Blood cell parameters at diagnosis, but not during chemotherapy, serve as prognostic biomarkers for executive functioning in people with aggressive lymphoma treated with chemotherapy

Blood cell parameters at diagnosis, but not during chemotherapy, serve as prognostic biomarkers for executive functioning in people with aggressive lymphoma treated with chemotherapy | 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 Research Article Blood cell parameters at diagnosis, but not during chemotherapy, serve as prognostic biomarkers for executive functioning in people with aggressive lymphoma treated with chemotherapy Delyse McCaffrey, Priscilla Gates, Haryana M. Dhillon, Carlene Wilson, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9233591/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Purpose Cognitive decline is common in people with aggressive lymphoma and may be linked to immune changes, yet it is unclear whether those who develop impairment show distinct biological profiles at diagnosis. We investigated whether greater cognitive decline after chemotherapy was associated with pre-treatment neutrophil-to-lymphocyte ratios (NLRs), platelet-to-lymphocyte ratios (PLRs) and systemic immune-inflammation indices (SIIs). Methods Multiple regression analyses examined correlations between cognitive deterioration and pre- and mid-treatment white blood cell ratios in people with aggressive lymphoma ( n = 30). Receiver operating characteristic analyses established diagnostic thresholds for NLRs, SIIs and PLRs to identify individuals at risk of cognitive impairment 6–8 weeks post-treatment. Trajectories of white blood cell ratios were also compared to healthy controls ( n = 72). Results Lower pre-treatment levels of NLRs, PLRs and SIIs correlated with greater cognitive decline during and after chemotherapy ( p 0.849; sensitivity > 0.75; specificity > 0.938) and 163.75 (AUC > 0.797; sensitivity > 0.833; specificity > 0.60), respectively. NLRs distinguished mild and moderate impairment, with thresholds of 2.255 for mild (AUC 0.763; sensitivity 0.75; specificity 0.688) and 1.6 for moderate impairment (AUC 0.928; sensitivity 0.75; specificity 0.95). Conclusion People with aggressive lymphoma who develop greater cognitive decline after chemotherapy already exhibit distinct blood cell profiles at diagnosis. Standard haematological markers may provide a low-cost, scalable approach for early risk identification, supporting targeted monitoring and early cognitive interventions. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Identifying biological markers that signal vulnerability to cognitive decline before treatment begins is essential for improving outcomes in cancer care. White blood cell ratios, such as neutrophil-to-lymphocyte ratios (NLRs), platelet-to-lymphocyte ratios (PLRs) and systemic immune-inflammation indices (SIIs), have been cross-sectionally linked to cognitive dysfunction in cancer patients as well as in various neurological and psychiatric conditions [ 1 – 4 ]. Previous research using the same cohort of people with aggressive lymphoma found objective neuropsychological performance generally improved over time [ 5 ]. However, there was considerable individual variability in these outcomes, some participants showed marked improvement, while others experienced cognitive decline. It is unknown whether individuals with greater cognitive decline following chemotherapy have distinct biological profiles at diagnosis, prior to any treatment. A ‘one-size-fits-all’ approach may obscure biologically driven vulnerabilities, and stratifying individuals based on biological indicators, such as white blood cell ratios, could identify subgroups at heightened risk for cognitive or psychological morbidity. Establishing this prognostic utility would allow early identification and targeted intervention in those most vulnerable to cancer and/or treatment-related cognitive decline. Cognitive impairment is a common and often overlooked consequence of non-central nervous system (CNS) cancer and its treatment [ 6 – 8 ]. Persisting for up to a decade post-therapy, these deficits impede patient decision-making, increase treatment resistance, and diminish quality of life and the ability to return to work [ 9 – 14 ]. Although functional brain impairment is associated with cancer treatments such as chemotherapy and the psychosocial burden of living with a cancer diagnosis, evidence suggests biological changes in the body and brain resulting from cancer itself causes initial cognitive changes [ 15 – 19 ]. Despite this significant burden, no biomarkers or other strategies exist to identify those at risk of cognitive decline, leaving a substantial unmet need in clinical practice. Early detection of at-risk individuals provides the opportunity to intervene early, preventing further cognitive decline. However, key barriers to using interventions such as psychological or behavioural treatments are that these strategies are time-consuming, often face-to-face, and are expensive and resource intense. It is not feasible or necessary to provide (p)rehabilitation to all patients, but it may be achievable if such programs were restricted to those who need it most by delineating those likely to have cognitive problems into survivorship. This emphasises the need for a prognostic indicator to triage patients that does not rely on resource-intensive neuropsychological testing. Individuals newly diagnosed with aggressive lymphoma represent an ideal population for testing the effectiveness of white blood cell ratios as biomarkers for cognitive impairment, due to the dynamic fluctuations in their blood cell numbers in response to both the cancer and its treatment [ 20 – 23 ]. The aim of this study was to examine whether cognitive deterioration after chemotherapy is associated with pre-treatment and mid-treatment blood cell profiles. Additionally, we aimed to determine diagnostic thresholds for inflammatory blood cell ratios that identify individuals at risk of chemotherapy-related cognitive impairment and to compare these trajectories with healthy controls. 2. MATERIALS AND METHODS 2.1 Study design and participants This study involved exploratory analyses of longitudinal data from people with newly diagnosed aggressive lymphoma enrolled in a feasibility study [ 5 ] and non-cancer control group data from a longitudinal cohort study comparing cognitive performance in patients with colorectal cancer to non-cancer controls [ 6 ]. Descriptive statistics for cognitive outcomes and patient characteristics were reported previously and analysed using R (version 3.6.1) [ 6 ]. Participants with aggressive lymphoma (n = 30; 18–78 years) were scheduled for curative combination chemotherapy, were English speaking and had an Eastern Cooperative Oncology Group performance status of 0–2. Exclusion criteria included CNS involvement, cranial radiation, life expectancy < 12 months, medical or psychiatric conditions impairing participation and substance misuse. Healthy controls (n = 72; 18–75 years) were excluded if they had a history of cancer, cognitive-impacting comorbidities, major psychiatric disorders, substance misuse, abnormal organ function or limited English proficiency. Their characteristics have been reported previously [ 5 , 6 ]. 2.2 Recruitment procedures Participants with newly diagnosed aggressive lymphoma were recruited from a specialised haematology department in Melbourne, Australia, and healthy controls from the community and six hospitals in Sydney. Both studies had ethics approval and all participants gave written informed consent. Assessments were conducted at diagnosis (T1), mid-chemotherapy (T2), and 6–8 weeks post-chemotherapy (T3); controls were assessed at baseline and 6 months. Neuropsychological tests and self-reported measures were completed (Table 1 ), with details reported previously [ 24 , 25 ]. A participant flow diagram is depicted in Fig. 1 Table 1 Neuropsychological tests and self-reported outcomes in people with aggressive lymphoma and healthy controls Category Measure Domain Assessed Lymphoma Cohort Healthy Controls Neuropsychological Test Verbal Learning Test-Revised (HVLT-R) Learning and memory ✔ ✔ Controlled Oral Word Association Test (COWAT) Verbal fluency and semantic function ✔ Stroop Colour and Word Test Inhibitory control ✔ Trail Making Test Part A Speed of information processing ✔ ✔ Trail Making Test Part B Cognitive flexibility ✔ ✔ Digit Span (WAIS-R) Attention and working memory ✔ ✔ Self-Report Measure QLQ-C30 Cognitive Functioning Scale (CF) General perceived cognition ✔ FACT-Cog (Cognitive Function Subscale) Memory and attention ✔ ✔ Cognitive Failures Questionnaire (CFQ) Attention and memory lapses ✔ FACT-G (General) Quality of life ✔ ✔ FACIT-F (Fatigue Subscale) Fatigue ✔ ✔ Full blood counts were used to calculate white blood cell ratios: NLRs (neutrophils ÷ lymphocytes), SIIs ((platelets × neutrophils) ÷ lymphocytes), and PLRs (platelets ÷ lymphocytes). All ratios were measured at each time point. 2.3 Statistical analyses 2.3.1 Robust mixed-effects models White blood cell ratio residuals were non-normally distributed, so robust linear mixed-effects models were used to compare longitudinal changes between lymphoma patients and healthy controls. Log-transformed data were used when raw values were skewed, as pairwise comparisons assume normality and equal variance. Normality was assessed using Shapiro-Wilk and Kolmogorov-Smirnov tests and Q-Q plots. Models included fixed effects for time and group-by-time interactions, with random intercepts for participants to account for repeated measures. For controls, the baseline assessment was T1, the 6-month follow-up was T3, and the average of the two (after confirming no difference via paired t-test) was used as T2. Significant interactions were followed by pairwise post-hoc comparisons. 2.3.2 Univariable and multivariable linear regression models Univariable and forced-entry multivariable linear regression models were used to determine whether people with aggressive lymphoma who experienced greater cognitive decline had distinct biological patterns at diagnosis and mid-chemotherapy. Cognitive change scores were calculated by normalising individual T-scores mid-chemotherapy and post-treatment to baseline (T1) using Z-scores, and post-treatment change scores were normalised to mid-chemotherapy T-scores to assess profiles during active treatment. Changes in cognition were regressed onto individual white blood cell ratios (PLR, NLR, SII) or categorical disease stage/symptom classification (1A-2B). Variables with p < 0.1 in univariable analyses were included in multivariable models. Significant covariates (age, gender, education) and baseline cognitive scores were included to control for confounding and individual differences. Normality and equal variance of residuals were checked using Q-Q plots and scatterplots; log transformation was applied if violated. One outlier at diagnosis (standardised residual 5) and correlated predictors were removed. Predictors were considered significant at p < 0.05. 2.3.3 Receiver operating characteristic analyses Receiver operating characteristic (ROC) analyses were conducted to generate optimal cut-off scores for white blood cell ratios at diagnosis that could accurately identify people with aggressive lymphoma at risk of cognitive impairment. ROC analyses were conducted between individual white blood cell ratios and TMT B T-scores 6–8 weeks after chemotherapy. The TMT B was chosen as it was most consistently correlated with white blood cell ratios over time and data were available for people with aggressive lymphoma and healthy controls. Thresholds used for identifying at-risk patients were set at 1.5 and 2 standard deviations below the mean scores of healthy controls. These thresholds reflect the ICCTF definition for cognitive impairment: either 2 or more test scores at or below − 1.5 standard deviations from the normative mean (for mild cognitive impairment) or 1 test score at or below − 2.0 standard deviations (for mild-moderate impairment) [ 26 ]. The area under the curve (AUC) was calculated to evaluate the performance of these thresholds, with higher AUC values indicating better discriminatory power. 2.3.4 R packages used Unless stated otherwise, all statistical analyses were performed using R (version 4.3.2). The “robustlmm” package in R was used to fit robust linear mixed-effects models [ 27 ] and pairwise post-hoc comparisons were performed using the “emmeans” package [ 28 ]. ROC curves were generated using the “pROC” package [ 29 ]. Q-Q plots and scatterplots were generated using the “ggplot2” package in R [ 30 ]. Figures were prepared using Graphpad Prism (9.2) and R (version 4.3.2). 