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Article

Not All Weight Loss Is Equal: Divergent Patterns and Prognostic Roles in Head and Neck Cancer Versus High-Grade B-Cell Lymphoma

by
Judith Büntzel
1,*,
Gina Westhofen
1,
Wilken Harms
1,
Markus Maulhardt
1,
Alexander Casimir Angleitner
1 and
Jens Büntzel
2
1
Department of Hematology and Medical Oncology, University Hospital, Robert-Koch-Straße 40, 37075 Göttingen, Germany
2
Department of Otolaryngology, Head Neck Surgery, Südharz Hospital, 99734 Nordhausen, Germany
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(15), 2530; https://doi.org/10.3390/nu17152530 (registering DOI)
Submission received: 30 June 2025 / Revised: 21 July 2025 / Accepted: 26 July 2025 / Published: 31 July 2025
(This article belongs to the Special Issue Health Challenges in Cancer Survivorship: Personalized Dietary Strategies, Exercise and Behavioral Intervention)
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Abstract

Background: Malnutrition and unintended weight loss are frequent in cancer patients and linked to poorer outcomes. However, data on long-term weight trajectories, particularly comparing different cancer entities, remain limited. Methods: In this retrospective, multicenter study, we analyzed 145 patients diagnosed with either head and neck cancer (HNC; n = 48) or high-grade B-cell lymphoma (HGBCL; n = 97). Body weight, C-reactive protein (CrP), albumin, and modified Glasgow Prognostic Score (mGPS) were assessed at diagnosis and at 3, 6, 9, and 12 months. Clinically relevant weight loss was defined as >5% from baseline. Survival analyses were performed for HGBCL patients. Results: Weight loss was common in both cohorts, affecting 32.2% at 3 months and persisting in 42.3% at 12 months. Nearly half of HNC patients had sustained >5% weight loss at one year, whereas HGBCL patients were more likely to regain weight, with significantly higher rates of weight gain at 6 and 12 months (p = 0.04 and p = 0.02). At baseline, HGBCL patients showed elevated CrP and lower albumin compared to HNC (both p < 0.001). Weight loss at 6 months was significantly associated with reduced overall survival in HGBCL (p < 0.01). Both Δweight at 6 months and mGPS emerged as useful prognostic indicators. Conclusions: This study reveals distinct patterns of weight change and systemic inflammation between HNC and HGBCL patients during the first year after diagnosis. These findings highlight the need for entity-specific nutritional monitoring and tailored supportive care strategies extending into survivorship. Prospective studies integrating body composition analyses are warranted to better guide long-term management.

1. Introduction

Malnutrition is a common occurrence in cancer patients, with up to 85% of individuals experiencing clinically relevant weight loss during treatment, depending on the cancer entity [1,2]. In addition to tumor type, the modality of cancer treatment also significantly influences weight trajectories throughout therapy [3]. Overall, malnutrition and weight loss are associated with worse prognosis, reduced quality of life, and an increased incidence of treatment-related toxicities [4,5,6,7,8].
With a growing number of cancer survivors, nutritional management has become a critical component of survivorship care, necessitating a multidisciplinary approach [9,10]. However, most current clinical guidelines—including the ESPEN clinical nutrition guideline for cancer patients [6,11] and the American Cancer Society’s nutrition and physical activity guideline for cancer survivors [12]—provide limited guidance on the long-term monitoring and management of weight and nutritional status beyond the active treatment phase. Tools such as the modified Prognostic Glasgow Score (mPGS) using albumin and C-reactive protein (CrP) apply nutrition and inflammation related markers for estimating prognosis in cancer patients [13,14]; however, this score is not a dynamic parameter suitable for follow-up.
Indeed, the majority of existing studies concentrate on weight changes occurring during active cancer treatment [15,16,17,18,19]. Particularly in lymphoma patients, clinical attention often centers on weight loss as part of the B-symptoms [18]. As yet, systematic follow-up data after completion of therapy remain scarce [20] and studies addressing weight trajectories in the survivorship phase predominantly target weight gain in breast cancer survivors [21,22].
Currently, the field underestimates the influence of the distinct pathophysiological mechanisms inherent to different cancer entities. For example, high-grade B-cell lymphoma (HGBCL) represents a systemic disease largely driven by inflammation, with immunochemotherapy being the standard of care [23,24]. In contrast, head and neck cancer (HNC) serves as a prime example of a malignancy where both the tumor itself and local treatments (e.g., surgery, radiotherapy) can directly impair nutritional intake due to local anatomical and functional disruption [25]. These two entities exemplify how differing disease characteristics may differentially affect nutritional status and metabolism. It is therefore reasonable to hypothesize that the course of body weight changes following diagnosis and treatment would also vary between cancer types.
Based on this hypothesis, the present study aimed to evaluate entity-specific patterns of weight loss and subsequent weight recovery in patients with HGBCL and HNC during the first twelve months after diagnosis. The findings are intended to inform and support the development of tailored survivorship care programs that address the nutritional needs of cancer patients in a differentiated manner.

