Abstract
Background
Older patients with cancer often have a worse prognosis than the general cancer patient population due to decreased immune responses, chronic inflammation status, and malnutrition. The present study aimed to develop a novel body mass index (BMI)-based inflammation index and investigate its prognostic significance in older patients with cancer.
Methods
This multicenter prospective study totally included 2376 cancer patients aged 65 years or older. Concordance index (C-index) and receiver operating characteristic (ROC) curves were applied to compare the prognostic value of various BMI-based inflammation indices. The overall survival (OS) was calculated and compared using the Kaplan–Meier method generated by the log-rank test. A Cox proportional hazard regression model was performed to identify independent predictors associated with OS. Subgroup analysis was used to explore the discriminative performance in different populations. The second outcomes included short-term survival, malnutrition and cachexia.
Results
We recorded 1112 death cases during a median follow-up of 17.43 months. Both the area under ROC curves and the C-index of the BMI to C-reactive protein/lymphocyte ratio (BCLR) were higher than other inflammation indices. There was a significant L-shaped association between BCLR and all-cause mortality in older patients with cancer (P < 0.001). A low BCLR was significantly associated with shortened OS and served as an independent risk factor for survival in older patients with cancer (hazard ratio = 1.51, 95% confidence interval = 1.32–1.73, P < 0.001). In the subgroup analysis, a low BCLR was an independent risk factor for prognosis inpatients with lung (HR = 1.40, 95%CI = 1.13–1.73, P = 0.002), upper gastrointestinal (HR = 1.32, 95%CI = 1.01–1.73, P = 0.041), hepatobiliary and pancreatic (HR = 1.78, 95%CI = 1.10–2.88, P = 0.020) and colorectal cancer (HR = 2.24, 95%CI = 1.57–3.18, P < 0.001). In addition, the prognostic value of low BCLR was also confirmed in underweight, normal as well as overweight and obesity populations. Multivariate logistic regression models showed that a low BCLR was an independent risk factor associated with short-term mortality, malnutrition and cancer cachexia in older patients with cancer. Two internal validation cohorts at a ratio of 7:3 confirmed the results of our study.
Conclusion
The BCLR is a promising indicator to assess nutritional-inflammatory status in older patients with cancer and can be used to identify patients with worse prognoses.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-026-07232-0.
Keywords: Body mass index, Inflammation, Prognosis, Cancer, Older patients
Introduction
Cancer constitutes the second leading cause of death worldwide, with an estimated 19.3 million new cancer cases and 10 million cancer-related deaths in 2020 [1]. Aging is deemed an independent risk factor for the increasing prevalence of cancer. Notably, patients aged 65 years or older account for 58% of newly diagnosed cancer in developed countries and 40% in developing countries [2]. As the number of individuals aged 65 years or older is expected to increase within the next few decades, the population of older patients with cancer will increase dramatically. Older patients with cancer are at high risk of chronic inflammation and malnutrition caused by the disease itself or anti-cancer treatments [3]. Therefore, identifying appropriate prognostic markers for this vulnerable population is urgently required.
Chronic low-level inflammation is one prominent feature of aging, involving the occurrence, progression, and complication of cancer [4]. It is clinically characterized by elevated levels of cytokines, such as C-reactive protein (CRP), interleukin-6 (IL-6), IL-18, and tumor necrosis factor-α (TNF-α) or, elevated counts of white blood cells (WBCs) and their subpopulations [4, 5]. The inflammatory environment is crucial in every stage in tumorigenesis, including initiation, promotion, malignant conversion, invasion, and metastasis [6]. Biomarkers in the peripheral blood that reflect the systemic inflammation level and immune function are considered prospective indictors predicting cancer patient prognosis [7]. Previous studies have examined the prognostic value of serum-based inflammatory markers and immune-related effectors, such as cytokines, WBC count, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and CRP [8]. Some studies also developed novel immune-inflammation combination indices or scoring systems, such as the systemic inflammation score, lymphocyte-CRP score, Glasgow Prognostic score, and so forth, to predict survival in various cancer types [9–12].
Malnutrition is another common condition in older patients with cancer, leading to higher mortality, greater functional decline, and poorer quality of life [13]. The body mass index (BMI) is widely applied to assess nutritional status by both clinicians and researchers due to its convenience. There is an increasing evidence showing that low BMI adversely affects prognosis in gastric [14] and colorectal [15] cancer patients. However, cancer-related malnutrition, is also characterized by systemic inflammation triggered by the disease itself [16]. Relying solely on BMI may not provide a comprehensive assessment of nutritional status or accurately predict survival. Therefore, our study aimed to develop a novel BMI-based inflammation index to evaluate the nutritional-inflammatory status and predict survival in older patients with cancer.
Materials and methods
Study population and design
This multicenter, prospective cohort study extracted data from the Investigation on Nutrition Status and its Clinical Outcomes of Common Cancers project in China (Registration number: ChiCTR1800020329) [17]. We initially included 6574 cancer patients aged 65 years or older from multiple clinical centers across China between 2013 and 2020. All patients had been admitted to the hospital receiving treatments including surgery, radiotherapy, chemotherapy, and other anti-cancer therapies. If patients were hospitalized more than twice during the study, only data from the first visit were included. Exclusion criteria were as follows: (a) 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 h. Finally, a total of 2376 patients were enrolled in the present study, separated into two internal validation cohorts of 1664 and 712 patients (Figure S1). All patients had signed informed consent before entering present study. This study protocol was approved by the ethical review committee of the participating institutions.