3. RESULTS 3.1 Lymphoma and control cohorts were well matched on age, sex and education The lymphoma and control cohorts were well matched for age (mean 57 ± 17 vs. 56 ± 11 years; median 61 [IQR 50–69] vs. 58 [IQR 46–63]; range 18–78 vs. 26–75 years) and education (13 ± 2 vs. 14 ± 3 years). The lymphoma group had a slightly higher proportion of males than controls (53% vs. 43%), supporting the comparability of the cohorts. In the lymphoma cohort, rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone (R-CHOP)-based regimens predominated, with 33% receiving six cycles. Variants administered for 2–4 cycles accounted for 3–10%, and rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone followed by two additional rituximab cycles was used in 13%. Less common regimens included mini rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone ×6 (7%) and doxorubicin, bleomycin, vinblastine and dacarbazine ×6 (10%) for individuals with Hodgkin lymphoma, consistent with standard treatment guidelines for aggressive lymphoma. Results are summarised in Table 2 and have been previously published in Gates et al., 2022, and Vardy et al., 2015 [ 5 , 25 ]. Table 2 Participant characteristics Characteristics Patients n = 30 Controls n = 72 n % n % Age at enrolment (years) Mean (SD) 57 (17) 56 (11) Median (IQR) 61 (50–69) 58 (46–63) Range 18–78 26–75 Sex Male 16 53 31 43 Female 14 47 41 57 Years of formal education Mean (SD) 13 (2) 14 (3) Median (IQR) 13 (12–14) 15 (11–15) Range 7–18 6–20 Diagnosis Diffuse large B cell lymphoma 20 67 Grade 3B follicular lymphoma 1 3 Hodgkin Lymphoma 4 13 Mantle cell lymphoma 1 3 T-cell lymphoma 3 10 Primary mediastinal B-cell lymphoma 1 3 Chemotherapy regimen ABVD×6 3 10 CHOP×6 2 7 Esc-BEACOPP×4 1 3 Mini R-CHOP×6 2 7 R-CHOP×2 1 3 R-CHOP×3 2 7 R-CHOP×4 3 10 R-CHOP×6 10 33 R-CHOP & HD MTX×2 1 3 R-CHOP & R×2 4 13 R-CHOP/R-DHAP×3 1 3 Chemotherapy treatment (days) Mean (SD) 102 (34) Median (IQR) 105 (105–114) Range 21–116 Abbreviations; ABVD, adriamycin, bleomycin, vinblastine, dacarbazine; R-CHOP, rituximab, cyclophosphamide, doxorubicin, vincristine, prednisolone; Esc-BEACOPP, bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, prednisolone; HD MTX, high-dose methotrexate; R-DHAP, rituximab, dexamethasone, cytarabine, cisplatin. 3.2 White blood cell ratios in individuals with aggressive lymphoma vs. healthy controls across time Temporal changes in blood cell ratios differed between lymphoma patients and healthy controls (group-by-time interactions: estimate > 0.0814, t > 2.470, p < 0.014). NLRs and SIIs were higher in patients at diagnosis and post-treatment but decreased mid-chemotherapy ( p < 0.05), while PLRs were elevated at all time points, peaking mid-chemotherapy and remaining high post-treatment ( p < 0.01 − 0.001) (Supplementary Fig. 1). 3.3 Cognitive change during and after chemotherapy correlated with white blood cells at diagnosis in people with aggressive lymphoma We then examined whether greater cognitive decline was linked to distinct blood cell profiles. As mid- to post-treatment cognitive changes were not associated with white blood cell ratios, analyses focused on relationships between baseline blood profiles and cognitive changes from diagnosis to mid- and post-chemotherapy. 3.3.1 Changes in cognitive flexibility and inhibitory control from baseline to mid-chemotherapy were correlated with white blood cells at diagnosis in people with aggressive lymphoma The univariable analyses identified TMT A and B, Stroop colour-word, Stroop interference and total recall change scores at diagnosis as significant correlates of NLRs and SIIs mid-chemotherapy ( p < 0.1). Only TMT B and Stroop interference change scores mid-chemotherapy were significant correlates of PLRs at diagnosis ( p < 0.1) (Supplementary Tables 1–3). After adjusting for age, total recall and Stroop Interference, mid-chemotherapy change scores were the only significant correlates of NLRs, explaining 40% of the variance (Adjusted R² = 0.406), with positive correlations for total recall (β = 0.497, p = 0.020) and Stroop Interference (β = 0.456, p = 0.030) (Fig. 2A-B). Mid-chemotherapy Stroop Interference change scores were the sole correlate of SII, explaining 46% of variance (Adjusted R² = 0.458; β = 0.440, p = 0.029) (Fig. 2C). For PLRs, mid-chemotherapy Stroop Interference and TMT-B change scores were significant correlates at diagnosis, explaining 34% of variance (Adjusted R² = 0.336), both positively associated (TMT-B β = 0.485, p = 0.044; Stroop β = 0.390, p = 0.048) (Fig. 2D-E). 3.3.2 Changes in inhibitory control and cognitive flexibility from baseline to 6–8 weeks post-chemotherapy were correlated to white blood cell ratios at diagnosis in individuals with aggressive lymphoma In the univariable analyses, TMT A and B, Stroop colour-word, Stroop interference, total category fluency and memory retention change scores 6–8 weeks post-treatment were identified as significant correlates of one or more white blood cell ratios at diagnosis ( p < 0.1) (Supplementary Tables 4–6). After controlling for age, mid-chemotherapy TMT-B change scores were the only significant correlate of NLRs, explaining 50% of variance (Adjusted R² = 0.495), with a positive association (β = 0.616, p = 0.008) (Fig. 3A). Mid-chemotherapy TMT-B and Stroop Interference change scores were significant correlates of SII, explaining 60% of variance (Adjusted R² = 0.603), both positively associated (TMT-B β = 0.527, p = 0.003; Stroop β = 0.360, p = 0.050) (Fig. 3B-C). For PLRs, mid-chemotherapy Stroop Interference change scores were the sole correlate at diagnosis, explaining 52% of variance (Adjusted R² = 0.518) with a positive association (β = 0.469, p = 0.036) (Fig. 3D). 3.3 Categorical disease staging and symptom classification did not inform subjective and objective measures of cognition Given severity of disease can influence the proportion of circulating white blood cell ratios and drive observed relationships, we investigated the utility of standard recommendations for evaluation, staging, and response assessment of patients with aggressive lymphoma as a screening tool to forecast changes in cognition [ 31 ]. We regressed cognitive change onto disease stage to determine whether individuals who experienced greater cognitive decline during and after chemotherapy were initially diagnosed at higher disease stages. The univariable results are presented in Supplementary Table 7. However, no significant relationships between stage-of-disease and cognitive change scores mid-treatment or post-treatment were detected at the multivariable level. 3.4 Generation of clinically relevant thresholds for white blood cell ratios to identify individuals with aggressive lymphoma at risk of cognitive impairment Standard categorical staging was not associated with cognitive change in people with aggressive lymphoma. However, individuals who experienced greater cognitive decline after chemotherapy already showed distinct blood cell profiles at diagnosis. Therefore, ROC analyses were conducted to determine optimal cut-off scores for white blood cell ratios at diagnosis to identify individuals at risk of mild and moderate cognitive impairment, as defined by the ICCTF [ 26 ]. 3.4.1 Cut-off scores for white blood cell ratios at diagnosis to detect mild cognitive impairment after chemotherapy ROC analyses of NLRs, SIIs and PLRs at baseline identified 12 lymphoma patients who performed 1.5 standard deviations below the healthy control mean on the TMT-B after chemotherapy. AUCs were 0.763 for NLR (threshold 2.255; 75% sensitivity, 68.8% specificity), 0.849 for SII (threshold 501.2; 75% sensitivity, 93.8% specificity) and 0.797 for PLR (threshold 163.75; 83.3% sensitivity, 68.8% specificity) (Supplementary Table 14; Supplementary Fig. 2) 3.4.2 Optimum cut-off scores for white blood cell ratios at diagnosis to detect moderate cognitive impairment 6–8 weeks after chemotherapy ROC analyses of NLR, SII and PLR at baseline identified eight lymphoma patients who performed 2 standard deviations below the healthy control mean on the TMT-B after chemotherapy. AUCs were 0.928 for NLR (threshold 1.6; 75% sensitivity, 95% specificity), 0.969 for SII (threshold 501.2; 100% sensitivity, 90% specificity) and 0.806 for PLR (threshold 163.75; 87.5% sensitivity, 60% specificity) (Supplementary Table 15; Supplementary Fig. 3). 4. DISCUSSION We have presented novel findings that individuals with aggressive lymphoma who experience greater cognitive change post-treatment already exhibit distinct blood cell profiles at diagnosis. Individuals with lower NLRs, PLRs, and SIIs at diagnosis showed greater deterioration in cognitive flexibility and inhibitory control both mid-chemotherapy and post-treatment. This suggests pre-treatment white blood cell ratios could be used to identify individuals at risk of decline in executive function. We then established clinically useful NLR, SII, and PLR cut-off scores for mild or moderate cognitive impairment, in line with international guidelines [ 26 ]. Individuals with NLRs, PLRs, or SIIs below these thresholds would be considered at risk of mild or moderate cognitive impairment 6–8 weeks post-chemotherapy (Fig. 4). Our findings highlight that routinely collected full blood counts can identify individuals at risk of cognitive impairment in those with newly diagnosed aggressive lymphoma. Using NLR, PLR and SII as a cognitive screening tool is scalable, cost-effective and requires minimal additional resources. This approach enables early intervention and triage based on expected prognosis, allowing for tailored prehabilitation strategies targeting executive function. 4.1 White blood cell ratios may be effective biomarkers for screening general executive dysfunction in people with aggressive lymphoma Using SII and PLR appears to be an effective approach to capture prospective general impairment in executive function in people with aggressive lymphoma. SII was found to be the best biomarker of executive dysfunction in people with aggressive lymphoma, with SIIs at diagnosis showing the strongest associations with cognitive change at both follow-up time points. The ROC analysis identified the optimum threshold for SII to detect mild and moderate impairment in executive functioning as 501.2. At this threshold, SIIs showed perfect discriminatory performance in detecting people with moderate impairment and correctly identified 75% of people with mild impairment, while maintaining good specificity (> 0.9). However, PLRs outperformed SIIs in identifying mild impairment, achieving 83.7% accuracy at a threshold of 163.75, but with moderate specificity (< 0.6). Relying solely on SIIs for detecting cognitive symptoms might overlook individuals at risk of mild cognitive impairment during the acute recovery phase. Therefore, using SIIs and PLRs together may better identify individuals with ratios below these thresholds as vulnerable to general cognitive impairment. In contrast, NLRs at diagnosis were useful for distinguishing between mild and moderate executive dysfunction in individuals with aggressive lymphoma post-chemotherapy, despite their slightly lower sensitivity. By employing two distinct NLR thresholds, the severity of cognitive impairment can be effectively differentiated. For mild impairment, an NLR cut-off of 2.255 offers 75% accuracy with good specificity (> 0.65). For moderate impairment, a lower NLR cut-off of 1.6 achieves the same 75% accuracy with high specificity (> 0.9). The findings underscore NLRs potential as a practical tool for categorising the severity of executive dysfunction. It is important to note that these cut-off values may fall within the range seen in healthy controls. However, similar ratio levels may reflect distinct underlying mechanisms in each group. In healthy individuals, lower systemic inflammation ratios might reflect a stable immunological profile with no pathological significance. In contrast, similarly low values in people with aggressive lymphoma may reflect transient immune suppression, treatment-related shifts in inflammatory status or a greater underlying cancer burden, all of which may contribute to increased vulnerability to cognitive impairment. 4.2 Diagnosis may represent a critical timepoint to identity cognitive vulnerability in people with aggressive lymphoma People who experienced greater decline in executive function during and after chemotherapy had lower baseline ratios of all white blood cell ratios. However, mid-treatment indices showed no association with cognitive change after chemotherapy. This highlights the pre-treatment phase as a critical window to identify individuals vulnerable to cognitive decline. In aggressive lymphoma, the balance of circulating white blood cell ratios is influenced by the cancer and its treatment over time. Those with more advanced staged disease often display increased lymphocyte proliferation in the bone marrow, displacing the production of platelets and neutrophils, thereby reducing their circulating counts [ 32 ]. Since lymphocytes are the denominator in calculations of NLR, PLR, and SII, these ratios tend to be lower at the time of diagnosis, when lymphocyte hyperproliferation occurs prior to any treatment intervention. Chemotherapy targets proliferating lymphocytes, which can lead to neutropenia and lymphopenia [ 33 – 35 ]. To counter these effects, G-CSF is often given concurrently or soon after chemotherapy to stimulate neutrophil expansion and elevate NLRs [ 36 ]. Post-treatment lymphocytes replenish slower compared to neutrophils and platelets. The combination(s) of these treatments therefore produce highly individualised outcomes on the proportions of white blood cell ratios during and after treatment. This could explain why white blood cell ratios mid-chemotherapy were not related to cognitive change 6–8 weeks post-treatment, especially as we recruited a representative heterogeneous cohort undergoing different chemotherapy regimens. The raw neutrophil, platelet, and lymphocyte counts further support this hypothesis; all three cell types were elevated prior to treatment, whereas neutrophil and lymphocytes significantly reduced during chemotherapy and remained low at 6–8 weeks post-treatment [ 37 ]. In contrast, platelet numbers were stable mid-chemotherapy but significantly reduced 6–8 weeks post-treatment. We considered the possibility that conventional categorical staging and symptom classification at diagnosis may be a simpler effective tool for predicting later cognitive outcomes but changes in subjective and objective cognition were not correlated with disease staging. Therefore, although the proportion of circulating white blood cell ratios are impacted by disease progression, our results suggest simple categorical staging and symptom classification is not sufficiently nuanced to accurately predict cognitive change in individuals with aggressive lymphoma. 