Comprehensive evaluation of serum nutritional markers in predicting prognosis among cancer patients with cachexia

doi:10.21203/rs.3.rs-2747206/v1

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Comprehensive evaluation of serum nutritional markers in predicting prognosis among cancer patients with cachexia

https://doi.org/10.21203/rs.3.rs-2747206/v1

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Purpose

Our objective is to comprehensively assess the prognostic value of three serum nutritional markers including albumin, prealbumin, and transferrin in patients with cancer cachexia.

Methods

Concordance index and receiver operating characteristic curves were applied to compare the prognostic performance of different markers. The primary outcome was overall survival (OS), with comprehensive survival analyses were performed. The second outcomes were short-term survival and quality of life.

Results

This multicenter prospective cohort study included 1303 cancer cachexia patients, with 592 death cases during a median follow-up of 20.23 months. Albumin was the most accurate prognostic predictor, followed by transferrin and prealbumin. Multivariate Cox analysis confirmed that low albumin (hazard ratio = 1.55, 95% confidence interval = 1.31–1.84, P < 0.001), prealbumin (HR = 1.33, 95%CI = 1.11–1.59, P = 0.002), and transferrin (HR = 1.46, 95%CI = 1.22–1.74, P < 0.001) were independent risk factors associated with survival in cancer cachexia patients. The prognostic value of low albumin was significant in patients with upper gastrointestinal, hepatobiliary and pancreatic and colorectal cancers; low prealbumin was significant in colorectal cancer; and low transferrin was significant in patients with upper gastrointestinal and colorectal cancer. All three nutritional markers were valuable in predicting survival for patients in advanced stages. The risks of short-term mortalit and poor quality of life were significantly increased in patients with low serum nutritional markers.

Conclusion

Low albumin, prealbumin, and transferrin levels were all independent prognostic factors for patients with cancer cachexia, especially in advanced stages.

Transferrin

Albumin

Prealbumin

Cachexia

Cancer

Prognosis

Cancer-related cachexia is a very common and devastating syndrome, with an estimated prevalence of 50–80% ranging across cancer types [1]. Cachexia is highly associated with cancers of the pancreas, esophagus, gastric system, lung, liver, and bowel; this group of malignancies accounts for half of all cancer-related deaths worldwide [2]. As cachexia is a multifactorial disorder characterized by involuntary loss of skeletal muscle mass and systemic inflammation, it adversely affects patient quality of life (QoL), treatment response, and survival [3]. Notably, almost one-fifth of cancer patients eventually die due to their extremely weakened state caused by cachexia [4]. Moreover, conventional nutritional supplementation alone cannot improve cachexia because of cancer-related metabolic alterations [5]. A spectrum of both tumor- and host-derived factors leads to hypercatabolism, muscle degradation, and acute phase response in cachexia patients [6]. The pathophysiology of cachexia is characterized by negative protein and energy balance. Although knowledge pertaining to cachexia has progressed, effective treatment is yet to be developed. An effective and convenient prognostic indicator for patients with cancer cachexia is urgently needed; to help inform better medical intervention.

Malnutrition reflects the severity of the disorder and fosters morbidity and mortality [7]. Serum hepatic protein levels, including albumin, prealbumin, and transferrin, are commonly used to identify nutritional risks in clinical practice [8]. Albumin, which accounts for more than half of blood proteins, reflects the protein status of the blood and internal organs [9]. Several studies have shown that pretreatment albumin levels are associated with the survival of cancer patients [10]. Prealbumin has a shorter half-life of 2 days in plasma; and has been recognized as a more sensitive indicator of nutritional status than albumin [11]. Transferrin also serves as an index of nutritional status and improvement in severe malnutrition [12]. Malnutrition, characterized by serum transferrin levels, predicts both short- and long-term outcomes in hospitalized patients [13]. However, no previous study has systemically investigated the prognostic value of these three serum nutritional markers in patients with cancer cachexia.

