This was a nationwide, multicenter cohort study. All patients were derived from the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) project of China which was registered online at https://www.chictr.org.cn (ID: ChiCTR1800020329). The full design of the INSCOC project has been described previously (36) and the detailed inclusion and exclusion criteria are shown in Supplementary Table 1. For the present study, we included 14,908 patients aged over 18 years who were diagnosed with cancer and/or were hospitalized for anti-cancer treatment from November 2011 to April 2019 at multiple centers in four geographical regions (east, south, west and north) of China. After excluding 509 patients with non-solid malignancies and 265 patients with an unclear pathological diagnosis, we finally included 14,134 patients with 17 types of cancer as the study population (Supplementary Figure 1). An independent cohort including 355 esophageal cancer patients diagnosed from December 2014 to November 2019 (not included in the INSCOC project) in our institution was used as the validation set to evaluate the prognostic performance of the FAIN. The study was approved by the Ethics Committees of all participating institutions and all data was analyzed anonymously. All participants in the study provided written consent for the scientific use of their data and the principles of the Declaration of Helsinki were followed.

The following information was collected at baseline within 48 h upon admission by a project-trained researcher via a face-to-face interview or physical examination: age, sex, smoking (active tobacco smoker before admission), alcohol drinking (once a week or more frequent alcohol consumption in the past 1 year, regardless of amount), tea consumption (once a week or more frequent tea consumption in the past 1 year, regardless of amount), comorbidities, height, weight, body mass index (BMI), mid-arm circumference (MAC, non-dominant arm), triceps skinfold thickness (TSF, non-dominant arm), handgrip strength (HGS, non-dominant hand), mid-arm muscle circumference (MAMC), calf circumference (CC, left calf), unintentional weight loss within and beyond 6 months, the NRS2002 score (≥ 3 indicating nutritional risk) (26), the PG-SGA score (27), the Karnofsky Performance Status (KPS) score (37) and the European Organization for Research and Treatment of Cancer QLQ-C30 score (QLQ-C30) (38).

In the present study, the BMI was also categorized as underweight (<18.5 kg/m²); normal (18.5 to <24 kg/m²), overweight (24 to <28 kg/m²), or obese (≥28 kg/m²) according to the Chinese recommendation (39). The detailed approaches and instruments used to obtain the anthropometric information (height, weight, BMI, MAC, TSF, HGS, MAMC, CC and weight loss) have been described previously (40), and are also shown in Supplementary Table 2. The gastrointestinal symptoms within the PG-SGA scale were extracted and analyzed independently. For the QLQ-C30, the global QOL scale was used in the present study, with a higher score indicating a better overall QOL.

The disease and treatment information, including the cancer site, clinical stage, differentiation grade, anticancer therapies used, serum indices, length of hospital stay, presence of intensive care unit stay, length of hospitalization, cost and thirty-day death were retrospectively retrieved from electronic medical records. Serum indices were all measured at the clinical laboratories of the participating institutions using fasting blood samples drawn upon admission.

Patients were followed annually after enrollment via telephone or face-to-face interviews to obtain the survival information. The all-cause mortality was the main outcome of the present study, and the overall survival time was calculated as the time interval (months) between the first admission and the patient's date of death, the date of the last valid follow-up, or April 2020.

A data-driven, outcome-oriented approach was used to create an index reflecting the nutritional and inflammatory impact on the patients' overall survival. First, Harrell's C-index was calculated to assess the prognostic impact of each baseline parameter. Then, the TSF (mm) (20), age (years), neutrophil to lymphocyte ratio (NLR, as an inflammatory marker, same unit for the neutrophils and lymphocytes, such as number/L) (15) and the serum albumin (g/L, as an inflammatory and prognostic marker) (41) were manually selected to develop the FAIN index since they showed the highest C-index within their respective categories. The prototypic definition of the FAIN was: (TSF + albumin)/(age + NLR). To maximize the prognostic value, the optimal formula of the FAIN was explored by multiplying each component with different coefficients (the other three parameters remained unchanged during the tuning of one parameter) and the corresponding C-index was observed. The FAIN index was finally determined to be: (TSF + albumin) / [age + 5 × (neutrophil count/lymphocyte count)].