2. Materials and Methods

As the aim of this study was to compare weight trajectories in two exemplary cancer entities (cancer as locoregional impairment vs. cancer as systemic disease), this retrospective, multicenter study included 48 patients diagnosed with HNC (treated between January 2020 and December 2022) and 97 patients diagnosed with HGBCL (between January 2021 and December 2022). Follow-up data were available until August 2023. Inclusion criteria were as follows: diagnosis of HNC or HGBCL, age ≥ 18 years, and availability of body weight measurements at a minimum of three different time points. The numbers of cases screened and included are depicted in Supplementary Figure S1. Clinical data, including follow-up, survival, treatment modalities, laboratory parameters (CrP, albumin), and body weight, were retrospectively retrieved from electronic medical records. We further checked whether treating physicians used nutritional interventions during and after cancer treatment of patients. The study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [26]; the corresponding checklist is provided in Appendix A. The study protocol was approved by the Ethics Committee of the University Medical Center Göttingen (approval number 12/6/24, approval date: 10 June 2024).
Relevant weight loss was defined as a decrease in body weight of >5%, in accordance with the international consensus criteria for cancer cachexia [27] and the Common Terminology Criteria for Adverse Events, Version 5.0 (CTCAE v5.0; [28]). We defined weight gain as an increase in body weight >5%. Patients with a decrease or increase in weight ≤5% were defined as patients with stable weight.
Body weight was assessed at five predefined time points: t0: at diagnosis, t1: 3 months after diagnosis, t2: 6 months after diagnosis, t3: 9 months after diagnosis, t4: 12 months after diagnosis. The rate of weight change (Δweight) was calculated as follows: Δweight = weight at tx − weight at t0. Albumin and CrP levels were used to calculate the mPGS, a validated prognostic tool for cancer patients. The score is calculated as follows: albumin > 35 g/L and CrP < 10 mg/dL—0 points; albumin > 35 g/L, CrP > 10 mg/dL—1 point; albumin < 35 g/L, CrP > 10 mg/dL—2 points [13,14].

Statistical Analysis and Data Handling

Data extraction and curation were performed using Microsoft Excel (Microsoft Excel 2021; Microsoft Corporation, Redmond, WA, USA). Descriptive statistics (mean, standard deviation, median, range, frequencies) were used to summarize demographic and clinical characteristics. Due to the retrospective nature of the study, missing data were present, particularly regarding body weight at individual follow-up time points. Analyses were conducted using the available data, and sample sizes were adjusted accordingly for each comparison. Statistical methods were chosen to account for non-normal distributions and potential effects of missing data. No imputation of missing values was performed.
Comparisons of categorical variables were conducted using Fisher’s exact test. The Mann–Whitney U test and Kruskal–Wallis test were applied for comparisons of non-parametric continuous variables, as appropriate. Survival analyses were performed using Kaplan–Meier estimators, with differences between groups assessed by the log-rank (Mantel–Cox) test. Kaplan–Meier curves were generated to visualize survival probabilities. The predictive value of body weight change (Δweight) for overall survival was evaluated using receiver operating characteristic (ROC) curve analysis. All statistical analyses were conducted using GraphPad Prism software (version 9.3.0; GraphPad Software, Boston, MA, USA). A two-tailed p-value < 0.05 was considered statistically significant; p-values between 0.05 and 0.15 were interpreted as a trend. The manuscript was reviewed and revised for language, grammar, style, and formatting using ChatGPT-4o, the June 2025 version of OpenAI’s large language model (OpenAI, L.L.C., San Francisco, CA, USA).

3. Results

3.1. Clinical Characteristics

A total of 145 patients were included in the analysis, comprising 48 patients with HNC and 97 patients with HGBCL. Age and body mass index (BMI) at baseline (time of cancer diagnosis) did not differ significantly between the two cohorts (Table 1). Among HNC patients, 14/48 underwent surgery alone, 11/48 received radio(chemo)therapy, and 23/48 were treated with surgery followed by adjuvant radio(chemo)therapy. HGBCL patients received systemic therapy according to national guidelines [24]. Within the HGBCL cohort, 11/97 received chimeric-antigen-receptor-T cell therapy and 19/97 underwent high-dose chemotherapy followed by autologous stem cell transplantation after failure of first-line treatment.

3.2. Weight Loss Is a Common Event in Cancer Patients

Weight loss is a frequent occurrence among cancer patients, with weight loss rates remaining relatively stable over the course of one year following diagnosis (Figure 1). At time-point t1, 32.20% of patients had experienced weight loss, and 42.31% still exhibited reduced weight compared to baseline (t0) one year later (t4). No significant differences in weight loss rates were observed between HNC and HGBCL patients. In both cohorts, the proportion of patients maintaining stable weight declined over time (HNC: t1 = 64.44% vs. t4 = 47.73%; HGBCL: t1 = 64.36% vs. t4 = 38.24%). Notably, HGBCL patients demonstrated a greater ability to regain weight over time, with significantly higher rates of weight gain observed at t2 (HNC: 0.00%; HGBCL: 13.33%; p = 0.04) and at t4 (HNC: 4.55%; HGBCL: 26.47%; p = 0.02, Table 1). For changes in Δweight over time refer to Supplementary Table S1.
Nutritional interventions were administered in 19.29% (27/140) of patients, with the vast majority belonging to the HNC cohort (26/27). Most interventions were initiated at baseline (t0: 27/140) and were less frequently applied at later time-points (t1: 12/140). Detailed information on the type of nutritional intervention was available for 26 patients: 9 received percutaneous endoscopic gastrostomy (PEG) feeding, 9 received intravenous nutrition, and 8 were supplemented with high-energy oral nutrition. Nutritional intervention at t0 did not significantly impact the rate of weight loss at t4 in HNC patients (p = 1.00).

3.3. Weight Loss Has No Influence on Recurrent Disease in HNC or HGBCL Patients, but on Survival

Overall, recurrent disease was documented in 10.42% (15/144) of patients. Weight loss between t1 and t4 was not significantly associated with recurrence in the overall study population. Subgroup analyses of HNC and HGBCL cohorts similarly revealed no increased risk of recurrence in patients experiencing weight loss.
Survival data were available for HGBCL patients. Those who exhibited weight loss at t1 (p = 0.052) and t3 (p = 0.073) showed a trend toward shorter overall survival compared to patients with stable weight or weight gain. Notably, patients with weight loss at t2 had significantly reduced overall survival (p < 0.01) (Figure 2).
While Δ(weightt1−t0) did not differ significantly between surviving and deceased HGBCL patients, significant differences were observed for Δ(weightt2−t0) and Δ(weightt3−t0) (Figure 3a). This effect was not seen at t4. Δ(weightt2−t0) emerged as a useful clinical marker for survival prediction, as demonstrated by ROC curve analysis (Figure 3b).