A 2017 classification of malnutrition from the European Society of Clinical Nutrition and Metabolism (ESPEN) defines cachexia as “chronic disease-associated malnutrition with inflammation” [14]. The liver, a major organ affected by cachexia, produces fewer proteins both in a protein-deficient state and in the presence of cytokine-induced inflammatory disorders [15]. Results from a previous study also showed that serum albumin and prealbumin levels were lower in patients with cancer cachexia than those without [16]. Serum albumin level also serves as a key factor in defining cancer cachexia and cancer-related malnutrition. Moreover, the prognostic value of low albumin levels has been confirmed in cancer patients with cachexia [17]. Prealbumin was considered as an independent risk factor for survival in patients with liver cancer [18] and resected esophageal squamous cell cancer [19]. A recent study also reported that serum transferrin and prealbumin may outperform albumin in identifying patients with esophageal cancer with malnutrition and poor prognosis [20]. However, it is still unclear which of these three hepatic proteins is the most accurate predictor for survival in cancer cachexia patients. Therefore, this study aimed to thoroughly explore the association between serum nutritional markers and survival in patients with cancer cachexia.

Study population and design

This multicenter prospective cohort study extracted data from the Investigation on Nutrition Status and its Clinical Outcomes of Common Cancers (INSCOC) project of China (registration number: ChiCTR1800020329) [21].

A total of 22783 patients pathologically diagnosed with cancer from 47 clinical centers were enrolled in this study from 2013 to 2020. The data from the first investigation was analyzed for patients with multiple admissions receiving cancer treatments, including surgery, radiotherapy, chemotherapy, and other anticancer therapies. The exclusion criteria were as follows: a) presence of clinically evident active infection or severe systemic immunodeficiency disease, b) important variables required for analysis in the study were lacking, and c) a hospital stay of less than 48 hours. A total of 4091 patients were eligible for the initial analysis, of which 1303 were diagnosed with cachexia based on the 2011 international consensus definition of cachexia (Figure S1). This study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by the ethics committees of all participating institutions. Written informed consent was obtained from all the participants prior to their participation.

Baseline Characteristics

We trained personnel to collect baseline characteristics, including sex, age, alcohol consumption, smoking status, diabetes, hypertension, type of cancer, tumor/node/metastasis (TNM) stage, treatment (e.g., surgery, chemotherapy, and radiotherapy), Nutrition Risk Screen 2002 (NRS 2002), Eastern Cooperative Oncology Group (ECOG) performance status, quality of life (QoL), body mass index (BMI), hand grip strength (HGS), calf circumference (CC), neutrophil-to-lymphocyte ratio (NLR), albumin, prealbumin, and transferrin. The following tumor types were included: lung, upper gastrointestinal, hepatobiliary and pancreatic, colorectal, urogenital (endometrial, cervical, bladder, prostate, and ovarian), and other cancers. All pathological staging was defined according to the eighth edition of the American Joint Committee on Cancer TNM staging system. Patients with NRS 2002 scores ≥ 3 were at a high nutritional risk [22]. The Eastern Cooperative Oncology Group performance status (ECOG-PS) was converted using the Karnofsky performance status (KPS) following the following rules: ECOG-PS 0 (KPS100), ECOG-PS 1(KPS 90–80), ECOG-PS 2 (KPS 70–60), ECOG-PS 3 (KPS 50–40), and ECOG-PS 4 (KPS 30 to 0) [23]. BMI was calculated as the weight in kilograms divided by the height in meters squared (kg/m²). HGS was measured in the nondominant hand using an electronic handgrip dynamometer. The patients were placed in the supine position with 90° of knee flexion to measure the CC.

Diagnosis of cachexia and outcomes

The definition and classification of cancer cachexia were formulated according to an international consensus [3]: (1) a weight loss > 5% over the past 6 months (in the absence of simple starvation); (2) BMI < 20 and any degree of weight loss > 2%; or (3) appendicular skeletal muscle index consistent with sarcopenia and any degree of weight loss > 2%. The assessment of skeletal muscle depletion was performed by mid–upper arm muscle area anthropometry (men < 32 cm², women < 18 cm²).

