Состояние питания, способность к антиоксидантам в рационе питания и разнообразие рациона питания у пациентов с недавно диагностированными гематологическими отклонениями: тематическое исследование.

Introduction

Hematological malignancies, including leukaemia, lymphoma, and multiple myeloma arise from the bone marrow or lymphoid tissues and account for a substantial proportion of the global cancer burden. Their incidence is expected to increase as populations age and exposure to lifestyle and environmental risk factors accumulates [ 1 , 2 ]. Nutritional status is an important determinant of treatment tolerance, complications, and survival in patients with cancer. In hematological malignancies, malnutrition is common due to tumour-related metabolic changes, systemic inflammation, and treatment-related side effects, and it adversely affects quality of life [ 3 ]. Reported prevalence rates of malnutrition in this population range from 17 to 43% [ 4 ]. The Global Leadership Initiative on Malnutrition (GLIM) provides a consensus-based framework for the diagnosis of malnutrition in adults, and scoping reviews indicate a wide but consistently high prevalence across oncology populations [ 5 ]. GLIM-based assessments in hematological malignancies have also reported variable prevalence rates; for example, 38.8% in patients with non-Hodgkin lymphoma [ 6 ].

Oxidative stress, defined as an imbalance between reactive oxygen species (ROS) and antioxidant defenses, contributes to carcinogenesis, although physiological ROS signaling remains essential for cellular regulation [ 7 , 8 ]. Dietary antioxidants such as vitamins C and E, carotenoids, selenium, and polyphenols, derived from fruits, vegetables, whole grains, and coffee, can neutralize free radicals and modulate inflammation [ 9 , 10 ]. Dietary total antioxidant capacity (DTAC) reflects the cumulative effect of these diet-derived antioxidants [ 11 ].

Meta-analyses have demonstrated inverse associations between higher DTAC and several cancers, including colorectal, pancreatic, endometrial, and breast cancer [ 12 , 13 ]. Similarly, dietary diversity scores (DDS) and related food biodiversity metrics have been associated with long-term health and lower gastrointestinal cancer risk [ 14 ]. However, evidence on DTAC and dietary diversity in hematological malignancies remains limited, as most studies have focused on circulating biomarkers rather than dietary intake [ 15 ].

To date, no studies have simultaneously examined DTAC, dietary diversity, and nutritional status in newly diagnosed, treatment-naive patients with hematological malignancies. Although hematological malignancies such as leukaemia, lymphoma, and multiple myeloma are biologically heterogeneous, they share common inflammatory, metabolic, and treatment-related pathways that justify combined evaluation in clinical research, particularly at diagnosis [ 16 ]. Including a healthy control group enables assessment of whether nutritional impairments are already present at diagnosis, independent of treatment effects. Identifying such differences may help inform preventive strategies and optimize supportive care.

Methods

Study design and participants

This case–control study was conducted at the hematology outpatient clinic of a tertiary university hospital between January and August 2025. The patient group included 34 individuals aged 18–65 years with newly diagnosed leukaemia, lymphoma, or multiple myeloma, who had not yet initiated treatment. The control group comprised 34 age- and sex-matched healthy volunteers without a history of malignancy or chronic disease, recruited from the same region. Matching was performed at the group level based on age and sex. Participants were eligible if they could undergo bioelectrical impedance analysis (BIA), were literate, and provided informed consent. Exclusion criteria were prior cancer diagnosis or treatment, conditions precluding BIA (e.g., pacemaker use, severe edema, pregnancy), and unwillingness to participate. Sample size was calculated using G*Power (version 3.1.9.2) based on the independent samples t-test (two-tailed). Assuming a medium effect size (d = 0.50), α = 0.05, and power (1 − β) = 0.80, the required sample size was 34 participants per group (total n = 68) [ 17 ]. Written informed consent was obtained from all participants. The study was approved by the University Non-Interventional Research Ethics Committee (Approval No: IZTUMUKCE2025/39) and conducted in accordance with the Declaration of Helsinki.

