Reported Outcomes Measurement Information System; SPPB, Short Physical Performance Battery.

Citation: Journal of the National Comprehensive Cancer Network 19, 9; 10.6004/jnccn.2020.7686

Clinical Outcomes

With a median follow-up of 21.4 months (range, 16.4–32.8 months), 9 patients died. Hospital utilization among this population was high during the study period and the 1-year follow up; 67% of patients (20/30) had ED visits, with a median of 1.5 visits (range, 1–6). Approximately 65% of the ED visits (33/51) resulted in a hospital admission; 15 patients were hospitalized at least once, with a median of 3 admissions (range, 1–7). The total inpatient LoS ranged from 2 to 41 days, with a median of 13 days. In univariable analysis, without adjustment for confounders, geriatric tools were associated with survival and hospital utilization among the participants (Table 2). Measures of objective function (SPPB scores) were associated with risk of death and hospital utilization. With each 1-unit increase in the SPPB score, the risk of death decreased by 20% (hazard ratio [HR], 0.80; 95% CI, 0.65–0.97; P=.03), LoS decreased by 1.29 days (95% CI, −2.46 to −0.13; P=.03), and odds of ED visit decreased by 26% (odds ratio [OR], 0.74; 95% CI, 0.52–1.05; P=.09). Patient-reported KPS was significantly associated with all 3 clinical outcomes. For each 10-unit increase in patient-reported KPS, the risk of death decreased by 31% (HR, 0.69; 95% CI, 0.49–0.99, P=.04), LoS decreased by 3.33 days (95% CI, −5.26 to −1.41; P=.0007), and odds of ED visit decreased by 45% (OR, 0.55; 95% CI, 0.30–1.01; P=.05). Physician-reported KPS was only associated with hospital LoS (estimate, −3.10; 95% CI, −5.90 to −0.31; P=.03). Chronologic age had no relationship with OS, LoS, or ED utilization (P=not significant).

Table 2.

Association Between Baseline Geriatric Assessment and Clinical Outcomes

Table 2.

Frailty

Cumulative geriatric deficits were quantified to estimate a cumulative burden of frailty at 72 distinct clinical visits. Standardized values of each geriatric metric (iADL, physical health score, MOS-PHS, anxiety scale, depression screen, SPPB, PROMIS, MHI-17, MOS subscales) for individual patient visits were clustered based on their similarity and visualized as a heatmap (Figure 2), with each column representing an individual patient visit and each row visualizing standardized scores of the geriatric domains (red indicating higher scores and blue indicating lower scores). Unsupervised clustering of the patients identified 2 frail cohorts. Patients were identified as either frail with high comorbidities, higher anxiety, and reports of depression (frail 1; n=9), or frail with low quality of life, poor physical function, and low social engagement (frail 2; n=11). The fit (n=22) cohort had fewer geriatric syndromes, and the intermediate (n=30) cohort had no specific pattern of geriatric deficits. Importantly, frail patients could not be captured with single assessments alone, but rather a comprehensive evaluation that clusters patients.

Figure 2.
Figure 2.

Frailty can be scored and categorized among older adults. Heatmap representation of unsupervised clustering of standardized scores divides data into 4 distinct groups. Comorbidities were measured by the physical health section of the OARS questionnaire; depression was addressed with a single yes or no question about frequent feelings of depression; a scale (0, best to 10, worst) was provided for average level of anxiety; PROMIS measured physical and mental health quality of life; social function was assessed by the MOS-SAL measure; iADL was taken from OARS; physical health was assessed by the MOS-PHS; social function was assessed by MOS-SAL; SPPB was used to evaluate objective physical function; emotional/informational and tangible social support was measured by MOS-SSS Scale; MHI-17 evaluated psychosocial functioning; DUREL measured religiosity and spirituality.

Abbreviations: DUREL, Duke University Religion Index; iADL, instrumental activities of daily living; MHI-17, Mental Health Inventory-17; MOS-PHS, Medical Outcomes Study-Physical Health Scale; MOS-SAL, Medical Outcomes Study-Social Activity Limitations Measure; MOS-SSS, Medical Outcomes Study-Social Support Survey; OARS, Older Americans Resources and Services; PROMIS, Patient-Reported Outcomes Measurement Information System; QoL, quality of life; SPPB, Short Physical Performance Battery.