Data collection and variables definition
Patients’ general information including age, gender, alcohol consumption, smoking status, comorbidities (diabetes and hypertension), type of cancer, tumor/node/metastasis (TNM) stage, and treatment (e.g., surgery, chemotherapy, and radiotherapy) was extracted from electronic medical records. The following tumor types were included: lung cancer, gastric cancer, hepatocellular carcinoma, breast cancer, esophageal cancer, bladder cancer, pancreatic cancer, prostate cancer, colorectal cancer, biliary malignant cancer, and other cancers. All pathologic staging was defined according to the eighth edition of the American Joint Committee on Cancer TNM staging system.
Nutrition-related variables were also collected. The BMI was calculated as weight (kg) divided by the height (m) squared (
). Anthropometric measurement was performed by professional personnel and followed the enteral and parenteral nutritional guideline to assess muscle mass and function including hand grip strength (HGS, kg) and calf circumference (CC, cm) [18]. HGS was measured in the non-dominant hand with an electronic hand grip dynamometer. Patients were asked to lie in the supine position with a 90° knee flexion to measure CC. The Nutritional Risk Screening 2002 (NRS 2002) tool was used to assess nutritional status and a score of three or greater was considered at risk of malnutrition [19].
Laboratory data were required from routine medical examination including WBC, neutrophil, lymphocyte, hemoglobin, platelet, albumin, and CRP. The Eastern Cooperative Oncology Group performance status (ECOG-PS) was converted using the Karnofsky performance status (KPS) according to the following rules: ECOG-PS 0 (KPS100), ECOG-PS 1(KPS 90 to 80), ECOG-PS 2 (KPS 70 to 60), ECOG-PS 3 (KPS 50 to 40), ECOG-PS 4 (KPS 30 to 0) [20]. To assess quality of life we utilized the 30-item European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, version 3.0 (QLQ-C30). This questionnaire consists of five multiitem function scales (physical, role, social, emotional, and cognitive function), three multi-item symptom scales (fatigue, pain, and nausea & vomiting), six single-item symptom scales (dyspnea, insomnia, appetite loss, constipation, diarrhea, and financial impact), and one two-item global QoL scale [21]. The 30-item Cancer Quality of Life Questionnaire summary score is 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 [22].
Definition of body mass index-based inflammation indices and outcomes
First, we selected three up-regulated inflammatory parameters: CRP, neutrophils, and platelets, as well as two down-regulated inflammatory parameters: albumin and lymphocytes. Dividing up-regulated inflammatory parameters by down-regulated inflammatory parameters, we determined six combined inflammation indices: neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), CRP to lymphocyte ratio (CLR), CRP to albumin ratio (CAR), platelet to albumin ratio (PAR) and neutrophil to albumin ratio (NAR). Secondly, we divided the BMI by NLR, PLR, CLR, CAR, PAR, and NAR to generate BNLR, BPLR, BCLR, BCAR, BPAR, and BNAR, respectively (Fig. 1).
Fig. 1.
Definition of body mass index-based inflammation index
To collect data on clinical outcomes, telephone follow-up surveys, periodical reexaminations, and readmission were applied to all patients. The primary outcome of our study was overall survival (OS), which was defined as the interval between the first assessment in the clinic until the date of death, date of withdrawal from the study, the end of follow-up (October 30, 2020), or the time of the last contact, whichever came first. The secondary outcomes included the short-term survival, malnourishment and cachexia. Short-term survival was defined as the clinical outcome of the patient within 90 days after treatment. The score of Patient-Generated Subjective Global Assessment (PG-SGA) greater than or equal to 4 is defined as malnutrition. We diagnosed cachexia according to the International Diagnostic Criteria for Cancer Cachexia [23].
Statistical analysis
Statistical analyses were performed using R software version 3.6.2. Continuous variables were expressed as median (interquartile range) and compared by a Mann–Whitney U test. Categorical variables were expressed as absolute number or percentage and compared by a χ2 test or Fisher’s exact test. Correlation between two variables was analyzed by a Pearson’s test. Concordance index (C-index) and receiver operating characteristic (ROC) curve were applied to assess the prognostic value of each indictor. Restricted cubic spline plot described the association between the interested variable and hazard ratio (HR) for mortality. We used an outcome-oriented method to set optimal cut-off points for continuous variables, which can maximize log-rank statistics. The OS 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, with HRs and 95% confidence intervals (CIs). Those variables which were significant at P < 0.05 in the univariate analyses were entered into the multivariate competing-risks regression models. Patients were divided into two groups by random partition to perform internal validation, with the proportion of approximately seven to three. Logistic regression analysis was applied to assess the association of nutritional-inflammatory index with short-term survival, PG-SGA and cachexia. A two-tailed P-value < 0.05 was considered statistically significant.