To collect data on clinical outcomes, telephone follow-up surveys, periodical reexaminations, and readmissions were provided to all patients. The primary outcome of our study was overall survival, which was defined as the interval between the first assessment in the clinic until the date of death, the date of withdrawal from the study, the end of follow-up (October 30th, 2020), or the time of the last contact, whichever came first. The secondary outcomes were short-term outcomes and QoL. The short-term survival refers to the prognostic outcome of the patient within three months of treatment. Quality of life was evaluated using the 30-item European Organization for Research and Treatment of Cancer QoL Questionnaire, version 3.0. This questionnaire consists of five multi-item functional scales (physical, role, social, emotional, and cognitive function), three multi-item symptom scales (fatigue, pain, and nausea and vomiting), six single-item symptom scales (dyspnea, insomnia, appetite loss, constipation, diarrhea, and financial impact), and a two-item global QoL scale [24]. The 30-item Cancer QoL Questionnaire summary score was calculated as follows: summary score = (physical functioning + role functioning + social functioning + emotional functioning + cognitive functioning + (100-fatigue) + (100-pain) + (100-nausea & vomiting) + (100-dyspnea) + (100-insomnia) + (100-appetite loss) + (100-constipation) + (100-diarrhea))/13 [25].

Statistical Analysis

Statistical analyses were performed using R software version 3.6.2. Continuous variables are expressed as median (interquartile range) and compared using the Mann-Whitney U test. Categorical variables are expressed as absolute numbers or percentages and compared using the χ2 test or Fisher’s exact test. Correlations between the two variables were analyzed using Pearson’s correlation coefficient. Concordance index (C-index) and receiver operating characteristic (ROC) curves were applied to assess the predictive value of the indicator. The dose-response relationship between transferrin and survival was evaluated using restricted cubic regression with three knots. We used an outcome-oriented method to set optimal cutoff points for continuous variables, which can maximize the log-rank statistics. Overall survival was calculated and compared using the Kaplan–Meier method generated by the log-rank test. Univariate and multivariate Cox regression analyses were performed to identify independent predictors associated with poor OS, using hazard ratios (HRs) and 95% confidence intervals (95%CIs). Logistic regression analysis was used to assess the association between the nutritional markers and short-term outcomes. Single variable significance was set at P < 0.05 in the univariate analyses. Significant variables were entered into the multivariate competing-risks regression models. Interaction terms were used to investigate whether there was any association between the main variables and the other clinical parameters. Statistical significance was defined as a two-tailed P-value < 0.05.

Clinical Characteristics

A total of 4091 cancer patients were analyzed in the first step, among which 1303 were diagnosed with cachexia based on the 2011 international consensus of cachexia. The median age of patients with cachexia was 60 years (IQR [53.00–66.00]), and the proportion of males and females was 59.6% and 40.4%, respectively. The most frequent tumors were upper gastrointestinal tract (29.2%), lung (27.8%), colorectal (19.2%), hepatobiliary and pancreatic (8.6%), and breast (4.8%). Ninety-six patients (7.4%) had stage I cancer, 211 (16.2%) had stage II, 386 (29.6%) had stage III, and 610 (46.8%) had stage IV cancer. Cancer cachexia patients were more likely to be male, older, have a history of smoking and drinking, have advanced tumor stage, have NRS2002 ≥ 3, ECOG grade > 1, lower BMI, calf circumference and hand grip strength, increased NLR, and decreased albumin, prealbumin, and transferrin levels. A box plot was created to compare the levels of the three nutritional markers between patients with and without cachexia in different tumor types (Fig. 1). Detailed demographic information, tumor-related characteristics, and laboratory data are presented in Table 1. There was a moderate positive correlation between albumin and transferrin (male: R = 0.46, P < 0.001; female: R = 0.38, P < 0.001) and prealbumin (male: R = 0.44, P < 0.001; female: R = 0.42, P < 0.001) levels. A weak positive correlation was observed between prealbumin and transferrin levels (male: R = 0.33, P < 0.001; female: R = 0.20, P < 0.001) (Figure S2).