4.3 White blood cell ratios may reflect neurobiological changes in aggressive lymphoma Lastly, white blood cell ratios at diagnosis were associated with specific types of cognitive impairment. Previous studies in this cohort have shown people with lymphoma had poorer performance on objective and subjective cognitive measures compared to healthy controls at diagnosis [ 7 ]. Our study extends these findings, by showing that those who experienced greater decline in executive function during and after chemotherapy, as assessed by the Stroop Interference Test and TMT B, displayed lower NLRs, SIIs, and PLRs at diagnosis. Deficits in similar cognitive domains have been reported in people with lymphoma before chemotherapy, although deficits were observed only at a single time point [ 38 ]. In our study, white blood cell ratios at diagnosis were not related to changes in subjective cognitive function, fatigue or impairment on other objective cognitive tests. This specificity suggests that white blood cell ratios may reflect neurobiological changes in the prefrontal cortex, anterior cingulate and parietal cortices, thalamus, and basal ganglia related to executive function [ 39 – 43 ] and are discrete from psychosocial factors that may influence subjective measures. 4.4 Limitations and future directions Strong relationships between white blood cell ratios and cognitive change were observed in a population of just 30 people with aggressive lymphoma receiving varying treatment regimens. However, a larger sample size may reveal additional links between white blood cell ratios and deficits across other cognitive domains. We were unable to determine whether the relationships between NLR, PLR, and SII and cognitive outcomes differed between Hodgkin lymphoma and non-Hodgkin lymphoma because there were too few participants with Hodgkin lymphoma in our cohort ( n = 4). Although our findings remained robust after excluding participants with Hodgkin lymphoma (Supplementary Tables 8–13). Future research should confirm that similar relationships exist between white blood cell ratios and cognitive change when both types of aggressive lymphoma are analysed separately. 4.5 Conclusions We have demonstrated that individuals with aggressive lymphoma who experience greater cognitive decline post-treatment may already exhibit distinct biological profiles at diagnosis. NLRs, SIIs, and PLRs can serve as valuable biomarkers for identifying those at risk of cognitive impairment, offering a simple and effective tool for patient triage. Early identification of at-risk individuals allows for timely interventions, potentially transforming clinical practice by preventing or mitigating cognitive decline. However, further research is needed to validate diagnostic cut-off scores for these biomarkers in a larger cohort and to assess the efficacy of cognitive prehabilitation interventions, such as brain training, in improving cognitive outcomes. Declarations Ethics declarations Ethical approval for the original lymphoma study was granted by Austin Health Human Research Ethics Committee (HREC) in Victoria, Australia. The study which collected the data for control groups had human research ethics committee approval at each institution. Both studies were conducted in compliance with the principles of the Declaration of Helsinki (2013) and the principles of Good Clinical Practice and the Australian National Statement on Ethical Conduct in Human Research. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent for publication Participants signed informed consent regarding publishing their data. Funding This work was supported by the Schizophrenia Research Institute, Neuroscience Research Australia (NeuRA) and The Sir Wilfred & C H (Roger) Brookes Charitable Foundation (IPAP2024 1119) to AKW. CSW is funded by the NSW Ministry of Health, Office of Health and Medical Research. CSW is a recipient of a National Health and Medical Research Council (Australia) Investigator Grant (#2009237). DM is the Christie Scholar and supported by the NeuRA PhD Pearls Program and funded by the Australian Government Department of Education, Skills and Employment Research Training Program. PG was supported by the Olivia Newton-John Cancer Wellness and Research Centre Supportive Care Research PhD scholarship through the Victorian Cancer Agency. We thank expert trained consumers Sandie Foreman and Kathryn Leaney (Cancer Voices NSW) for their input. Authorship contribution statement D.M: Datacuration, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. P.G: Data curation, Investigation, Methodology, Writing – review & editing. H.M.D: Data curation, Writing – review & editing. C.W: Conceptualization, Methodology, Funding acquisition, Writing – review & editing. J.L.V: Conceptualization, Methodology, Funding acquisition, Writing – review & editing. C.S.W: Conceptualization, Supervision, Writing – review & editing. A.K.W: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing. 7. ACKNOWLEDGEMENTS We extend our gratitude to the participants who generously volunteered their time for this study. We also thank our collaborators, Professor Mei Krishnasamy and Associate Professor Karla Gough, for their invaluable PhD supervision and contributions to the original recruitment study. Additionally, we acknowledge Professor Michael Dickinson for providing a clinical perspective into these data and the manuscript. References Zhou, L., X. Ma, and W. Wang, Immune dysregulation is associated with symptom dimensions and cognitive deficits in schizophrenia: accessible evidence from complete blood count. BMC Psychiatry, 2024. 24 (1): p. 48. Llorca-Bofí, V., et al., Inflammatory blood cells and ratios at remission for psychosis relapse prediction: A three-year follow-up of a cohort of first episodes of schizophrenia. Schizophrenia Research, 2024. 267 : p. 24-31. Ryan, K.M., M. Lynch, and D.M. McLoughlin, Blood cell ratios in mood and cognitive outcomes following electroconvulsive therapy. J Psychiatr Res, 2022. 156 : p. 729-736. Ohtani, R., et al., High-Density Lipoprotein Subclasses and Mild Cognitive Impairment: Study of Outcome and aPolipoproteins in Dementia (STOP-Dementia). Journal of Alzheimer's Disease, 2018. 66 : p. 289-296. Gates, P., et al., Cancer-related cognitive impairment in patients with newly diagnosed aggressive lymphoma undergoing standard chemotherapy: a longitudinal feasibility study. Support Care Cancer, 2022. 30 (9): p. 7731-7743. Vardy, J.L., et al., Cognitive Function in Patients With Colorectal Cancer Who Do and Do Not Receive Chemotherapy: A Prospective, Longitudinal, Controlled Study. J Clin Oncol, 2015. 33 (34): p. 4085-92. Gates, P., et al., Cancer-related cognitive impairment and wellbeing in patients with newly diagnosed aggressive lymphoma compared to population norms and healthy controls: an exploratory study. Supportive Care in Cancer, 2024. 32 (4): p. 238. Buskbjerg, C.R., et al., Cognitive impairment and associations with structural brain networks, endocrine status, and risk genotypes in newly orchiectomized testicular cancer patients. Brain Imaging and Behavior, 2022. 16 (1): p. 199-210. Götze, H., et al., Depression and anxiety in long-term survivors 5 and 10 years after cancer diagnosis. Support Care Cancer, 2020. 28 (1): p. 211-220. Koppelmans, V., et al., Neuropsychological Performance in Survivors of Breast Cancer More Than 20 Years After Adjuvant Chemotherapy. Journal of Clinical Oncology, 2012. 30 (10): p. 1080-1086. Ahles, T.A., et al., Neuropsychologic impact of standard-dose systemic chemotherapy in long-term survivors of breast cancer and lymphoma. J Clin Oncol, 2002. 20 (2): p. 485-93. van der Willik, K.D., et al., Inflammation markers and cognitive performance in breast cancer survivors 20 years after completion of chemotherapy: a cohort study. Breast Cancer Research, 2018. 20 (1): p. 135. Von Ah, D. and A. Crouch, Relationship of perceived everyday cognitive function and work engagement in breast cancer survivors. Supportive Care in Cancer, 2021. 29 (8): p. 4303-4309. Maurer, T., et al., Health-related quality of life in a cohort of breast cancer survivors over more than 10 years post-diagnosis and in comparison to a control cohort. Cancers, 2021. 13 (8): p. 1854. McCaffrey, D., et al., Cancer activates microglia to the same extent as chronic stress throughout stress neurocircuitry in a mouse model of breast cancer. Psychoneuroendocrinology, 2022. 146 : p. 105938. Walker, A.K., et al., Low dose aspirin blocks breast cancer-induced cognitive impairment in mice. PloS one, 2018. 13 (12): p. e0208593. Pyter, L.M., et al., Novel rodent model of breast cancer survival with persistent anxiety-like behavior and inflammation. Behavioural brain research, 2017. 330 : p. 108-117. Yan, W., et al., Heterotopic 4T1 breast cancer transplantation induces hippocampal inflammation and depressive-like behaviors in mice. Metab Brain Dis, 2022. 37 (8): p. 2955-2963. Pyter, L.M., et al., Mammary tumors induce select cognitive impairments. Brain, Behavior, and Immunity, 2010. 24 (6): p. 903-907. Mackall, C.L., et al., Lymphocyte depletion during treatment with intensive chemotherapy for cancer. Blood, 1994. 84 (7): p. 2221-8. Maloney, D.G., et al., IDEC-C2B8 (Rituximab) anti-CD20 monoclonal antibody therapy in patients with relapsed low-grade non-Hodgkin's lymphoma. Blood, 1997. 90 (6): p. 2188-95. Maloney, D.G., et al., Phase I clinical trial using escalating single-dose infusion of chimeric anti-CD20 monoclonal antibody (IDEC-C2B8) in patients with recurrent B-cell lymphoma. Blood, 1994. 84 (8): p. 2457-66. Dale, D.C., Colony-stimulating factors for the management of neutropenia in cancer patients. Drugs, 2002. 62 Suppl 1 : p. 1-15. Gates, P., et al., Longitudinal exploration of cancer-related cognitive impairment in patients with newly diagnosed aggressive lymphoma: protocol for a feasibility study. BMJ Open, 2020. 10 (9): p. e038312. Vardy, J.L., et al., Cognitive function in patients with colorectal cancer who do and do not receive chemotherapy: a prospective, longitudinal, controlled study. Journal of Clinical Oncology, 2015. 33 (34): p. 4085-4092. Wefel, J.S., et al., International Cognition and Cancer Task Force recommendations to harmonise studies of cognitive function in patients with cancer. Lancet Oncol, 2011. 12 (7): p. 703-8. Koller, M., robustlmm: an R package for robust estimation of linear mixed-effects models. Journal of statistical software, 2016. 75 : p. 1-24. Lenth, R., et al., Emmeans: Estimated marginal means, aka least-squares means (R package version 1.5. 1.)[Computer software]. The Comprehensive R Archive Network. Available online: https://CRAN. R-project. org/package= emmeans (accessed on 27 September 2022), 2021. Robin, X., et al., pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 2011. 12 (1): p. 77. Wickham, H., W. Chang, and M.H. Wickham, Package ‘ggplot2’. Create elegant data visualisations using the grammar of graphics. Version, 2016. 2 (1): p. 1-189. Cheson, B.D., et al., Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol, 2014. 32 (27): p. 3059-68. Board, P.A.T.E. PDQ Non-Hodgkin Lymphoma Treatment . 2023 [cited 2023 12/07/2024]; Available from: https://www.cancer.gov/types/lymphoma/patient/adult-nhl-treatment-pdq. Anand, U., et al., Cancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics. Genes & Diseases, 2023. 10 (4): p. 1367-1401. Mackall, C.L., et al., Lymphocyte depletion during treatment with intensive chemotherapy for cancer. 1994. Truong, N.T.H., et al., Effects of Chemotherapy Agents on Circulating Leukocyte Populations: Potential Implications for the Success of CAR-T Cell Therapies. Cancers, 2021. 13 (9): p. 2225. Ozer, H., et al., 2000 update of recommendations for the use of hematopoietic colony-stimulating factors: evidence-based, clinical practice guidelines. American Society of Clinical Oncology Growth Factors Expert Panel. J Clin Oncol, 2000. 18 (20): p. 3558-85. McCaffrey, D., et al., Cognitive impairment is associated with altered blood cell profiles in aggressive lymphoma. Support Care Cancer, 2026. 34 (2): p. 120. Fayette, D., et al., Cognitive impairment associated with Hodgkin’s lymphoma and chemotherapy. Neuroscience Letters, 2023. 