Results
Clinical characteristics
A total of 2376 patients aged 65 years or older were enrolled in our study after excluding patients who met the exclusion criteria. The median age was 70 years old (interquartile range (IQR) [67.00–74.00]), with the proportion of males and females being 68.2% and 31.8%, respectively. The percentage of patients with lung cancer was the largest at 34.2%, followed by upper gastrointestinal cancer 21.9% and colorectal cancer 19.1%. There were only 6.4% patients with hepato-biliary and pancreatic cancer and 18.4% patients with other cancer types. Majority of the patients had advanced stage disease, with the percentage of stage Ⅳ disease being 49.6% and stage Ⅲ being 24.5%. The median BMI in all population was 22.19 
(IQR [19.79–24.39]). During the median follow-up of 17.43 months, we observed 1112 cases of death.
Comparison of performance and discrimination of the BMI-based inflammation index
To select a robust indictor for survival in older patients with cancer, we compared the C-index in 13 variables related to immunity, inflammation, and nutrition (Table S1). BCLR showed the largest C-index of 0.636, with a 95% CI ranging from 0.619 to 0.654. Similarly, the area under the curve (AUC) of BCLR was higher than that of other biomarkers (1-year AUC = 0.658, 3-year AUC = 0.668) (Fig. 2). The level of BCLR in different cancer types is shown in Figure S2. Hepatobiliary cancer patients had the lowest BCLR level while breast cancer patients had the highest. There was a weak positive correlation between BCLR and albumin (Males: R = 0.36, P < 0.001; Females: R = 0.37, P < 0.001) and a weak negative correlation between BCLR and neutrophils (Males: R=-0.22, P < 0.001; Females: R=-0.27, P < 0.001). In men, a very weak positive association was observed between BCLR with QLQ-C30 (R = 0.21, P < 0.001) and hemoglobin (R = 0.20, P < 0.001) (Figure S3).
Fig. 2.
Comparison of 1-year and 3-year ROC curves between thirteen variables. Abbreviation: ROC, receiver operating characteristic; AUC, area under curve
Patient baseline characteristics stratified by BCLR
We set a cut-off point of 6.42 for BCLR using standardized log-rank statistics, based on which all older patients with cancer were divided into two groups (Figure S4). There were 1262 patients with low BCLR and 1114 patients with high BCLR. Statistical results indicated that patients with low BCLR tended to be male, with older ages, advanced TNM stages, a history of diabetes, smoking, drinking, and loss of weight, and with lower BMI, CC, HGS, albumin, hemoglobin, lymphocyte, and QLQ-C30. Additionally, the patients with low BCLR were more likely to have increased WBC, neutrophil, platelet, CRP, NRS 2002 scores, and ECOG grade. More detailed demographic information, tumor-related characteristics, and laboratory data are presented in Table 1. Moreover, the median summary score of quality of life in low BCLR population was 84.87 (IQR [75.22–93.14]), notably lower than patients with high BCLR (92.05, IQR [84.20–97.44], P < 0.001). Scores of every domain in two groups are also listed in Table S2.
Table 1.
Patients baseline characteristics stratified by BCLR
| Variables | All patients (n = 2376) |
Low BCLR (n = 1262) |
High BCLR (n = 1114) |
P value |
|---|---|---|---|---|
| Age, years | 70.00(67.00–74.00) | 70.00(67.00–75.00) | 69.00(66.00–74.00) | <0.001* |
| Gender (Male/Female) | 1621/755(68.2%/31.8%) | 888/374(70.4%/29.6%) | 733/381(65.8%/34.2%) | 0.019* |
| Diabetes(Yes/No) | 343/2033(14.4%/85.6%) | 202/1060(16.0%/84.0%) | 141/973(12.7%/87.3%) | 0.024* |
| Hypertension(Yes/No) | 748/1628(31.5%/68.5%) | 415/847(32.9%/67.1%) | 333/781(29.9%/70.1%) | 0.128 |
| Smoking(Yes/No) | 1238/1138(52.1%/47.9%) | 696/566(55.2%/44.8%) | 542/572(48.7%/51.3%) | 0.002* |
| Drinking(Yes/No) | 563/1813(23.7%/76.3%) | 320/942(25.4%/74.6%) | 243/871(21.8%/78.2%) | 0.048* |
| Type of cancer | 0.001* | |||
