Cognitive Function Among Cancer Patient Undergoing Chemotherapy：A Latent Profile Analysis

Cognitive Function Among Cancer Patient Undergoing Chemotherapy：A Latent Profile Analysis | 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 Cognitive Function Among Cancer Patient Undergoing Chemotherapy：A Latent Profile Analysis Hongyu Xue, Qiqi Chen, Yingtong Meng, Xiaohua Ge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8632498/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chemotherapy-induced cognitive dysfunction is a multifaceted and intricate experience that can significantly impact a patient's daily life, mental well-being,and social functioning.This study aimed to elucidate the profiles and determinants of cognitive function in cancer patients receiving chemotherapy. Methods A cross-sectional study was conducted with 612 cancer patients receiving chemotherapy. Participants were recruited through convenience sampling from those receiving treatment at a tertiary-level hospital in Shanghai. General information questionnaire, Functional Assessment of Cancer Therapy-Cognitive Function(FACT-Cog), The10-item Kessler Psychological Distress Scale(K10), and Social Support Rating Scale(SSRS) were used to collect the data. Latent profle analysis was used to explore the latent profles of cognitive function in cancer patients.Analysis.Furthermore,Univariate analysis and Binomial logistic regression analysis were conducted to identify the key influences on these profiles. Results Based on the latent profile analysis results,three potential categories for the type of cognitive function among cancer patients were identified:moderate cognition-stable group (Class 1,20.75%),cognitive impairment group (Class 2,11.60%),high cognition-overall good group (Class 3,67.75%).Among people with various subtypes of cognitive function,there were statistically significant distinctions in cancer stage and cardiovascular disease(P< 0.05). Conclusions Cognitive function among chemotherapy patients is highly heterogeneous and multifactorial.Healthcare professionals should focus on promoting diverse cognitive activities to delay the decline in cognitive function. Figures Figure 1 Introduction Cancer constitutes a worldwide health crisis, profoundly impacting human wellbeing and quality of life.According to the statistical data from GLOBOCAN 2022, nearly 20 million new cancer cases were reported worldwide, with China accounting for approximately one-fourth of this total. Specifically, 4.82 million new cancer cases were projected in China, including 2.53 million in males and 2.29 million in females, imposing a substantial disease burden on the country[ 1 ].While cancer treatments have made significant strides in boosting survival rates for numerous cancer types[ 2 , 3 ], the medical professionals are now turning attention to addressing the lingering side effects of these therapies that can take a serious toll on patients’ quality of life.Of these,chemotherapy-related cognitive impairment(CRCI), more commonly known as “chemo-brain”, is becoming a widespread and debilitating issue for many cancer survivors. CRCI refers to in nonneurological cancer patients during or after treatment[ 4 ].The manifestation of CRCI is heterogeneous, encompassing deficits in memory, attention, executive function, and processing speed[ 5 ] , [ 6 ].It affects an estimated 17–75% of patients and significantly diminishes quality of life by reducing occupational and social capacities, notably in the domains of autonomy, work resumption, social interactions, and confidence[ 7 – 9 ].The pathogenesis of CRCI is complex and multifactorial.Existing literature primarily explores the roles of potential biological, medical, and sociodemographic factors in the cognitive function of patients undergoing chemotherapy.For instance, advanced age, lower educational attainment, medical comorbidities such as vascular risk factors, and prolonged chemotherapy exposure are associated with increased risk of cognitive decline[ 10 – 13 ].Furthermore, psychosocial factors may also be closely linked to cognitive performance. Anxiety and depressive symptoms are common in cancer patients, often stemming from concerns about disease progression and adverse treatment effects.Research indicates an association between such negative affect,particularly depression, and diminished cognitive function[ 14 ].Despite previous studies have identified various risk factors for cognitive impairment in cancer patients undergoing chemotherapy,the majority have focused primarily on reporting overall prevalence or examining linear associations with single factors.Limited attention has been paid to the heterogeneity of cognitive performance within this population. Currently, there is no standardized assessment tool for cancer-related cognitive impairment,with most evaluations relying on subjective rating scales.Previous studies have predominantly classified cognitive function based on total questionnaire scores. However,this variable-centered analytical approach fails to capture diversity among individuals and does not explore latent information within the data,suggesting that it may not adequately reflect heterogeneity in patients' cognitive performance. Latent profile analysis(LPA)serves as a person-oriented statistical technique that uncovers distinct clusters of individuals sharing comparable response profiles across multiple measured variables[ 15 ].This methodology hits the nail on the head for examining multifaceted constructs such as CRCI,allowing researchers to sort patients into unique cognitive phenotype categories and compare the distribution of influencing factors across these groups.The heterogeneity in cancer types, treatment protocols, and individual resilience results in diverse cognitive presentations.As such, it's crucial to use latent profile analysis to pinpoint the distinct cognitive patterns within a homogenous group of chemotherapy patients.Therefore,This study aimed to describe the current status of cognitive function in cancer patients undergoing chemotherapy and to employ LPA to identify the subgroups and their influencing factors of cognitive function in cancer patients. Methods Study Population and Procedures In this cross-sectional investigation, participants were patients currently undergoing treatment at a tertiary-level hospital in Shanghai.Participation was limited to adults aged 18 and above who were undergoing chemotherapy treatment for various malignancies,including but not limited to colon,pancreatic,gastric,ovarian,bladder,prostate,and lung cancers, as well as non-Hodgkin's lymphoma and cancers of unknown primary origin.Individuals were excluded from the study if they presented with neurological conditions,psychiatric comorbidities requiring pharmacological intervention such as depression or anxiety disorders,bipolar disease,or psychotic disorders,or if they had a secondary cancer diagnosis,metastasis, or recurrent disease. Sample size Logistic regression demands a sample size that's at least ten times greater than the number of independent variables-with 18 predictors in this case, that means we needed a minimum of 180 participants to begin with. Factoring in a potential 20% attrition rate, we had to aim for no fewer than 225 subjects to ensure adequate statistical power.[ 16 ]. Measures Demographic and Clinical Characteristics Questionnaires ascertained socio-demographic,clinical and psychosocial factors and self-reported cognition.Survivor treatment data were abstracted from medical records. Cognitive Function Perceived cognitive impairment was assessed using the Functional Assessment of Cancer Therapy-Cognitive Function, a 37-item instrument specifically designed to evaluate subjective cognitive function in cancer patients[ 17 , 18 ].Developed by Wagner et al.