797 : p. 137082. Schroyen, G., et al., Neuroinflammation and Its Association with Cognition, Neuronal Markers and Peripheral Inflammation after Chemotherapy for Breast Cancer. Cancers, 2021. 13 (16): p. 4198. Kesler, S.R., J.S. Kent, and R. O'Hara, Prefrontal cortex and executive function impairments in primary breast cancer. Arch Neurol, 2011. 68 (11): p. 1447-53. Silverman, D.H.S., et al., Altered frontocortical, cerebellar, and basal ganglia activity in adjuvant-treated breast cancer survivors 5–10 years after chemotherapy. Breast Cancer Research and Treatment, 2007. 103 (3): p. 303-311. Miao, H., et al., Long-term cognitive impairment of breast cancer patients after chemotherapy: A functional MRI study. European Journal of Radiology, 2016. 85 (6): p. 1053-1057. Simó, M., et al., Cognitive and brain structural changes in a lung cancer population. J Thorac Oncol, 2015. 10 (1): p. 38-45. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterialpaper1170226.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 05 May, 2026 Editor assigned by journal 05 May, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 26 Mar, 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-9233591","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616403437,"identity":"7169c513-8326-4795-86d1-fd10a3aec89a","order_by":0,"name":"Delyse McCaffrey","email":"","orcid":"","institution":"Neuroscience Research Australia","correspondingAuthor":false,"prefix":"","firstName":"Delyse","middleName":"","lastName":"McCaffrey","suffix":""},{"id":616403439,"identity":"16e88514-1dd8-4f38-bb62-f0b1e3411b66","order_by":1,"name":"Priscilla Gates","email":"","orcid":"","institution":"Peter MacCallum Cancer Centre","correspondingAuthor":false,"prefix":"","firstName":"Priscilla","middleName":"","lastName":"Gates","suffix":""},{"id":616403441,"identity":"1fd47fdf-a7a2-41fa-bf1c-6a08a2c6bc9f","order_by":2,"name":"Haryana M. Dhillon","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Haryana","middleName":"M.","lastName":"Dhillon","suffix":""},{"id":616403442,"identity":"f4e4f68f-e0e4-417b-8d11-fbaf63f121c8","order_by":3,"name":"Carlene Wilson","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Carlene","middleName":"","lastName":"Wilson","suffix":""},{"id":616403443,"identity":"1095f91c-c8df-447c-9972-d0fd82292f05","order_by":4,"name":"Janette L. Vardy","email":"","orcid":"","institution":"University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Janette","middleName":"L.","lastName":"Vardy","suffix":""},{"id":616403444,"identity":"c39b553a-1c5c-424e-8a9c-bd79a021fc37","order_by":5,"name":"Cynthia Shannon Weickert","email":"","orcid":"","institution":"Neuroscience Research Australia","correspondingAuthor":false,"prefix":"","firstName":"Cynthia","middleName":"Shannon","lastName":"Weickert","suffix":""},{"id":616403454,"identity":"82b6b29a-d0a5-47d5-97df-2724faba43e7","order_by":6,"name":"Adam K. Walker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYLCCBwcYGPjhvAPEaEkAqpJsIFmLAVwlIS38DTyGHxLO2OQb324+Jl1RwyDHdyOB8TMPHi0SB3iMJRJupFluu3MsTfLMMQZjyRsJzNL4tDAc4N0gkfDhsIHZjRwzyQY2hsQNNxIY8GqRP8C7+UfCh/8GxjPyv0k2/GOoB2ph/o1Pi8EB3m1Ahx0wMJDIYZNsbGNIMLiRwIbXFsPD/N8sEs4kG0jcSDO2bOyTMJx55mGb5Rw8WuSOtyXf+HDMzoB/RvLDmw3fbOT5jicfvvEGn/eZUbkSQMzYgE/DKBgFo2AUjAIiAADiOU8/QEJdcAAAAABJRU5ErkJggg==","orcid":"","institution":"Neuroscience Research Australia","correspondingAuthor":true,"prefix":"","firstName":"Adam","middleName":"K.","lastName":"Walker","suffix":""}],"badges":[],"createdAt":"2026-03-26 11:39:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9233591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9233591/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107298215,"identity":"31d676fc-e68c-460e-b02a-e2c2d15a428b","added_by":"auto","created_at":"2026-04-20 07:07:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant flowchart. \u003c/strong\u003eA total of 55 patients were initially screened, with 33 eligible based on the inclusion criteria. All 33 eligible patients were approached, with 30 consenting to participate. Reasons for ineligibility (\u003cem\u003en\u003c/em\u003e= 25) included comorbidities impacting compliance with assessments (\u003cem\u003en\u003c/em\u003e = 11) and treatment delivered elsewhere (\u003cem\u003en\u003c/em\u003e = 3). Additionally, 3 patients declined participation, and 3 were distressed or overwhelmed by diagnosis and/or treatment. The study included 30 treatment-naive patients with aggressive lymphoma undergoing standard combination chemotherapy. Healthy controls (\u003cem\u003en\u003c/em\u003e = 72) were included for comparison at baseline (T1) and 6-month follow-up (T2) assessments.\u003c/p\u003e","description":"","filename":"FigurePanelspaper11702261.png","url":"https://assets-eu.researchsquare.com/files/rs-9233591/v1/a0139a79ceaaf51ac4ba29fd.png"},{"id":107485782,"identity":"7cf3261e-e13b-4978-9034-71f054821512","added_by":"auto","created_at":"2026-04-22 02:36:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBlood cell parameters at diagnosis correlated with cognitive functioning mid-chemotherapy in people with aggressive lymphoma. \u003c/strong\u003eMultivariable\u003cstrong\u003e \u003c/strong\u003elinear regression analyses of significant relationships between white blood cell ratios at diagnosis and self-reported/objective measures of cognitive functioning during chemotherapy. \u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e SIIs at diagnosis were positively correlated with Trail Making Test Part B T-scores mid-chemotherapy, β = 0.5746, \u003cem\u003ep\u003c/em\u003e = 0.0042.\u003cem\u003e\u003cstrong\u003e (B) \u003c/strong\u003e\u003c/em\u003eNLRs at diagnosis were\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003epositively related to Stroop interference T-scores after two rounds of chemotherapy,\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eβ = 0.4167, \u003cem\u003ep\u003c/em\u003e = 0.0028. \u003cem\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003c/em\u003ePLRs at diagnosis were\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003epositively correlated with Trail Making Test Part B T-scores after two rounds of chemotherapy, β = 0.3498, \u003cem\u003ep\u003c/em\u003e = 0.0073. \u003cem\u003e\u003cstrong\u003e(D) \u003c/strong\u003e\u003c/em\u003eSIIs at diagnosis were\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003epositively correlated with Stroop interference T-scores after two rounds of chemotherapy\u003cem\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003c/em\u003eβ = 0.5572, \u003cem\u003ep\u003c/em\u003e = 0.0007. \u003cem\u003e\u003cstrong\u003e(E) \u003c/strong\u003e\u003c/em\u003ePLRs at diagnosis were\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003epositively correlated with Stroop interference T-scores after two rounds of chemotherapy\u003cem\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003c/em\u003eβ = 0.3314, \u003cem\u003ep\u003c/em\u003e = 0.0030. \u003cem\u003eAbbreviations; PLR: Platelet-to-lymphocyte ratio, NLR: Neutrophil-to-lymphocyte ratio, SII: Systemic immune-inflammation index\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FigurePanelspaper11702262.png","url":"https://assets-eu.researchsquare.com/files/rs-9233591/v1/a3faf24e7202266c1eee8d53.png"},{"id":107298217,"identity":"21e0baf5-671f-4903-9d63-aabed14bc8ec","added_by":"auto","created_at":"2026-04-20 07:07:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBlood cell parameters at diagnosis correlated with cognitive functioning 6-8 weeks after chemotherapy in people with aggressive lymphoma. \u003c/strong\u003eMultivariable linear regression analyses of significant relationships between white blood cell ratios at diagnosis and objective measures of cognitive functioning 6-8 weeks after chemotherapy. \u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e NLRs at diagnosis were positively correlated with Trail Making Test Part B T-scores 6-8 weeks after chemotherapy, β = 0.3178, \u003cem\u003ep\u003c/em\u003e = 0.0013.\u003cem\u003e\u003cstrong\u003e (B) \u003c/strong\u003e\u003c/em\u003eNLRs at diagnosis were\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003epositively related to Stroop interference T-scores 6-8 weeks after chemotherapy β = 0.2544, \u003cem\u003ep\u003c/em\u003e = 0.0332. \u003cem\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003c/em\u003eSIIs at diagnosis were positively correlated with Trail Making Test Part B T-scores 6-8 weeks after chemotherapy,\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eβ = 0.4891, \u003cem\u003ep\u003c/em\u003e = 0.0003. \u003cem\u003e\u003cstrong\u003e(D) \u003c/strong\u003e\u003c/em\u003eSIIs at diagnosis were\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003epositively related to Stroop interference T-scores 6-8 weeks after chemotherapy,\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eβ = 0.5849, \u003cem\u003ep\u003c/em\u003e = 0.0002. (\u003cem\u003e\u003cstrong\u003eE) \u003c/strong\u003e\u003c/em\u003ePLRs at diagnosis were positively correlated with Trail Making Test Part B T-scores 6-8 weeks after chemotherapy,\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eβ = 0.2285, \u003cem\u003ep\u003c/em\u003e = 0.0137. \u003cem\u003e\u003cstrong\u003e(F) \u003c/strong\u003e\u003c/em\u003ePLRs at diagnosis were\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003epositively correlated to Stroop interference T-scores 6-8 weeks after chemotherapy,\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eβ = 0.3657, \u003cem\u003ep\u003c/em\u003e = 0.0034. \u003cem\u003eAbbreviations; PLR: Platelet-to-lymphocyte ratio, NLR: Neutrophil-to-lymphocyte ratio, SII: Systemic immune-inflammation index\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FigurePanelspaper11702263.png","url":"https://assets-eu.researchsquare.com/files/rs-9233591/v1/cc538eeba0961da63ee2885c.png"},{"id":107298218,"identity":"23703d39-7104-4fa9-a002-42ef0c268ca1","added_by":"auto","created_at":"2026-04-20 07:07:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":211173,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBlood cell parameters as prognostic biomarkers for cognitive impairment people with aggressive lymphoma.\u003c/strong\u003e The figure illustrates the utility for blood cell parameters measured at baseline (T1) to identify mild and moderate cognitive impairment in individuals with aggressive lymphoma 6-8 weeks post-treatment (T3). Individuals below the cut-off scores for NLRs (2.225), SII (501.2), and PLR (163.75) at T1 are classified as at-risk for general cognitive impairment at T3. NLRs can further distinguish between mild and moderate cognitive impairment, with those having NLRs below 1.6 classified as vulnerable to moderate cognitive impairment at T3. Individuals with scores above these thresholds are classified as not at risk for cognitive impairment. \u003cem\u003eAbbreviations; PLR: Platelet-to-lymphocyte ratio, NLR: Neutrophil-to-lymphocyte ratio, SII: Systemic immune-inflammation index\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FigurePanelspaper11702264.png","url":"https://assets-eu.researchsquare.com/files/rs-9233591/v1/342c4c049987c7a0e0bc87ec.png"},{"id":107705438,"identity":"125e0192-51b1-4297-a33b-b8be7ca682cd","added_by":"auto","created_at":"2026-04-24 09:12:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":805442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9233591/v1/f7fe242c-6775-4b87-a684-263d177ee705.pdf"},{"id":107298214,"identity":"938767f9-7682-4e02-b8c4-ab9b1ff091a1","added_by":"auto","created_at":"2026-04-20 07:07:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":675226,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialpaper1170226.docx","url":"https://assets-eu.researchsquare.com/files/rs-9233591/v1/c4ca1caaade0a62425845eaa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blood cell parameters at diagnosis, but not during chemotherapy, serve as prognostic biomarkers for executive functioning in people with aggressive lymphoma treated with chemotherapy","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIdentifying biological markers that signal vulnerability to cognitive decline before treatment begins is essential for improving outcomes in cancer care. White blood cell ratios, such as neutrophil-to-lymphocyte ratios (NLRs), platelet-to-lymphocyte ratios (PLRs) and systemic immune-inflammation indices (SIIs), have been cross-sectionally linked to cognitive dysfunction in cancer patients as well as in various neurological and psychiatric conditions [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous research using the same cohort of people with aggressive lymphoma found objective neuropsychological performance generally improved over time [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, there was considerable individual variability in these outcomes, some participants showed marked improvement, while others experienced cognitive decline. It is unknown whether individuals with greater cognitive decline following chemotherapy have distinct biological profiles at diagnosis, prior to any treatment. A \u0026lsquo;one-size-fits-all\u0026rsquo; approach may obscure biologically driven vulnerabilities, and stratifying individuals based on biological indicators, such as white blood cell ratios, could identify subgroups at heightened risk for cognitive or psychological morbidity. Establishing this prognostic utility would allow early identification and targeted intervention in those most vulnerable to cancer and/or treatment-related cognitive decline.