| Lung | 813(34.2%) | 463(36.7%) | 350(31.4%) | |
| Upper gastrointestinal | 521(21.9%) | 279(22.1%) | 242(21.7%) | |
| Hepato-Biliary and pancreatic | 151(6.4%) | 93(7.4%) | 58(5.2%) | |
| Colorectal | 454(19.1%) | 224(17.7%) | 230(20.6%) | |
| Others | 437(18.4%) | 203(16.1%) | 234(21.0%) | |
| TNM stages | <0.001* | |||
| Ⅰ | 200(8.4%) | 77(6.1%) | 123(11.0%) | |
| Ⅱ | 415(17.5%) | 178(14.1%) | 237(21.3%) | |
| Ⅲ | 582(24.5%) | 267(21.2%) | 315(28.3%) | |
| Ⅳ | 1179(49.6%) | 740(58.6%) | 439(39.4%) | |
| Surgery(Yes/No) | 914/1462(38.5%/61.5%) | 444/818(35.2%/64.8%) | 470/644(42.2%/57.8%) | 0.001* |
| Radiotherapy(Yes/No) | 180/2196(7.6%/92.4%) | 111/1151(8.8%/91.2%) | 69/1045(6.2%/93.8%) | 0.021* |
| Chemotherapy(Yes/No) | 1032/1344(43.4%/56.6%) | 532/730(42.2%/57.8%) | 500/614(44.9%/55.1%) | 0.195 |
| Weight loss>5% in 6 month | <0.001* | |||
| Yes | 723(30.4%) | 477(37.8%) | 246(22.1%) | |
| No | 1653(69.6%) | 785(62.2%) | 868(77.9%) | |
| NRS2002(<3/≥3) | 1137/1239(47.9%/52.1%) | 488/774(38.7%/61.3%) | 649/465(58.3%/41.7%) | <0.001* |
| ECOG grade(≤ 1/>1) | 1011/1365(42.6%/57.4%) | 415/847(32.9%/67.1%) | 596/518(53.5%/46.5%) | <0.001* |
| QLQ-C30 | 88.89(79.32–95.51) | 84.87(75.22–93.14) | 92.05(84.20-97.44) | <0.001* |
| Body Mass Index, kg/m² | 22.19(19.79–24.39) | 21.64(19.20-23.88) | 22.72(20.57–24.88) | <0.001* |
| Calf circumference, cm | 32.50(30.00–35.00) | 32.00(29.35–34.30) | 33.00(31.00-35.50) | <0.001* |
| Hand grip strength, kg | 22.00(16.08–28.40) | 20.80(15.00-27.30) | 23.65(17.60-29.98) | <0.001* |
| Albumin, g/L | 38.30(34.60–41.40) | 36.10(32.70–39.50) | 40.30(37.62-43.00) | <0.001* |
| Hemoglobin, g/L | 124.00(109.00-137.00) | 118.00(103.00-133.00) | 130.00(116.00-141.00) | <0.001* |
White blood cell, X g/L |
6.20(4.90–8.03) | 6.88(5.24–8.99) | 5.76(4.64–7.03) | <0.001* |
Neutrophil, X g/L |
4.02(2.92–5.61) | 4.73(3.37–6.66) | 3.46(2.60–4.51) | <0.001* |
Lymphocyte, X g/L |
1.40(1.02–1.83) | 1.20(0.85–1.64) | 1.59(1.20-2.00) | <0.001* |
Platelet, X g/L |
214.00(164.00-272.00) | 227.00(171.00-291.00) | 200.00(158.00-252.00) | <0.001* |
| C-reactive protein, mg/L | 5.20(2.50–22.00) | 20.32(9.14–46.48) | 2.30(0.86–3.20) | <0.001* |
Abbreviation BCLR body mass index/C-creative protein to lymphocyte ratio, TNM tumor/node/metastasis, NRS2002 Nutrition Risk Screen 2002, ECOG Eastern Cooperative Oncology Group performance status, QLQ-C30 the 30-item European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, version 3.0
Association between BCLR and all-cause mortality
Restricted cubic spline plot exhibited a significant L-shape association between BCLR and HR for all-cause mortality, with P-value for nonlinearity < 0.001 (Fig. 3). As the value of BCLR decreased per standard deviation, the risk of mortality rose to 1.17 (95%CI = 1.07–1.28, P < 0.001) after adjusting for gender, smoking, drinking, diabetes, hypertension, tumor type, tumor stage, treatment, NRS 2002, ECOG, HGS, CC, albumin, hemoglobin and platelet levels. We divided the BCLR into quartiles and set the fourth quartile as the reference. Compared with the fourth quartile (Q4, > 15.65), the third quartile (Q3, 5.48 to 15.65), the second quartile (Q2, 1.23 to 5.48) and the first quartile (Q1, ≤ 1.23) were all associated with a worse prognosis. The HR for mortality in Q3, Q2, and Q1 were 1.22 (95%CI = 1.01–1.48, P = 0.042), 1.49 (95%CI = 1.24–1.80, P < 0.001), and 1.88 (95%CI = 1.56–2.28, P < 0.001), respectively after adjusting for confounding factors (P value for trend < 0.001) (Table 2).
Fig. 3.
Cubic spline plot shows relation between BCLR and hazard ratio for mortality. Abbreviation: HR, hazard ratio; BCLR, body mass index/C-creative protein to lymphocyte ratioA: Unadjusted model; B: Adjusted for gender, smoking, drinking, diabetes, hypertension, tumor type, tumor stage, treatment, NRS2002, ECOG, HGS, CC, albumin, hemoglobin and platelet
Table 2.