[ 19 ]based on clinical experts' and cancer patients' self-reported cognitive issues.It consists of four subscales:Items 1 to 20 measured perceived cognitive impairment (PCI), Items 21 to 24 measured perceived cognitive ability(PCA),Items 25 to 33measured comments from others(OTH),and Items 34 to 37measured impact of cognitive impairment on quality of life(QOL).Patients are required to recall the frequency of each situation occurring over the past week, using a 5-point Likert scale from 0 (never) to 4 (several times a day). The perceived cognitive ability dimension is scored positively, while all other dimensions require reverse scoring. Higher scale scores indicate better cognitive function.PCI subscales scores<54 were defined significant subjective CRCI. Assessment of Psychological Distress and Social Support Psychological distress encompasses a range of stress-related symptoms that often serve as indicators for prevalent mental health conditions like anxiety and depression. For our investigation, we employed the 10-item Kessler Psychological Distress Scale (K10) to measure these emotional states. The K10 comprises 10 questions designed to evaluate depression, anxiety, and self-worth[ 20 , 21 ].Participants rated their responses on a 5-point scale. Scores ranging from 10 to 15 fall within typical parameters, while those between 16 and 31 suggest mild distress; values from 22 to 29 indicate moderate psychological strain, and scores spanning 30 to 50 point to severe symptomatology. When it came to evaluating social support, we employed the Social Support Rating Scale (SSRS), a purpose-built questionnaire designed to gauge the support systems available to patients.This tool comprises ten items distributed across three subscales:objective support,subjective support, and utilization of social support[ 22 ]. Ethical Considerations This research was in adherence to the Declaration of Helsinki and was granted ethical approval by the Hospital Ethics Committee.Strict adherence to the following ethical standards was maintained:Participants were fully voluntary;prior to data collection,each individual was granted written,informed consent. Researchers laid out the study's intent,procedures,the duration of their time required,possible risks,how their information would be kept private, and their option to exit at any stage in a language everyone could understand.Participants' concerns were dealt with diligently,and signed consent forms were collected as evidence of their willingness to participate.All personal data and findings would be solely utilized for scholarly endeavors,and participant privacy,including any identifying information,would be strictly safeguarded. Lastly,the Equity Principle was followed,ensuring that every qualified candidate had an equal shot to take part in the research. Data Analysis The statistical analyses were performed utilizing the Statistical Package for the Social Science version 25 (SPSS 25) and Mplus 8.3.To flesh out the cognitive function profiles of our cancer cohort, we resorted to a Latent Profiles Analysis using the FACT-Cog questionnaire.To evaluate model fit for each estimated class solution, we relied on the Bayesian Information Criterion (BIC), wherein lower values signify better model fit[ 23 ]. ll-Rubin (VLRM) test served to compare improvements between adjacent class configurations (e.g.,2 class solution vs. 3 class solution). Statistically significant p-values (< .05) from these comparisons demonstrated meaningful enhancements in model fit when adding an additional class.Entropy values above 0.75 were considered ideal for evaluating classification quality[ 24 ].Non-normally distributed data are presented as median (quartile range).The Kruskal-Wallis test was employed for comparative analyses across different groups.Count data were presented in the format [n (%)].Depending on the data characteristics,either the Chi-square test or Fisher's exact probability test was utilized to examine differences in population distribution across cognitive function subtypes.Finally,multinomial logistic regression analysis was conducted to investigate the effects of these cognitive function subtypes. Results Participants’ Sociodemographic and Clinical Characteristics The complete questionnaire data of 612 survivors was evaluated.Participants ranged in age from 28–82 years and were largely well-educated (Table 1 ).Men accounted for 50.3% of the participants, and women accounted for 49.7% of the participants. Furthermore, 89.7% of the participants were married.The distribution of disease stages was as follows:stage I,11.3% stage II, 29.9%; stage III, 41.5%; and stage IV, 17.3%. In addition, the scores of psychological distress and social support were 13.00 and 38.00, respectively. Table 1 Basic Characteristics Characteristics Classification N(Percentage)/Median(IQR) Sex Male 308(50.3) Female 304(49.7) Age 18–40 39(6.4) 41–65 331(54.1) >65 242(39.5) Education level Primary school or below 107(17.5) Junior high school 197(32.2) Senior high school/vocational school 139(22.7) College degree or above 169(27.6) Marital status Married 549(89.7) Others 63(10.3) Occupationa status Retired 351(57.4) Employed 155(25.3) No 106(17.3) Per-capita monthly household income (¥) 5000 404(66.0) BMI <18.5 56(9.2) 18.5–24.0 371(60.6) ≥ 24.0 185(30.2) Cancer categories Digestive system 422(69.0) Respiratory system 28(4.6) Reproductive system 138(22.5) Others 24(3.9) Tumor stages Ⅰ 69(11.3) Ⅱ 183(29.9) Ⅲ 254(41.5) Ⅳ 106(17.3) Chemotherapy cycles 1 ~ 3 331(54.1) 3 ~ 6 159(26.0) >6 71(11.6) After chemotherapy 51(8.3) Cardiovascular disease Yes 176(28.8) No 436(71.2) Diabetes Yes 71(11.6) No 541(88.4) Psychological Distress 13(12, 15) Social Support 38(34, 42) Latent profile analysis for the cancer-related cognitive function in cancer survivors Table 2 showcases the fit indices for LPA across one to five possible classes.While the AIC, BIC, and aBIC values demonstrated a consistent downward trend as the number of classes increased, the Lo-Mendell-Rubin test failed to show any significant difference for the five-class solution (P > 0.05). The three-profile model ultimately emerged as the optimal choice, as it boasted the highest Entropy value despite the two-profile model also showing strong statistical results. The two-profile solution was ultimately ruled out due to its limited practical value in explaining the underlying data structure. Table 2 Latent Profile Analysis Fit Statistics for Cognitive Function Categories Model AIC BIC aBIC ENTROPY LMRT BLRT Class Probability 1 12338.418 12373.752 12348.354 - - - - 2 11095.877 11153.295 11112.022 0.943 <0.01 <0.01 19.94/80.07 3 10794.261 10873.762 10816.616 0.890 0.033 <0.01 20.75/11.60/67.65 4 10639.811 10741.395 10668.375 0.873 0.034 <0.01 7.68/17.97/59.41/15.03 5 10535.363 10659.031 10570.137 0.850 0.117 <0.01 5.39/10.29/19.77/51.47/13.07 Participants were categorized into three distinct cohorts through latent profile model clustering analysis, as illustrated in Fig. 1 .Class 1, comprising 127 participants (20.75%), exhibited average scores that fell squarely in the middle range for every assessment item and dimension,indicating “moderate cognition-stable group”.Class 2, consisting of 71 individuals (11.60%), demonstrated significantly lower cognitive abilities, which is why they were designated as the “Cognitive impairment group”.Meanwhile, Class 3 represented the largest contingent with 414 participants (67.75%), who showed superior cognitive functioning across the board, leading to their classification as the “high cognition-overall good group”. Heterogeneity of sociodemographic and disease-related characteristics between subgroups There were no significant differences in age, gender, BMI, education level, Marital status,per-capita monthly household income, chemotherapy cycles and diabetes between three subgroup (Table 3 ).In terms of occupationa status,cancer categories,tumor stages and cardiovascular disease, significant differences existed between the three subgroups ( P = 0.007, P = 044, P = 0,005, P <0.01). Table 3 Cognitive Function Variations Across Subtypes with Relevant Variables. Variables Classification Class1 Class2 Class3 P Sex Male 70(55.1) 40(56.3) 198(47.8) 0.199 Femal 57(44.9) 31(43.7) 216(52.2) Age 18–40 11(8.7) 2(2.8) 26(6.3) 0.502 41–65 63(49.6) 41(57.7) 227(54.8) >65 53(41.7) 28(39.4) 161(38.9) Education level Primary school or below 22(17.3) 16(22.5) 69(16.7) 0.674 Junior high school 47(37.0) 21(29.6) 129(31.2) Senior high school/vocational school 27(21.3) 13(18.3) 99(23.9) College degree or above 31(24.4) 21(29.6) 117(28.3) Marital status Married 111(87.4) 62(87.3) 376(90.8) 0.422 Others 16(12.6) 9(12.7) 38(9.2) Occupationa status Retired 77(60.6) 28(39.4) 246(59.4) 0.007 Employed 33(26.0) 29(40.8) 93(22.5) No 17(13.4) 14(19.7) 75(18.1) Per-capita monthly household income (¥) 5000 88(69.3) 46(64.8) 270(65.2) BMI <18.5 12(9.4) 8(11.3) 36(8.7) 0.486 18.5–24.0 77(60.6) 36(50.7) 258(62.3) ≥ 24.0 38(29.9) 27(38.0) 120(29.0) Cancer categories Digestive system 92(72.4) 51(71.8) 279(67.4) 0.044 Respiratory system 3(2.4) 8(11.3) 17(4.1) Reproductive system 28(22.0) 9(12.7) 101(24.4) Others 4(3.1) 3(4.2) 17(4.1) Tumor stages Ⅰ 9(7.1) 2(2.8) 58(14.0) 0.005 Ⅱ 34(26.8) 16(22.5) 133(32.1) Ⅲ 58(45.7) 35(49.3) 161(38.9) Ⅳ 26(20.5) 18(25.4) 62(15.0) Chemotherapy cycles 1 ~ 3 70(55.1) 37(52.1) 224(54.1) 0.875 3 ~ 6 28(22.0) 18(25.4) 113(27.3) >6 17(13.4) 10(14.1) 44(10.6) After chemotherapy 12(9.4) 6(6.9) 33(8.0) Cardiovascular disease Yes 42(33.1) 33(46.5) 101(24.4) <0.01 No 85(66.9) 38(53.5) 313(75.6) Diabetes Yes 22(17.3) 9(12.7) 40(9.7) 0.059 No 105(82.7) 62(87.3) 374(90.3) Psychological Distress 13(12, 14) 13(11, 15) 13(11, 15) 0.477 Social Support 37(34, 