Dual-Energy X-Ray Absorptiometry and Anthropometry for Assessment of Nutritional Status at Diagnosis in Children with Cancer: A Single-Center Experience from India

Abstract

Gauri Kapoor

Background The survival of children with cancer has improved owing to advances in chemotherapy and better supportive care, and nutritional support is an important component of the latter especially in low- and middle-income countries like India.

Materials and Methods A prospective observational study of 137 newly diagnosed children with cancer aged less than 18 years was planned. Nutritional assessment was done using dual-energy X-ray absorptiometry (DXA), anthropometry, and serum albumin. Patients were followed for 3 months for assessment of treatment-related morbidity.

Results Lean body mass (LBM; DXA), mid-upper arm circumference (MUAC), and body mass index detected undernutrition in 44, 45, and 14% patients, respectively. Combination of arm anthropometry (MUAC and triceps skinfold thickness) with serum albumin categorized patients as adequately nourished (32%), moderately depleted (18%), and severely depleted (49%). Patients with hematological malignancy had a higher prevalence of undernutrition but there was no difference in morbidities between the undernourished and adequately nourished children by any parameter. Hypoalbuminemia observed in 25% patients was associated with significant chemotherapy delays (p, 0.01) and interval admissions (p, 0.03). Using LBM as a criterion, linear regression analysis revealed MUAC (R ² = 0.681) as the best predictor of undernutrition with lowest standard error.

Conclusion Our study reports undernutrition among two-fifths of newly diagnosed patients of childhood cancer associated with high prevalence of sarcopenia and adiposity. These findings are of utmost clinical relevance in planning interventional strategies.

Keywords

undernutrition - arm anthropometry - LBM

Introduction

The survival of children with cancer has improved substantially over the past few decades. This has been possible owing to improvements in surgical and radiation techniques, use of risk and response-based treatment approaches to chemotherapy, and better supportive care. Undernutrition and low socioeconomic status have been reported to be associated with higher treatment-related toxicity, abandonment of therapy, and poor overall survival.[1] Hence, nutritional assessment and intervention are now recognized to be important components of supportive care in the management of children with cancer.

It is estimated that at least 80% of children with cancer live in low- and middle-income countries (LMICs) and the average prevalence of undernutrition in this population is 50% (30–80%).[2] Various tools have been used for the assessment of nutritional status, of which anthropometry is the most common and inexpensive method. However, due to the use of a wide range of anthropometric indices it is often difficult to make meaningful comparisons. Other methods of assessing nutritional status include biochemical tests and dual-energy X-ray absorptiometry (DXA). The latter has the additional advantage of being a widely accepted and precise tool for measuring body composition.[3] DXA has been used most often in children with leukemia but there are scant published data from LMICs.

Hence, the present investigation was undertaken to study the nutritional status of newly diagnosed children with cancer using DXA, anthropometry, and biochemistry, and to compare them using lean body mass (LBM) by DXA as the criterion measure of nutritional status. In addition, the impact of nutritional status on treatment-related morbidity during the first 3 months of therapy was evaluated.

Material and Methods

Study Design

This prospective observational study was performed in the Department of Pediatric Hematology and Oncology of a tertiary care comprehensive cancer center, Rajiv Gandhi Cancer Hospital and Research Centre, New Delhi, India. It is a 500-bed specialized cancer hospital run by a trust and caters to individuals mainly from the low and middle socioeconomic strata.

Patients

Newly diagnosed patients less than 18 years of age were recruited between June 2014 and December 2016. Patients with a history of prior treatment, those with chronic preexisting diseases, and those at relapse were excluded. Patient demographics, duration of illness, history of weight loss, type of cancer (hematological malignancy [HM] or solid tumor [ST]), and its stage/risk group were recorded in a predesigned study pro forma. The socioeconomic status was determined as per the Kuppuswamy scale which is based on education and occupation of the head of the family and total family income per month.[4] This scale defines five socioeconomic classes: upper, upper middle, lower middle, upper lower, and lower. The study was approved by the Institutional Ethics Committee and conducted after obtaining appropriate informed consent from the parent.