Citation: Journal of the National Comprehensive Cancer Network 19, 9; 10.6004/jnccn.2020.7686

Biologic Correlates

Specific biomarkers of aging were explored to determine a relationship with PBTL molecular markers of aging and PBMC epigenetic age with a frailty phenotype. PBTL mRNA isolates (n=54) were analyzed using the OSU_Senescence nanostring codeset in relationship to frailty phenotypes defined earlier (frail, intermediate, fit). Patients defined as frail had upregulation of killer cell immunoglobulin-like receptors (KIRs) compared with intermediate and fit cohorts (P<.05) (Figure 3). Also noted was downregulation of aryl hydrocarbon receptor (AhR) and CD86 among frail and intermediate cohorts compared with fit cohorts (P<.05) (supplemental eFigure 1; available with this article at JNCCN.org). Epigenetic age was then explored among PBMC DNA samples across diseases (n=19) using high-throughput Levine/Horvath mPhenoAge clock analyses (Figure 4A). Median mPhenoAge and chronologic age, respectively, were 60.2 and 70 years for plasma cell dyscrasias, 61.7 and 77.5 years for leukemia, and 65.5 and 79 years for NHL. Methylation data were imputed as appropriate for the few missing values to derive age. Samples were analyzed to examine the impact of exercise on PBMCs epigenetic age in older adults with hematologic malignancy. Paired PBMC DNA isolates were obtained in 10 patients and analyzed using high-throughput mPhenoAge clock analyses. PBMC mPhenoAge was similar across hematologic disease types, and after the 6-month structured exercise intervention mPhenoAge decreased in 3 of 10 patients but remained stable in the remaining patients (Figure 4B).

Figure 3.
Figure 3.

Association of OSU_Senescence panel markers with frailty phenotype. Age-corrected gene expression for patient cohorts (frail, intermediate, fit).

Abbreviation: KIR, killer cell immunoglobulin-like receptor.

Citation: Journal of the National Comprehensive Cancer Network 19, 9; 10.6004/jnccn.2020.7686

Figure 4.
Figure 4.

(A) mPhenoAge by disease type, derived from EPIC methylation chip. (B) Change in mPhenoAge with exercise by frailty cluster. Ten patients with both frailty data and epigenetic mPhenoAge data were analyzed at baseline (visit 1) and after an exercise intervention (visit 3). Patients were categorized by cumulative geriatric deficits, and the change in mPhenoAge with exercise was trended.

Abbreviation: NHL, non-Hodgkin lymphoma.

Citation: Journal of the National Comprehensive Cancer Network 19, 9; 10.6004/jnccn.2020.7686

Discussion

According to subjective and objective metrics, functional deficits in older patients with hematologic malignancy undergoing active treatment improved with participation in the OEP exercise program. Objective markers of physical function (SPPB) correlated with mortality and hospital utilization among this population, but no significant relationship between age and clinical outcomes was observed. Our results show that participation in the OEP leads to improved functional status and physical quality of life among older adults with hematologic malignancy. Established measures of function using the SPPB provide reliable predictors of clinical outcomes, and exploring novel biomarkers of age may enhance our approach to physiologic status.

Proactively engaging patients in physical activity during treatment is a preventive approach to mitigating functional decline. The OEP is a means to safely improve fitness levels in a patient population that rarely utilizes exercise as part of the care plan. Large population-based studies have demonstrated that patients with hematologic malignancy report among the poorest HRQoL.34 However, exercise is reported to improve HRQoL in patients with cancer undergoing active treatment,35 and our results show that physical quality of life using patient-reported outcomes is improved with a short exercise intervention. The association between slower gait and clinical outcomes has been reported in a hematologic malignancy population36; our results show that functional deficits in vulnerable older adults can be improved with a structured exercise program. Moving from assessment to intervention is an important next step for geriatric oncology.