41) 38(34, 41) 38(35, 42) 0.236 Cognitive Profile Variance Analysis in Oncology Patients Following up on the univariate findings,a multinomial logistic regression was employed to pinpoint characteristics linked to various cognitive function profiles.The three latent cognitive categories revealed through latent profile analysis served as our outcome measure,with the high cognition-overall good group standing as our baseline for comparison.All independent variables demonstrating statistical significance ( p < 0.05) from the univariate tests were incorporated into this model.Categorical variables were transformed through dummy coding based on their specific characteristics,with the complete coding framework outlined in Table 4 . Table 4 Coding Scheme For Independent Variables. variable Categories code Occupationa status Retired 1 Employed 2 No 3 Cardiovascular disease Yes 1 No 2 Cancer categories Digestive system 1 Respiratory system 2 Reproductive system 3 Others 4 The logistic regression outcomes suggested that survivors with robust cognitive abilities were more likely to include cancer survivors who was in the early cancer stages or without cardiovascular disease (OR = 0.324, 95% CI = 0.136–0.773,OR = 0.609,95% CI = 0.386–0.961)(Table 5 ). Table 5 Multinomial Logistic Regression On Cognitive Function Subtypes. Model terms Class1 Class2 P OR 95%CI P OR 95%CI Mean difference in initial 0.126 0.972 Occupationa status Retired 0.408 1.128 0.709–2.335 0.054 0.487 0.234–1.012 Employed 0.090 1.803 0.913–3.560 0.060 2.067 0.970–4.405 Cancer categories Digestive system 0.579 1.378 0.444–4.281 0.671 0.751 0.201–2.812 Respiratory system 0.549 0.598 0.111–3.218 0.992 0.991 0.195–5.035 Reproductive system 0.578 1.406 0.423–2.675 0.345 0.490 0.112–2.154 Cardiovascular disease No 0.033 0.609 0.386–0.961 <0.01 0.276 0.155–0.491 Tumor stages Ⅰ 0.011 0.324 0.136–0.773 0.005 0.106 0.022–0.503 Ⅱ 0.09 0.586 0.316–1.086 0.096 0.505 0.226–1.129 Ⅲ 0.477 0.816 0.466–1.429 0.377 0.733 0.367–1.461 Discussion In this investigation, latent profile analysis was employed to classify the cognitive capabilities of cancer survivors,revealing significant variability among participants.The results suggested three distinct cognitive subtypes emerged from the data:moderate cognition-stable group (Class 1),cognitive impairment group (Class 2), high cognition-overall good group (Class 3) .Simultaneously, a comparison of scores across all domains between the three patient categories was conducted to more objectively and scientifically identify priority areas requiring intervention. High cognition-overall good group,comprising the majority of participants(67.75%),was characterized by superior global cognitive performance. This group outperformed the other classes across specific domains, including attention, executive function, memory, and concentration. For this subgroup, interventions should focus on structured health education aimed at early identification of cognitive changes and their contributing factors.Cognitive impairment group,which accounted for 11.60% of the sample, exhibited the most pronounced cognitive deficits.Since many cancer patients significantly reduce their physical activity after diagnosis, this combination of inactivity and illness may heighten the risk of cognitive decline.Therefore,this subgroup should be specifically encouraged to engage in moderate-to-vigorous physical activity as a neuroprotective strategy to support brain health. A range of physiological, psychological, environmental, health-related, and medication-related factors may contribute to cognitive decline. Our study identified that, among these variables, cancer stage and cardiovascular disease were the most significant factors influencing cognitive function in cancer survivors. Patients in the early stages of cancer typically demonstrated better cognitive function.Early-stage cancer patients typically exhibit well-preserved functional reserve and consequently demonstrate a milder systemic inflammatory response.Experimentally,sustained systemic inflammation is now regarded as a key driver of chemotherapy-linked cognitive impairment[ 25 , 26 ].In the tumor microenvironment, pro-inflammatory cytokines can enter the bloodstream, cross the blood-brain barrier, and activate neuroinflammatory responses[ 27 ].This resulting neuroinflammatory state shares features with inflammatory processes observed in idiopathic neurodegeneration[ 28 ].In contrast, patients with advanced-stage cancer typically require more intensive treatment regimens. The associated toxicity of these chemotherapeutic agents may further adversely affect cognitive function. For example, in a breast cancer cohort study, Demos-Davies reported that exposure to multiple cytotoxic agents was associated with a threefold increase in the likelihood of cognitive decline compared to hormone therapy alone[ 29 ].Therefore,nurses should implementing systematic cognitive monitoring at baseline and longitudinally throughout the diagnostic and therapeutic continuum, extending to early-stage patients.For patients with advanced disease or those undergoing aggressive treatment regimens, proactive education about potential cognitive changes is essential. The study showed that the presence of cardiovascular diseases may elevate the risk of cognitive dysfunction.Emerging evidence from large longitudinal studies with extended follow-up continues to indicate that higher blood pressure (BP) during midlife (ages 45–65) is linked to a more pronounced decline in cognitive function over time[ 30 ].Elevated BP can induce endothelial dysfunction and excessive shear stress,which disrupt the integrity of the blood–brain barrier.This kind of disruption paves the way for peripheral immune cells, the ones that hail from outside the central nervous system, to infiltrate the brain,and it boosts the firing up of those pro-inflammatory signaling channels[ 31 ].This can set off neuroinflammatory reactions,a known factor in cancer patients who've survived their bout with the disease[ 32 , 33 ].Additionally, microglial pro-inflammatory activity can initiate neuroinflammation and play a role in the development of cancer-related cognitive impairment (CRCI) [ 34 , 35 ]. What can be done in nursing care is to Integrate cardiovascular risk evaluation, including BP monitoring, into the routine care of cancer patients, particularly those in midlife and beyond, as part of a comprehensive cognitive risk stratification. Study limitations However, several limitations should be considered when interpreting our results. First, our single-site design limits the generalizability of the findings. Future studies should recruit participants from multiple centers across diverse regions and healthcare settings to enhance generalizability. Furthermore, the cross-sectional design precludes causal inference and cannot capture cognitive changes over time; longitudinal studies are needed to address these limitations. Finally, although we adjusted for key variables including sociodemographic characteristics, clinical features, psychological distress, and social support, other potential confounders were not assessed. These may include physical activity levels and symptom burden, which could influence cognitive function. Resource constraints prevented comprehensive measurement of these factors, and future studies should incorporate a broader range of potential confounders. Conclusion This study sort cancer survivors receiving chemotherapy into three cognitive subgroups:moderate cognition-stable group (Class 1),cognitive impairment group (Class 2), high cognition-overall good group (Class 3) .Tumor stage and the presence of cardiovascular comorbidities influence the latent-profile classification of cognitive function in patients receiving chemotherapy.This underscores the need for medical staffs to perform early categorization of a patient's cognitive status.Implementing targeted interventions based on this assessment can help prevent or mitigate cognitive decline, thereby improving overall quality of life. Data availability All members of the research team can view and use all data from the study. Data can be provided to the publisher from the corresponding author. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Hongyu Xue, Qiqi Chen and Xiaohua Ge conceived and designed the project; Hongyu Xue, Qiqi Chen, and Yingtong Meng investigated, analyzed, and revised the original draft; Xiaohua Ge supervised the project. All the authors have read, edited, and approved the final version of the manuscript. Ethics declarations The researchers strictly followed the Declaration of Helsinki during the study. The study was ethically reviewed by the Ethics Committee of Xin Hua Hospital. Participants were informed in detail about the purpose of the study, methodology, and content, etc. Participants were also assured that they could withdraw at any time during the study and that there would be no impact on their follow-up care. Participant data remained anonymous, were kept by the researcher, and were not given to others. Consent to participate All of the participants provided Informed consent in this study. Data availability All members of the research team can view and use all data from the study. Data can be provided to the publisher from the corresponding author. References Zheng R S; Chen R; Han B F; Wang S M; Li L; Sun K X; Zeng H M; Wei W Q; He J. Analysis of the prevalence of malignant tumors in China, 2022. Chinese Journal of Oncology, 2024, 46, 221–231, doi:10.3760/cma.j.cn112152-20240119-00035. Wagle, N.S.; Nogueira, L.; Devasia, T.P.; Mariotto, A.B.; Yabroff, K.R.; Islami, F.; Jemal, A.; Alteri, R.; Ganz, P.A.; Siegel, R.L. 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An Introduction to Latent Variable Mixture Modeling (Part 1): Overview and Cross-Sectional Latent Class and Latent Profile Analyses., doi:10.1093/jpepsy/jst084. Wang, M.-C.; Deng, Q.; Bi, X.; Ye, H.; Yang, W. Performance of the Entropy as an Index of Classification Accuracy in Latent Profile Analysis: A Monte Carlo Simulation Study. Acta Psychologica Sinica 2017 , 49 , 1473–1482, doi:10.3724/SP.J.1041.2017.01473. Yu, S.; Zhao, J.; Wang, M.; Cheng, G.; Li, W.; Tang, L.; Yao, S.; Pang, L.; Yin, X.; Jing, Y.; et al. The Correlation between Neutrophil-to-Lymphocyte Ratio, Carcinoembryonic Antigen, and Carbohydrate Antigen 153 Levels with Chemotherapy-Related Cognitive Impairment in Early-Stage Breast Cancer Patients. Front. Med. 2022 , 9 , 945433, doi:10.3389/fmed.2022.945433. Duivon, M.; Lequesne, J.; Di Meglio, A.; Pradon, C.; Vaz-Luis, I.; Martin, A.-L.; Everhard, S.; Broutin, S.; Rigal, O.; Bousrih, C.; et al. Inflammation at Diagnosis and Cognitive Impairment Two Years Later in Breast Cancer Patients from the Canto-Cog Study. Breast Cancer Res 2024 , 26 , 93, doi:10.1186/s13058-024-01850-5. Olson, B.; Marks, D.L. Pretreatment Cancer-Related Cognitive Impairment—Mechanisms and Outlook. Cancers 2019 , 11 , 687, doi:10.3390/cancers11050687. Michael T, H.; J, C., Monica; El, K., Joseph; E, L., Gary; Frederic, B. Neuroinflammation in Alzheimer’s Disease. The Lancet Neurology 2015 , 14 , 388–405, doi:10.1016/S1474-4422(15)70016-5. Demos-Davies, K.; Lawrence, J.; Seelig, D. Cancer Related Cognitive Impairment: A Downside of Cancer Treatment. Front Oncol 2024 , 14 , 1387251, doi:10.3389/fonc.2024.1387251. Kang, M.; Ang, T.F.A.; Devine, S.A.; Sherva, R.; Mukherjee, S.; Trittschuh, E.H.; Scollard, P.; Lee, M.; Choi, S.; Klinedinst, B.; et al. Genome‐wide Pleiotropy Analysis of Longitudinal Blood Pressure and Harmonized Cognitive Performance Measures. Alzheimers Dement 2025 , 21 , e70681, doi:10.1002/alz.70681. Youwakim, J.; Girouard, H. Inflammation: A Mediator between Hypertension and Neurodegenerative Diseases. Am J Hypertens 2021 , 34 , 1014–1030, doi:10.1093/ajh/hpab094. Fleming, B.; Edison, P.; Kenny, L. Cognitive Impairment after Cancer Treatment: Mechanisms, Clinical Characterization, and Management. Bmj 2023 , 380 , e071726, doi:10.1136/bmj-2022-071726. Oppegaard, K.R.; Armstrong, T.S.; Anguera, J.A.; Kober, K.M.; Kelly, D.L.; Laister, R.C.; Saligan, L.N.; Ayala, A.P.; Kuruvilla, J.; Alm, M.W.; et al. Blood-Based Biomarkers of Cancer-Related Cognitive Impairment in Non-Central Nervous System Cancer: A Scoping Review. Critical Reviews in Oncology/Hematology 2022 , 180 , 103822, doi:10.1016/j.critrevonc.2022.103822. Torre, M.; Feany, M. Increased Senescence and Microglial Activation in a Mouse Model of Chemotherapy-Related Cognitive Impairment. J. Neuropathol. Exp. Neurol. 2022 , 81 , 446–446. Wang, J.; Zhang, H.; Augenreich, M.; Martinez-Lemus A, L.; Liu, Z.; Kang, X.; Lu, B.; Chang, H.-M.; Yeh, E.T.H.; Cata, J.; et al. Microglia-Mediated Synaptic Dysfunction Contributes to Chemotherapy-Related Cognitive Impairment. Journal of Neurochemistry 2025 , 169 , e70024, doi:10.1111/jnc.70024. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8632498","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593578754,"identity":"abb4e94c-c3c6-4a99-bb19-d4f8bbc6a814","order_by":0,"name":"Hongyu Xue","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Xue","suffix":""},{"id":593578756,"identity":"cb17affd-4824-4ca9-a267-d23597e8aa51","order_by":1,"name":"Qiqi Chen","email":"","orcid":"","institution":"XinHua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiqi","middleName":"","lastName":"Chen","suffix":""},{"id":593578759,"identity":"7647773b-02eb-4f71-a4d5-bf1934398302","order_by":2,"name":"Yingtong Meng","email":"","orcid":"","institution":"XinHua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingtong","middleName":"","lastName":"Meng","suffix":""},{"id":593578761,"identity":"1b9953e8-e223-4f4b-92e5-a1722104eadc","order_by":3,"name":"Xiaohua Ge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYJCCA2DE3sBwgLEBLGBApBaeAyRogeiSSGBgIEqLvHuP4YEfFXcS+2e+MTxcuOOwvG578waGHxXbGPhnN2DVYnjmWMLBnjPPEmfcTks4PPPMYcNtZ44VMPacuc0gcecAdi0zkg8c4G07nNhwO/nAYSCDcduNHANmxrbbDAYgp2LTMv9hw8G/QC3zbx5sAGmx33b/DX4t8hLMYMMTN9yAMrbd4MGvxYAH6AWZM4eNN54BMnjb0pO3nUkrAPruNo/EDRy2tJ8x/vim4rDsvONnjD/ztlnbbjt+eOODHxW35fhn4LAFa6iABHmwqgfZ0oBLZhSMglEwCkYBDAAADFZzoSDjcaoAAAAASUVORK5CYII=","orcid":"","institution":"XinHua Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Ge","suffix":""}],"badges":[],"createdAt":"2026-01-18 16:08:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8632498/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8632498/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103321481,"identity":"4359a7b8-48f5-4020-8179-fbd1ab98ce0e","added_by":"auto","created_at":"2026-02-24 11:56:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30713,"visible":true,"origin":"","legend":"\u003cp\u003eThe Characteristic Distribution of 3 Potential Profiles of Cognitive Function Patients With Cancer\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8632498/v1/8c38654e64d0cdd5159b9fe2.png"},{"id":104979387,"identity":"c31fdcb1-9782-4cd6-8032-a3ed578eee1f","added_by":"auto","created_at":"2026-03-19 12:57:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1493011,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8632498/v1/ade6dbd9-3b34-4835-b63e-332f09df5c3d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Function Among Cancer Patient Undergoing Chemotherapy：A Latent Profile Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer constitutes a worldwide health crisis, profoundly impacting human wellbeing and quality of life.According to the statistical data from GLOBOCAN 2022, nearly 20\u0026nbsp;million new cancer cases were reported worldwide, with China accounting for approximately one-fourth of this total. Specifically, 4.82\u0026nbsp;million new cancer cases were projected in China, including 2.53\u0026nbsp;million in males and 2.29\u0026nbsp;million in females, imposing a substantial disease burden on the country[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].While cancer treatments have made significant strides in boosting survival rates for numerous cancer types[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the medical professionals are now turning attention to addressing the lingering side effects of these therapies that can take a serious toll on patients\u0026rsquo; quality of life.Of these,chemotherapy-related cognitive impairment(CRCI), more commonly known as \u0026ldquo;chemo-brain\u0026rdquo;, is becoming a widespread and debilitating issue for many cancer survivors.\u003c/p\u003e \u003cp\u003eCRCI refers to in nonneurological cancer patients during or after treatment[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].The manifestation of CRCI is heterogeneous, encompassing deficits in memory, attention, executive function, and processing speed[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].It affects an estimated 17\u0026ndash;75% of patients and significantly diminishes quality of life by reducing occupational and social capacities, notably in the domains of autonomy, work resumption, social interactions, and confidence[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].The pathogenesis of CRCI is complex and multifactorial.Existing literature primarily explores the roles of potential biological, medical, and sociodemographic factors in the cognitive function of patients undergoing chemotherapy.For instance, advanced age, lower educational attainment, medical comorbidities such as vascular risk factors, and prolonged chemotherapy exposure are associated with increased risk of cognitive decline[\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Furthermore, psychosocial factors may also be closely linked to cognitive performance. Anxiety and depressive symptoms are common in cancer patients, often stemming from concerns about disease progression and adverse treatment effects.Research indicates an association between such negative affect,particularly depression, and diminished cognitive function[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].Despite previous studies have identified various risk factors for cognitive impairment in cancer patients undergoing chemotherapy,the majority have focused primarily on reporting overall prevalence or examining linear associations with single factors.Limited attention has been paid to the heterogeneity of cognitive performance within this population.