\u003c/p\u003e \u003cp\u003eCognitive impairment is a common and often overlooked consequence of non-central nervous system (CNS) cancer and its treatment [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Persisting for up to a decade post-therapy, these deficits impede patient decision-making, increase treatment resistance, and diminish quality of life and the ability to return to work [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although functional brain impairment is associated with cancer treatments such as chemotherapy and the psychosocial burden of living with a cancer diagnosis, evidence suggests biological changes in the body and brain resulting from cancer itself causes initial cognitive changes [\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite this significant burden, no biomarkers or other strategies exist to identify those at risk of cognitive decline, leaving a substantial unmet need in clinical practice. Early detection of at-risk individuals provides the opportunity to intervene early, preventing further cognitive decline. However, key barriers to using interventions such as psychological or behavioural treatments are that these strategies are time-consuming, often face-to-face, and are expensive and resource intense. It is not feasible or necessary to provide (p)rehabilitation to all patients, but it may be achievable if such programs were restricted to those who need it most by delineating those likely to have cognitive problems into survivorship. This emphasises the need for a prognostic indicator to triage patients that does not rely on resource-intensive neuropsychological testing.\u003c/p\u003e \u003cp\u003eIndividuals newly diagnosed with aggressive lymphoma represent an ideal population for testing the effectiveness of white blood cell ratios as biomarkers for cognitive impairment, due to the dynamic fluctuations in their blood cell numbers in response to both the cancer and its treatment [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The aim of this study was to examine whether cognitive deterioration after chemotherapy is associated with pre-treatment and mid-treatment blood cell profiles. Additionally, we aimed to determine diagnostic thresholds for inflammatory blood cell ratios that identify individuals at risk of chemotherapy-related cognitive impairment and to compare these trajectories with healthy controls.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and participants\u003c/h2\u003e \u003cp\u003eThis study involved exploratory analyses of longitudinal data from people with newly diagnosed aggressive lymphoma enrolled in a feasibility study [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and non-cancer control group data from a longitudinal cohort study comparing cognitive performance in patients with colorectal cancer to non-cancer controls [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Descriptive statistics for cognitive outcomes and patient characteristics were reported previously and analysed using R (version 3.6.1) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParticipants with aggressive lymphoma (n\u0026thinsp;=\u0026thinsp;30; 18\u0026ndash;78 years) were scheduled for curative combination chemotherapy, were English speaking and had an Eastern Cooperative Oncology Group performance status of 0\u0026ndash;2. Exclusion criteria included CNS involvement, cranial radiation, life expectancy\u0026thinsp;\u0026lt;\u0026thinsp;12 months, medical or psychiatric conditions impairing participation and substance misuse.\u003c/p\u003e \u003cp\u003eHealthy controls (n\u0026thinsp;=\u0026thinsp;72; 18\u0026ndash;75 years) were excluded if they had a history of cancer, cognitive-impacting comorbidities, major psychiatric disorders, substance misuse, abnormal organ function or limited English proficiency. Their characteristics have been reported previously [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Recruitment procedures\u003c/h2\u003e \u003cp\u003eParticipants with newly diagnosed aggressive lymphoma were recruited from a specialised haematology department in Melbourne, Australia, and healthy controls from the community and six hospitals in Sydney. Both studies had ethics approval and all participants gave written informed consent. Assessments were conducted at diagnosis (T1), mid-chemotherapy (T2), and 6\u0026ndash;8 weeks post-chemotherapy (T3); controls were assessed at baseline and 6 months. Neuropsychological tests and self-reported measures were completed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with details reported previously [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A participant flow diagram is depicted in Fig.\u0026nbsp;1\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNeuropsychological tests and self-reported outcomes in people with aggressive lymphoma and healthy controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDomain Assessed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLymphoma Cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHealthy Controls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuropsychological Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVerbal Learning Test-Revised (HVLT-R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLearning and memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControlled Oral Word Association Test (COWAT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVerbal fluency and semantic function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStroop Colour and Word Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibitory control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrail Making Test Part A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpeed of information processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrail Making Test Part B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCognitive flexibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigit Span (WAIS-R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAttention and working memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Report Measure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQLQ-C30 Cognitive Functioning Scale (CF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneral perceived cognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFACT-Cog (Cognitive Function Subscale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMemory and attention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognitive Failures Questionnaire (CFQ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAttention and memory lapses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFACT-G (General)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuality of life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFACIT-F (Fatigue Subscale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e✔\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFull blood counts were used to calculate white blood cell ratios: NLRs (neutrophils\u0026thinsp;\u0026divide;\u0026thinsp;lymphocytes), SIIs ((platelets \u0026times; neutrophils) \u0026divide; lymphocytes), and PLRs (platelets\u0026thinsp;\u0026divide;\u0026thinsp;lymphocytes). All ratios were measured at each time point.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analyses\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Robust mixed-effects models\u003c/h2\u003e \u003cp\u003eWhite blood cell ratio residuals were non-normally distributed, so robust linear mixed-effects models were used to compare longitudinal changes between lymphoma patients and healthy controls. Log-transformed data were used when raw values were skewed, as pairwise comparisons assume normality and equal variance. Normality was assessed using Shapiro-Wilk and Kolmogorov-Smirnov tests and Q-Q plots. Models included fixed effects for time and group-by-time interactions, with random intercepts for participants to account for repeated measures. For controls, the baseline assessment was T1, the 6-month follow-up was T3, and the average of the two (after confirming no difference via paired t-test) was used as T2. Significant interactions were followed by pairwise post-hoc comparisons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Univariable and multivariable linear regression models\u003c/h2\u003e \u003cp\u003eUnivariable and forced-entry multivariable linear regression models were used to determine whether people with aggressive lymphoma who experienced greater cognitive decline had distinct biological patterns at diagnosis and mid-chemotherapy. Cognitive change scores were calculated by normalising individual T-scores mid-chemotherapy and post-treatment to baseline (T1) using Z-scores, and post-treatment change scores were normalised to mid-chemotherapy T-scores to assess profiles during active treatment. Changes in cognition were regressed onto individual white blood cell ratios (PLR, NLR, SII) or categorical disease stage/symptom classification (1A-2B). Variables with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariable analyses were included in multivariable models. Significant covariates (age, gender, education) and baseline cognitive scores were included to control for confounding and individual differences. Normality and equal variance of residuals were checked using Q-Q plots and scatterplots; log transformation was applied if violated. One outlier at diagnosis (standardised residual \u0026lt; -3) was excluded. Multicollinearity was assessed using variance inflation factors (VIFs\u0026thinsp;\u0026gt;\u0026thinsp;5) and correlated predictors were removed. Predictors were considered significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Receiver operating characteristic analyses\u003c/h2\u003e \u003cp\u003eReceiver operating characteristic (ROC) analyses were conducted to generate optimal cut-off scores for white blood cell ratios at diagnosis that could accurately identify people with aggressive lymphoma at risk of cognitive impairment. ROC analyses were conducted between individual white blood cell ratios and TMT B T-scores 6\u0026ndash;8 weeks after chemotherapy. The TMT B was chosen as it was most consistently correlated with white blood cell ratios over time and data were available for people with aggressive lymphoma and healthy controls. Thresholds used for identifying at-risk patients were set at 1.5 and 2 standard deviations below the mean scores of healthy controls. These thresholds reflect the ICCTF definition for cognitive impairment: either 2 or more test scores at or below \u0026minus;\u0026thinsp;1.5 standard deviations from the normative mean (for mild cognitive impairment) or 1 test score at or below \u0026minus;\u0026thinsp;2.0 standard deviations (for mild-moderate impairment) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The area under the curve (AUC) was calculated to evaluate the performance of these thresholds, with higher AUC values indicating better discriminatory power.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 R packages used\u003c/h2\u003e \u003cp\u003eUnless stated otherwise, all statistical analyses were performed using R (version 4.3.2). The \u0026ldquo;robustlmm\u0026rdquo; package in R was used to fit robust linear mixed-effects models [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and pairwise post-hoc comparisons were performed using the \u0026ldquo;emmeans\u0026rdquo; package [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. ROC curves were generated using the \u0026ldquo;pROC\u0026rdquo; package [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Q-Q plots and scatterplots were generated using the \u0026ldquo;ggplot2\u0026rdquo; package in R [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Figures were prepared using Graphpad Prism (9.2) and R (version 4.3.2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Lymphoma and control cohorts were well matched on age, sex and education\u003c/h2\u003e \u003cp\u003eThe lymphoma and control cohorts were well matched for age (mean 57\u0026thinsp;\u0026plusmn;\u0026thinsp;17 vs. 56\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years; median 61 [IQR 50\u0026ndash;69] vs. 58 [IQR 46\u0026ndash;63]; range 18\u0026ndash;78 vs. 26\u0026ndash;75 years) and education (13\u0026thinsp;\u0026plusmn;\u0026thinsp;2 vs. 14\u0026thinsp;\u0026plusmn;\u0026thinsp;3 years). The lymphoma group had a slightly higher proportion of males than controls (53% vs. 43%), supporting the comparability of the cohorts.