Table 1

Patients baseline characteristics
Variables	All patients (n = 4091)	Cachexia patients (n = 1303)	Non-cachexia patients (n = 2788)	P value
Age, years	59.00(51.00–65.00)	60.00(53.00–66.00)	59.00(50.00–65.00)	<0.001*
Gender (Male/Female)	2184/1907(53.4%/46.6%)	777/526(59.6%/40.4%)	1407/1381(50.5%/49.5%)	<0.001*
Diabetes(Yes/No)	351/3740(8.6%/91.4%)	110/1193(8.4%/91.6%)	241/2547(8.6%/91.4%)	0.877
Hypertension(Yes/No)	732/3359(17.9%/82.1%)	234/1069(18.0%/82.0%)	498/2290(17.9%/82.1%)	0.975
Smoking(Yes/No)	1714/2377(41.9%/58.1%)	600/703(46.0%/54.0%)	1114/1674(40.0%/60.0%)	<0.001*
Drinking(Yes/No)	898/3193(22.0%/78.0%)	333/970(25.6%/74.4%)	565/2223(20.3%/79.7%)	<0.001*
Type of cancer				<0.001*
Lung	1370(33.5%)	362(27.8%)	1008(36.2%)
Upper gastrointestinal	752(18.4%)	381(29.2%)	371(13.3%)
Hepatobiliary and pancreatic	181(4.4%)	112(8.6%)	69(2.5%)
Colorectal	634(15.5%)	250(19.2%)	384(13.8%)
Breast	690(16.9%)	63(4.8%)	627(22.5%)
Others	464(11.3%)	135(10.4%)	329(11.8%)
TNM stages				<0.001*
Ⅰ	417(10.2%)	96(7.4%)	321(11.5%)
Ⅱ	842(20.6%)	211(16.2%)	631(22.6%)
Ⅲ	1151(28.1%)	386(29.6%)	765(27.4%)
Ⅳ	1681(41.1%)	610(46.8%)	1071(38.4%)
Surgery(Yes/No)	2021/2070(49.4%/50.6%)	647/656(49.7%/50.3%)	1374/1414(49.3%/50.7%)	0.851
Radiotherapy(Yes/No)	507/3584(12.4%/87.6%)	149/1154(11.4%/88.6%)	358/2430(12.8%/87.2%)	0.222
Chemotherapy(Yes/No)	2516/1575(61.5%/38.5%)	731/572(56.1%/43.9%)	1785/1003(64.0%/36.0%)	<0.001*
NRS2002(<3/≥3)	2987/1104(73.0%/27.0%)	504/799(38.7%/61.3%)	2483/305(89.1%/10.9%)	<0.001*
ECOG grade(≤ 1/>1)	2063/2028(50.4%/49.6%)	526/777(40.4%/59.6%)	1537/1251(55.1%/44.9%)	<0.001*
Body mass index, kg/m²	22.58(20.20-24.82)	20.40(18.55–22.89)	23.37(21.23–25.46)	<0.001*
Calf circumference, cm	33.50(31.00–36.00)	32.00(30.00-34.35)	34.00(32.00-36.50)	<0.001*
Hand grip strength, kg	24.80(19.00-31.07)	23.60(18.00-30.15)	25.15(19.60–31.60)	<0.001*
Neutrophil to lymphocyte ratio (NLR)	2.36(1.58–3.75)	2.74(1.68–4.60)	2.24(1.54–3.42)	<0.001*
Albumin, g/L	40.00(36.60–42.90)	38.60(34.90–42.10)	40.50(37.40–43.40)	<0.001*
Prealbumin, g/L	0.22(0.17–0.26)	0.20(0.15–0.24)	0.23(0.18–0.27)	<0.001*
Transferrin, g/L	2.27(1.89–2.67)	2.13(1.75–2.54)	2.33(1.96–2.73)	<0.001*
Abbreviations: TNM, tumor/node/metastasis; NRS 2002, Nutrition Risk Screen 2002; ECOG, Eastern Cooperative Oncology Group.