Data collection

Dietary total antioxidant capacity (DTAC) was estimated using the ferric reducing antioxidant power (FRAP) method based on a validated food frequency questionnaire (FFQ). Macro- and micronutrient intakes and dietary diversity score (DDS) were derived from 3-day dietary records. Nutritional status was evaluated using the GLIM criteria, along with fat-free mass index (FFMI), and mid-upper arm circumference (MUAC).

Dietary total antioxidant capacity (DTAC) calculation

Long-term dietary intake was assessed using a semi-quantitative FFQ covering the previous six months. Reported consumption frequencies were converted into daily intakes (g/day) using standard portion sizes. Antioxidant values were assigned using a standardized database developed by Carlsen et al. [ 18 ] based on the FRAP method; no de novo laboratory analyses were performed. This database includes antioxidant capacity values for more than 3,100 foods. For each food item, the corresponding FRAP value (mmol/100 g) was multiplied by the estimated daily intake, and total DTAC was expressed as mmol/day [ 18 ].

Dietary diversity score (DDS)

Dietary diversity was assessed using the dietary diversity score (DDS), an indicator of overall diet quality reflecting the variety of foods consumed across major food groups [ 19 ]. The DDS method was based on approaches described by the FAO and Kant et al. (1991), adapted to Turkish portion standards (TBSA, 2019). Five major food groups, grains, vegetables, fruits, meat and meat products, and dairy products, were considered, comprising 23 subgroups. Dietary intake was assessed using 3-day prospective dietary records (two weekdays and one weekend day), and DDS was calculated as the mean of these records. Records were collected during face-to-face interviews to ensure completeness and accuracy. A subgroup was considered “consumed” if at least half of the defined portion size was reported. Each major food group contributed up to 2 points, yielding a total DDS ranging from 0 to 10. Scores were categorized as low (< 3.5), moderate (3.5–6.5), or high dietary diversity (> 6.5) [ 20 ].

Dietary records were analyzed using the Nutrition Information System (BEBİS 9.0, Pasifik Company, 2016) to estimate mean daily energy and macro- and micronutrient intakes. Nutrient adequacy was evaluated according to the Recommended Dietary Allowances (RDA), and classified as inadequate (< 67% of RDA), adequate (67–133%), or excessive (> 133%) [ 21 ].

Assessment of nutritional status

Nutritional status was evaluated in two steps. First, malnutrition risk was screened using the Malnutrition Universal Screening Tool (MUST), which assesses body mass index (BMI), unintentional weight loss over the past 3–6 months, and reduced food intake due to disease, yielding a score categorized as low (0), medium (1), or high risk (≥ 2) [ 22 ]. Individuals identified at risk were further assessed according to the GLIM criteria. A diagnosis of malnutrition required at least one phenotypic criterion (weight loss, low BMI, or reduced muscle mass assessed by FFMI) and one etiologic criterion (reduced intake/absorption or inflammation). In the patient group, the etiological criterion of inflammation was considered fulfilled due to presence of a newly diagnosed hematological malignancy, representing disease-related inflammation as defined by the GLIM framework. This approach is supported by a recent systematic review indicating that a cancer diagnosis may serve as a valid surrogate for inflammation within the GLIM framework. In the control group, the absence of clinical inflammation was confirmed by medical history; therefore, serum inflammatory biomarkers (e.g., C-reactive protein) were not collected to avoid unnecessary invasive procedures [ 5 ]. Body composition was measured using bioelectrical impedance analysis (BIA, TANITA BC-418 MA) under standardized conditions. Fat-free mass (FFM) was calculated and normalized for height to obtain FFMI (kg/m 2 ), with cut-offs of < 17 kg/m 2 for men and < 15 kg/m 2 for women indicating reduced muscle mass, as recommended by the GLIM [ 5 ]. BMI was calculated as weight/height 2 and classified according to World Health Organization criteria (2011). Waist circumference risk thresholds were ≥ 94 cm for men and ≥ 80 cm for women [ 23 ]. Mid-upper arm circumference (MUAC) was meaured at the midpoint between the acromion and olecranon; values < 24 cm in men and < 23 cm in women were considered indicative of malnutrition risk [ 24 ].