\u003c/p\u003e \u003cp\u003eIn the lymphoma cohort, rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone (R-CHOP)-based regimens predominated, with 33% receiving six cycles. Variants administered for 2\u0026ndash;4 cycles accounted for 3\u0026ndash;10%, and rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone followed by two additional rituximab cycles was used in 13%. Less common regimens included mini rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone \u0026times;6 (7%) and doxorubicin, bleomycin, vinblastine and dacarbazine \u0026times;6 (10%) for individuals with Hodgkin lymphoma, consistent with standard treatment guidelines for aggressive lymphoma. Results are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and have been previously published in Gates et al., 2022, and Vardy et al., 2015 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge at enrolment (years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e57 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e61 (50\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (46\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e18\u0026ndash;78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYears of formal education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e13 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e13 (12\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (11\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDiagnosis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse large B cell lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 3B follicular lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHodgkin Lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMantle cell lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-cell lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary mediastinal B-cell lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChemotherapy regimen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABVD\u0026times;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOP\u0026times;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsc-BEACOPP\u0026times;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMini R-CHOP\u0026times;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-CHOP\u0026times;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-CHOP\u0026times;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-CHOP\u0026times;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-CHOP\u0026times;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-CHOP \u0026amp; HD MTX\u0026times;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-CHOP \u0026amp; R\u0026times;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-CHOP/R-DHAP\u0026times;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChemotherapy treatment (days)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e102 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e105 (105\u0026ndash;114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e21\u0026ndash;116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAbbreviations; ABVD, adriamycin, bleomycin, vinblastine, dacarbazine; R-CHOP, rituximab, cyclophosphamide, doxorubicin, vincristine, prednisolone; Esc-BEACOPP, bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, prednisolone; HD MTX, high-dose methotrexate; R-DHAP, rituximab, dexamethasone, cytarabine, cisplatin.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 White blood cell ratios in individuals with aggressive lymphoma vs. healthy controls across time\u003c/h2\u003e \u003cp\u003eTemporal changes in blood cell ratios differed between lymphoma patients and healthy controls (group-by-time interactions: estimate\u0026thinsp;\u0026gt;\u0026thinsp;0.0814, t\u0026thinsp;\u0026gt;\u0026thinsp;2.470, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.014). NLRs and SIIs were higher in patients at diagnosis and post-treatment but decreased mid-chemotherapy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while PLRs were elevated at all time points, peaking mid-chemotherapy and remaining high post-treatment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u0026thinsp;\u0026minus;\u0026thinsp;0.001) (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Cognitive change during and after chemotherapy correlated with white blood cells at diagnosis in people with aggressive lymphoma\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe then examined whether greater cognitive decline was linked to distinct blood cell profiles. As mid- to post-treatment cognitive changes were not associated with white blood cell ratios, analyses focused on relationships between baseline blood profiles and cognitive changes from diagnosis to mid- and post-chemotherapy.\u003c/p\u003e \u003cp\u003e \u003cem\u003e3.3.1 Changes in cognitive flexibility and inhibitory control from baseline to mid-chemotherapy were correlated with white blood cells at diagnosis in people with aggressive lymphoma\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe univariable analyses identified TMT A and B, Stroop colour-word, Stroop interference and total recall change scores at diagnosis as significant correlates of NLRs and SIIs mid-chemotherapy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Only TMT B and Stroop interference change scores mid-chemotherapy were significant correlates of PLRs at diagnosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) (Supplementary Tables\u0026nbsp;1\u0026ndash;3).\u003c/p\u003e \u003cp\u003eAfter adjusting for age, total recall and Stroop Interference, mid-chemotherapy change scores were the only significant correlates of NLRs, explaining 40% of the variance (Adjusted R\u0026sup2; = 0.406), with positive correlations for total recall (β\u0026thinsp;=\u0026thinsp;0.497, p\u0026thinsp;=\u0026thinsp;0.020) and Stroop Interference (β\u0026thinsp;=\u0026thinsp;0.456, p\u0026thinsp;=\u0026thinsp;0.030) (Fig.\u0026nbsp;2A-B). Mid-chemotherapy Stroop Interference change scores were the sole correlate of SII, explaining 46% of variance (Adjusted R\u0026sup2; = 0.458; β\u0026thinsp;=\u0026thinsp;0.440, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) (Fig.\u0026nbsp;2C). For PLRs, mid-chemotherapy Stroop Interference and TMT-B change scores were significant correlates at diagnosis, explaining 34% of variance (Adjusted R\u0026sup2; = 0.336), both positively associated (TMT-B β\u0026thinsp;=\u0026thinsp;0.485, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044; Stroop β\u0026thinsp;=\u0026thinsp;0.390, p\u0026thinsp;=\u0026thinsp;0.048) (Fig.\u0026nbsp;2D-E).\u003c/p\u003e \u003cp\u003e \u003cem\u003e3.3.2 Changes in inhibitory control and cognitive flexibility from baseline to 6\u0026ndash;8 weeks post-chemotherapy were correlated to white blood cell ratios at diagnosis in individuals with aggressive lymphoma\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn the univariable analyses, TMT A and B, Stroop colour-word, Stroop interference, total category fluency and memory retention change scores 6\u0026ndash;8 weeks post-treatment were identified as significant correlates of one or more white blood cell ratios at diagnosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) (Supplementary Tables\u0026nbsp;4\u0026ndash;6).\u003c/p\u003e \u003cp\u003eAfter controlling for age, mid-chemotherapy TMT-B change scores were the only significant correlate of NLRs, explaining 50% of variance (Adjusted R\u0026sup2; = 0.495), with a positive association (β\u0026thinsp;=\u0026thinsp;0.616, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) (Fig.\u0026nbsp;3A). Mid-chemotherapy TMT-B and Stroop Interference change scores were significant correlates of SII, explaining 60% of variance (Adjusted R\u0026sup2; = 0.603), both positively associated (TMT-B β\u0026thinsp;=\u0026thinsp;0.527, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Stroop β\u0026thinsp;=\u0026thinsp;0.360, p\u0026thinsp;=\u0026thinsp;0.050) (Fig.\u0026nbsp;3B-C). For PLRs, mid-chemotherapy Stroop Interference change scores were the sole correlate at diagnosis, explaining 52% of variance (Adjusted R\u0026sup2; = 0.518) with a positive association (β\u0026thinsp;=\u0026thinsp;0.469, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) (Fig.\u0026nbsp;3D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.3 Categorical disease staging and symptom classification did not inform subjective and objective measures of cognition\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eGiven severity of disease can influence the proportion of circulating white blood cell ratios and drive observed relationships, we investigated the utility of standard recommendations for evaluation, staging, and response assessment of patients with aggressive lymphoma as a screening tool to forecast changes in cognition [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We regressed cognitive change onto disease stage to determine whether individuals who experienced greater cognitive decline during and after chemotherapy were initially diagnosed at higher disease stages. The univariable results are presented in Supplementary Table\u0026nbsp;7. However, no significant relationships between stage-of-disease and cognitive change scores mid-treatment or post-treatment were detected at the multivariable level.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 Generation of clinically relevant thresholds for white blood cell ratios to identify individuals with aggressive lymphoma at risk of cognitive impairment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStandard categorical staging was not associated with cognitive change in people with aggressive lymphoma. However, individuals who experienced greater cognitive decline after chemotherapy already showed distinct blood cell profiles at diagnosis. Therefore, ROC analyses were conducted to determine optimal cut-off scores for white blood cell ratios at diagnosis to identify individuals at risk of mild and moderate cognitive impairment, as defined by the ICCTF [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003e3.4.1 Cut-off scores for white blood cell ratios at diagnosis to detect\u003c/em\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003emild cognitive impairment\u003c/span\u003e \u003cem\u003eafter chemotherapy\u003c/em\u003e\u003c/p\u003e \u003cp\u003eROC analyses of NLRs, SIIs and PLRs at baseline identified 12 lymphoma patients who performed 1.5 standard deviations below the healthy control mean on the TMT-B after chemotherapy. AUCs were 0.763 for NLR (threshold 2.255; 75% sensitivity, 68.8% specificity), 0.849 for SII (threshold 501.2; 75% sensitivity, 93.8% specificity) and 0.797 for PLR (threshold 163.75; 83.3% sensitivity, 68.8% specificity) (Supplementary Table\u0026nbsp;14; Supplementary Fig.\u0026nbsp;2)\u003c/p\u003e \u003cp\u003e \u003cem\u003e3.4.2 Optimum cut-off scores for white blood cell ratios at diagnosis to detect\u003c/em\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003emoderate cognitive impairment\u003c/span\u003e \u003cem\u003e6\u0026ndash;8 weeks after chemotherapy\u003c/em\u003e\u003c/p\u003e \u003cp\u003eROC analyses of NLR, SII and PLR at baseline identified eight lymphoma patients who performed 2 standard deviations below the healthy control mean on the TMT-B after chemotherapy. AUCs were 0.928 for NLR (threshold 1.6; 75% sensitivity, 95% specificity), 0.969 for SII (threshold 501.2; 100% sensitivity, 90% specificity) and 0.806 for PLR (threshold 163.75; 87.5% sensitivity, 60% specificity) (Supplementary Table\u0026nbsp;15; Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eWe have presented novel findings that individuals with aggressive lymphoma who experience greater cognitive change post-treatment already exhibit distinct blood cell profiles at diagnosis. Individuals with lower NLRs, PLRs, and SIIs at diagnosis showed greater deterioration in cognitive flexibility and inhibitory control both mid-chemotherapy and post-treatment. This suggests pre-treatment white blood cell ratios could be used to identify individuals at risk of decline in executive function. We then established clinically useful NLR, SII, and PLR cut-off scores for mild or moderate cognitive impairment, in line with international guidelines [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Individuals with NLRs, PLRs, or SIIs below these thresholds would be considered at risk of mild or moderate cognitive impairment 6\u0026ndash;8 weeks post-chemotherapy (Fig.\u0026nbsp;4). Our findings highlight that routinely collected full blood counts can identify individuals at risk of cognitive impairment in those with newly diagnosed aggressive lymphoma. Using NLR, PLR and SII as a cognitive screening tool is scalable, cost-effective and requires minimal additional resources. This approach enables early intervention and triage based on expected prognosis, allowing for tailored prehabilitation strategies targeting executive function.