Nutritional Assessment

All newly diagnosed patients underwent anthropometric assessment and blood biochemistry including serum albumin, prior to initiation of cancer treatment. To avoid measurement bias, all assessments were done by the same person and included weight, height, mid-upper arm circumference (MUAC), and triceps skinfold thickness (TSFT). Weight without shoes was recorded on an electronic scale (Avery) to the nearest 0.1 kg. Height was recorded by a stadiometer to the nearest 0.1 cm. Body mass index (BMI) was calculated by weight (kg) divided by height (m) squared. The z-score for BMI for age was derived in reference to the Centers for Disease Control and Prevention growth charts.[5]

MUAC was measured midway between the acromion and olecranon of the nondominant upper arm by nonstretchable measuring tape to the nearest 0.1 cm. TSFT was measured using a Harpenden caliper to the nearest 0.1 mm in the posterior line with the arm hanging loosely (at the same level as the site used for MUAC), by lifting the skin and fat away from the underlying muscle. MUAC and TSFT were measured thrice and the average value was recorded. These variables were interpreted in accordance with Frisancho percentile charts.[6] From these two measurements arm muscle circumference (AMC) was calculated by the formula: AMC = MUAC – (TSFT × 3.14). This was interpreted by Frisancho percentile charts.[6]

Hypoalbuminemia was defined at a cutoff value of 3.5 g/dL.[7]

Analysis of body composition was done for children more than 3 years of age by DXA using a Hologic densitometer (Model: Explorer S/N 91531). The absolute values of LBM and fat mass (FM) were obtained. Canadian normative data were used to determine z-scores.[8]

The cutoffs for undernutrition by various anthropometric/DXA indices were BMI z-score < –2 (World Health Organization), MUAC/AMC/TSFT < 10th centile, and LBM/FM z-score < –1.645 (equivalent to 5th centile). Based on a combination of arm anthropometry and serum albumin three categories of nutritional status were described as previously published[9] and proposed in a study from St. Jude Children's Research Hospital.[10] Patients were classified as adequately nourished if both MUAC and TSFT > 10th percentile and serum albumin > 3.5 g/dL; severely depleted if MUAC or TSFT < 5th percentile or serum albumin < 3.2 g/dL; and the rest were regarded as moderately depleted.

The patients were followed for 3 months after initiation of anticancer therapy to record the incidence of treatment-related morbidities. These included episodes of febrile neutropenia, chemotherapy delay (> 3 days), interval hospitalization (days), and mucositis (> grade 2, common terminology criteria for adverse events, CTCAE ver 5.0).

Statistical Analysis

Categorical variables are presented as number and percentage (%) and continuous variables as mean ± standard deviation and median (range) as per the distribution of data. Normality of data was tested by the Kolmogorov–Smirnov test. In case of nonnormal variable nonparametric test was used. Quantitative variables were compared using unpaired t-test or Mann–Whitney test between adequately nourished and inadequately nourished children. Spearman's correlation coefficients were used to test the association between undernutrition and anthropometric parameters. Qualitative variables were correlated using chi-square test and “N – 1” chi-square test for comparison of proportions.[11] Univariate linear regression analysis was used to identify covariates on the basis of their significance (p < 0.05). Six measurements were entered in stepwise linear regression analysis for predicting LBM, coefficient of determination (R ²) and 95% confidence interval were reported for a multivariable model. A p-value of less than 0.05 was considered statistical significance. The data analysis was performed using SPSS software (IBM SPSS statistics for Windows, version 23.0, IBM Corp., Armonk, New York, United States). The occurrence of treatment-related morbidities observed in the first 3 months of therapy were recorded and analyzed according to the nutritional status and type of malignancy.

Results

The characteristics of 137 newly diagnosed pediatric cancer patients are presented in [Table 1]. Their mean age was 10.5 (±4.2) years and the M:F gender ratio was 4.7:1. The majority of patients belonged to the low and middle socioeconomic strata (96%). Anthropometric assessments and serum albumin were available for all patients and DXA for 101 of them (age > 3 years). History of weight loss was present in 29% of them, median duration of illness was 54 days, 55% had hematological malignancies, and 25% had metastatic or high-risk disease at diagnosis. The mean values for BMI (z-score –0.617), MUAC (5th percentile), TSFT (50–75th percentile), LBM (z-score –1.399), FM (z-score 0.446), and serum albumin (3.9 g/dL) were calculated (not shown in [Table 1]).