3.4. CrP and Albumin as Indicators for Systemic Inflammation in HGBCL Patients

CrP and albumin levels were compared between HNC and HGBCL patients. At baseline, HGBCL patients exhibited significantly higher CrP levels and lower albumin concentrations than HNC patients. Following initiation of systemic therapy, CrP levels in HGBCL patients became comparable to those in HNC patients, supporting the interpretation of elevated CrP as a marker of systemic inflammation. Nevertheless, albumin levels remained significantly lower in HGBCL patients throughout the first six months and continued to be lower even one year post-diagnosis (Table 2, Figure 4a,b).
The mGPS differed significantly between cohorts only at baseline, with a higher median score in HGBCL patients (median mGPS(HGBCL) = 1 [0–2] vs. mGPS(HNC) = 0 [0–2]; p < 0.001). Since survival data were available only for the HGBCL cohort, we assessed whether mGPS could predict poor outcomes. An mGPS of 2 was significantly associated with mortality in HGBCL patients at t0 (p = 0.031), t1 (p = 0.040), and t2 (p = 0.010) (Fisher’s exact test).

4. Discussion

In our retrospective study, we observed clinically relevant weight loss in both HNC and HGBCL patients over the first year following diagnosis. HGBCL patients demonstrated a more favorable trajectory, with many regaining weight, whereas nearly half of the HNC patients failed to return to their baseline weight. Both patterns—persistent weight loss among HNC survivors [29] and weight gain following HGBCL treatment—have been previously reported [20,30].
Persistent weight loss in HNC patients is well documented. Studies report rates of post-treatment weight loss reaching nearly 95% in this group [29,31,32]. The discrepancy between the 95% reported by Jin et al. and our lower proportion can likely be attributed to our stricter definition of weight loss, categorizing only patients with a significant reduction of >5% as “weight loss” cases.
At first glance, post-treatment weight gain in HGBCL patients appears more favorable than the prolonged weight loss observed in HNC patients. However, prior studies focusing on body composition have shown that lymphoma patients frequently lose muscle mass during treatment [20,33] while simultaneously gaining visceral fat. Notably, significant weight gain (>5%) has been linked to increases in visceral adiposity in HGBCL patients [20]. Moreover, weight gain itself has been discussed as an unfavorable prognostic marker. In a study on female non-Hodgkin lymphoma patients, those with weight gain after treatment had significantly shorter overall survival compared to patients maintaining stable weight [30].
We intentionally chose to compare these two cancer entities because weight loss is prevalent in both groups but driven by fundamentally different mechanisms. This distinction was also reflected in the inflammatory profiles of our cohorts. At diagnosis, HGBCL patients exhibited significantly higher CrP levels and lower albumin concentrations than HNC patients.
Inflammation plays a crucial role in HGBCL, shaping the tumor microenvironment and influencing treatment response [23]. Tumor-induced inflammation is a known driver of fat and muscle catabolism, ultimately leading to malnutrition and cancer cachexia [34]. Effective treatment of HGBCL often resolves this pro-inflammatory state, as evidenced by the decline in mean CrP and increase in albumin levels over time in our HGBCL cohort. By contrast, HNC patients primarily undergo local treatments such as surgery or radio(chemo)therapy aimed at tumor eradication [35]. Consequently, they face a different spectrum of side effects—including dysgeusia, xerostomia, dysphagia, and odynophagia—which are closely linked to persistent post-treatment weight loss [25,29].
These distinct patterns of weight change between HNC and HGBCL already highlight the need for supportive care strategies tailored to the specific challenges of each cancer type. Nutritional interventions are critical during treatment in patients with malnutrition, and routine screening is advocated by international guidelines [6,11]. However, our findings emphasize that monitoring weight trajectories should extend well into cancer survivorship. Both weight loss and weight gain have been shown to impact long-term survival in lymphoma patients [30]. The detrimental effects of weight gain may be partly explained by adverse changes in body composition, such as muscle loss accompanied by increased fat mass [30], which elevate the risk of metabolic syndrome and cardiovascular disease [36,37,38]. Considering that nearly half of HNC patients in our study exhibited > 5% weight loss compared to baseline even twelve months after diagnosis, it is essential to enhance supportive care efforts addressing long-term sequelae of cancer treatment. Preventing ongoing weight loss has itself been shown to positively influence both the physical and mental health of cancer survivors [39,40].
In line with previous studies [41,42,43], we were able to demonstrate that weight loss serves as a predictor of poorer overall survival. As noted, cancer-associated inflammation is closely tied to body mass loss [34]. Various prognostic scores incorporating inflammatory markers have been proposed to predict overall survival in HGBCL [23], yet none have been adopted into routine clinical practice. Our findings show that (1) Δweight six months after diagnosis effectively predicts patient survival, and (2) mGPS is a simple and well-established tool also suitable for HGBCL.
To our knowledge, this is the first study comparing real-world data on both HNC and HGBCL patients, providing insight into their distinct weight trajectories. Although weight data were not available for every patient at each time point throughout the one-year observation period, we still identified significant differences between the two cohorts, including during post-treatment recovery. By incorporating inflammatory biomarkers, we further established the connection between systemic inflammation and weight loss.
While the use of real-world data enhances the external validity of our results, several limitations must be acknowledged. The retrospective design of our study inherently introduces risks of selection bias, incomplete data capture, and limitations in causal inference. Inclusion was restricted to patients with weight measurements at multiple time points, potentially over-representing individuals with better follow-up adherence and survival outcomes. Due to the limited number of patients and the exploratory character of our study, we chose a simple design forgoing subgroup analysis that included prognostic features. Additionally, data were not consistently available at all planned time points, and particularly small sample sizes at t3 and t4 may have affected statistical power and the robustness of conclusions. Moreover, the absence of body composition assessments leaves unresolved questions about the qualitative nature of observed weight changes. Future research should consider prospective designs incorporating both anthropometric data and body composition analyses (e.g., via computer tomography scans or bio-impedance analysis).