The GA has been used in various oncology settings to calculate chemotherapy toxicity, predict clinical outcomes, and estimate the risk of death toxicity.3741 Within our study, we demonstrated that some geriatric variables were more dynamic than others throughout the exercise intervention, whereas others, such as social engagement, depression, and anxiety, remained unchanged. Most patients (>80%) did not report depression upon study enrollment, and anxiety was minimal at all study time points. The small sample size may not be large enough to detect a difference, although when using a more detailed inventory of mental health (MHI-17), active exercise resulted in improved mental well-being. Meta-analysis has shown that exercise enhances social functioning and engagement at similar time points (∼12 weeks) during active chemotherapy,35 whereas in our study, we saw no difference in social support or engagement. However, we can speculate that many of the studies in the meta-analysis involved patients with solid tumors, and those undergoing active treatment for hematologic malignancies are more socially restrictive due to neutropenia.

Patient-reported physical quality of life improved by the end of our study, and physician-reported KPS was incongruent with patient-reported KPS. Performance status metrics are not tantamount to frailty. Frailty is highly prevalent among older adults and confers a high risk for adverse health outcomes, including mortality, institutionalization, falls, and hospitalization.42,43 Frailty has been defined as a biologic syndrome of decreased reserve and resistance to stressors, resulting from cumulative declines across multiple physiologic systems, and causing vulnerability to adverse outcomes.44 In our study, using an established GA method (the Hurria GA), we determined that patients clustered into 2 distinct types of frail syndromes: those with more reported comorbidities, higher anxiety/depression, low social support (comorbid frailty), and those with low quality of life, poor physical function/health, and less social activities (functional frailty). Both clusters identified as frail, but each had unique clinical phenotypes. This methodology may be clinically relevant where a classic frailty phenotype (ie, those with low physical function, exhaustion) may have >1 hallmark, as we propose here, with distinct frailty clusters: functional frailty and multimorbidity frailty. Here we explore how a constellation of geriatric factors influence clustering to determine frailty status; however, our clustering results and definitive conclusions are tempered by a small sample population in this pilot work.

There are study limitations that limit the generalizability of these results. This is a pilot study without a control group for the exercise intervention, and given the exploratory analysis of the aging biomarkers we did not include a validation cohort of biologic specimens. The small sample size, predominately composed of patients with multiple myeloma and who were White and female, is not representative of all patients with hematologic malignancy. Furthermore, dropout during the study limits the extrapolation of these results to the larger hematologic malignancy patient population. We also recognize that patients enrolled had highly variable treatments and were at various stages of the treatment trajectory (induction, maintenance, relapse, and refractory), and disease status was not captured throughout the 6-month study period. It has been understood that patients with hematologic malignancy who have disease control have improved HRQoL,45 and this may be a confounding factor that we have not accounted for within our study set. In contrast, our findings may also imply that exercise may be meaningful at every stage of cancer therapy and relevant for cancer survivorship. Importantly, this pilot work lays the groundwork for future larger studies, specifically designed for older adults to improve functional decline and target meaningful endpoints for quality of life and restoration of function.

Exploring biomarkers of aging to denote and measure physiologic age is an emerging concept in cancer research. There is a dynamic interplay between cancer and aging. We and others have shown that chemotherapy accelerates aging of the adaptive immune system,15 and there is a modest relationship between markers of senescence and fatigue.46 Here we focused on preliminary results of PBTL immunosenescent profiles and PBMC epigenetic clocks. A panel of RNA markers of T-cell cellular senescence, immunosenescence, exhaustion, and anergy were used to explore differences in frailty phenotypes. In this exploratory analysis, we identified upregulation of KIR activating subgroups on CD3-selected T cells. KIR is a major regulator of natural killer cell activity, but is also expressed on CD8-positive T cells and is associated with impaired function, effector memory phenotype, and aging.47,48 Patients with KIR overexpression in our population primarily clustered into the frail category by cumulative geriatric deficits. This may suggest that patients who are frail exhibit unique changes in their T-cell immune profile. In addition, we showed downregulation of AhR, a ligand-dependent transcription factor best established in the role of environmental toxins such as dioxin. AhR is also reported to drive immune checkpoint inhibition through T helper 17 cells and regulatory T-cell generation.49,50 In our preliminary analysis, AhR was relatively suppressed among patients with frail and intermediate status, as was CD86, a costimulatory signal required for T-cell proliferation and IL-2 production. These alterations in PBTL expression require further investigation to understand the implications of these changes among patients who are phenotypically aged.