Table 1 Patient characteristics

Characteristics	All patients n =137 (%)	Hematological malignancy n =76 (%)	Solid tumor n = 61 (%)	p-Value
Age in years Mean (SD)	10.5 (4.2)	9.8 (4.2)	11.3 (4.0)	0.045[b]
Gender  Male  Female	113 (82.4) 24 (17.5)	63 (82.8) 13 (17.1)	50 (82) 11 (18)	0.887[d]
Duration of illness in days, median (IQR)	54 (21–75)	45 (15–60)	60 (30–90)	0.003
Weight loss	40 (29.2)	17 (22.3)	23 (37.7)	0.054
Kuppuswamy scale  Class I (upper SE)  Class II, III (middle SE)  Class IV, V[a] (lower SE)	6 (4.3) 95 (69.2) 36 (26.2)	3 (3.9) 53 (69.6) 20 (26.3)	3 (4.9) 42 (68.8) 16 (26.2)	0.992[d]
Risk group
Localized  High risk/metastatic	102 (74.5) 35 (25)	57 (74.9) 19 (25)	45 (73.5) 16/61 (26.2)	< 0.001[d]
Mean (SD)
BMI, kg/m2	16.9 (3.6)	16.2 (2.7)	18.7 (4.1)	< 0.001[b]
MUAC, cm	20.2 (4.8)	18.7 (4.1)	22.5 (4.1)	< 0.001[b]
TSFT, mm	13.7 (7.8)	11.8 (6.1)	17.4 (9.6)	< 0.001[b]
LBM, kg	25.2 (10.8)	22.2 (0.9)	28.2 (1.1)	0.006[c]
Fat mass, kg	11.6 (7.4)	9.2 (5.2)	14.0 (8.5)	0.001[c]
Albumin, g/dL	3.9 (0.56)	3.8 (0.64)	4.08 (0.48)	0.003[b]

Abbreviations: BMI, body mass index; HM, hematological malignancy; IQR, interquartile range; LBM, lean body mass; MUAC, mid-upper arm circumference; SD, standard deviation; SE, socioeconomic; ST, solid tumor; TSFT, triceps skinfold thickness.

Note: Localized: those that were not metastatic or high risk; metastatic/high risk: ALL high risk, NHL Group C; HL stage IV; solid tumor stage IV.

^a None of the patients belonged to Kuppuswamy socioeconomic class V. z: z-score; centile: percentile.

^b Student's t-test for two independent samples.

^c Mann–Whitney U test.

^d Chi-square test. All comparisons are between HM and ST.

The prevalence of undernutrition and obesity by the different measures is depicted in [Table 2]. The various anthropometric tools detected undernutrition among 14% (BMI), 23% (TSFT), 45% (MUAC), and 64% (AMC) of the patients. Serum albumin levels detected 25% patients to be undernourished and the prevalence was higher among those with HM than in those with ST (p, 0.008). Inclusion of serum albumin to arm anthropometry in the categorization resulted in a final distribution of adequately nourished 32%, moderately depleted 18%, and severely depleted 49%.

Table 2 Nutritional status by various parameters and by malignancy type

Characteristics	All patients	Hematological malignancy	Solid tumor	p-Value[a]
BMI for age	134	75	59
z < –3	7 (5.2)	4 (5.3)	3 (5.1)	0.731
z ≥ –3 to < –2	12 (9.0)	8 (10.7)	4 (6.8)
z ≥ –2 to +2	115 (85.8)	63 (84.0)	52 (88.1)
MUAC	128	74	54
< 5th centile	50 (39.0)	35 (47.3)	15 (27.8)	0.026
5th–10th centile	8 (6.3)	6 (8.1)	2 (3.7)
> 10th–85th centile	70 (54.7)	33 (44.6)	37 (68.5)
TSFT	97	61	36
< 5th centile	10 (10.3)	8 (13.1)	2 (5.6)	0.111
5th–10th centile	12 (12.4)	10 (16.4)	2 (5.6)
> 10th–85th centile	75 (77.3)	43 (70.5)	32 (88.9)
AMC	134	75	59
< 5th centile	75 (55.9)	48 (64.0)	27 (45.8)	0.108
5th–10th centile	11 (8.2)	5 (6.7)	6 (10.1)
> 10th–85th centile	48 (35.9)	22 (29.3)	26 (44.1)
LBM z-score	99	50	49
z < –2	33 (33.3)	20 (40.0)	13 (26.5)
z > –2 to –1.65	11 (11.1)	7 (14.0)	4 (8.2)	0.152
z > –1.65 to +2	55 (55.6)	23 (46.0)	32 (65.3)
Fat mass z-score	94	50	44
z < –2	0 (0.0)	0 (0.0)	0 (0.0)	NE
z > –2 to –1.65	0 (0.0)	0 (0.0)	0 (0.0)
z > –1.65 to +2	94 (100.0)	50 (100.0)	44 (100.0)
Serum albumin	137	76	61
< 3.2 g/dL	6 (4.3)	5 (6.5)	1 (1.6)	0.003
3.2–3.5 g/dL	28 (20.4)	22 (28.9)	6 (9.8)
> 3.5 g/dL	103 (75.1)	49 (64.4)	54 (88.5)
MUAC, TSFT, alb	105	37	68
Severely depleted	52 (49.5%)	36 (52.9)	16 (43.2)	0.202
Moderate depleted	19 (18.1%)	14 (20.6)	5 (13.5)
Adequately nourished	34 (32.4%)	18 (26.5)	16 (43.2)