Continuous data are shown as the medians [interquartile range] and were compared using Wilcoxon's rank-sum test. Categorical data were expressed as numbers (percentage) and compared using a Chi-squared test. The two-variable correlation was examined using a Spearman's rank correlation test. The baseline NRI, PNI and CONUT indices were also calculated to compare their prognostic value with the FAIN according to the following approaches: NRI = (1.519 × serum albumin, g/L) + (41.7 × present weight / usual weight); PNI = 10*serum albumin (g/dl) + 0.005*total lymphocyte count (mm³); CONUT includes the serum albumin level, total lymphocyte counts and serum total cholesterol level. The detailed scoring method of the CONUT has been described previously (31).

A restricted cubic spline was used to flexibly analyze the potential non-linear associations of the continuous FAIN index with survival. The potential non-linearity was tested using a likelihood ratio test with P < 0.05 indicating a non-linear relationship. We also categorized the continuous FAIN as a dichotomous variable to define the low and high groups using the median value and the optimal stratification (OS)-defined threshold. The OS method selects the threshold for a continuous factor by maximizing the between-group log-rank statistic for the overall survival (42). We also categorized the FAIN in tertiles, quartiles and quintiles to partially minimize the limitations associated with the variable dichotomization. The associations between the FAIN categories and survival was evaluated using Kaplan-Meier curves and log-rank tests. Multivariable-adjusted Cox proportional hazards models were used and hazard ratios (HR) with 95% confidence intervals (95%CIs) were calculated to estimate the association between the FAIN and mortality. We used the Schoenfeld individual test and Kaplan-Meier curves to statistically and visually estimate the proportional hazards assumption for each covariate adjusted (Schoenfeld test P > 0.05 indicates that the proportional hazards assumption is satisfied). The linearity assumption between covariates and outcome was confirmed by the Martingale residual plots.

Incremental models with increasing numbers of covariates were created. A dual-direction stepwise method based on the Bayesian Information Criterion (BIC) was used to help select the significant covariates. Model 1 was an unadjusted crude model. Model 2 was adjusted for the age at baseline. Model 3 was adjusted for age, sex and the BIC-screened independent predictors, including the tumor stage, radical surgery, curative chemotherapy, serum prealbumin level, HGS, the NRS2002 score, length of hospital stay and cancer type. Model 4 was adjusted for all variables in Model 3, plus the calf circumference, PG-SGA score, KPS score and the global QOL score.

Sensitivity analyses were performed to test the robustness of the multivariate Cox regression models by excluding the patients who died within the first 3 (Model 5), 6 (Model 6) and 12 months (Model 7), respectively. Multiplicative interactions were tested by adjusting the cross-product terms. Those covariates showing a statistically significant multiplicative interaction (P < 0.05) were defined as potential effect modifiers and subgroup analyses were performed in different strata of these variables to evaluate the modification of the associations observed in the overall population. The proportional hazards assumption and linearity assumption were also confirmed for the Cox regression models obtained through stratification using the approaches described above. All tests were two-sided, and P < 0.05 was regarded as statistically significant. All analyses were performed using R (version 3.6.3, http://www.rproject.org).

The study included 7,468 men and 6,666 women with a mean age of 57 years. The tumors were most frequently located in the lung (22.9%), colorectum (15.7%), breast (15.6%), stomach (10.6%), esophagus (8.8%), and nasopharynx (7.9%). The predominant clinical stages were III (39.9%) and IV (24.0%). There were 3,266 (23.1%) underweight patients, 7,396 (52.3%) patients with a normal weight, 2,761 (19.5%) overweight patients, and 711 (5.0%) obese patients. There were 3,241 deaths among 14,134 patients during a median follow-up time of 42 months. The patient age (C-index = 0.583, 95%CI = 0.573–0.593), albumin (C-index = 0.595, 95%CI = 0.585–0.605), NLR (C-index = 0.580, 95%CI = 0.570–0.590), and TSF (C-index = 0.589, 95%CI = 0.579–0.599) showed the highest prognostic value among the various baseline data (reflecting the demographic, inflammatory and anthropometric/nutritional dimensions, Table 1), so these were incorporated to develop the FAIN index. A coefficient screen showed that the FAIN had a maximal C-index when the NLR value was multiplied by 5 (Supplementary Table 3). Thus, the formula to generate the FAIN was determined to be: [TSF (mm) + albumin (g/L)]/[age (years) + 5 × (neutrophil count/lymphocyte count)] and the FAIN index was calculated for each patient using the baseline data.