Evaluating phenotypic age using epigenetic clocks in patients with hematologic malignancy has not been previously described. Across hematologic diseases, the mPhenoAge was younger than the chronologic age. With an exercise intervention, the mPhenoAge remained stable for most patients and improved for some over a short period, and this is hypothesis-generating for future work. Our exploratory work is limited by a small dataset, yet incorporating novel biomarkers outside of the traditional aging markers used in hematologic malignancy, such as albumin, C-reactive protein, and inflammatory cytokines, presents a unique approach to the complex process of physiologic aging.

Conclusions

Increasing the years of healthy life, with full functional capacity, while undergoing cancer care therapy is important for older adults. Here we identify that the OEP can improve functional capacity and improve measures of HRQoL. Importantly, geriatric measures of iADL dependence, mental health, and self-reported physical health also improved with an exercise intervention. Among older adults with hematologic malignancy, OS was associated with patient-reported KPS and SPPB scores; these metrics are readily measurable in an oncology clinic and may be used to facilitate treatment decisions and identify occult vulnerability. The need for reproducible biomarkers of aging is especially important in the field of malignant hematology, due to the large numbers of older adults affected, heterogeneity in older adult fitness, and diverse treatment strategies. Exploring aging biomarkers is important to characterize physiologic aging, allowing for a multitude of future research opportunities within this dynamic interaction of aging, cancer, and cancer therapeutics.

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    Horvath S. Recent updates on the epigenetic clock. Gerontologist 2016;56(Suppl 3):35.

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    Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 2018;10:573591.

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    Yang XJ, Hill K, Moore K, et al. Effectiveness of a targeted exercise intervention in reversing older people’s mild balance dysfunction: a randomized controlled trial. Phys Ther 2012;92:2437.

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    • PubMed
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  • 21.

    Basch E, Abernethy AP, Mullins CD, et al. Recommendations for incorporating patient-reported outcomes into clinical comparative effectiveness research in adult oncology. J Clin Oncol 2012;30:42494255.

    • Crossref
    • PubMed
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  • 22.

    Hurria A, Gupta S, Zauderer M, et al. Developing a cancer-specific geriatric assessment: a feasibility study. Cancer 2005;104:19982005.

  • 23.

    Fillenbaum GG, Smyer MA. The development, validity, and reliability of the OARS multidimensional functional assessment questionnaire. J Gerontol 1981;36:428434.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Fillenbaum GG. Screening the elderly. A brief instrumental activities of daily living measure. J Am Geriatr Soc 1985;33:698706.

  • 25.

    Stewart M. The Medical Outcomes Study 36-item short-form health survey (SF-36). Aust J Physiother 2007;53:208.

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    Loprinzi CL, Laurie JA, Wieand HS, et al. Prospective evaluation of prognostic variables from patient-completed questionnaires. J Clin Oncol 1994;12:601607.

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    Stewart AL, Ware JE. Measuring Functioning and Well-Being: The Medical Outcomes Study Approach. Durham, NC: Duke University Press; 1992.

    • Search Google Scholar
    • Export Citation
  • 28.

    Sherbourne CD. Pain measures. In: Stewart A, Ware J, eds. Measuring Functioning and Well-Being: The Medical Outcomes Study Approach. Durham, NC: Duke University Press; 1992:220234.

    • Search Google Scholar
    • Export Citation
  • 29.

    Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med 1991;32:705714.

  • 30.

    Fortin JP, Triche TJ Jr, Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics 2017;33:558560.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007;8:118127.

  • 32.

    Leek J, Johnson W, Parker H, et al. sva: Surrogate Variable Analysis. R package version 3.10.0; 2015.

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    Troyanskaya O, Cantor M, Sherlock G, et al. Missing value estimation methods for DNA microarrays. Bioinformatics 2001;17:520525.

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    Kent EE, Ambs A, Mitchell SA, et al. Health-related quality of life in older adult survivors of selected cancers: data from the SEER-MHOS linkage. Cancer 2015;121:758765.

    • Crossref
    • PubMed
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    Mishra SI, Scherer RW, Snyder C, et al. Exercise interventions on health-related quality of life for people with cancer during active treatment. Cochrane Database Syst Rev 2012;8:CD008465.