Statistical analysis

Data were analysed using IBM SPSS Statistics 25.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were presented as mean ± standard deviation, median (min–max), or percentages. Group comparisons were performed using the Mann–Whitney U test or Kruskal–Wallis test for non-normally distributed variables, and the chi-square or Fisher’s exact test for categorical variables. Correlations were assessed using Spearman’s rank correlation coefficient. To account for potential confounding, analysis of covariance (ANCOVA) was performed to adjust for smoking status in DTAC comparisons. Homogeneity of variances was assessed using Levene’s test; when this assumption was violated ( p < 0.05), Welch ANOVA with Games–Howell post hoc tests was applied. To control for Type I error in multiple comparisons, Bonferroni correction was applied for DTAC comparisons across GLIM categories (adjusted α = 0.016). For other nutritional and dietary parameters, results were interpreted within an exploratory framework. Statistical significance was set at p < 0.05.

Results

Participant characteristics

The mean age was 53.6 ± 9.3 years in the case group and 51.5 ± 8.9 years in the control group, with no significant difference between groups. The sex distribution was also similar. Smoking status differed significantly, with a higher proportion of current and former smokers among patients compared with controls. The mean duration of smoking was longer in patients than in controls (17.2 ± 15.0 vs. 6.2 ± 12.2 years, p = 0.001). Among patients, 35.3% were diagnosed with lymphoma, 32.4% with leukaemia, and 32.4% with multiple myeloma. Participant characteristics are presented in Table 1 .

Nutritional status

As shown in Table 2 , BMI and FFMI did not differ significantly between groups; however MUAC was lower in patients. The prevalence of low muscle mass according to FFMI classification was significantly higher in patients than in controls (32.4% vs. 11.8%, p = 0.023). Based on MUST, 67.6% of patients were at risk of malnutrition, whereas no risk was detected in controls ( p < 0.001). Similarly, no malnutrition was detected in the control according to GLIM criteria.

Dietary total antioxidant capacity (DTAC) and dietary diversity

As shown in Table 3 , no significant differences were observed between patients and controls in DDS or in the distribution of low, moderate, and high dietary diversity categories. In contrast, DTAC levels were significantly lower in patients than in controls (3.85 ± 1.58 vs. 8.68 ± 3.21 mmol/day, p ≤ 0.001).

As shown in Table 4 , DTAC levels were significantly higher in controls than in patients, including those without malnutrition risk according to MUST ( p < 0.001). Among patients, DTAC differed across GLIM categories (Kruskal–Wallis p = 0.034); however, pairwise comparisons with Bonferroni correction (α = 0.016) were not significant. Due to unequal variances (Levene’s test p = 0.004), Welch ANOVA was applied and confirmed higher DTAC levels in controls ( p < 0.001). This difference remained significant after adjustment for smoking status using ANCOVA ( p < 0.001).

According to RDA-based evaluation (Table 5 ), energy, carbohydrate (%), protein (g), zinc, vitamin E, and folate intakes were significantly lower in patients, whereas other nutrients did not differ between groups ( p > 0.05). Correlation analyses (Table 6 ) showed that, in patients, DTAC was positively correlated with energy intake, protein intake (g/day and % energy), and vitamin E (r = 0.296–0.464), and negatively correlated with calcium intake. DTAC was also positively associated with the consumption of legumes, olive oil, nuts, vegetables (particularly green vegetables), fruits (including citrus), whole-grain bread, herbal tea, and coffee, with the strongest correlation observed for coffee (r = 0.719).