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.1 White blood cell ratios may be effective biomarkers for screening general executive dysfunction in people with aggressive lymphoma\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUsing SII and PLR appears to be an effective approach to capture prospective general impairment in executive function in people with aggressive lymphoma. SII was found to be the best biomarker of executive dysfunction in people with aggressive lymphoma, with SIIs at diagnosis showing the strongest associations with cognitive change at both follow-up time points. The ROC analysis identified the optimum threshold for SII to detect mild and moderate impairment in executive functioning as 501.2. At this threshold, SIIs showed perfect discriminatory performance in detecting people with moderate impairment and correctly identified 75% of people with mild impairment, while maintaining good specificity (\u0026gt;\u0026thinsp;0.9). However, PLRs outperformed SIIs in identifying mild impairment, achieving 83.7% accuracy at a threshold of 163.75, but with moderate specificity (\u0026lt;\u0026thinsp;0.6). Relying solely on SIIs for detecting cognitive symptoms might overlook individuals at risk of mild cognitive impairment during the acute recovery phase. Therefore, using SIIs and PLRs together may better identify individuals with ratios below these thresholds as vulnerable to general cognitive impairment. In contrast, NLRs at diagnosis were useful for distinguishing between mild and moderate executive dysfunction in individuals with aggressive lymphoma post-chemotherapy, despite their slightly lower sensitivity. By employing two distinct NLR thresholds, the severity of cognitive impairment can be effectively differentiated. For mild impairment, an NLR cut-off of 2.255 offers 75% accuracy with good specificity (\u0026gt;\u0026thinsp;0.65). For moderate impairment, a lower NLR cut-off of 1.6 achieves the same 75% accuracy with high specificity (\u0026gt;\u0026thinsp;0.9). The findings underscore NLRs potential as a practical tool for categorising the severity of executive dysfunction. It is important to note that these cut-off values may fall within the range seen in healthy controls. However, similar ratio levels may reflect distinct underlying mechanisms in each group. In healthy individuals, lower systemic inflammation ratios might reflect a stable immunological profile with no pathological significance. In contrast, similarly low values in people with aggressive lymphoma may reflect transient immune suppression, treatment-related shifts in inflammatory status or a greater underlying cancer burden, all of which may contribute to increased vulnerability to cognitive impairment.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Diagnosis may represent a critical timepoint to identity cognitive vulnerability in people with aggressive lymphoma\u003c/h2\u003e \u003cp\u003ePeople who experienced greater decline in executive function during and after chemotherapy had lower baseline ratios of all white blood cell ratios. However, mid-treatment indices showed no association with cognitive change after chemotherapy. This highlights the pre-treatment phase as a critical window to identify individuals vulnerable to cognitive decline. In aggressive lymphoma, the balance of circulating white blood cell ratios is influenced by the cancer and its treatment over time. Those with more advanced staged disease often display increased lymphocyte proliferation in the bone marrow, displacing the production of platelets and neutrophils, thereby reducing their circulating counts [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Since lymphocytes are the denominator in calculations of NLR, PLR, and SII, these ratios tend to be lower at the time of diagnosis, when lymphocyte hyperproliferation occurs prior to any treatment intervention. Chemotherapy targets proliferating lymphocytes, which can lead to neutropenia and lymphopenia [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. To counter these effects, G-CSF is often given concurrently or soon after chemotherapy to stimulate neutrophil expansion and elevate NLRs [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Post-treatment lymphocytes replenish slower compared to neutrophils and platelets. The combination(s) of these treatments therefore produce highly individualised outcomes on the proportions of white blood cell ratios during and after treatment. This could explain why white blood cell ratios mid-chemotherapy were not related to cognitive change 6\u0026ndash;8 weeks post-treatment, especially as we recruited a representative heterogeneous cohort undergoing different chemotherapy regimens. The raw neutrophil, platelet, and lymphocyte counts further support this hypothesis; all three cell types were elevated prior to treatment, whereas neutrophil and lymphocytes significantly reduced during chemotherapy and remained low at 6\u0026ndash;8 weeks post-treatment [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In contrast, platelet numbers were stable mid-chemotherapy but significantly reduced 6\u0026ndash;8 weeks post-treatment. We considered the possibility that conventional categorical staging and symptom classification at diagnosis may be a simpler effective tool for predicting later cognitive outcomes but changes in subjective and objective cognition were not correlated with disease staging. Therefore, although the proportion of circulating white blood cell ratios are impacted by disease progression, our results suggest simple categorical staging and symptom classification is not sufficiently nuanced to accurately predict cognitive change in individuals with aggressive lymphoma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 White blood cell ratios may reflect neurobiological changes in aggressive lymphoma\u003c/h2\u003e \u003cp\u003eLastly, white blood cell ratios at diagnosis were associated with specific types of cognitive impairment. Previous studies in this cohort have shown people with lymphoma had poorer performance on objective and subjective cognitive measures compared to healthy controls at diagnosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Our study extends these findings, by showing that those who experienced greater decline in executive function during and after chemotherapy, as assessed by the Stroop Interference Test and TMT B, displayed lower NLRs, SIIs, and PLRs at diagnosis. Deficits in similar cognitive domains have been reported in people with lymphoma before chemotherapy, although deficits were observed only at a single time point [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our study, white blood cell ratios at diagnosis were not related to changes in subjective cognitive function, fatigue or impairment on other objective cognitive tests. This specificity suggests that white blood cell ratios may reflect neurobiological changes in the prefrontal cortex, anterior cingulate and parietal cortices, thalamus, and basal ganglia related to executive function [\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and are discrete from psychosocial factors that may influence subjective measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations and future directions\u003c/h2\u003e \u003cp\u003eStrong relationships between white blood cell ratios and cognitive change were observed in a population of just 30 people with aggressive lymphoma receiving varying treatment regimens. However, a larger sample size may reveal additional links between white blood cell ratios and deficits across other cognitive domains. We were unable to determine whether the relationships between NLR, PLR, and SII and cognitive outcomes differed between Hodgkin lymphoma and non-Hodgkin lymphoma because there were too few participants with Hodgkin lymphoma in our cohort (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4). Although our findings remained robust after excluding participants with Hodgkin lymphoma (Supplementary Tables\u0026nbsp;8\u0026ndash;13). Future research should confirm that similar relationships exist between white blood cell ratios and cognitive change when both types of aggressive lymphoma are analysed separately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Conclusions\u003c/h2\u003e \u003cp\u003eWe have demonstrated that individuals with aggressive lymphoma who experience greater cognitive decline post-treatment may already exhibit distinct biological profiles at diagnosis. NLRs, SIIs, and PLRs can serve as valuable biomarkers for identifying those at risk of cognitive impairment, offering a simple and effective tool for patient triage. Early identification of at-risk individuals allows for timely interventions, potentially transforming clinical practice by preventing or mitigating cognitive decline. However, further research is needed to validate diagnostic cut-off scores for these biomarkers in a larger cohort and to assess the efficacy of cognitive prehabilitation interventions, such as brain training, in improving cognitive outcomes.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the original lymphoma study was granted by Austin Health Human Research Ethics Committee (HREC) in Victoria, Australia.\u0026nbsp;The study which collected the data for control groups had human research ethics committee approval at each institution.\u0026nbsp;Both studies were conducted in compliance with the principles of the Declaration of Helsinki (2013) and the principles of Good Clinical Practice and the Australian National Statement on Ethical Conduct in Human Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study. \u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants signed informed consent regarding publishing their data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Schizophrenia Research Institute, Neuroscience Research Australia (NeuRA) and The\u0026nbsp;Sir Wilfred \u0026amp; C H (Roger) Brookes Charitable Foundation\u0026nbsp;(IPAP2024 1119) to AKW. CSW is funded by the NSW Ministry of Health, Office of Health and Medical Research. CSW is a recipient of a National Health and Medical Research Council (Australia) Investigator Grant (#2009237). DM is the Christie Scholar and supported by the NeuRA PhD Pearls Program and funded by the Australian Government Department of Education, Skills and Employment Research Training Program. PG was supported by the Olivia Newton-John Cancer Wellness and Research Centre Supportive Care Research PhD scholarship through the Victorian Cancer Agency. We thank expert trained consumers Sandie Foreman and Kathryn Leaney (Cancer Voices NSW) for their input.\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.M:\u0026nbsp;\u003c/strong\u003eDatacuration, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP.G:\u003c/strong\u003e Data curation, Investigation, Methodology, Writing – review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH.M.D:\u003c/strong\u003e Data curation, Writing – review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.W:\u003c/strong\u003e Conceptualization, Methodology, Funding acquisition, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ.L.V:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Funding acquisition, Writing – review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.S.W:\u003c/strong\u003e Conceptualization, Supervision, Writing – review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.K.W:\u003c/strong\u003e Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e7. ACKNOWLEDGEMENTS\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the participants who generously volunteered their time for this study. We also thank our collaborators, Professor Mei Krishnasamy and Associate Professor Karla Gough, for their invaluable PhD supervision and contributions to the original recruitment study. Additionally, we acknowledge Professor Michael Dickinson for providing a clinical perspective into these data and the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhou, L., X. Ma, and W. Wang, \u003cem\u003eImmune dysregulation is associated with symptom dimensions and cognitive deficits in schizophrenia: accessible evidence from complete blood count.\u003c/em\u003e BMC Psychiatry, 2024. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 48.\u003c/li\u003e\n\u003cli\u003eLlorca-Bof\u0026iacute;, V., et al., \u003cem\u003eInflammatory blood cells and ratios at remission for psychosis relapse prediction: A three-year follow-up of a cohort of first episodes of schizophrenia.\u003c/em\u003e Schizophrenia Research, 2024. \u003cstrong\u003e267\u003c/strong\u003e: p. 24-31.\u003c/li\u003e\n\u003cli\u003eRyan, K.M., M. Lynch, and D.M. McLoughlin, \u003cem\u003eBlood cell ratios in mood and cognitive outcomes following electroconvulsive therapy.\u003c/em\u003e J Psychiatr Res, 2022. \u003cstrong\u003e156\u003c/strong\u003e: p. 729-736.\u003c/li\u003e\n\u003cli\u003eOhtani, R., et al., \u003cem\u003eHigh-Density Lipoprotein Subclasses and Mild Cognitive Impairment: Study of Outcome and aPolipoproteins in Dementia (STOP-Dementia).\u003c/em\u003e Journal of Alzheimer\u0026apos;s Disease, 2018. \u003cstrong\u003e66\u003c/strong\u003e: p. 289-296.\u003c/li\u003e\n\u003cli\u003eGates, P., et al., \u003cem\u003eCancer-related cognitive impairment in patients with newly diagnosed aggressive lymphoma undergoing standard chemotherapy: a longitudinal feasibility study.\u003c/em\u003e Support Care Cancer, 2022. \u003cstrong\u003e30\u003c/strong\u003e(9): p. 7731-7743.\u003c/li\u003e\n\u003cli\u003eVardy, J.L., et al., \u003cem\u003eCognitive Function in Patients With Colorectal Cancer Who Do and Do Not Receive Chemotherapy: A Prospective, Longitudinal, Controlled Study.\u003c/em\u003e J Clin Oncol, 2015. \u003cstrong\u003e33\u003c/strong\u003e(34): p. 4085-92.\u003c/li\u003e\n\u003cli\u003eGates, P., et al., \u003cem\u003eCancer-related cognitive impairment and wellbeing in patients with newly diagnosed aggressive lymphoma compared to population norms and healthy controls: an exploratory study.\u003c/em\u003e Supportive Care in Cancer, 2024. \u003cstrong\u003e32\u003c/strong\u003e(4): p. 238.