5. Conclusions

This study highlights distinct patterns of weight change and systemic inflammation in patients with head and neck cancer (HNC) and high-grade B-cell lymphoma (HGBCL) over the first year following diagnosis. While weight loss was prevalent in both groups, HGBCL patients showed a greater capacity for regaining weight, whereas nearly half of HNC patients continued to exhibit significant weight loss one year post-diagnosis. Importantly, we identified Δweight at six months and the mGPS as useful tools for predicting survival outcomes in HGBCL. These findings underscore the necessity of entity-specific nutritional monitoring and supportive care strategies that extend well beyond active treatment, aiming to address the unique metabolic and inflammatory challenges associated with different cancer types. Prospective studies incorporating detailed assessments of body composition are warranted to further optimize long-term survivorship care.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu17152530/s1, Figure S1: Flow chart depicting included cases of head heck (HNC) and high grade b-cell lymphoma (HGBCL) patients; Table S1: Δweight over time. HNC—head neck cancer, HGBCL—high grade b-cell lymphoma, SD—standard deviation.

Author Contributions

Conceptualization, J.B. (Judith Büntzel) and J.B. (Jens Büntzel); methodology, J.B. (Judith Büntzel), A.C.A. and M.M.; software, J.B. (Judith Büntzel); validation, J.B. (Judith Büntzel), A.C.A. and M.M.; formal analysis, J.B. (Judith Büntzel) and A.C.A.; investigation, all authors; resources, J.B. (Judith Büntzel) and J.B. (Jens Büntzel); data curation, J.B. (Judith Büntzel) and J.B. (Jens Büntzel); writing—original draft preparation, J.B. (Judith Büntzel) and J.B. (Jens Büntzel); writing—review and editing, all authors; visualization, J.B. (Judith Büntzel), W.H. and G.W.; supervision, J.B. (Jens Büntzel); project administration, J.B. (Judith Büntzel). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of University Medical Center Göttingen (protocol/approval number 12/6/24, approval date: 10 June 2024)).

Informed Consent Statement

Patient consent was waived due to the anonymous, retrospective analysis of data.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
HNCHead and neck cancer
HGBCLHigh grade B-cell lymphoma
HRHazard ratio
CrPC-reactive protein
STROBEStrengthening the Reporting of Observational Studies in Epidemiology
CTCAECommon Terminology Criteria for Adverse Events
mPGSmodified Prognostic Glasgow Score

Appendix A

STROBE Statement—checklist of items that should be included in reports of observational studies, adapted from [26], Vandenbroucke et al. 2007, Creative Commons Attribution License.
Item NoRecommendationPage
No
Title and abstract1(a) Indicate the study’s design with a commonly used term in the title or the abstract1
(b) Provide in the abstract an informative and balanced summary of what was done and what was found1
Introduction
Background/rationale2Explain the scientific background and rationale for the investigation being reported2
Objectives3State specific objectives, including any prespecified hypotheses2
Methods
Study design4Present key elements of study design early in the paper3
Setting5Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection3
Participants6Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants3
Variables7Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable3
Data sources/measurement8 *For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group3
Bias9Describe any efforts to address potential sources of bias3
Study size10Explain how the study size was arrived at3
Quantitative variables11Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why3
Statistical methods12(a) Describe all statistical methods, including those used to control for confounding3
(b) Describe any methods used to examine subgroups and interactions3
(c) Explain how missing data were addressed3
(d) Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategyn. a.
(e) Describe any sensitivity analysesn. a.
Results
Participants13 *(a) Report numbers of individuals at each stage of study—e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed3ff
(b) Give reasons for non-participation at each stagen. a.
(c) Consider use of a flow diagramn. a.
Descriptive data14 *(a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders3ff
(b) Indicate number of participants with missing data for each variable of interest5
Outcome data15 *Cohort study—Report numbers of outcome events or summary measures over timen. a.
Case-control study—Report numbers in each exposure category, or summary measures of exposuren. a.
Cross-sectional study—Report numbers of outcome events or summary measures3ff
Main results16(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence interval). Make clear which confounders were adjusted for and why they were included3ff
(b) Report category boundaries when continuous variables were categorized3ff
(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time periodn. a.
Other analyses17Report other analyses done—e.g., analyses of subgroups and interactions, and sensitivity analyses3ff
Discussion
Key results18Summarise key results with reference to study objectives9ff
Limitations19Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias10f
Interpretation20Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence9ff
Generalisability21Discuss the generalisability (external validity) of the study results9ff
Other information
Funding22Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based11