\u003c/p\u003e \u003cp\u003eCurrently, there is no standardized assessment tool for cancer-related cognitive impairment,with most evaluations relying on subjective rating scales.Previous studies have predominantly classified cognitive function based on total questionnaire scores. However,this variable-centered analytical approach fails to capture diversity among individuals and does not explore latent information within the data,suggesting that it may not adequately reflect heterogeneity in patients' cognitive performance.\u003c/p\u003e \u003cp\u003eLatent profile analysis(LPA)serves as a person-oriented statistical technique that uncovers distinct clusters of individuals sharing comparable response profiles across multiple measured variables[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].This methodology hits the nail on the head for examining multifaceted constructs such as CRCI,allowing researchers to sort patients into unique cognitive phenotype categories and compare the distribution of influencing factors across these groups.The heterogeneity in cancer types, treatment protocols, and individual resilience results in diverse cognitive presentations.As such, it's crucial to use latent profile analysis to pinpoint the distinct cognitive patterns within a homogenous group of chemotherapy patients.Therefore,This study aimed to describe the current status of cognitive function in cancer patients undergoing chemotherapy and to employ LPA to identify the subgroups and their influencing factors of cognitive function in cancer patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population and Procedures\u003c/h2\u003e \u003cp\u003eIn this cross-sectional investigation, participants were patients currently undergoing treatment at a tertiary-level hospital in Shanghai.Participation was limited to adults aged 18 and above who were undergoing chemotherapy treatment for various malignancies,including but not limited to colon,pancreatic,gastric,ovarian,bladder,prostate,and lung cancers, as well as non-Hodgkin's lymphoma and cancers of unknown primary origin.Individuals were excluded from the study if they presented with neurological conditions,psychiatric comorbidities requiring pharmacological intervention such as depression or anxiety disorders,bipolar disease,or psychotic disorders,or if they had a secondary cancer diagnosis,metastasis, or recurrent disease.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eLogistic regression demands a sample size that's at least ten times greater than the number of independent variables-with 18 predictors in this case, that means we needed a minimum of 180 participants to begin with. Factoring in a potential 20% attrition rate, we had to aim for no fewer than 225 subjects to ensure adequate statistical power.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eQuestionnaires ascertained socio-demographic,clinical and psychosocial factors and self-reported cognition.Survivor treatment data were abstracted from medical records.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCognitive Function\u003c/h3\u003e\n\u003cp\u003ePerceived cognitive impairment was assessed using the Functional Assessment of Cancer Therapy-Cognitive Function, a 37-item instrument specifically designed to evaluate subjective cognitive function in cancer patients[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Developed by Wagner et al.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]based on clinical experts' and cancer patients' self-reported cognitive issues.It consists of four subscales:Items 1 to 20 measured perceived cognitive impairment (PCI), Items 21 to 24 measured perceived cognitive ability(PCA),Items 25 to 33measured comments from others(OTH),and Items 34 to 37measured impact of cognitive impairment on quality of life(QOL).Patients are required to recall the frequency of each situation occurring over the past week, using a 5-point Likert scale from 0 (never) to 4 (several times a day). The perceived cognitive ability dimension is scored positively, while all other dimensions require reverse scoring. Higher scale scores indicate better cognitive function.PCI subscales scores\u0026lt;54 were defined significant subjective CRCI.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Psychological Distress and Social Support\u003c/h2\u003e \u003cp\u003ePsychological distress encompasses a range of stress-related symptoms that often serve as indicators for prevalent mental health conditions like anxiety and depression. For our investigation, we employed the 10-item Kessler Psychological Distress Scale (K10) to measure these emotional states. The K10 comprises 10 questions designed to evaluate depression, anxiety, and self-worth[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].Participants rated their responses on a 5-point scale. Scores ranging from 10 to 15 fall within typical parameters, while those between 16 and 31 suggest mild distress; values from 22 to 29 indicate moderate psychological strain, and scores spanning 30 to 50 point to severe symptomatology.\u003c/p\u003e \u003cp\u003eWhen it came to evaluating social support, we employed the Social Support Rating Scale (SSRS), a purpose-built questionnaire designed to gauge the support systems available to patients.This tool comprises ten items distributed across three subscales:objective support,subjective support, and utilization of social support[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e This research was in adherence to the Declaration of Helsinki and was granted ethical approval by the Hospital Ethics Committee.Strict adherence to the following ethical standards was maintained:Participants were fully voluntary;prior to data collection,each individual was granted written,informed consent. Researchers laid out the study's intent,procedures,the duration of their time required,possible risks,how their information would be kept private, and their option to exit at any stage in a language everyone could understand.Participants' concerns were dealt with diligently,and signed consent forms were collected as evidence of their willingness to participate.All personal data and findings would be solely utilized for scholarly endeavors,and participant privacy,including any identifying information,would be strictly safeguarded. Lastly,the Equity Principle was followed,ensuring that every qualified candidate had an equal shot to take part in the research.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analyses were performed utilizing the Statistical Package for the Social Science version 25 (SPSS 25) and Mplus 8.3.To flesh out the cognitive function profiles of our cancer cohort, we resorted to a Latent Profiles Analysis using the FACT-Cog questionnaire.To evaluate model fit for each estimated class solution, we relied on the Bayesian Information Criterion (BIC), wherein lower values signify better model fit[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. ll-Rubin (VLRM) test served to compare improvements between adjacent class configurations (e.g.,2 class solution vs. 3 class solution). Statistically significant p-values (\u0026lt;\u0026thinsp;.05) from these comparisons demonstrated meaningful enhancements in model fit when adding an additional class.Entropy values above 0.75 were considered ideal for evaluating classification quality[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].Non-normally distributed data are presented as median (quartile range).The Kruskal-Wallis test was employed for comparative analyses across different groups.Count data were presented in the format [n (%)].Depending on the data characteristics,either the Chi-square test or Fisher's exact probability test was utilized to examine differences in population distribution across cognitive function subtypes.Finally,multinomial logistic regression analysis was conducted to investigate the effects of these cognitive function subtypes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u0026rsquo; Sociodemographic and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eThe complete questionnaire data of 612 survivors was evaluated.Participants ranged in age from 28\u0026ndash;82 years and were largely well-educated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).Men accounted for 50.3% of the participants, and women accounted for 49.7% of the participants. Furthermore, 89.7% of the participants were married.The distribution of disease stages was as follows:stage I,11.3% stage II, 29.9%; stage III, 41.5%; and stage IV, 17.3%. In addition, the scores of psychological distress and social support were 13.00 and 38.00, respectively.