    • Search Google Scholar
    • Export Citation
  • 36.

    Liu MA, DuMontier C, Murillo A, et al. Gait speed, grip strength, and clinical outcomes in older patients with hematologic malignancies. Blood 2019;134:374382.

  • 37.

    Hurria A, Togawa K, Mohile SG, et al. Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study. J Clin Oncol 2011;29:34573465.

    • Crossref
    • Search Google Scholar
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    Hurria A, Mohile S, Gajra A, et al. Validation of a prediction tool for chemotherapy toxicity in older adults with cancer. J Clin Oncol 2016;34:23662371.

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    Extermann M, Boler I, Reich RR, et al. Predicting the risk of chemotherapy toxicity in older patients: the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH) score. Cancer 2012;118:33773386.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 40.

    Muffly LS, Boulukos M, Swanson K, et al. Pilot study of comprehensive geriatric assessment (CGA) in allogeneic transplant: CGA captures a high prevalence of vulnerabilities in older transplant recipients. Biol Blood Marrow Transplant 2013;19:429434.

    • Crossref
    • PubMed
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    • Export Citation
  • 41.

    Mohile SG, Dale W, Somerfield MR, et al. Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: ASCO guideline for geriatric oncology. J Clin Oncol 2018;36:23262347.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Rockwood K, Stadnyk K, MacKnight C, et al. A brief clinical instrument to classify frailty in elderly people. Lancet 1999;353:205206.

  • 43.

    Speechley M, Tinetti M. Falls and injuries in frail and vigorous community elderly persons. J Am Geriatr Soc 1991;39:4652.

  • 44.

    Rodríguez-Mañas L, Féart C, Mann G, et al. Searching for an operational definition of frailty: a Delphi method based consensus statement: the frailty operative definition-consensus conference project. J Gerontol A Biol Sci Med Sci 2013;68:6267.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Weaver KE, Forsythe LP, Reeve BB, et al. Mental and physical health-related quality of life among U.S. cancer survivors: population estimates from the 2010 National Health Interview Survey. Cancer Epidemiol Biomarkers Prev 2012;21:21082117.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    Rosko AE, Huang Y, Benson DM, et al. Use of a comprehensive frailty assessment to predict morbidity in patients with multiple myeloma undergoing transplant. J Geriatr Oncol 2019;10:479485.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47.

    Alter G, Rihn S, Streeck H, et al. Ligand-independent exhaustion of killer immunoglobulin-like receptor-positive CD8+ T cells in human immunodeficiency virus type 1 infection. J Virol 2008;82:96689677.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 48.

    Boelen L, Debebe B, Silveira M, et al. Inhibitory killer cell immunoglobulin-like receptors strengthen CD8+ T cell-mediated control of HIV-1, HCV, and HTLV-1. Sci Immunol 2018;3:29.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 49.

    Singh NP, Singh UP, Rouse M, et al. Dietary indoles suppress delayed-type hypersensitivity by inducing a switch from proinflammatory Th17 cells to anti-inflammatory regulatory T cells through regulation of microRNA. J Immunol 2016;196:11081122.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50.

    Mezrich JD, Fechner JH, Zhang X, et al. An interaction between kynurenine and the aryl hydrocarbon receptor can generate regulatory T cells. J Immunol 2010;185:31903198.

    • Crossref
    • PubMed
    • Search Google Scholar
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Submitted June 3, 2020; final revision received November 13, 2020; accepted for publication November 13, 2020.

Published online March 26, 2021.

Author contributions: Study design: Rosko, Sucheston-Campbell, Huang, Burd, Naughton. Patient enrollment: Rosko, Wall, Baiocchi, Benson, Brammer, Byrd, Efebera, Maddocks, Rogers. Research and data analysis: Rosko, Sucheston-Campbell, Tang, Ozer, Huang, Burd. Manuscript preparation: All authors.

Disclosures: Dr. Rosko has reported receiving grant/research support from Janssen, Millennium, and Regeneron. Dr. Rogers has reported receiving grant/research support from Genentech, AbbVie, and Janssen; serving on advisory boards for Acerta Pharma, AstraZeneca, and Pharmacyclics; and receiving travel funds from AztraZeneca. The remaining authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.