In controls, DTAC was positively correlated with the intake of cheese, eggs, vegetables, fruits, whole-grain bread, nuts, butter, and black tea, with moderate correlation strength.

Discussion

According to the GLIM criteria, malnutrition was highly prevalent at diagnosis among patients with hematological malignancies (35.3% moderate and 26.5% severe), whereas it was absent in the control group. This finding indicates that nutritional compromise is already present at diagnosis and highlights the vulnerability of this patient population. Early identification of malnutrition supports the need for routine nutritional screening and timely supportive interventions at diagnosis. While GLIM provides a standardized framework, comprehensive nutritional assessment in complex patient populations may benefit from integration with biochemical, hematological, and immunological parameters [ 25 ].

The prevalence observed in our study appears higher than in some previous reports. For example, Bian et al. reported a malnutrition prevalence of 44% across cancer patients, while Zou et al. found 38.8% in non-Hodgkin lymphoma [ 6 , 26 ]. Similarly, GLIM-defined malnutrition was reported in 25.8% of hospitalized hematological patients [ 27 ]These differences may reflect variations in cancer type, disease stage and patient characteristics. Notably, our assessment was performed prior to treatment initiation, allowing evaluation of disease-related effects on nutritional status.

Reduced muscle mass was a prominent finding, with 32.4% of patients presenting with low FFMI compared to 11.8% in controls. This suggests an early decline in muscle reserves before treatment, consistent with previous studies linking muscle depletion to disease burden, systemic inflammation, and catabolic processes [ 28 – 30 ]. Importantly, reduced muscle mass has been associated with poorer overall and progression-free survival, underscoring its prognostic relevance and the importance of early nutritional and supportive interventions [ 31 , 32 ]. Nutrient intake patterns further support the early nutritional deficits in patients. Compared with controls, patients had lower energy and protein intake, which may predispose them to malnutrition and reduced treatment tolerance. In addition, a greater proportion of total energy was derived from carbohydrates, suggesting an imbalanced macronutrient distribution and overall poorer diet quality.

Consistent with previous studies, insufficient protein intake is associated with muscle wasting and poorer survival outcomes [ 33 , 34 ]. Micronutrient inadequacies were also evident, particularly for zinc, vitamin E, and folate, nutrients essential for immune function, redox balance, and cellular metabolism [ 35 – 37 ]. Lower intake of these micronutrients may further compromise antioxidant defense and immune competence. This is biologically plausible, as nutritional status influences oxidative stress pathways and adipokine regulation, potentially contributing to a pro-oxidative metabolic environment in malnourished individuals [ 38 ]. Beyond individual nutrients, patients exhibited significantly lower DTAC than controls. Although evidence in hematological malignancies is limited, higher DTAC has been associated with reduced risk of several cancers, including prostate, colorectal, and lung cancer [ 39 – 41 ]. The lower DTAC observed in patients may partly reflect reduced consumption of antioxidant-rich foods such as vegetables, fruits, and coffee. Given that dietary antioxidant capacity contributes to metabolic stability and treatment tolerance, the coexistence of low DTAC and malnutrition at diagnosis may represent a combined nutritional vulnerability. Consistent with this, poorer nutritional status was associated with lower DTAC, suggesting a link between malnutrition severity and reduced dietary antioxidant capacity.

Smoking was more prevalent among patients and may have contributed to increased oxidative stress. However, lower DTAC levels remained significant after adjustment for smoking, indicating that suboptimal dietary intake is an independent contributor to reduced antioxidant capacity [ 42 , 43 ]. Although DTAC tended to decrease with worsening GLIM-defined malnutrition, this trend did not reach statistical significance, likely due to the limited sample size within subgroups.