Abbreviations: AMC, arm muscle circumference; BMI, body mass index; LBM, lean body mass; MUAC, TSFT, alb: nutritional status based on MUAC, TSFT, and serum albumin; MUAC, mid-upper arm circumference; TSFT, triceps skinfold thickness.

^a Chi-square test.

For analysis of body composition by DXA application of LBM z-score < –2 and < –1.645 (equivalent to 5th percentile) revealed 33 and 44% patients to be undernourished (or sarcopenic), respectively. Using FM by DXA, all patients had z-scores between –1.645 and +2 and none had low FM. Further analysis revealed that among patients detected to be adequately nourished by MUAC, 25% (13/51) were sarcopenic by LBM ([Supplementary Table S1], available online only).

To study the relationship between the various tools a correlation analysis was performed ([Table 3]). The anthropometric parameters MUAC (r = 0.68) and BMI (r = 0.82) correlated well with LBM while TSFT correlated well with FM (r = 0.86) with a significant p-value < 0.0001 ([Supplemental Table S1], available online only). Correlations were considered to be statistically significant by taking Bonferroni adjustment into account.

Table 3 Linear regression analysis of nutrition parameters taking LBM as gold standard

	Univariate analysis				Multivariate analysis
Factor	B	95% CI	R 2	p-Value	B	95% CI	R 2	p-Value
BMI	2003.5	1517.2–2431.7	0.465	< 0.0001
MUAC	1798.8	1553.1–2044.5	0.681	< 0.0001	2758.2	2418.4–3098.0	0.786	< 0.0001
TSFT	603.2	337.4–829.0	0.221	< 0.0001	−702.0	–901.9 to –502.1		< 0.0001
AMC	293.5	261.8–325.2	0.773	< 0.0001
Fat mass	0.976	0.759–1.193	0.446	< 0.0001
MUAC, TSFT, alb	6858.5	3079.2–10637.8	0.340	< 0.0001

Abbreviations: AMC, arm muscle circumference; BMI, body mass index; CI, confidence interval; LBM, lean body mass; MUAC, mid-upper arm circumference; MUAC, TSFT, alb: nutritional status based on MUAC, TSFT, and serum albumin; TSFT, triceps skin fold thickness.

Univariate and multivariate linear regression analyses were done to compare the various nutrition parameters using LBM by DXA as the criterion measure. It was observed that MUAC (R ² = 0.681) was the best predictor of undernutrition with the lowest standard error ([Table 3]). The multivariate regression analysis shows that the two arm anthropometric parameters, MUAC and TSFT, accounted for 78.6% of the variation in LBM.

Overall patients with HM had significantly more morbidities than those with ST, irrespective of their nutritional status ([Table 4]). The undernourished and adequately nourished patients experienced similar treatment-related toxicities in both groups. Among patients with hypoalbuminemia a higher proportion were observed to have febrile neutropenia (p, 0.039), chemotherapy delays (p = 0.003), and interval admissions (p = 0.025). No association was found between nutritional status and socioeconomic strata.