Association between BCLR and HR for all-cause mortality in older patients with cancer
| Groups | No. of patients (N = 2376) |
Crude model | Adjusted Model * | Adjusted Model** | |||
|---|---|---|---|---|---|---|---|
| HR(95%CI) | P-value | HR(95%CI) | P-value | HR(95%CI) | P-value | ||
| By per SD | 1.43(1.29–1.57) | <0.001* | 1.26(1.15–1.38) | <0.001* | 1.17(1.07–1.28) | <0.001* | |
| By quartile | |||||||
| Q4(>15.65) | 594 | Reference | Reference | Reference | |||
| Q3(5.48 to 15.65) | 594 | 1.41(1.17–1.71) | <0.001* | 1.23(1.02–1.49) | 0.033* | 1.22(1.01–1.48) | 0.042* |
| Q2(1.23 to 5.48) | 595 | 2.12(1.77–2.54) | <0.001* | 1.68(1.40–2.02) | <0.001* | 1.49(1.24–1.80) | <0.001* |
| Q1(≤ 1.23) | 593 | 3.11(2.62–3.70) | <0.001* | 2.45(2.05–2.91) | <0.001* | 1.88(1.56–2.28) | <0.001* |
| P value for trend | <0.001* | <0.001* | <0.001* | ||||
Abbreviation SD, standard deviation, HR hazard ratio, CI confidence interval
Survival analysis and prognostic significance stratified by BCLR
Kaplan–Meier curve analysis showed that compared to high BCLR, low BCLR had shorter OS in older patients with cancer (Log-rank P < 0.001) (Fig. 4). Multivariate Cox regression confirmed low BCLR was an independent risk factor for prognosis, after adjusting for gender, smoking, tumor type, tumor stage, surgery, radiotherapy, chemotherapy, NRS 2002, ECOG, HGS, CC, albumin, hemoglobin and platelet counts (HR = 1.51, 95%CI = 1.32–1.73, P < 0.001) (Table 3). We also performed a sensitivity analysis to confirm these results by excluding patients who died within 6 months after the beginning of our study. Similarly, low BCLR was still negatively related to survival and was an unfavorable factor for prognosis after adjusting for confounding factors (HR = 1.40, 95%CI = 1.20–1.64, P < 0.001) (Figure S5).
Fig. 4.
Kaplan-Meier curve stratified by BCLR in older cancer patients
Table 3.
Univariate and multivariate Cox regression analyses predicting overall survival
| Variables | No. of patients | Univariate | Multivariate | P value | |
|---|---|---|---|---|---|
| Hazard ratio(95%CI) | P value | Hazard ratio(95%CI) | |||
| Gender | |||||
| Female | 755 | Reference | |||
| Male | 1621 | 1.26(1.11–1.44) | <0.001* | 1.36(1.17–1.58) | <0.001* |
| Smoking | |||||
| No | 1138 | Reference | |||
| Yes | 1238 | 1.18(1.05–1.32) | 0.007* | 0.97(0.85–1.11) | 0.670 |
| Drinking | |||||
| No | 1813 | Reference | |||
| Yes | 563 | 1.10(0.96–1.26) | 0.150 | ||
| Diabetes | |||||
| No | 2033 | Reference | |||
| Yes | 343 | 1.10(0.94–1.30) | 0.235 | ||
| Hypertension | |||||
| No | 1628 | Reference | |||
| Yes | 748 | 1.06(0.94–1.20) | 0.365 | ||
| Type of cancer | |||||
| Lung | 813 | Reference | |||
| Upper gastrointestinal | 521 | 0.80(0.68–0.93) | 0.003* | 0.88(0.74–1.03) | 0.118 |
| Hepato-Biliary and pancreatic | 151 | 1.32(1.06–1.65) | 0.013* | 1.40(1.11–1.77) | 0.004* |
| Colorectal | 454 | 0.52(0.44–0.62) | <0.001* | 0.69(0.57–0.84) | <0.001* |
| Others | 437 | 0.48(0.40–0.58) | <0.001* | 0.56(0.46–0.68) | <0.001* |
| TNM stages | |||||
| Ⅰ | 200 | Reference | |||
| Ⅱ | 415 | 1.41(0.99-2.00) | 0.059 | 1.19(0.83–1.70) | 0.335 |
| Ⅲ | 582 | 2.45(1.76–3.41) | <0.001* | 1.97(1.41–2.75) | <0.001* |
| Ⅳ | 1179 | 5.03(3.67–6.90) | <0.001* | 3.14(2.27–4.33) | <0.001* |
| Surgery | |||||
| No | 1462 | Reference | |||
| Yes | 914 | 0.58(0.51–0.66) | <0.001* | 0.71(0.61–0.82) | <0.001* |
| Radiotherapy | |||||
| No | 2196 | Reference | |||
| Yes | 180 | 1.45(1.19–1.76) | <0.001* | 1.16(0.95–1.41) | 0.151 |
| Chemotherapy | |||||
| No | 1344 | Reference | |||
| Yes | 1032 | 1.20(1.07–1.35) | 0.002* | 1.34(1.18–1.53) | <0.001* |
| NRS2002 | |||||
| <3 | 1137 | Reference | |||
| ≥ 3 | 1239 | 1.56(1.39–1.76) | <0.001* | 1.11(0.98–1.26) | 0.115 |
| ECOG grade | |||||
| ≤ 1 | 1011 | Reference | |||
| >1 | 1365 | 2.32(2.04–2.64) | <0.001* | 1.73(1.52–1.99) | 0.001* |
| Hand grip strength, kg | |||||
| High(≥ 18.5) | 1572 | Reference | |||
| Low(<18.5) | 804 | 1.42(1.26–1.60) | <0.001* | 1.24(1.09–1.42) | 0.001* |
| Calf circumference, cm | |||||
| High(≥ 35.0) | 640 | Reference | |||
| Low(<35.0) | 1736 | 1.42(1.24–1.63) | <0.001* | 1.17(1.01–1.35) | 0.031* |
| Albumin, g/L | |||||
| Normal (≥ 35.00) | 1735 | Reference | |||
| Low(<35.00) | 641 | 1.74(1.53–1.96) | <0.001* | 1.20(1.05–1.38) | 0.009* |
| Hemoglobin, g/L | |||||
| Normal (≥ 120.00) | 1383 | Reference | |||
| Low(<120.00) | 993 | 1.52(1.36–1.71) | <0.001* | 1.20(1.05–1.36) | 0.006* |