Funding: Research reported in this publication was supported by the NCI of the NIH under award number UG1CA189823 Alliance for Clinical Trials in Oncology NCORP Research Base, K23 CA208010-01, and UG1CA233331.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Correspondence: Ashley E. Rosko, MD, Division of Hematology, The Ohio State University, A345 Starling Loving Hall, 320 West 10th Avenue, Columbus, OH 43210. Email: Ashley.Rosko@osumc.edu

View associated content

Supplementary Materials

  • View in gallery

    (A) SPPB measure of physical function improved to normal scores by visit 2 and were sustained. (B) Patient-reported KPS increased significantly and the improvement was sustained across all time points, whereas no significant change was found across study visits in physician-reported KPS. (C) Physical health scores, as measured by the MOS-PHS, increased significantly at the second. (D) PROMIS physical health T-score improved significantly at the third and final visit.

    Abbreviations: KPS, Karnofsky performance status; MOS-PHS, Medical Outcomes Study-Physical Health Scale; PROMIS, Patient-Reported Outcomes Measurement Information System; SPPB, Short Physical Performance Battery.

  • View in gallery

    Frailty can be scored and categorized among older adults. Heatmap representation of unsupervised clustering of standardized scores divides data into 4 distinct groups. Comorbidities were measured by the physical health section of the OARS questionnaire; depression was addressed with a single yes or no question about frequent feelings of depression; a scale (0, best to 10, worst) was provided for average level of anxiety; PROMIS measured physical and mental health quality of life; social function was assessed by the MOS-SAL measure; iADL was taken from OARS; physical health was assessed by the MOS-PHS; social function was assessed by MOS-SAL; SPPB was used to evaluate objective physical function; emotional/informational and tangible social support was measured by MOS-SSS Scale; MHI-17 evaluated psychosocial functioning; DUREL measured religiosity and spirituality.

    Abbreviations: DUREL, Duke University Religion Index; iADL, instrumental activities of daily living; MHI-17, Mental Health Inventory-17; MOS-PHS, Medical Outcomes Study-Physical Health Scale; MOS-SAL, Medical Outcomes Study-Social Activity Limitations Measure; MOS-SSS, Medical Outcomes Study-Social Support Survey; OARS, Older Americans Resources and Services; PROMIS, Patient-Reported Outcomes Measurement Information System; QoL, quality of life; SPPB, Short Physical Performance Battery.

  • View in gallery

    Association of OSU_Senescence panel markers with frailty phenotype. Age-corrected gene expression for patient cohorts (frail, intermediate, fit).

    Abbreviation: KIR, killer cell immunoglobulin-like receptor.

  • View in gallery

    (A) mPhenoAge by disease type, derived from EPIC methylation chip. (B) Change in mPhenoAge with exercise by frailty cluster. Ten patients with both frailty data and epigenetic mPhenoAge data were analyzed at baseline (visit 1) and after an exercise intervention (visit 3). Patients were categorized by cumulative geriatric deficits, and the change in mPhenoAge with exercise was trended.

    Abbreviation: NHL, non-Hodgkin lymphoma.

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    Hurria A, Gupta S, Zauderer M, et al. Developing a cancer-specific geriatric assessment: a feasibility study. Cancer 2005;104:19982005.

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    Fillenbaum GG, Smyer MA. The development, validity, and reliability of the OARS multidimensional functional assessment questionnaire. J Gerontol 1981;36:428434.

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    • PubMed
    • Search Google Scholar
    • Export Citation
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    Fillenbaum GG. Screening the elderly. A brief instrumental activities of daily living measure. J Am Geriatr Soc 1985;33:698706.

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    Stewart M. The Medical Outcomes Study 36-item short-form health survey (SF-36). Aust J Physiother 2007;53:208.

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    Stewart AL, Ware JE. Measuring Functioning and Well-Being: The Medical Outcomes Study Approach. Durham, NC: Duke University Press; 1992.

    • Search Google Scholar
    • Export Citation
  • 28.