Table 4 Proportion of patients experiencing morbidities during first 3 months of therapy by type of malignancy and nutritional status

Characteristic	Febrile neutropenia N (%)	Chemotherapy delay N (%)	Interval admissions N (%)	Mucositis > grade 2 N (%)
Malignancy
HM (N = 76)	61 (80)	65 (85)	44 (58)	30 (39)
ST (N = 61)	22 (36)	27 (44)	11 (18)	21 (34)
p-value	< 0.001	< 0.001	< 0.001	0.548
Nutrition parameter
BMI
UN (19)	15 (78)	15 (78)	6 (31)	9 (47)
AN (115)	68 (59)	76 (66)	48 (41)	41 (35)
p-value	0.116	0.302	0.414	0.317
MUAC
UN (58)	38 (63)	42 (45)	40 (72)	25 (40)
AN (70)	42 (60)	44 (34)	41 (62)	24 (34)
p-value	0.729	0.206	0.234	0.485
LBM
UN (44)	25 (57)	29 (66)	19(43)	22 (50)
AN (55)	33 (60)	31 (56)	15 (27)	18 (33)
p-value	0.764	0.314	0.097	0.089
Albumin
UN (34)	26 (76)	30 (88)	20 (58)	14 (41)
AN (103)	58 (56)	62 (60)	38 (36)	37 (35)
p-value	0.039	0.003	0.025	0.530
MUAC, TSFT, alb
Sev + mod (71)	48 (68)	53 (75)	34 (48)	29 (41)
AN (34)	18 (53)	20 (59)	11 (32)	10 (29)
p-value	0.138	0.096	0.123	0.236

Abbreviations: AN, adequately nourished; BMI, body mass index; HM, hematological malignancy; LBM, lean body mass; MUAC, mid-upper arm circumference; MUAC, TSFT, alb: nutritional status based on MUAC, TSFT, and serum albumin; Sev + mod, severely depleted, moderately depleted; ST, solid tumor; TSFT, triceps skin fold thickness; UN, undernourished.

Note: p-Value is calculated using “N – 1” chi-squared test by Campbell (2007). Bold letter present statistical significance at 5% level.

Discussion

The nutritional status of children with cancer is extremely important, since a good nutritional status enables them to cope better with cancer therapy and allows us to administer intensive treatments often required for curative strategies. Better understanding of the burden of undernutrition and how it affects the different body compartments is also relevant as it helps develop the foundation for nutrition intervention. This is a single-center study from a tertiary care cancer center from North India that caters to patients from the middle and low socioeconomic strata of society. It is a challenge to compare data from other studies as several factors influence the interpretation of nutrition data, the most compelling being the use of different anthropometric tools in the assessment of nutritional status and lack of uniformity in defining cutoffs for undernutrition. There are very few investigators who have used DXA in newly diagnosed pediatric oncology patients and none from India. Moreover, while comparing nutrition data between patients from high-income countries and LMICs it is important to consider differences in body composition that may be attributable to differences in ethnicity, environmental, and dietary factors. Hence, the authors proposed the use of LBM by DXA along with other anthropometric indices to offset this ambiguity.

In the present study, nutritional evaluation by arm anthropometry (MUAC) and DXA (LBM) revealed that approximately two-fifths of the patients were undernourished while overweight and obesity were quite rare at the time of diagnosis. The observed mean values for anthropometric indices: BMI z-score, MUAC, and TSFT were higher than those reported by studies from two large centers in India.[12] [13] The incidence of undernutrition reported in these studies was also higher (70–90%) and probably attributable to the fact that these centers cater to patients from relatively lower socioeconomic strata of society.[12] [13] Internationally, the results of the present study match closely with the reported prevalence in other LMIC, that is, from Central America and Brazil.[9] [14]

The findings of our study revealed BMI to be an imprecise measure of nutritional status especially at diagnosis, and compared with LBM and MUAC its use misclassified nearly 30% of the patients as normally nourished. Anthropometric indices based on weight and height often underestimate undernutrition as they are unable to account for tumor-related factors like weight of tumor, edema, etc., especially in children presenting with advanced and large volume disease. Body composition analyses and arm anthropometry on the other hand remain unaffected to a significant extent by tumor mass and thus are better able to assess the nutritional status in children with cancer. Similar observations have been made by others in India and abroad.[12] [15] [16] [17] [18] [19] [20] [21] [22]