The baseline patient characteristics, as stratified by the median-dichotomized FAIN, are presented in Table 2. Compared to the FAIN low group, the FAIN high group was associated with a higher value/rate of total protein, pre-albumin, albumin, transferrin, alanine transaminase, cholesterol, triglycerides, high density lipoprotein, low density lipoprotein, hemoglobin, red blood cells, platelets, lymphocytes, weight, BMI, MAC, TSF, HGS, CC, radical surgery, postoperative adjuvant chemotherapy, KPS score, global QOL score, NRI score, and PNI score, and was associated with a lower value/rate of age, male sex, smoking, alcohol drinking, tea consumption, hypertension, diabetes, coronary heart disease, chronic biliary disease, anemia, urea nitrogen, creatinine, total bilirubin, direct bilirubin, glucose, white blood cells, neutrophils, C-reactive protein, NLR, height, MAMC, weight loss within and beyond 6 months, curative radiotherapy, curative chemotherapy, other anticancer therapy, NRS2002 score, PG-SGA score, gastrointestinal symptoms (no appetite, nausea, vomiting, constipation, dry mouth, things taste funny or have no taste, dysphagia, feel full quickly, abdominal pain and other symptoms) and the CONUT score. As was expected, the cancer types, clinical stage and differentiation grade were also different between the low and high FAIN groups. Additionally, a univariate analysis on the short-term outcomes also showed that a higher FAIN was associated with a shorter length of hospital stay, fewer incidents of an intensive care unit stay, lower costs during hospitalization and lower rates of thirty-day mortality (all P < 0.05).

Sex-specific spearman's rank correlation tests were performed to assess the degree of relevance for the associations of the continuous FAIN with the BMI, weight loss beyond 6 months, CC, HGS, C-reactive protein, NRS2002 score, PG-SGA score, KPS score and global QOL score (Figure 1). The results were similar for both genders, showing a positive correlation between the FAIN and BMI (Figure 1A), CC (Figure 1C), HGS (Figure 1D), KPS score (Figure 1H) and global QOL score (Figure 1I), and a negative correlation between FAIN and weight loss (Figure 1B), C-reactive protein (Figure 1E), NRS2002 score (Figure 1F) and PG-SGA score (Figure 1G, all P < 0.05).

The Harrell's C-index of the FAIN was statistically compared to those calculated for the NRI, PNI, CONUT, NRS2002, and PG-SGA. The results showed that the FAIN had the highest prognostic value, with a C-index = 0.634 (95%CI = 0.624–0.644) compared to the NRI (C-index = 0.599, 95%CI = 0.589–0.609, P < 0.001), PNI (C-index = 0.595, 95%CI = 0.585–0.605, P < 0.001), CONUT (C-index = 0.575, 95%CI = 0.565–0.585, P < 0.001), NRS2002 (C-index = 0.578, 95%CI = 0.568–0.587, P < 0.001), and PG-SGA (C-index = 0.593, 95%CI = 0.583–0.603, P < 0.001).

A restricted cubic spline analysis showed that the continuous FAIN index was associated with a reduced mortality risk (P < 0.001) and no significant non-linearity was observed for this relationship (P = 0.489). The optimal threshold of the FAIN was 0.82, as determined by the OS (high: ≥0.82; low: <0.82, Figure 2A). Kaplan-Meier curves demonstrated that patients with a higher FAIN had better overall survival than those in the lower groups, regardless of the categorization approach used (all P < 0.001). For the FAIN tertiles, quartiles and quintiles, the tests for P of the trend indicated that the FAIN was monotonically associated with better overall survival of the patients (all P for trend <0.001, Figures 2B–F).

The results of the multivariable Cox proportional hazards models on the associations between the FAIN and mortality are shown in Table 3. The FAIN was analyzed as a continuous variable, per each standard deviation and as a categorical variable. In the fully-adjusted model (Model 4), the FAIN was independently associated with a reduced death hazard as both a continuous variable (HR = 0.57, 95%CI = 0.47–0.68) and for separation by one standard deviation (HR = 0.83, 95%CI = 0.78–0.88). Similar results were observed when the FAIN was dichotomized based on the OS (HR = 0.71, 95%CI = 0.64–0.78) or the median value (HR = 0.78, 95%CI = 0.71–0.86). These associations were all sustained when excluding those patients died within the first 3 (Model 5), 6 (Model 6) and 12 months (Model 7), except that the association between the median-dichotomized FAIN and mortality was not sustained in Model 7. This partially supports the use of the OS to generate an outcome-oriented FAIN threshold.