\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\u003eBasic Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eN(Percentage)/Median(IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e308(50.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e304(49.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e39(6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e331(54.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e242(39.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e107(17.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e197(32.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior high school/vocational school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e139(22.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e169(27.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e549(89.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e63(10.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eOccupationa status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e351(57.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e155(25.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e106(17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePer-capita monthly household income (\u0026yen;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e84(13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3000\u0026thinsp;~\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e124(20.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e404(66.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e56(9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.5\u0026ndash;24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e371(60.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e185(30.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCancer categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigestive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e422(69.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e28(4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReproductive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e138(22.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e24(3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eTumor stages\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e69(11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e183(29.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e254(41.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e106(17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy cycles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e331(54.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026thinsp;~\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e159(26.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e71(11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfter chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e51(8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e176(28.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e436(71.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e71(11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e541(88.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychological Distress\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(12, 15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Support\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(34, 42)\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLatent profile analysis for the cancer-related cognitive function in cancer survivors\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showcases the fit indices for LPA across one to five possible classes.While the AIC, BIC, and aBIC values demonstrated a consistent downward trend as the number of classes increased, the Lo-Mendell-Rubin test failed to show any significant difference for the five-class solution (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The three-profile model ultimately emerged as the optimal choice, as it boasted the highest Entropy value despite the two-profile model also showing strong statistical results. The two-profile solution was ultimately ruled out due to its limited practical value in explaining the underlying data structure.\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\u003eLatent Profile Analysis Fit Statistics for Cognitive Function Categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eENTROPY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eClass Probability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12338.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12373.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12348.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11095.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11153.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11112.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.94/80.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e10794.261\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e10873.762\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10816.616\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.890\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e20.75/11.60/67.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10639.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10741.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10668.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.68/17.97/59.41/15.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10535.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10659.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10570.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.39/10.29/19.77/51.47/13.07\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\u003eParticipants were categorized into three distinct cohorts through latent profile model clustering analysis, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.Class 1, comprising 127 participants (20.75%), exhibited average scores that fell squarely in the middle range for every assessment item and dimension,indicating \u0026ldquo;moderate cognition-stable group\u0026rdquo;.Class 2, consisting of 71 individuals (11.60%), demonstrated significantly lower cognitive abilities, which is why they were designated as the \u0026ldquo;Cognitive impairment group\u0026rdquo;.Meanwhile, Class 3 represented the largest contingent with 414 participants (67.75%), who showed superior cognitive functioning across the board, leading to their classification as the \u0026ldquo;high cognition-overall good group\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity of sociodemographic and disease-related characteristics between subgroups\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThere were no significant differences in age, gender, BMI, education level, Marital status,per-capita monthly household income, chemotherapy cycles and diabetes between three subgroup (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).In terms of occupationa status,cancer categories,tumor stages and cardiovascular disease, significant differences existed between the three subgroups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;044,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,005,\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCognitive Function Variations Across Subtypes with Relevant Variables.\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\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClass1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClass2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClass3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198(47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216(52.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227(54.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161(38.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129(31.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior high school/vocational school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99(23.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117(28.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111(87.