| Platelet, | |||||
| Normal(≤ 300.00) | 1973 | Reference | |||
| High(>300.00) | 403 | 1.28(1.11–1.49) | 0.001* | 1.05(0.90–1.22) | 0.525 |
| BCLR | |||||
| High(≥ 6.42) | 1114 | Reference | |||
| Low(<6.42) | 1262 | 2.22(1.96–2.51) | <0.001* | 1.51(1.32–1.73) | <0.001* |
Those variables found significant at P < 0.05 in the univariate analyses were entered into the multivariate Cox regression analyses
Abbreviation CI confidence interval, TNM tumor/node/metastasis, NRS2002 Nutrition Risk Screen 2002, ECOG Eastern Cooperative Oncology Group performance status, BCLR body mass index/C-creative protein to lymphocyte ratio;
Further we plotted Kaplan–Meier curves according to tumor types and found that older patients with low BCLR had shorter OS in lung cancer, upper gastrointestinal cancer, hepato-biliary and pancreatic cancer, and colorectal cancer (Figure S6). Moreover, low BCLR was an independent negative prognostic factor in older patients with lung cancer (HR = 1.40, 95%CI = 1.13–1.73, P = 0.002), upper gastrointestinal cancer (HR = 1.32, 95%CI = 1.01–1.73, P = 0.041), hepato-biliary and pancreatic cancer (HR = 1.78, 95%CI = 1.10–2.88, P = 0.020) and colorectal cancer (HR = 2.24, 95%CI = 1.57–3.18, P < 0.001). In addition, low BCLR had an adverse impact on survival in I, II, III and IV TNM stages among older patients with cancer (Figure S7). When combined with low BCLR, the prognostic value of TNM stage was also improved. The 1-year and 3-year AUC of TNM were 0.666 and 0.705, respectively, and the AUC for TNM plus BCLR were 0.706 and 0.745, respectively (Figure S8). Notably, the prognostic value of low BCLR among older cancer patients was also confirmed in underweight (BMI<18.5 
), normal (18.5 
≤BMI<24.0 
) and overweight and obesity (BMI ≥ 24.0 
) populations (Figure S9). We also performed subgroup analyses based on various clinical characteristics, including gender, HGS, CC, NRS 2002, ECOG grade, tumor type, TNM stage, albumin and hemoglobin. The results confirmed the predictive value of low BCLR for worse prognosis in all subgroups (Fig. 5).
Fig. 5.
Subgroup analysis of BCLR in predicting survival. Abbreviation: CI, confidence interval; NRS2002, Nutrition Risk Screen 2002; ECOG, Eastern Cooperative Oncology Group performance status; BCLR, body mass index/C-creative protein to lymphocyte ratio;Adjusted for gender, tumor type, tumor stage, surgery, chemotherapy, ECOG, HGS, CC, albumin and hemoglobin
Randomized internal validation of low BCLR in the older patients with cancer
To evaluate the performance of BCLR in predicting prognosis in older patients with cancer, we further randomly assigned the total population into two validation groups, with a seven to three ratio based on computer-generated random numbers. There were 1664 patients in validation group A and 712 patients in validation group B. The baseline characteristics are displayed in Table S3. The low BCLR group included 874 (52.5%) patients and 790 (47.5%) patients were classified into high BCLR group in validation cohort (A) The low BCLR group comprised 388 (54.5%) patients and 324 (45.5%) patients were in high BCLR group in validation cohort (B) Multivariate analysis confirmed that low BCLR was an independent, unfavorable factor for survival in both validation groups after adjusting for confounding factors (Validation cohort A, HR = 1.44, 95%CI = 1.24–1.69, P < 0.001; Validation cohort B, HR = 1.69, 95%CI = 1.30–2.20, P < 0.001) (Table S4).
Relationship between BCLR and secondary outcomes
We also performed Logistic regression to explore the relationship between BCLR and secondary outcomes in older patients with cancer (Table S5). Low BCLR was an independent risk factor of short-term survival after adjusting confounder factors (OR = 3.58, 95%CI = 2.47–5.33, P < 0.001). We divided the BCLR into quartiles and set the fourth quartile as the reference. Compared with patients in high BCLR (Q4, > 15.65), the risk of mortality within 90 days was more than 3 times higher (OR = 5.45, 95%CI = 3.30–9.49, P < 0.001) in patients with low BCLR (Q1, ≤ 1.23). In similar trend, low BCLR was an independent risk factor associated with malnutrition (OR = 2.66, 95%CI = 2.18–3.26, P < 0.001) and cachexia (OR = 2.12, 95%CI = 1.77–2.55, P < 0.001). The risk of developing malnutrition and cachexia rose to more than 4 (OR = 6.01, 95%CI = 4.33–8.45, P < 0.001) and 2 times (OR = 2.64, 95%CI = 2.04–3.43, P < 0.001) in patients with low BCLR (Q4 vs. Q1).