References

  1. Büthe, L.; Westhofen, G.; Hille, A.; Büntzel, J. Symptom Burden and Dietary Changes Among Older Adults with Cancer: A Cross-Sectional Study. Curr. Oncol. 2024, 31, 7663–7685. [Google Scholar] [CrossRef]
  2. Aktas, A.; Kadakia, K.C.; Waldman, J.; Walsh, D. Weight Loss in Cancer and the 2017 Common Terminology Criteria for Adverse Events—Dangerous and Misleading. J. Cachexia Sarcopenia Muscle 2025, 16, e13754. [Google Scholar] [CrossRef] [PubMed]
  3. Bossi, P.; Delrio, P.; Mascheroni, A.; Zanetti, M. The Spectrum of Malnutrition/Cachexia/Sarcopenia in Oncology According to Different Cancer Types and Settings: A Narrative Review. Nutrients 2021, 13, 1980. [Google Scholar] [CrossRef] [PubMed]
  4. Bozzetti, F. Forcing the Vicious Circle: Sarcopenia Increases Toxicity, Decreases Response to Chemotherapy and Worsens with Chemotherapy. Ann. Oncol. 2017, 28, 2107–2118. [Google Scholar] [CrossRef] [PubMed]
  5. Büntzel, J.; Micke, O.; Kisters, K.; Büntzel, J.; Mücke, R. Malnutrition and Survival—Bioimpedance Data in Head Neck Cancer Patients. In Vivo 2019, 33, 979–982. [Google Scholar] [CrossRef]
  6. Muscaritoli, M.; Arends, J.; Bachmann, P.; Baracos, V.; Barthelemy, N.; Bertz, H.; Bozzetti, F.; Hütterer, E.; Isenring, E.; Kaasa, S.; et al. ESPEN Practical Guideline: Clinical Nutrition in Cancer. Clin. Nutr. Edinb. Scotl. 2021, 40, 2898–2913. [Google Scholar] [CrossRef]
  7. Nishikawa, H.; Goto, M.; Fukunishi, S.; Asai, A.; Nishiguchi, S.; Higuchi, K. Cancer Cachexia: Its Mechanism and Clinical Significance. Int. J. Mol. Sci. 2021, 22, 8491. [Google Scholar] [CrossRef]
  8. Bossi, P.; De Luca, R.; Ciani, O.; D’Angelo, E.; Caccialanza, R. Malnutrition Management in Oncology: An Expert View on Controversial Issues and Future Perspectives. Front. Oncol. 2022, 12, 910770. [Google Scholar] [CrossRef]
  9. Demark-Wahnefried, W.; Rogers, L.Q.; Alfano, C.M.; Thomson, C.A.; Courneya, K.S.; Meyerhardt, J.A.; Stout, N.L.; Kvale, E.; Ganzer, H.; Ligibel, J.A. Practical Clinical Interventions for Diet, Physical Activity, and Weight Control in Cancer Survivors. CA Cancer J. Clin. 2015, 65, 167–189. [Google Scholar] [CrossRef]
  10. Siwik, C.J.; Jhaveri, K.; Cohen, J.A.; Barulich, M.; Chang, A.; Levin, A.O.; Goyal, N.G.; Melisko, M.; Chesney, M.A.; Shumay, D. Survivorship Wellness: A Multidisciplinary Group Program for Cancer Survivors. Support. Care Cancer 2023, 31, 655. [Google Scholar] [CrossRef]
  11. Arends, J.; Baracos, V.; Bertz, H.; Bozzetti, F.; Calder, P.C.; Deutz, N.E.P.; Erickson, N.; Laviano, A.; Lisanti, M.P.; Lobo, D.N.; et al. ESPEN Expert Group Recommendations for Action against Cancer-Related Malnutrition. Clin. Nutr. Edinb. Scotl. 2017, 36, 1187–1196. [Google Scholar] [CrossRef]
  12. Rock, C.L.; Thomson, C.A.; Sullivan, K.R.; Howe, C.L.; Kushi, L.H.; Caan, B.J.; Neuhouser, M.L.; Bandera, E.V.; Wang, Y.; Robien, K.; et al. American Cancer Society Nutrition and Physical Activity Guideline for Cancer Survivors. CA Cancer J. Clin. 2022, 72, 230–262. [Google Scholar] [CrossRef]
  13. Luan, C.-W.; Kuo, L.-T.; Wang, Y.-T.; Liao, C.-T.; Kang, C.-J.; Lee, Y.-C.; Chen, K.-Y.; Lai, C.-H.; Tsai, Y.-H.; Huang, E.I.; et al. Utility of Modified Glasgow Prognostic Score for Head and Neck Squamous Cell Carcinoma: Systematic Review and Meta-Analysis. Head Neck 2023, 45, 1856–1867. [Google Scholar] [CrossRef]
  14. Wu, T.-H.; Tsai, Y.-T.; Chen, K.-Y.; Yap, W.-K.; Luan, C.-W. Utility of High-Sensitivity Modified Glasgow Prognostic Score in Cancer Prognosis: A Systemic Review and Meta-Analysis. Int. J. Mol. Sci. 2023, 24, 1318. [Google Scholar] [CrossRef] [PubMed]
  15. Shen, L.-J.; Chen, C.; Li, B.-F.; Gao, J.; Xia, Y.-F. High Weight Loss during Radiation Treatment Changes the Prognosis in Under-/Normal Weight Nasopharyngeal Carcinoma Patients for the Worse: A Retrospective Analysis of 2433 Cases. PLoS ONE 2013, 8, e68660. [Google Scholar] [CrossRef]
  16. Park, S.; Han, B.; Cho, J.W.; Woo, S.-Y.; Kim, S.; Kim, S.J.; Kim, W.S. Effect of Nutritional Status on Survival Outcome of Diffuse Large B-Cell Lymphoma Patients Treated with Rituximab-CHOP. Nutr. Cancer 2014, 66, 225–233. [Google Scholar] [CrossRef] [PubMed]
  17. Jin, Y.-N.; Xia, T.-L.; Mai, D.-M.; Yao, J.-J.; Jiang, C.; He, W.-Z.; Xia, L.-P. The Prognostic Value of Weight Loss during Radiotherapy among Patients with Nasopharyngeal Carcinoma: A Large-Scale Cohort Study. BMC Cancer 2022, 22, 505. [Google Scholar] [CrossRef]