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62(87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e376(90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38(9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eOccupationa status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246(59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93(22.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75(18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePer-capita monthly household income (\u0026yen;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3000\u0026thinsp;~\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87(21.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270(65.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.5\u0026ndash;24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e258(62.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(29.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCancer categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigestive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92(72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51(71.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e279(67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReproductive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101(24.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eTumor stages\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133(32.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161(38.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62(15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy cycles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e224(54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026thinsp;~\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113(27.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44(10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfter chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33(8.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101(24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e313(75.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105(82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62(87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e374(90.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychological Distress\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(12, 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(11, 15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(11, 15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Support\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(34, 41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(34, 41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38(35, 42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.236\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=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCognitive Profile Variance Analysis in Oncology Patients\u003c/h2\u003e \u003cp\u003eFollowing up on the univariate findings,a multinomial logistic regression was employed to pinpoint characteristics linked to various cognitive function profiles.The three latent cognitive categories revealed through latent profile analysis served as our outcome measure,with the high cognition-overall good group standing as our baseline for comparison.All independent variables demonstrating statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from the univariate tests were incorporated into this model.Categorical variables were transformed through dummy coding based on their specific characteristics,with the complete coding framework outlined in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoding Scheme For Independent Variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecode\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eOccupationa status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCancer categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigestive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReproductive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\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\u003eThe logistic regression outcomes suggested that survivors with robust cognitive abilities were more likely to include cancer survivors who was in the early cancer stages or without cardiovascular disease (OR\u0026thinsp;=\u0026thinsp;0.324, 95% CI\u0026thinsp;=\u0026thinsp;0.136\u0026ndash;0.773,OR\u0026thinsp;=\u0026thinsp;0.609,95% CI\u0026thinsp;=\u0026thinsp;0.386\u0026ndash;0.961)(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial Logistic Regression On Cognitive Function Subtypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel terms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eClass1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eClass2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean difference in initial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.126\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 \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupationa status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.709\u0026ndash;2.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.234\u0026ndash;1.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.913\u0026ndash;3.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.970\u0026ndash;4.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.444\u0026ndash;4.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.201\u0026ndash;2.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u0026ndash;3.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.195\u0026ndash;5.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.423\u0026ndash;2.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.112\u0026ndash;2.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.386\u0026ndash;0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.155\u0026ndash;0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor stages\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.136\u0026ndash;0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.022\u0026ndash;0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.316\u0026ndash;1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.226\u0026ndash;1.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.466\u0026ndash;1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.367\u0026ndash;1.461\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"},{"header":"Discussion","content":"\u003cp\u003eIn this investigation, latent profile analysis was employed to classify the cognitive capabilities of cancer survivors,revealing significant variability among participants.The results suggested three distinct cognitive subtypes emerged from the data:moderate cognition-stable group (Class 1),cognitive impairment group (Class 2), high cognition-overall good group (Class 3) .Simultaneously, a comparison of scores across all domains between the three patient categories was conducted to more objectively and scientifically identify priority areas requiring intervention.\u003c/p\u003e \u003cp\u003eHigh cognition-overall good group,comprising the majority of participants(67.75%),was characterized by superior global cognitive performance. This group outperformed the other classes across specific domains, including attention, executive function, memory, and concentration. For this subgroup, interventions should focus on structured health education aimed at early identification of cognitive changes and their contributing factors.Cognitive impairment group,which accounted for 11.60% of the sample, exhibited the most pronounced cognitive deficits.Since many cancer patients significantly reduce their physical activity after diagnosis, this combination of inactivity and illness may heighten the risk of cognitive decline.Therefore,this subgroup should be specifically encouraged to engage in moderate-to-vigorous physical activity as a neuroprotective strategy to support brain health.\u003c/p\u003e \u003cp\u003eA range of physiological, psychological, environmental, health-related, and medication-related factors may contribute to cognitive decline. Our study identified that, among these variables, cancer stage and cardiovascular disease were the most significant factors influencing cognitive function in cancer survivors.