\u003c/li\u003e\n\u003cli\u003eBuskbjerg, C.R., et al., \u003cem\u003eCognitive impairment and associations with structural brain networks, endocrine status, and risk genotypes in newly orchiectomized testicular cancer patients.\u003c/em\u003e Brain Imaging and Behavior, 2022. \u003cstrong\u003e16\u003c/strong\u003e(1): p. 199-210.\u003c/li\u003e\n\u003cli\u003eG\u0026ouml;tze, H., et al., \u003cem\u003eDepression and anxiety in long-term survivors 5 and 10 years after cancer diagnosis.\u003c/em\u003e Support Care Cancer, 2020. \u003cstrong\u003e28\u003c/strong\u003e(1): p. 211-220.\u003c/li\u003e\n\u003cli\u003eKoppelmans, V., et al., \u003cem\u003eNeuropsychological Performance in Survivors of Breast Cancer More Than 20 Years After Adjuvant Chemotherapy.\u003c/em\u003e Journal of Clinical Oncology, 2012. \u003cstrong\u003e30\u003c/strong\u003e(10): p. 1080-1086.\u003c/li\u003e\n\u003cli\u003eAhles, T.A., et al., \u003cem\u003eNeuropsychologic impact of standard-dose systemic chemotherapy in long-term survivors of breast cancer and lymphoma.\u003c/em\u003e J Clin Oncol, 2002. \u003cstrong\u003e20\u003c/strong\u003e(2): p. 485-93.\u003c/li\u003e\n\u003cli\u003evan der Willik, K.D., et al., \u003cem\u003eInflammation markers and cognitive performance in breast cancer survivors 20 years after completion of chemotherapy: a cohort study.\u003c/em\u003e Breast Cancer Research, 2018. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 135.\u003c/li\u003e\n\u003cli\u003eVon Ah, D. and A. Crouch, \u003cem\u003eRelationship of perceived everyday cognitive function and work engagement in breast cancer survivors.\u003c/em\u003e Supportive Care in Cancer, 2021. \u003cstrong\u003e29\u003c/strong\u003e(8): p. 4303-4309.\u003c/li\u003e\n\u003cli\u003eMaurer, T., et al., \u003cem\u003eHealth-related quality of life in a cohort of breast cancer survivors over more than 10 years post-diagnosis and in comparison to a control cohort.\u003c/em\u003e Cancers, 2021. \u003cstrong\u003e13\u003c/strong\u003e(8): p. 1854.\u003c/li\u003e\n\u003cli\u003eMcCaffrey, D., et al., \u003cem\u003eCancer activates microglia to the same extent as chronic stress throughout stress neurocircuitry in a mouse model of breast cancer.\u003c/em\u003e Psychoneuroendocrinology, 2022. \u003cstrong\u003e146\u003c/strong\u003e: p. 105938.\u003c/li\u003e\n\u003cli\u003eWalker, A.K., et al., \u003cem\u003eLow dose aspirin blocks breast cancer-induced cognitive impairment in mice.\u003c/em\u003e PloS one, 2018. \u003cstrong\u003e13\u003c/strong\u003e(12): p. e0208593.\u003c/li\u003e\n\u003cli\u003ePyter, L.M., et al., \u003cem\u003eNovel rodent model of breast cancer survival with persistent anxiety-like behavior and inflammation.\u003c/em\u003e Behavioural brain research, 2017. \u003cstrong\u003e330\u003c/strong\u003e: p. 108-117.\u003c/li\u003e\n\u003cli\u003eYan, W., et al., \u003cem\u003eHeterotopic 4T1 breast cancer transplantation induces hippocampal inflammation and depressive-like behaviors in mice.\u003c/em\u003e Metab Brain Dis, 2022. \u003cstrong\u003e37\u003c/strong\u003e(8): p. 2955-2963.\u003c/li\u003e\n\u003cli\u003ePyter, L.M., et al., \u003cem\u003eMammary tumors induce select cognitive impairments.\u003c/em\u003e Brain, Behavior, and Immunity, 2010. \u003cstrong\u003e24\u003c/strong\u003e(6): p. 903-907.\u003c/li\u003e\n\u003cli\u003eMackall, C.L., et al., \u003cem\u003eLymphocyte depletion during treatment with intensive chemotherapy for cancer.\u003c/em\u003e Blood, 1994. \u003cstrong\u003e84\u003c/strong\u003e(7): p. 2221-8.\u003c/li\u003e\n\u003cli\u003eMaloney, D.G., et al., \u003cem\u003eIDEC-C2B8 (Rituximab) anti-CD20 monoclonal antibody therapy in patients with relapsed low-grade non-Hodgkin\u0026apos;s lymphoma.\u003c/em\u003e Blood, 1997. \u003cstrong\u003e90\u003c/strong\u003e(6): p. 2188-95.\u003c/li\u003e\n\u003cli\u003eMaloney, D.G., et al., \u003cem\u003ePhase I clinical trial using escalating single-dose infusion of chimeric anti-CD20 monoclonal antibody (IDEC-C2B8) in patients with recurrent B-cell lymphoma.\u003c/em\u003e Blood, 1994. \u003cstrong\u003e84\u003c/strong\u003e(8): p. 2457-66.\u003c/li\u003e\n\u003cli\u003eDale, D.C., \u003cem\u003eColony-stimulating factors for the management of neutropenia in cancer patients.\u003c/em\u003e Drugs, 2002. \u003cstrong\u003e62 Suppl 1\u003c/strong\u003e: p. 1-15.\u003c/li\u003e\n\u003cli\u003eGates, P., et al., \u003cem\u003eLongitudinal exploration of cancer-related cognitive impairment in patients with newly diagnosed aggressive lymphoma: protocol for a feasibility study.\u003c/em\u003e BMJ Open, 2020. \u003cstrong\u003e10\u003c/strong\u003e(9): p. e038312.\u003c/li\u003e\n\u003cli\u003eVardy, J.L., et al., \u003cem\u003eCognitive function in patients with colorectal cancer who do and do not receive chemotherapy: a prospective, longitudinal, controlled study.\u003c/em\u003e Journal of Clinical Oncology, 2015. \u003cstrong\u003e33\u003c/strong\u003e(34): p. 4085-4092.\u003c/li\u003e\n\u003cli\u003eWefel, J.S., et al., \u003cem\u003eInternational Cognition and Cancer Task Force recommendations to harmonise studies of cognitive function in patients with cancer.\u003c/em\u003e Lancet Oncol, 2011. \u003cstrong\u003e12\u003c/strong\u003e(7): p. 703-8.\u003c/li\u003e\n\u003cli\u003eKoller, M., \u003cem\u003erobustlmm: an R package for robust estimation of linear mixed-effects models.\u003c/em\u003e Journal of statistical software, 2016. \u003cstrong\u003e75\u003c/strong\u003e: p. 1-24.\u003c/li\u003e\n\u003cli\u003eLenth, R., et al., \u003cem\u003eEmmeans: Estimated marginal means, aka least-squares means (R package version 1.5. 1.)[Computer software].\u003c/em\u003e The Comprehensive R Archive Network. Available online: https://CRAN. R-project. org/package= emmeans (accessed on 27 September 2022), 2021.\u003c/li\u003e\n\u003cli\u003eRobin, X., et al., \u003cem\u003epROC: an open-source package for R and S+ to analyze and compare ROC curves.\u003c/em\u003e BMC Bioinformatics, 2011. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 77.\u003c/li\u003e\n\u003cli\u003eWickham, H., W. Chang, and M.H. Wickham, \u003cem\u003ePackage \u0026lsquo;ggplot2\u0026rsquo;.\u003c/em\u003e Create elegant data visualisations using the grammar of graphics. Version, 2016. \u003cstrong\u003e2\u003c/strong\u003e(1): p. 1-189.\u003c/li\u003e\n\u003cli\u003eCheson, B.D., et al., \u003cem\u003eRecommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification.\u003c/em\u003e J Clin Oncol, 2014. \u003cstrong\u003e32\u003c/strong\u003e(27): p. 3059-68.\u003c/li\u003e\n\u003cli\u003eBoard, P.A.T.E. \u003cem\u003ePDQ Non-Hodgkin Lymphoma Treatment\u003c/em\u003e. 2023 [cited 2023 12/07/2024]; Available from: https://www.cancer.gov/types/lymphoma/patient/adult-nhl-treatment-pdq.\u003c/li\u003e\n\u003cli\u003eAnand, U., et al., \u003cem\u003eCancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics.\u003c/em\u003e Genes \u0026amp; Diseases, 2023. \u003cstrong\u003e10\u003c/strong\u003e(4): p. 1367-1401.\u003c/li\u003e\n\u003cli\u003eMackall, C.L., et al., \u003cem\u003eLymphocyte depletion during treatment with intensive chemotherapy for cancer.\u003c/em\u003e 1994.\u003c/li\u003e\n\u003cli\u003eTruong, N.T.H., et al., \u003cem\u003eEffects of Chemotherapy Agents on Circulating Leukocyte Populations: Potential Implications for the Success of CAR-T Cell Therapies.\u003c/em\u003e Cancers, 2021. \u003cstrong\u003e13\u003c/strong\u003e(9): p. 2225.\u003c/li\u003e\n\u003cli\u003eOzer, H., et al., \u003cem\u003e2000 update of recommendations for the use of hematopoietic colony-stimulating factors: evidence-based, clinical practice guidelines. American Society of Clinical Oncology Growth Factors Expert Panel.\u003c/em\u003e J Clin Oncol, 2000. \u003cstrong\u003e18\u003c/strong\u003e(20): p. 3558-85.\u003c/li\u003e\n\u003cli\u003eMcCaffrey, D., et al., \u003cem\u003eCognitive impairment is associated with altered blood cell profiles in aggressive lymphoma.\u003c/em\u003e Support Care Cancer, 2026. \u003cstrong\u003e34\u003c/strong\u003e(2): p. 120.\u003c/li\u003e\n\u003cli\u003eFayette, D., et al., \u003cem\u003eCognitive impairment associated with Hodgkin\u0026rsquo;s lymphoma and chemotherapy.\u003c/em\u003e Neuroscience Letters, 2023. \u003cstrong\u003e797\u003c/strong\u003e: p. 137082.\u003c/li\u003e\n\u003cli\u003eSchroyen, G., et al., \u003cem\u003eNeuroinflammation and Its Association with Cognition, Neuronal Markers and Peripheral Inflammation after Chemotherapy for Breast Cancer.\u003c/em\u003e Cancers, 2021. \u003cstrong\u003e13\u003c/strong\u003e(16): p. 4198.\u003c/li\u003e\n\u003cli\u003eKesler, S.R., J.S. Kent, and R. O\u0026apos;Hara, \u003cem\u003ePrefrontal cortex and executive function impairments in primary breast cancer.\u003c/em\u003e Arch Neurol, 2011. \u003cstrong\u003e68\u003c/strong\u003e(11): p. 1447-53.\u003c/li\u003e\n\u003cli\u003eSilverman, D.H.S., et al., \u003cem\u003eAltered frontocortical, cerebellar, and basal ganglia activity in adjuvant-treated breast cancer survivors 5\u0026ndash;10 years after chemotherapy.\u003c/em\u003e Breast Cancer Research and Treatment, 2007. \u003cstrong\u003e103\u003c/strong\u003e(3): p. 303-311.\u003c/li\u003e\n\u003cli\u003eMiao, H., et al., \u003cem\u003eLong-term cognitive impairment of breast cancer patients after chemotherapy: A functional MRI study.\u003c/em\u003e European Journal of Radiology, 2016. \u003cstrong\u003e85\u003c/strong\u003e(6): p. 1053-1057.\u003c/li\u003e\n\u003cli\u003eSim\u0026oacute;, M., et al., \u003cem\u003eCognitive and brain structural changes in a lung cancer population.\u003c/em\u003e J Thorac Oncol, 2015. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 38-45.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9233591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9233591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eCognitive decline is common in people with aggressive lymphoma and may be linked to immune changes, yet it is unclear whether those who develop impairment show distinct biological profiles at diagnosis. We investigated whether greater cognitive decline after chemotherapy was associated with pre-treatment neutrophil-to-lymphocyte ratios (NLRs), platelet-to-lymphocyte ratios (PLRs) and systemic immune-inflammation indices (SIIs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMultiple regression analyses examined correlations between cognitive deterioration and pre- and mid-treatment white blood cell ratios in people with aggressive lymphoma (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30). Receiver operating characteristic analyses established diagnostic thresholds for NLRs, SIIs and PLRs to identify individuals at risk of cognitive impairment 6\u0026ndash;8 weeks post-treatment. Trajectories of white blood cell ratios were also compared to healthy controls (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;72).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLower pre-treatment levels of NLRs, PLRs and SIIs correlated with greater cognitive decline during and after chemotherapy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). SIIs and PLRs were the most effective biomarkers, with optimal thresholds of 501.2 (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.849; sensitivity\u0026thinsp;\u0026gt;\u0026thinsp;0.75; specificity\u0026thinsp;\u0026gt;\u0026thinsp;0.938) and 163.75 (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.797; sensitivity\u0026thinsp;\u0026gt;\u0026thinsp;0.833; specificity\u0026thinsp;\u0026gt;\u0026thinsp;0.60), respectively. NLRs distinguished mild and moderate impairment, with thresholds of 2.255 for mild (AUC 0.763; sensitivity 0.75; specificity 0.688) and 1.6 for moderate impairment (AUC 0.928; sensitivity 0.75; specificity 0.95).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePeople with aggressive lymphoma who develop greater cognitive decline after chemotherapy already exhibit distinct blood cell profiles at diagnosis. Standard haematological markers may provide a low-cost, scalable approach for early risk identification, supporting targeted monitoring and early cognitive interventions.\u003c/p\u003e","manuscriptTitle":"Blood cell parameters at diagnosis, but not during chemotherapy, serve as prognostic biomarkers for executive functioning in people with aggressive lymphoma treated with chemotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 07:07:46","doi":"10.21203/rs.3.rs-9233591/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-05T20:26:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T20:24:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T07:28:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Supportive Care in Cancer","date":"2026-03-26T11:32:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c61caec7-6b8f-4272-9266-3a504da5a381","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"3","date":"2026-05-05T20:26:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T20:24:03+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T20:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 07:07:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9233591","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9233591","identity":"rs-9233591","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}