Discussion
This prospective cohort study aimed to develop a novel BMI-based inflammation index to comprehensively assess the nutritional-inflammatory status and investigate its prognostic value in older patients with cancer. In total, we constructed six BMI-based inflammation indices and found that BCLR (BMI by CRP-to-lymphocyte ratio [CLR]) had the most reliable prognostic value. BCLR showed a positive correlation with nutritional parameters, such as albumin, and a negative correlation with inflammatory parameters such as neutrophil count. A restricted cubic spline described the association between BCLR and HRs of all-cause mortality, which was L-shaped after adjusting for confounding factors. We set a cut-off point of 6.42 for BCLR in older patients with cancer and assessed the prognostic value of BCLR with various outcomes including OS, short-term survival, malnourishment and cachexia. Low BCLR was confirmed as an independent, unfavorable factor for prognosis in lung cancer, upper gastrointestinal cancer, hepato-biliary and pancreatic cancer, and colorectal cancer. The result was unchanged in sensitivity analysis, which had excluded patients who died within 6 months of study initiation to avoid the effects of cause inversion. We further verified the clinical significance of BCLR in the prognostic assessment of older patients with cancer using two internal randomized validation cohorts. Another finding of our study was patients with low BCLR had a higher risk of dying in short-term, developing malnutrition and cachexia. It was reasonable considering the baseline characteristic showed low BCLR was associated with older age, comorbidities, history of smoking and drinking, advanced TNM stage and elevated inflammation status. These findings highlight the significance of screening the nutritional-inflammatory status in older patients with cancer and provide physicians with methods to design better and earlier clinical interventions.
Cancer patients aged 65 years and older are a large, growing population at high risk of weight loss conditions including cancer-related malnutrition, sarcopenia, and cachexia [24]. Several detrimental outcomes arise due to lack of nutrition, such as high risk mortality, worse quality of life, impaired physical function, and resistance to treatment [25]. Of note, systemic inflammation responses also exacerbate weight loss by promoting catabolic metabolism, increasing insulin resistance, or suppressing the energy entering cells [26, 27]. The European Society for Clinical Nutrition and Metabolism (ESPEN) also recommended dividing malnutrition into cases with inflammation and without inflammation and suggested integrating inflammation suppression therapy into traditional nutritional strategies [16, 28]. While various prognostic systemic inflammation markers have been developed to reflect the inflammatory status in cancer patients, our research indicates that the integration of BMI with these markers not only provides a more comprehensive assessment of the nutritional-inflammatory profile but also significantly improves the predictive accuracy as evidenced by enhanced C-index and AUC values when compared to the use of inflammation markers alone. This also aligns with the GLIM consensus, which integrates BMI and inflammation for a more comprehensive evaluation of malnutrition.
BMI is a useful tool to assess nutritional status for both clinicians and researchers; however, its significance in predicting survival is debatable [29]. This may partly be because BMI does not reflect inflammation level in patients, which is crucial for the prognosis of older patients with cancer. In patients with a high inflammatory burden and normal or high BMI, the BMI may fail to accurately reflect and predict survival [30]. In line with our hypothesis, the C-index and AUC value of both 1-year and 3-year mortality were significantly higher in BMI-based inflammation indices than that of BMI. BCLR showed a slightly higher predictive accuracy than other biomarkers in predicting OS.This may be attributable to the combination of three important components in BCLR including BMI, CRP levels, and lymphocyte count, which comprehensively reflect nutrition, inflammation, and immune status of the body. BMI is a convenient method for initial nutritional evaluation and low BMI is associated with worse prognosis in cancer patients [14, 15], and [23]. CRP, an acute-phase reactant, is elevated after an inflammatory stimulus. A previous study observed 10,408 individuals who had CRP measured at baseline and up to 16 years later and found that an elevated level of CRP was associated with increased risk of cancer and cancer-related early death [31]. Lymphocytes play a critical role in immune regulation and tumor cell-specific killing, closely related with the development of cancer [32]. Limited previous studies have assessed the prognostic value of combining BMI and inflammatory markers. Consistent with our study, the impact of the serum CRP-to-BMI ratio on the prognosis of patients with resected colorectal cancer has previously been reported, confirming that it is an independent risk factor for survival [33]. Previous evidence also found that preoperative elevated NLR and decreased BMI had a significant negative effect on the survival of patients with resectable gastric or gastroesophageal junction adenocarcinomas [34]. The results of our study showed that BCLR had a slightly improvement than the combination of BMI and NLR in predicting survival.