\u003c/p\u003e \u003cp\u003ePatients in the early stages of cancer typically demonstrated better cognitive function.Early-stage cancer patients typically exhibit well-preserved functional reserve and consequently demonstrate a milder systemic inflammatory response.Experimentally,sustained systemic inflammation is now regarded as a key driver of chemotherapy-linked cognitive impairment[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].In the tumor microenvironment, pro-inflammatory cytokines can enter the bloodstream, cross the blood-brain barrier, and activate neuroinflammatory responses[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].This resulting neuroinflammatory state shares features with inflammatory processes observed in idiopathic neurodegeneration[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].In contrast, patients with advanced-stage cancer typically require more intensive treatment regimens. The associated toxicity of these chemotherapeutic agents may further adversely affect cognitive function. For example, in a breast cancer cohort study, Demos-Davies reported that exposure to multiple cytotoxic agents was associated with a threefold increase in the likelihood of cognitive decline compared to hormone therapy alone[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].Therefore,nurses should implementing systematic cognitive monitoring at baseline and longitudinally throughout the diagnostic and therapeutic continuum, extending to early-stage patients.For patients with advanced disease or those undergoing aggressive treatment regimens, proactive education about potential cognitive changes is essential.\u003c/p\u003e \u003cp\u003eThe study showed that the presence of cardiovascular diseases may elevate the risk of cognitive dysfunction.Emerging evidence from large longitudinal studies with extended follow-up continues to indicate that higher blood pressure (BP) during midlife (ages 45\u0026ndash;65) is linked to a more pronounced decline in cognitive function over time[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].Elevated BP can induce endothelial dysfunction and excessive shear stress,which disrupt the integrity of the blood\u0026ndash;brain barrier.This kind of disruption paves the way for peripheral immune cells, the ones that hail from outside the central nervous system, to infiltrate the brain,and it boosts the firing up of those pro-inflammatory signaling channels[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].This can set off neuroinflammatory reactions,a known factor in cancer patients who've survived their bout with the disease[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].Additionally, microglial pro-inflammatory activity can initiate neuroinflammation and play a role in the development of cancer-related cognitive impairment (CRCI) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. What can be done in nursing care is to Integrate cardiovascular risk evaluation, including BP monitoring, into the routine care of cancer patients, particularly those in midlife and beyond, as part of a comprehensive cognitive risk stratification.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eHowever, several limitations should be considered when interpreting our results. First, our single-site design limits the generalizability of the findings. Future studies should recruit participants from multiple centers across diverse regions and healthcare settings to enhance generalizability. Furthermore, the cross-sectional design precludes causal inference and cannot capture cognitive changes over time; longitudinal studies are needed to address these limitations. Finally, although we adjusted for key variables including sociodemographic characteristics, clinical features, psychological distress, and social support, other potential confounders were not assessed. These may include physical activity levels and symptom burden, which could influence cognitive function. Resource constraints prevented comprehensive measurement of these factors, and future studies should incorporate a broader range of potential confounders.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study sort cancer survivors receiving chemotherapy into three cognitive subgroups:moderate cognition-stable group (Class 1),cognitive impairment group (Class 2), high cognition-overall good group (Class 3) .Tumor stage and the presence of cardiovascular comorbidities influence the latent-profile classification of cognitive function in patients receiving chemotherapy.This underscores the need for medical staffs to perform early categorization of a patient's cognitive status.Implementing targeted interventions based on this assessment can help prevent or mitigate cognitive decline, thereby improving overall quality of life.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll members of the research team can view and use all data from the study. Data can be provided to the publisher from the corresponding author.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHongyu Xue, Qiqi Chen and Xiaohua Ge conceived and designed the project; Hongyu Xue, Qiqi Chen, and Yingtong Meng investigated, analyzed, and revised the original draft; Xiaohua Ge supervised the project. All the authors have read, edited, and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers strictly followed the Declaration of Helsinki during the study. The study was ethically reviewed by the Ethics Committee of Xin Hua Hospital. Participants were informed in detail about the purpose of the study, methodology, and content, etc. Participants were also assured that they could withdraw at any time during the study and that there would be no impact on their follow-up care. Participant data remained anonymous, were kept by the researcher, and were not given to others.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the participants provided Informed consent in this study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll members of the research team can view and use all data from the study. Data can be provided to the publisher from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZheng R S; Chen R; Han B F; Wang S M; Li L; Sun K X; Zeng H M; Wei W Q; He J. Analysis of the prevalence of malignant tumors in China, 2022. 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Microglia-Mediated Synaptic Dysfunction Contributes to Chemotherapy-Related Cognitive Impairment. \u003cem\u003eJournal of Neurochemistry\u003c/em\u003e\u003cstrong\u003e2025\u003c/strong\u003e, \u003cem\u003e169\u003c/em\u003e, e70024, doi:10.1111/jnc.70024.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8632498/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8632498/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChemotherapy-induced cognitive dysfunction is a multifaceted and intricate experience that can significantly impact a patient's daily life, mental well-being,and social functioning.This study aimed to elucidate the profiles and determinants of cognitive function in cancer patients receiving chemotherapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional study was conducted with 612 cancer patients receiving chemotherapy. Participants were recruited through convenience sampling from those receiving treatment at a tertiary-level hospital in Shanghai. General information questionnaire, Functional Assessment of Cancer Therapy-Cognitive Function(FACT-Cog), The10-item Kessler Psychological Distress Scale(K10), and Social Support Rating Scale(SSRS) were used to collect the data. Latent profle analysis was used to explore the latent profles of cognitive function in cancer patients.Analysis.Furthermore,Univariate analysis and Binomial logistic regression analysis were conducted to identify the key influences on these profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the latent profile analysis results,three potential categories for the type of cognitive function among cancer patients were identified:moderate cognition-stable group (Class 1,20.75%),cognitive impairment group (Class 2,11.60%),high cognition-overall good group (Class 3,67.75%).Among people with various subtypes of cognitive function,there were statistically significant distinctions in cancer stage and cardiovascular disease(P\u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCognitive function among chemotherapy patients is highly heterogeneous and multifactorial.Healthcare professionals should focus on promoting diverse cognitive activities to delay the decline in cognitive function.\u003c/p\u003e","manuscriptTitle":"Cognitive Function Among Cancer Patient Undergoing Chemotherapy：A Latent Profile Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 11:54:09","doi":"10.21203/rs.3.rs-8632498/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dc3f2f0d-f0dd-4f64-86a7-df8cb81d9cf2","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T12:56:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 11:54:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8632498","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8632498","identity":"rs-8632498","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}