The results were also similar when the FAIN was analyzed as tertiles, quartiles or quintiles. Compared to those in the lowest tertile, patients in the highest tertile had a significantly reduced death hazard (Model 4, HR = 0.65, 95%CI = 0.58–0.74). Similar patterns were observed for the highest quartile (Model 4, HR = 0.59, 95%CI = 0.51–0.68, vs. the lowest quartile) and the highest quintile (Model 4, HR = 0.55, 95%CI = 0.46–0.65, vs. the lowest quintile). These associations were all sustained in the sensitivity analysis (Model 5–7) and tests for P of the trend showed that the positive associations between the FAIN and overall survival were all “dose-dependent” (all P for trend <0.001).

All covariates were screened for potential interactive effects, and the patient sex, tumor stage, curative chemotherapy, prealbumin, cancer type, PG-SGA category, KPS score and global QOL score showed statistically significant interactions with the FAIN (all P < 0.05). The fully-adjusted models were then repeated in different variable strata to study the effect modifications (Table 4). The positive association between the FAIN and overall survival (HR = 0.57, 95%CI = 0.47–0.68, as continuous) was strengthened in female patients (HR = 0.40, 95%CI = 0.30–0.54), those with stage III tumors (HR = 0.49, 95%CI = 0.36–0.67), patients who did not receive curative chemotherapy (HR = 0.51, 95%CI = 0.41–0.64), the higher serum prealbumin group (HR = 0.52, 95%CI = 0.40–0.67), those with lung cancer (HR = 0.47, 95%CI = 0.34–0.66), colorectal cancer (HR = 0.39, 95%CI = 0.23–0.64) and other cancers (HR = 0.53, 95%CI = 0.38–0.73), those in PG-SGA category B (HR = 0.48, 95%CI = 0.38–0.63), with a higher KPS (HR = 0.52, 95%CI = 0.43–0.64) and in the higher global QOL group (HR = 0.39, 95%CI = 0.26–0.58). In contrast, this relationship was attenuated in male patients (HR = 0.70, 95%CI = 0.56–0.88), those with a lower tumor stage (stage I, HR = 0.86, 95%CI = 0.45–1.64; stage II, HR = 0.72, 95%CI = 0.46–1.13), patients who received curative chemotherapy (HR = 0.69, 95%CI = 0.49–0.98), the lower serum prealbumin group (HR = 0.63, 95%CI = 0.48–0.82), those with breast (HR = 0.90, 95%CI = 0.36–2.25) and gastric cancer (HR = 0.97, 95%CI = 0.71–1.34), patients in PG-SGA category A (HR = 0.59, 95%CI = 0.37–0.93) and C (HR = 0.83, 95%CI = 0.61–1.12), with a lower KPS (HR = 0.76, 95%CI = 0.45–1.29) and those in the lower global QOL group (HR = 0.63, 95%CI = 0.51–0.77).

The prognostic impact of the FAIN was further assessed in an independent multicenter lung cancer cohort (n = 227) which was not used for the derivation of the FAIN. The baseline characteristics of the validation cohort are shown in Supplementary Table 4. The Harrell's C-index of the FAIN in the validation cohort was 0.639 (95%CI = 0.586–0.691) which was higher than the NRI (C-index = 0.589, 95%CI = 0.535–0.643, P = 0.030), PNI (C-index = 0.591, 95%CI = 0.536–0.647, P = 0.031), CONUT (C-index = 0.572, 95%CI = 0.518–0.627, P < 0.001), NRS2002 (C-index = 0.554, 95%CI = 0.502–0.607, P = 0.003), and PG-SGA (C-index = 0.585, 95%CI = 0.535–0.634, P = 0.031). Consistent with the findings in the original dataset, a multivariable Cox regression analysis in the validation cohort also showed that the FAIN was independently associated with a reduced death hazard, both as a continuous variable (HR = 0.21, 95%CI = 0.06–0.76) and when assessed as per one standard deviation (HR = 0.70, 95%CI = 0.53–0.94, Supplementary Table 5).