  18. Gürsoy, V.; Hunutlu, F.-Ç.; Pinar, I.-E.; Göktuğ, M.-R.; Ali, R.; Özkocaman, V.; Özkalemkaş, F. The Clinical Impacts of the Controlling Nutritional Status Score on Patients with Hodgkin Lymphoma. Eur. Rev. Med. Pharmacol. Sci. 2023, 27, 9916–9927. [Google Scholar] [CrossRef]
  19. Khan, M.S.; Butler, J.; Anker, M. Weight Gain Among Cancer Patients Receiving Chemotherapy—Facts and Numbers. J. Cachexia Sarcopenia Muscle 2025, 16, e13694. [Google Scholar] [CrossRef]
  20. Xiao, D.Y.; Luo, S.; O’Brian, K.; Sanfilippo, K.M.; Ganti, A.; Riedell, P.; Lynch, R.C.; Liu, W.; Kahl, B.S.; Cashen, A.F.; et al. Longitudinal Body Composition Changes in Diffuse Large B-Cell Lymphoma Survivors: A Retrospective Cohort Study of United States Veterans. JNCI J. Natl. Cancer Inst. 2016, 108, djw145. [Google Scholar] [CrossRef]
  21. Campbell, N.J.; Barton, C.; Cutress, R.I.; Copson, E.R. Impact of Obesity, Lifestyle Factors and Health Interventions on Breast Cancer Survivors. Proc. Nutr. Soc. 2023, 82, 47–57. [Google Scholar] [CrossRef] [PubMed]
  22. Walker, J.; Joy, A.A.; Vos, L.J.; Stenson, T.H.; Mackey, J.R.; Jovel, J.; Kao, D.; Madsen, K.L.; Wong, G.K.-S. Chemotherapy-Induced Weight Gain in Early-Stage Breast Cancer: A Prospective Matched Cohort Study Reveals Associations with Inflammation and Gut Dysbiosis. BMC Med. 2023, 21, 178. [Google Scholar] [CrossRef] [PubMed]
  23. Cvetković, Z.; Marković, O.; Marinković, G.; Pejić, S.; Vučić, V. Tumor Microenvironment, Inflammation, and Inflammatory Prognostic Indices in Diffuse Large B-Cell Lymphomas: A Narrative Review. Int. J. Mol. Sci. 2025, 26, 5670. [Google Scholar] [CrossRef] [PubMed]
  24. Diffuses Großzelliges B-Zell-Lymphom. Available online: https://www.onkopedia.com/de/onkopedia/guidelines/diffuses-grosszelliges-b-zell-lymphom (accessed on 28 June 2025).
  25. Ackerman, D.; Laszlo, M.; Provisor, A.; Yu, A. Nutrition Management for the Head and Neck Cancer Patient. Cancer Treat. Res. 2018, 174, 187–208. [Google Scholar] [CrossRef]
  26. Vandenbroucke, J.P.; von Elm, E.; Altman, D.G.; Gøtzsche, P.C.; Mulrow, C.D.; Pocock, S.J.; Poole, C.; Schlesselman, J.J.; Egger, M. STROBE Initiative Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration. PLoS Med. 2007, 4, e297. [Google Scholar] [CrossRef]
  27. Fearon, K.; Strasser, F.; Anker, S.D.; Bosaeus, I.; Bruera, E.; Fainsinger, R.L.; Jatoi, A.; Loprinzi, C.; MacDonald, N.; Mantovani, G.; et al. Definition and Classification of Cancer Cachexia: An International Consensus. Lancet Oncol. 2011, 12, 489–495. [Google Scholar] [CrossRef]
  28. Common Terminology Criteria for Adverse Events (CTCAE)|Protocol Development|CTEP. Available online: https://ctep.cancer.gov/protocolDevelopment/electronic_applications/ctc.htm (accessed on 21 June 2025).
  29. Jin, S.; Lu, Q.; Sun, Y.; Xiao, S.; Zheng, B.; Pang, D.; Yang, P. Nutrition Impact Symptoms and Weight Loss in Head and Neck Cancer during Radiotherapy: A Longitudinal Study. BMJ Support. Palliat. Care 2021, 11, 17–24. [Google Scholar] [CrossRef]
  30. Han, X.; Stevens, J.; Bradshaw, P.T. Body Mass Index, Weight Change and Survival in Non-Hodgkin Lymphoma Patients in Connecticut Women. Nutr. Cancer 2013, 65, 43–50. [Google Scholar] [CrossRef]
  31. Ottosson, S.; Zackrisson, B.; Kjellén, E.; Nilsson, P.; Laurell, G. Weight Loss in Patients with Head and Neck Cancer during and after Conventional and Accelerated Radiotherapy. Acta Oncol. Stockh. Swed. 2013, 52, 711–718. [Google Scholar] [CrossRef]
  32. Abu Zaid, Z.; Kay Neoh, M.; Mat Daud, Z.A.; Md Yusop, N.B.; Ibrahim, Z.; Abdul Rahman, Z.; Jamhuri, N.; Abdul Azim, A.Z. Weight Loss in Post-Chemoradiotherapy Head and Neck Cancer Patients. Nutrients 2022, 14, 548. [Google Scholar] [CrossRef]
  33. Albano, D.; Dondi, F.; Treglia, G.; Tucci, A.; Ravanelli, M.; Farina, D.; Bertagna, F. Longitudinal Body Composition Changes Detected by [18F]FDG PET/CT during and after Chemotherapy and Their Prognostic Role in Elderly Hodgkin Lymphoma. Cancers 2022, 14, 5147. [Google Scholar] [CrossRef] [PubMed]
  34. McGovern, J.; Dolan, R.D.; Skipworth, R.J.; Laird, B.J.; McMillan, D.C. Cancer Cachexia: A Nutritional or a Systemic Inflammatory Syndrome? Br. J. Cancer 2022, 127, 379–382. [Google Scholar] [CrossRef] [PubMed]