    Sherbourne CD. Pain measures. In: Stewart A, Ware J, eds. Measuring Functioning and Well-Being: The Medical Outcomes Study Approach. Durham, NC: Duke University Press; 1992:220234.

    • Search Google Scholar
    • Export Citation
  • 29.

    Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med 1991;32:705714.

  • 30.

    Fortin JP, Triche TJ Jr, Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics 2017;33:558560.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007;8:118127.

  • 32.

    Leek J, Johnson W, Parker H, et al. sva: Surrogate Variable Analysis. R package version 3.10.0; 2015.

  • 33.

    Troyanskaya O, Cantor M, Sherlock G, et al. Missing value estimation methods for DNA microarrays. Bioinformatics 2001;17:520525.

  • 34.

    Kent EE, Ambs A, Mitchell SA, et al. Health-related quality of life in older adult survivors of selected cancers: data from the SEER-MHOS linkage. Cancer 2015;121:758765.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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    Mishra SI, Scherer RW, Snyder C, et al. Exercise interventions on health-related quality of life for people with cancer during active treatment. Cochrane Database Syst Rev 2012;8:CD008465.

    • Search Google Scholar
    • Export Citation
  • 36.

    Liu MA, DuMontier C, Murillo A, et al. Gait speed, grip strength, and clinical outcomes in older patients with hematologic malignancies. Blood 2019;134:374382.

  • 37.

    Hurria A, Togawa K, Mohile SG, et al. Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study. J Clin Oncol 2011;29:34573465.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 38.

    Hurria A, Mohile S, Gajra A, et al. Validation of a prediction tool for chemotherapy toxicity in older adults with cancer. J Clin Oncol 2016;34:23662371.

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    Extermann M, Boler I, Reich RR, et al. Predicting the risk of chemotherapy toxicity in older patients: the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH) score. Cancer 2012;118:33773386.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 40.

    Muffly LS, Boulukos M, Swanson K, et al. Pilot study of comprehensive geriatric assessment (CGA) in allogeneic transplant: CGA captures a high prevalence of vulnerabilities in older transplant recipients. Biol Blood Marrow Transplant 2013;19:429434.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Mohile SG, Dale W, Somerfield MR, et al. Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: ASCO guideline for geriatric oncology. J Clin Oncol 2018;36:23262347.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Rockwood K, Stadnyk K, MacKnight C, et al. A brief clinical instrument to classify frailty in elderly people. Lancet 1999;353:205206.

  • 43.

    Speechley M, Tinetti M. Falls and injuries in frail and vigorous community elderly persons. J Am Geriatr Soc 1991;39:4652.

  • 44.

    Rodríguez-Mañas L, Féart C, Mann G, et al. Searching for an operational definition of frailty: a Delphi method based consensus statement: the frailty operative definition-consensus conference project. J Gerontol A Biol Sci Med Sci 2013;68:6267.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Weaver KE, Forsythe LP, Reeve BB, et al. Mental and physical health-related quality of life among U.S. cancer survivors: population estimates from the 2010 National Health Interview Survey. Cancer Epidemiol Biomarkers Prev 2012;21:21082117.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    Rosko AE, Huang Y, Benson DM, et al. Use of a comprehensive frailty assessment to predict morbidity in patients with multiple myeloma undergoing transplant. J Geriatr Oncol 2019;10:479485.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47.

    Alter G, Rihn S, Streeck H, et al. Ligand-independent exhaustion of killer immunoglobulin-like receptor-positive CD8+ T cells in human immunodeficiency virus type 1 infection. J Virol 2008;82:96689677.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 48.

    Boelen L, Debebe B, Silveira M, et al. Inhibitory killer cell immunoglobulin-like receptors strengthen CD8+ T cell-mediated control of HIV-1, HCV, and HTLV-1. Sci Immunol 2018;3:29.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 49.

    Singh NP, Singh UP, Rouse M, et al. Dietary indoles suppress delayed-type hypersensitivity by inducing a switch from proinflammatory Th17 cells to anti-inflammatory regulatory T cells through regulation of microRNA. J Immunol 2016;196:11081122.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50.

    Mezrich JD, Fechner JH, Zhang X, et al. An interaction between kynurenine and the aryl hydrocarbon receptor can generate regulatory T cells. J Immunol 2010;185:31903198.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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