Dietary diversity did not differ significantly between groups; however, nearly half of the patients exhibited low diversity, reflecting suboptimal dietary patterns. While a diverse diet is generally associated with higher intake of antioxidants (e.g., polyphenols and vitamins) that support redox balance, evidence regarding its relationship with cancer outcomes remains inconsistent. Some studies suggest protective effects of diverse consumption of fruits, vegetables, and dairy products [ 14 , 44 , 45 ], whereas others report no significant associations with cancer risk [ 46 ]. Importantly, DDS reflects dietary variety rather than nutrient adequacy. In our study, the the absence of differences in DDS masked underlying inadequacies in energy, protein, and micronutrient intake among patients, suggesting that DDS alone may be insufficient to evaluate nutritional status in hematological malignancies. Similarly, although controls consumed more antioxidant-rich foods, this was not fully captured by DDS, indicating that the score may not adequately reflect intake quantity or overall diet quality.

The positive association between DTAC and dietary diversity is consistent with findings in other populations, suggesting that greater dietary variety may enhance antioxidant intake [ 47 ]. These findings highlight the potential importance of improving both dietary quality and diversity to support antioxidant defense in this patient group.

Strengths and limitations

The main strength of this study is the simultaneous evaluation of nutritional status, DTAC, and DDS in patients with hematological malignancies, a combination that has not been previously examined in this population at diagnosis.

However, several limitations should be considered. First, the case group included biologically and clinically distinct malignancies (leukaemia, lymphoma, and multiple myeloma). Although this approach reflects real-world hematology practice, the sample size did not allow formal subgroup analyses. Therefore, the findings should be interpreted as representing shared metabolic and inflammatory features at diagnosis rather than disease-specific effects. Second, the case–control design allows identification of associations but precludes causal inference. Third, although analyses were adjusted for smoking, residual confounding cannot be excluded, particularly given the higher prevalence of smoking among patients. Due to the limited sample size, adjustment was restricted to key confounders. Fourth, DTAC was estimated using FFQ-based dietary data and the FRAP method, which reflects dietary antioxidant exposure rather than in vivo antioxidant status. In addition, FFQ-based assessments may be subject to recall bias, and the FRAP method does not account for differences in bioavailability or all antioxidant mechanisms. DTAC values may also be influenced by high-consumption items such as coffee. Finally, the single-center design and reliance on self-reported dietary records may limit generalizability. Given the number of nutritional comparisons performed, the analyses should be considered exploratory and interpreted with caution regarding potential Type I error.

Clinical implications and future directions

Our findings suggest that malnutrition and inadequate dietary antioxidant intake are already present at diagnosis in patients with hematological malignancies. These results support the integration of early nutritional screening and timely dietitian involvement into routine clinical care at diagnosis. Early nutritional assessment, along with strategies to improve overall diet quality and intake of antioxidant-rich foods, may contribute to better treatment tolerance and clinical outcomes. Personalized nutritional counseling and ongoing supportive monitoring may help address the early nutritional vulnerability observed in this population.

Future longitudinal and interventional studies with larger, disease-specific cohorts are needed to confirm these findings and to further explore the role of dietary antioxidant capacity in clinical outcomes, including survival and quality of life.

Conclusıon

Newly diagnosed patients with hematological malignancies had lower DTAC, higher prevalence of malnutrition, and more frequent inadequacies in energy, protein, and key micronutrients compared with healthy controls. Although dietary diversity scores did not differ significantly, reduced diet quality and lower muscle mass indicated early nutritional vulnerability at diagnosis. These findings support the importance of early nutritional assessment and timely supportive interventions in this patient population. While the results suggest a potential role for dietary antioxidant capacity in maintaining redox balance, the observational design precludes causal inference. Future prospective and interventional studies are needed to confirm these associations and to evaluate the impact of early nutritional strategies on clinical outcomes.

Acknowledgements

The authors would like to thank all participants for their valuable contribution to this study

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by B.N.K., M.Y., and F.D.K. The first draft of the manuscript was written by B.N.K., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

Data Availability Statement

No datasets were generated or analysed during the current study.