Comparison between albumin, prealbumin and transferrin in predicting survival

During a median follow-up of 20.23 months, we recorded a total of 592 deaths in patients with cancer cachexia. The C-index for OS was the highest for albumin (0.608, IQR [0.584–0.633]), followed by transferrin (0.595, IQR [0.571–0.619]) and prealbumin (0.572, IQR [0.546–0.597]) (Table S1). The time-dependent ROC curve showed a similar trend: the 1-year AUC of albumin, transferrin, and prealbumin were 0.650, 0.633, and 0.600, respectively; and the 3-year AUC of albumin, transferrin, and prealbumin were 0.615, 0.598, and 0.571, respectively (Fig. 2).

Restricted cubic spline plots showed an association between serum nutritional markers and the HR for all-cause mortality in cancer patients with cachexia (Fig. 3). The risk of cancer-related mortality increased as serum albumin and transferrin levels decreased, with adjusted P values of 0.015 and 0.018, respectively. After adjusting for confounding factors, a significant nonlinear correlation was observed between prealbumin and mortality (P = 0.012). The optimal cutoff value of albumin, prealbumin, and transferrin for cancer cachexia patients in our study was 38.7 g/L, 0.17 g/L, and 2.29 g/L, respectively, as calculated by standardized log-rank statistics (Figure S3).

Survival analysis of albumin, prealbumin, and transferrin in cancer cachexia patients

Kaplan-Meier curves showed that patients in the low albumin, prealbumin, and transferrin groups had markedly poorer prognoses than those in the high serum nutritional marker group (Fig. 4). After adjusting for age, sex, smoking, drinking, BMI, HGS, CC, tumor type, tumor stage, surgery, chemotherapy, NRS2002, ECOG grade, and NLR, multivariate Cox proportional risk regression analysis showed that low albumin (HR = 1.55, 95%CI = 1.31–1.84, P < 0.001), prealbumin (HR = 1.33, 95%CI = 1.11–1.59, P = 0.002), and transferrin (HR = 1.46, 95%CI = 1.22–1.74, P < 0.001) were independent risk factors for prognosis (Table 2). We also performed two sensitivity analyses to confirm the prognostic value in patients with cachexia (Figure S4). First, we excluded 88 patients with liver diseases such as chronic hepatitis, cirrhosis, and liver cancer. Low albumin (adjusted HR = 1.54, 95%CI [1.30–1.84], P < 0.001), prealbumin (adjusted HR = 1.25, 95%CI [1.04–1.51], P = 0.016), and transferrin (adjusted HR = 1.51, 95%CI [1.26–1.82], P < 0.001) levels were still associated with shorter OS and were independently unfavorable factors for prognosis. Secondly, 175 patients who died within 6 months after the beginning of this study were also excluded, the overall result was unchanged, with adjusted HR of albumin, prealbumin and transferrin of 1.41 (95%CI [1.17–1.71], P < 0.001), 1.28 (95%CI [1.04–1.57], P = 0.019) and 1.36 (95%CI [1.11–1.66], P = 0.003).

Table 2

Cox proportional hazard regression analyses of albumin, prealbumin and transferrin
Groups	No. of patients	Crude model				Adjusted Model**
Groups	No. of patients	HR(95%CI)	P-value	HR(95%CI)	P-value	HR(95%CI)	P-value
Albumin
By per SD		1.34(1.25–1.45)	<0.001*	1.24(1.15–1.34)	<0.001*	1.19(1.09–1.29)	<0.001*
≥38.7g/L	650	Reference		Reference		Reference
<38.7g/L	653	1.87(1.59–2.20)	<0.001*	1.61(1.37–1.91)	<0.001*	1.55(1.31–1.84)	<0.001*
Prealbumin
By per SD		1.21(1.11–1.31)	<0.001*	1.18(1.08–1.28)	<0.001*	1.12(1.03–1.22)	0.011*
≥0.17g/L	846	Reference		Reference		Reference
<0.17g/L	457	1.58(1.35–1.86)	<0.001*	1.40(1.19–1.66)	<0.001*	1.33(1.11–1.59)	0.002*
Transferrin
By per SD		1.30(1.20–1.42)	<0.001*	1.16(1.07–1.27)	<0.001*	1.13(1.04–1.24)	0.006*
≥2.29g/L	527	Reference		Reference		Reference
<2.29g/L	776	1.73(1.46–2.05)	<0.001*	1.49(1.25–1.77)	<0.001*	1.46(1.22–1.74)	<0.001*
Abbreviation: HR, hazard ratio; CI, confidence interval; SD, standard deviation .
*Adjusted for age, sex, tumor type and TNM stage.
**Adjusted for variables found significant at P < 0.05 in the univariate analyses, including age, sex, drinking, smoking, tumor type, TNM stage, surgery, chemotherapy, NRS2002, ECOG, BMI, HGS, CC and NLR.