DXA is a reliable tool for measurement of body composition in clinical practice despite its limited availability outside of resource-rich settings. There is little published data on use of LBM by DXA to assess nutrition status in pediatric oncology patients at diagnosis[18] [23] [24] from LMICs. In the present study, LBM detected a higher prevalence of undernutrition (44%) when compared with that detected by BMI (14%). We observed a good concordance between LBM and MUAC in detecting undernutrition. A strong correlation of LBM was seen with MUAC (and AMC), while FM correlated well with TSFT ([Table 3]). A study on Canadian patients by Barr et al reported a similar correlation for LBM and MUAC but observed a weaker correlation between TSFT and FM.[18] A previous report from Mexico[23] conducted among patients with acute lymphoblastic leukemia, DXA detected more undernourished patients than BMI (20% vs. 11.8%), although correlation of the various parameters with each other was not described. Adopting LBM as the clinical gold standard, we found MUAC to have the best value in prediction of undernutrition among the various anthropometric indices.

Interestingly, in the present analysis, LBM and MUAC detected undernutrition among 44 to 45% of patients while TSFT detected this in just 16% of patients. Sarcopenia or skeletal muscle depletion with or without fat loss has been shown to be a hallmark of cancer cachexia.[22] One may postulate that in our undernourished population arm muscle is lost before arm fat which is preserved till the very last.

It is noteworthy that none of our patients had low FM z-score (< –1.645), in other words FM as detected by DXA was preserved among all patients, even those observed to be undernourished by other parameters (MUAC, TSFT, and LBM). Furthermore, at times of acute stress/disease (cancer) central fat (as measured by FM) is relatively preserved compared with peripheral fat (as measured by TSFT).[25] This associated with mean high FM (z-score 0.44) and TSFT (50th–75th centile) at diagnosis would appear to indicate that our patient population have a high baseline arm and whole body fat.

We also observed that more than a quarter (28%) of our adequately nourished patients (by MUAC) was sarcopenic (by LBM). These findings concur with published reports on ethnic variations in body composition showing that for a given BMI, South Asian populations have lower LBM and higher visceral fat compared with their western counterparts.[26]

These findings on body composition, that is, high body fat and low muscle mass, have important implications on interventions, in terms of type of dietary and physical activity recommendations to our cancer patients.

We studied the incidence of various treatment-related morbidities during the first 3 months of cancer therapy, and found all toxicities to occur more frequently among patients with HM than those with ST (p = 0.01). Although patients with HM had a higher prevalence of undernutrition, there was no difference in morbidities between the undernourished and adequately nourished children either by anthropometry or DXA. This is in contrast to other studies which found more episodes of febrile neutropenia and delays in chemotherapy among undernourished patients.[15] [27] We believe this to be probably due to heterogeneity in distribution of cancer type, stage/risk group, and intensity of treatment in the two nutrition categories. We observed hypoalbuminemia to occur in one-fourth of the patients and low albumin levels at diagnosis correlated with higher incidence of febrile neutropenia, chemotherapy delays, and longer interval admissions ([Table 4]). It is well known that low serum albumin is an excellent marker of metabolic stress due to various reasons including cancer and infections in addition to malnutrition. It has been previously shown to be a predictor of both morbidity and mortality, and hence clinicians must keep this in mind while planning treatment and supportive care for patients with hypoalbuminemia. In fact, investigators have combined arm anthropometry with serum albumin in nutrition intervention algorithms to predict patients at high risk.[9] [10]

The strengths of our study are that it was designed prospectively and appears to be the only one from an LMIC that evaluated nutritional status and body composition using DXA and compared it with various anthropometric indices. Moreover, we recorded and analyzed the morbidities among all the patients and correlated them with cancer type and nutritional status. The limitations include its relatively small sample size and skewed gender ratio in favor of males as also reported from other studies in India.[28] Although dietary assessment and interventions were done, these were not systematically planned or done sequentially. Indian reference values for DXA parameters were not available for our population.

Conclusion

Our study reports undernutrition among two-fifths of newly diagnosed patients of childhood cancer belonging to low- and middle-income socioeconomic strata, as detected by arm anthropometry and corroborated using LBM by DXA as the criterion measure. Body composition analyses revealed our patients to be more sarcopenic and adipose even when adequately nourished by anthropometry. These findings are extremely relevant in planning intervention strategies for newly diagnosed patients. Larger studies having a more homogenous cohort of patients, with serial longitudinal nutritional status assessments may help determine the influence of nutritional status at diagnosis on the final clinical outcome.

Conflict of Interest

None declared.

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Artikel online veröffentlicht:
25. April 2022