  35. Anderson, G.; Ebadi, M.; Vo, K.; Novak, J.; Govindarajan, A.; Amini, A. An Updated Review on Head and Neck Cancer Treatment with Radiation Therapy. Cancers 2021, 13, 4912. [Google Scholar] [CrossRef]
  36. Verhaegen, A.A.; Van Gaal, L.F. Drugs That Affect Body Weight, Body Fat Distribution, and Metabolism. In Endotext; Feingold, K.R., Ahmed, S.F., Anawalt, B., Blackman, M.R., Boyce, A., Chrousos, G., Corpas, E., de Herder, W.W., Dhatariya, K., Dungan, K., et al., Eds.; MDText.com, Inc.: South Dartmouth, MA, USA, 2000. [Google Scholar]
  37. Pinto, B.M.; Trunzo, J.J. Health Behaviors during and after a Cancer Diagnosis. Cancer 2005, 104, 2614–2623. [Google Scholar] [CrossRef]
  38. Anderson, A.S.; Martin, R.M.; Renehan, A.G.; Cade, J.; Copson, E.R.; Cross, A.J.; Grimmett, C.; Keaver, L.; King, A.; Riboli, E.; et al. Cancer Survivorship, Excess Body Fatness and Weight-Loss Intervention-Where Are We in 2020? Br. J. Cancer 2021, 124, 1057–1065. [Google Scholar] [CrossRef]
  39. Ganzer, H.; Touger-Decker, R.; Byham-Gray, L.; Murphy, B.A.; Epstein, J.B. The Eating Experience after Treatment for Head and Neck Cancer: A Review of the Literature. Oral Oncol. 2015, 51, 634–642. [Google Scholar] [CrossRef]
  40. Van Liew, J.R.; Brock, R.L.; Christensen, A.J.; Karnell, L.H.; Pagedar, N.A.; Funk, G.F. Weight Loss after Head and Neck Cancer: A Dynamic Relationship with Depressive Symptoms. Head Neck 2017, 39, 370–379. [Google Scholar] [CrossRef]
  41. Camus, V.; Lanic, H.; Kraut, J.; Modzelewski, R.; Clatot, F.; Picquenot, J.M.; Contentin, N.; Lenain, P.; Groza, L.; Lemasle, E.; et al. Prognostic Impact of Fat Tissue Loss and Cachexia Assessed by Computed Tomography Scan in Elderly Patients with Diffuse Large B-Cell Lymphoma Treated with Immunochemotherapy. Eur. J. Haematol. 2014, 93, 9–18. [Google Scholar] [CrossRef]
  42. Go, S.-I.; Park, S.; Kang, M.H.; Kim, H.-G.; Kim, H.R.; Lee, G.-W. Clinical Impact of Prognostic Nutritional Index in Diffuse Large B Cell Lymphoma. Ann. Hematol. 2019, 98, 401–411. [Google Scholar] [CrossRef]
  43. Jhaveri, K.; Thapa, R.; Ercan, D.; Saha, A.; Noble, J.; Singh, P.; Fahrmann, J.; Saini, N.; Wu, R.; Dennison, J.B.; et al. Sarcopenia and Skeletal Muscle Loss after CAR T-Cell Therapy in Diffuse Large B Cell Lymphoma. Clin. Cancer Res. 2025, 31, 2756–2766. [Google Scholar] [CrossRef]
Nutrients 17 02530 g001
Figure 1. Weight trajectory of patients with head and neck cancer (HNC) or high-grade B-cell lymphoma (HGBCL); (a) weight loss over time is a common event in cancer patients. Subgroup analysis of HNC (b) and HGBCL (c).
Figure 1. Weight trajectory of patients with head and neck cancer (HNC) or high-grade B-cell lymphoma (HGBCL); (a) weight loss over time is a common event in cancer patients. Subgroup analysis of HNC (b) and HGBCL (c).
Nutrients 17 02530 g001
Nutrients 17 02530 g002
Figure 2. Weight loss at t2 is signifcantly associated with reduced survival in patients with high grad B-cell lymphoma. Kaplan-Meier Curve, log-rank (Mantel–Cox) test. HR—hazard ratio; CI—confidence intervall.
Figure 2. Weight loss at t2 is signifcantly associated with reduced survival in patients with high grad B-cell lymphoma. Kaplan-Meier Curve, log-rank (Mantel–Cox) test. HR—hazard ratio; CI—confidence intervall.
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Nutrients 17 02530 g003
Figure 3. Δweight is a predictor of outcome in high-grade B-cell lymphoma (HGBCL) patients. (a) Plots comparing Δweight of survivors and deceased patients at t1–t3. (b) Receiver Operator Curve (ROC) analysis of the corresponding Δweight time-points. *—p < 0.05, **—p < 0.01.
Figure 3. Δweight is a predictor of outcome in high-grade B-cell lymphoma (HGBCL) patients. (a) Plots comparing Δweight of survivors and deceased patients at t1–t3. (b) Receiver Operator Curve (ROC) analysis of the corresponding Δweight time-points. *—p < 0.05, **—p < 0.01.
Nutrients 17 02530 g003
Nutrients 17 02530 g004
Figure 4. Head and neck cancer (HNC) and high-grade B-cell lymphoma (HGBCL) show significantly lower albumin and C-reactive protein (CrP) levels at t0 (a). Albumin levels remain significantly lower in HGBCL patients over the course of three months (b), six months (c) and twelve months (d). **—p ≤ 0.01, ***—p ≤ 0.001, ****—p ≤ 0.0001.
Figure 4. Head and neck cancer (HNC) and high-grade B-cell lymphoma (HGBCL) show significantly lower albumin and C-reactive protein (CrP) levels at t0 (a). Albumin levels remain significantly lower in HGBCL patients over the course of three months (b), six months (c) and twelve months (d). **—p ≤ 0.01, ***—p ≤ 0.001, ****—p ≤ 0.0001.