Subgroup analysis

We performed subgroup analyses based on various clinicopathological characteristics including age, sex, tumor type, TNM stage and ECOG grade (Fig. 5). Low albumin and prealbumin levels were independent risk factors for survival in all age, sex, and ECOG grade groups. However, the prognostic performance of transferrin in patients aged 65 years was not statistically significant. Low albumin levels were associated with high-risk mortality in patients with upper gastrointestinal cancer, hepatobiliary and pancreatic cancer, and colorectal cancer. The predictive ability of low prealbumin levels for survival was significant in patients with colorectal cancer. Low transferrin levels were confirmed as a prognostic predictor in patients with upper gastrointestinal cancer and colorectal cancer. Notably, the prognostic value of the three nutritional markers was significant in patients with advanced TNM stage (stage III and IV).

Association between nutritional markers with short-term survival and QoL

We performed a multivariate logistic regression analysis to investigate the impact of serum nutritional markers on short-term survival. As the values of albumin and transferrin decreased per standard deviation, the risk of mortality within 90 days rose to 1.87 (95%CI = 1.50–2.35, P < 0.001) and 1.55 (95%CI = 1.19–2.05, P = 0.001), respectively, after adjusting for confounding factors (Table 3). A similar trend was observed for prealbumin; however, it was not statistically significant as a continuous variable. When classified by cut-off value, we found that low albumin (OR = 3.90, 95%CI = 2.27–7.06, P < 0.001), prealbumin (OR = 1.63, 95%CI = 1.01–2.64, P = 0.044), and transferrin (OR = 1.98, 95%CI = 1.17–3.48, P = 0.013) were all independent risk factors for short-term survival. Moreover, patients with low levels of serum nutritional markers were more likely to have poorer QoL (Table S2). The median summary score of QoL in the low albumin population was 83.72 (IQR [70.94–92.78]), notably lower than that in patients with high albumin (88.35, IQR [77.46–95.90], P < 0.001). Low transferrin level was also an adverse factor of QoL, with a summary score of 84.62 in the low transferrin group and 87.78 in the high transferrin group (P = 0.002). Similarly, patients with low prealbumin levels had significantly poorer QoL than those with high prealbumin levels. Notably, prealbumin had an effective stratification value in each domain of the QLQ-C3 in patients with cancer cachexia.

Table 3

Logistic regression analyses of nutritional marker associated with short-term survival
Groups	No. of patients	Crude model				Adjusted Model**
Groups	No. of patients	OR(95%CI)	P-value	OR(95%CI)	P-value	OR(95%CI)	P-value
Albumin
By per SD		1.92(1.56–2.37)	<0.001*	1.90(1.54–2.37)	<0.001*	1.87(1.50–2.35)	<0.001*
≥38.7g/L	650	Reference		Reference		Reference
<38.7g/L	653	4.19(2.49–7.44)	<0.001*	4.18(2.44–7.53)	<0.001*	3.90(2.27–7.06)	<0.001*
Prealbumin
By per SD		1.18(0.94–1.49)	0.149	1.14(0.91–1.45)	0.261	1.10(0.88–1.38)	0.424
≥0.17g/L	846	Reference		Reference		Reference
<0.17g/L	457	1.89(1.21–2.95)	0.005*	1.81(1.14–2.89)	0.012*	1.63(1.01–2.64)	0.044*
Transferrin
By per SD		1.61(1.26–2.08)	<0.001*	1.56(1.21–2.03)	<0.001*	1.55(1.19–2.05)	0.001*
≥2.29g/L	527	Reference		Reference		Reference
<2.29g/L	776	2.24(1.36–3.84)	0.002*	2.10(1.25–3.66)	0.006*	1.98(1.17–3.48)	0.013*
Abbreviation: OR, odds ratio; CI, confidence interval; SD, standard deviation.
*Adjusted for age, sex, tumor type and TNM stage.
**Adjusted for age, sex, drinking, smoking, tumor type, TNM stage, surgery, chemotherapy, NRS2002, ECOG.