We also performed subgroups analyses to assess the prognostic value of BCLR in each tumor type. Low BCLR was independently associated with worse survival in lung cancer, upper gastrointestinal cancer, hepato-biliary and pancreatic cancer, and colorectal cancer. The prognostic value of CLR has been observed in patients with pancreatic cancer, colorectal cancer, and oral cavity squamous cell carcinoma [35–37]. A recent study also investigated six combinations of inflammatory indices including NLR, PLR, CAR, NAR, PAR, and CLR in pancreatic cancer patients and confirmed that CLR had the highest prediction accuracy for survival and was an independent risk factor [35]. However, the present study found that BCLR showed higher predictive ability for OS than other inflammation-based biomarkers including CLR. In the TNM stratification analyses, BCLR could also significantly stratify the prognosis of patients with the same cancer stage. We also performed ROC curves to compare the predictive value for all-cause mortality within each TNM stage with or without low BCLR. A difference of 0.025 from reference was considered better discrimination [38]. The predictive ability of TNM stage for both 1- and 3-year survival was clearly improved when combined with low BCLR. This indicates that BCLR can be used as an effective complement to the traditional TNM staging for prognosis assessment. Additionally, results of our study showed BCLR had an excellent performance in predicting survival in patients with different BMI levels, which implies the potential prognostic role of BCLR not only in patients with underweight and normal weight but also in those patients with overweight or obesity.
This study has some limitations. First, the reference value of several markers, such as HGS and CC, in the older population was lacking, therefore, we calculated cut-off points for them using an outcome-oriented method. Second, the heterogeneity in tumor types and treatments likely influenced our results. Although we conducted a subgroup analysis, larger studies targeting more specific populations are needed. In addition, all patients enrolled in our study were from multicenter medical institutions in China. The performance of BCLR in predicting survival in cancer patients beyond the Chinese population is unknown. Third, while our study developed a valuable prognostic indicator to assess the nutritional-inflammatory status in older patients with cancer, the C-index and AUC values were modest and not superior to ECOG-PS, suggesting that the results should be interpreted with caution. Furthermore, although BCLR demonstrated relatively better performance compared with other BMI- or inflammation-based biomarkers, its advantage remained moderate rather than pronounced. Future longitudinal and mechanistic studies are needed to validate the robustness and explore the underlying biological interaction between BMI and inflammation in cancer-related prognosis.
In conclusion, our present study developed a novel BMI-based inflammation index to evaluate nutritional-inflammatory status in older patients with cancer. Compared with other indicators, BCLR showed the best performance in predicting the prognosis of older patients with cancer. Evaluation of BCLR could identify patients at risk of malnutrition and inflammatory burden and be a useful prognostic marker in clinical practice.
Supplementary Information
Acknowledgements
We would like to express our sincere thanks to the INSCOC project members for their substantial work on data collection and patient follow-up.
Abbreviations
- CRP
C-reactive protein
- IL
interleukin
- OS
overall survival
- TNF-α
tumor necrosis factor-α
- WBCs
white blood cells
- BMI
body mass index
- TNM
tumor/node/metastasis
- NRS 2002
Nutrition Risk Screen 2002
- ECOG
Eastern Cooperative Oncology Group
- ECOG-PS
Eastern Cooperative Oncology Group performance status
- KPS
Karnofsky performance status
- BMI
body mass index
- HGS
hand grip strength
- CC
calf circumference
- QoL
quality of life
- NLR
neutrophil to lymphocyte ratio
- PLR
platelet to lymphocyte ratio
- CLR
CRP to lymphocyte ratio
- CAR
CRP to albumin ratio
- PAR
platelet to albumin ratio
- NAR
neutrophil to albumin ratio
- BNLR
BMI to the ratio of neutrophil to lymphocyte
- BPLR
BMI to the ratio of platelet to lymphocyte
- BCLR
BMI to the ratio of CRP to lymphocyte
- BCAR
BMI to the ratio of CRP to albumin
- BPAR
BMI to the ratio of platelet to albumin
- BNAR
BMI to the ratio of neutrophil to albumin
- PG-SGA
Patient-Generated Subjective Global Assessment
- IQR
interquartile range
- C-index
concordance index
- ROC
receive operating characteristic
- HR
hazard ratio
- CIs
confidence intervals
Authors’ 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, WL and MY provided essential materials. XZ and JXH analyzed data and performed statistical analysis. XZ and JXH wrote drafts of manuscript. MY has made significant contributions to the revision. 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.
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).
Data availability
All data generated or analysed during this study are included in this published article and its supplementary information files.
Declarations
Ethics approval and consent to participate
This study was planned and conducted in accordance with the principles of the Declaration of Helsinki, and was firstly approved by the Medical Ethics Committee of First Affiliated Hospital of Sun yat-sen University (reference number: 2013-82). This study was also approved by the Medical Ethics Committee at each of the collaborating hospitals. All participants were informed of the purpose of this research and signed informed consent. All methods were carried out in accordance with relevant guidelines and regulations.
Consent for publication
Not applicable.
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.
Jia-Xin Huang, Xi Zhang and Min Yang contributed equally to this work.
Contributor Information
Han-Ping Shi, Email: shihp@ccmu.edu.cn.
Ming-Hua Cong, Email: congmh@cicams.ac.cn.
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Data Availability Statement
All data generated or analysed during this study are included in this published article and its supplementary information files.