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Table 1. Clinical characteristics and weight development. HNC—head and neck cancer, HGBCL—high-grade B-cell lymphoma, SD—standard deviation. HNC and HGBCL patients were compared to detect differences between cohorts. Bold was used for subheadings and significant p-values.
Table 1. Clinical characteristics and weight development. HNC—head and neck cancer, HGBCL—high-grade B-cell lymphoma, SD—standard deviation. HNC and HGBCL patients were compared to detect differences between cohorts. Bold was used for subheadings and significant p-values.
HNC + HGBCLHNCHGBCLp-Value
N [patients]1454897
Sex
Male1014160
Female44737
Age [years] +/− SD63.08 +/− 12.7761.89 +/− 9.7363.67 +/− 13.990.43
Body-Mass-Index [kg/m2] +/− SD25.78 +/− 7.3126.42 +/− 5.8727.23 +/− 2.180.46
T1 (3 months)
Stable weight, N [patients]76/118 (64.41%)29/45 (64.44%)47/73 (64.38%)
Weight loss, N [patients] 38/118 (32.20%)16/45 (35.56%)22/73 (30.14%)0.69
Weight gain, N [patients] 4/118 (3.39%)0/45 (0.00%)4/73 (5.48%)0.29
T2 (6 months)
Stable weight, N [patients] 50/106 (47.17%)20/46 (43.48%)30/60 (50.00%)
Weight loss, N [patients] 48/106 (45.28%)26/46 (56.52%)22/60 (36.67%)0.22
Weight gain, N [patients] 8/106 (7.55%)0/46 (0.00%)8/60 (13.33%)0.04
T3 (9 months)
Stable weight, N [patients] 38/87 (43.68%)18/44 (40.91%)20/43 (46.51%)
Weight loss, N [patients]40/87 (45.98%)24/44 (54.55%)16/43 (37.21%)0.26
Weight gain, N [patients]9/87 (10.34%)2/44 (4.55%)7/43 (16.28%)0.27
T4 (12 months)
Stable weight, N [patients]34/78 (43.59%)21/44 (47.73%)13/34 (38.24%)
Weight loss, N [patients]33/78 (42.31%)21/44 (47.73%)12/34 (35.29%)1.00
Weight gain, N [patients] 11/78 (14.10%)2/44 (4.55%)9/34 (26.47%)0.02
B-symptoms
B symptoms, N [patients]33/1300/4833/810.0001
Nutritional intervention t0 N [patients]27/14026/481/920.0001
Nutritional intervention t1 N [patients]12/14011/481/920.0001
Table 2. Changes in C-reactive protein and albumin over time. HNC—head and neck cancer, HGBCL—high-grade B-cell lymphoma, CrP—C-reactive protein. Bold was used for subheadings and significant p-values.
Table 2. Changes in C-reactive protein and albumin over time. HNC—head and neck cancer, HGBCL—high-grade B-cell lymphoma, CrP—C-reactive protein. Bold was used for subheadings and significant p-values.
HNC + HGBCLHNCHGBCLp-Value
t0 (diagnosis of cancer)
CrP [mg/dL]26.65 ± 51.175.64 ± 8.3837.03 ± 59.60<0.001
albumin [mg/L]35.03 ± 10.6444.15 ± 4.3630.01 ± 9.70<0.001
t1 (3 months)
CrP [mg/dL]18.92 ± 33.7216.51 ± 27.2920.13 ± 36.450.99
albumin [mg/L]33.39 ± 11.4741.48 ± 4.9230.08 ± 11.74<0.001
t2 (6 months)
CrP [mg/dL]24.84 ± 62.458.87 ± 14.4232.13 ± 73.590.18
albumin [mg/L]34.47 ± 10.5641.16 ± 5.6731.31 ± 10.85<0.001
t3 (9 months)
CrP [mg/dL]10.48 ± 16.8115.05 ± 12.189.28 ± 17.630.12
albumin [mg/L]29.73 ± 13.0138.78 ±6.7927.14 ± 13.210.75
t4 (12 months)
CrP [mg/dL]24.75 ± 37.6714.68 ± 18.0231.72 ± 45.360.35
albumin [mg/L]37.46 ± 10.5144.65 ± 2.3431.70 ± 10.950.003
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Büntzel, J.; Westhofen, G.; Harms, W.; Maulhardt, M.; Angleitner, A.C.; Büntzel, J. Not All Weight Loss Is Equal: Divergent Patterns and Prognostic Roles in Head and Neck Cancer Versus High-Grade B-Cell Lymphoma. Nutrients 2025, 17, 2530. https://doi.org/10.3390/nu17152530

AMA Style

Büntzel J, Westhofen G, Harms W, Maulhardt M, Angleitner AC, Büntzel J. Not All Weight Loss Is Equal: Divergent Patterns and Prognostic Roles in Head and Neck Cancer Versus High-Grade B-Cell Lymphoma. Nutrients. 2025; 17(15):2530. https://doi.org/10.3390/nu17152530

Chicago/Turabian Style

Büntzel, Judith, Gina Westhofen, Wilken Harms, Markus Maulhardt, Alexander Casimir Angleitner, and Jens Büntzel. 2025. "Not All Weight Loss Is Equal: Divergent Patterns and Prognostic Roles in Head and Neck Cancer Versus High-Grade B-Cell Lymphoma" Nutrients 17, no. 15: 2530. https://doi.org/10.3390/nu17152530

APA Style

Büntzel, J., Westhofen, G., Harms, W., Maulhardt, M., Angleitner, A. C., & Büntzel, J. (2025). Not All Weight Loss Is Equal: Divergent Patterns and Prognostic Roles in Head and Neck Cancer Versus High-Grade B-Cell Lymphoma. Nutrients, 17(15), 2530. https://doi.org/10.3390/nu17152530

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