Malnutrition has been recognized for decades as a predictor of survival in patients with cancer. Serum protein, a non-invasively and easily quantified biomarker, is widely used to assess nutritional risk in clinical practice. As a result, a number of previous studies have explored the prognostic value of serum nutritional markers in cancer patients. Serum albumin has been described as an independent unfavorable factor for survival in patients with lung, gastric, colorectal, pancreatic, and breast cancer [10]. Some studies have also combined albumin with other parameters to predict the prognosis of patients with cancer. The prognostic nutritional index (PNI), calculated using albumin and lymphocyte counts in the serum, is an effective prognostic factor for various malignancies, especially digestive system carcinomas [26]. A recent meta-analysis reported that a low albumin-fibrinogen ratio (AFR) or a high fibrinogen-prealbumin ratio (FPR) may be promising prognostic markers for cancers [27]. In severe cases, malnutrition can progress to cachexia, a specific cancer-related malnourishment characterized by loss of lean body mass [28]. However, research on serum nutritional markers in the cancer cachexia population is lacking. The present study, comprehensively assessed the association between albumin, prealbumin, and transferrin levels and prognosis in cancer cachexia patients for the first time.

Consistently with previous studies, our results showed that cachexia patients have a lower level of serum hepatic protein than general cancer patients. This may be because several alterations were observed in the liver of patient with cachexia. In addition to muscle and adipose tissues, cachexia also affects other major organs, including liver protein synthesis [29]. Excessive amino acids resulting from muscle protein degradation result in an acute phase response (APR) and energy wasting in the liver [30]. Cachexia progression can also result in collagen deposition and fibrosis [31]. Poor protein-energy intake, impaired liver synthetic function, and inflammatory status result in low circulating hepatic protein levels among patients with cachexia [32]. Thus, the present study specifically set optimal cutoff values of albumin, prealbumin and transferrin for cancer cachexia population determined by standardized log-rank statistics, with figures of 38.7, 0.17, and 2.29 g/L respectively. Kaplan-Meier curves showed that patients with low serum nutritional marker levels had an obviously shorter OS time than those with high levels. Adjusting for age, sex, drinking, smoking, tumor type, TNM stage, surgery, chemotherapy, NRS2002, ECOG, BMI, HGS, CC, and NLR, multivariate Cox proportional risk regression confirmed that low albumin, prealbumin, and transferrin levels were all independent risk factors for prognosis in cancer patients with cachexia. The results were unchanged in two sensitive analyses, with one excluding patients with liver disorders and the other excluding patients who died within six months of the beginning of our study.

Chiang et al. recently reported that the AUC value for diagnosing malnutrition was largest for prealbumin, followed by transferrin and albumin, with optimal breakpoints of 0.21, 2.06, and 43.0 g/L, respectively, for diagnosing malnutrition in esophageal cancer [20]. Milano et al. also showed that prealbumin was the most sensitive indicator of nutrition, and its levels and rates of change had prognostic significance in cancer of the large bowel [33]. These results are in accordance with the hallmarks of prealbumin levels. With its much shorter half-life, prealbumin is a more sensitive indicator of nutritional status than albumin [11]. However, the current study found the opposite result for predicting survival in patients with cancer cachexia: albumin had the most accurate prognostic value, followed by transferrin and prealbumin. The Nutrition Area of the Spanish Society of Endocrinology and Nutrition (SEEN) also states that albumin remains the strongest marker for long-term nutrition-related prognosis, especially mortality, although prealbumin has a reasonably good value for long-term mortality up to three years [34]. We speculate that prealbumin may outperform albumin in assessing nutritional status, but its prognostic performance in cancer cachexia patients is not as accurate as that of albumin. Routine checking of all three markers will help comprehensively reflect nutritional risk, predict survival, and provide timely intervention.

The development of cancer cachexia differs according to tumor site, TNM stage, and individual conditions [35]. Therefore, we performed subgroup analyses based on clinical variables including age, sex, tumor type, TNM stage, and ECOG grade. All three hepatic proteins were promising predictors of survival in patients with advanced TNM stages. This may be because advanced patients often have a worse nutritional status due to reduced food intake and increased energy consumption. A low albumin level is an independent unfavorable factor for cachexia patients with upper gastrointestinal, hepatobiliary and pancreatic and colorectal cancers. The prognostic value of transferrin has been observed in upper gastrointestinal and colorectal cancer. Low prealbumin levels only have prognostic discrimination performance in patients with cachexia and colorectal cancer. However, Meier et al. confirmed that serum ferritin and transferrin levels have independent and excellent capabilities to determine prognosis in patients with end-stage liver disease [36]. A meta-analysis involving ten studies also showed that low pretreatment serum prealbumin level was significantly associated with poor prognosis in patients with liver cancer [18]. Considering the limited number of hepatobiliary and pancreatic cancer patients in our study, large-scale clinical research is required to further verify the prognostic value of prealbumin and transferrin in these populations. For lung cancer patients with cachexia, none of the three serum nutritional markers were statistically significant in predicting survival in our study.

We also assessed the association of nutritional markers with short-term survival and QoL, which provides a good reference for comprehensively evaluating the prognostic value and clinical application prospects in cancer cachexia patients. Multivariate logistic regression analysis showed that the odds of mortality within 90 days were higher in patients with low albumin, prealbumin, and transferrin levels. Similarly, Miyamoto et al. found that the CRP/albumin ratio is an independent factor for predicting survival within two weeks [37]. Oyama et al. confirmed the value of the PNI in predicting life expectancy within 12 weeks in patients with end-stage gastric cancer [38]. We assessed QoL using the QLQ-C30 and found that patients with lower levels of nutritional markers had a significantly poorer QoL. This is unsurprising as malnutrition is associated with a decline in QoL. Additionally, compared to albumin and transferrin, the discrimination value of prealbumin was significant in all domains of the QLQ-C30. This may be because the concentration of serum prealbumin reflects dietary intake rather than overall nutritional status [39].

This study had some limitations. First, owing to the limited number of patients with hepatobiliary and pancreatic cancer, the prognostic value in this subgroup may be biased. Second, this was an observational study, and there may be some unmeasured or measured confounders that could have affected the results. Third, all patients enrolled in our study were from a multicenter medical institution in China. Accordingly, the performance of serum nutritional markers in predicting survival in patients with cancer cachexia beyond the Chinese population is unknown. Large-scale international prospective clinical studies are required to overcome these biases.

In conclusion, serum nutritional marker levels are generally lower in cancer patients with cachexia. The present study reported that low albumin, prealbumin, and transferrin levels were associated with worse prognosis in patients with cancer cachexia. Albumin had the most accurate prognostic performance, followed by those of transferrin and prealbumin. The effect on survival was more significant in patients with advanced cancer. These results highlight the value of routinely assessing serum nutritional markers in clinical practice, which may help predict the prognosis of patients with cancer cachexia.

Acknowledgments

We would like to express our sincere thanks to the INSCOC project members for their substantial work on data collection and patient follow-up.

Declarations of interest: none.

Funding

This work was supported by the National Multidisciplinary Cooperative Diagnosis and Treatment Capacity Project for Major Diseases: Comprehensive Treatment and Management of Critically Ill Elderly Inpatients (No. 2019.YLFW).

Author contributions

All authors have read and approved the manuscript. MHC and HPS conceived and designed the study. XZ, MHC and HPS formulated research goals and designed study models. MT, QZ, LD CHS and WL provided essential materials. XZ and JXH analyzed data and performed statistical analysis. XZ and JXH wrote drafts of manuscript. MHC had primary responsibility for final content. All authors assisted with the interpretation of the findings, commented on drafts of the manuscript, and approved the final version.

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Comprehensive evaluation of serum nutritional markers in predicting prognosis among cancer patients with cachexia

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