Introduction
C ancer is a major public health problem globally, with an estimated 19.3 million new cases and 10 million deaths in 2020 ( 1 ). The highest incidence worldwide occurs in the elderly and represents the fourth most frequent cause of death in older adults ( 2 ). It is estimated that by 2035, the incidence of cancer in older adults will double ( 3 ) and by 2050 the number of older adults with cancer will triple, reaching 446 million ( 4 ).
Cancer can cause a decrease in food intake, energy expenditure at rest and alterations in the metabolism of nutrients, being one of the main inflammatory diseases related to age ( 5 , 6 ). On the other hand, inflammation affects cell metabolism, muscle strength and energy regulation, predisposing the development of sarcopenia ( 6 ). In this way, cancer is frequently associated with rapid weight loss as well as a significant deterioration of muscle mass that may be mediated by sarcopenia or by the development of cachexia ( 7 , 8 ). In this sense, cancer-induced sarcopenia is associated with an increase in the adverse effects of treatment, unfavorable outcomes and lower survival ( 5 ). Sarcopenia represents a problem related to various long-term diseases and older adults with cancer have a higher risk of presenting this disease ( 9 ).
The diagnosis of sarcopenia is made by measuring grip strength, physical performance, and muscle mass. Ideally, the latter is determined by dual energy X-ray absorptiometry (DXA); however, there are several techniques that have different cut-off points, producing variability of results and, consequently, difficulty in diagnosing sarcopenia ( 9 ) and the need for different tools for adequate diagnosis.
SARC-F is an instrument that assesses the risk of sarcopenia and can be used both in clinical settings and in community health care. It has a moderate to low sensitivity and a very high specificity ( 10 ), for which an improved variant was proposed.
SARC-CalF is an instrument to which the measurement of calf circumference was added, increasing the sensitivity from 29.5–33.3% to 60.7–66.7% ( 11 , 12 ). Previous studies have been conducted using the SARC-F and/or SARC-CalF scales for screening of the risk of sarcopenia in older adults with cancer in the United States ( 13 ) and Brazil ( 14 , 15 ). However, these studies had limitations due to the short follow-up time and study population ( 13 , 14 , 15 ), in addition to having a cross-sectional design that did not allow causal association to be evaluated in one of these studies ( 15 ).
Cancer increases the risk of sarcopenia, and both diseases are associated with important complications such as morbidity, disability and mortality ( 16 , 17 ). It is necessary to evaluate the risk of sarcopenia as a risk factor for mortality, especially in this vulnerable group. However, there are no previous studies that evaluate the association of interest using both scales or their diagnostic performance. In addition, it is relevant to have accessible and low-cost tools that allow screening for sarcopenia in primary care in rural areas ( 18 , 19 ), considering the lack of health personnel and infrastructure ( 20 ). For this reason, this study aims to evaluate the role of SARC-F and SARC-CalF scores as risk factors for mortality in older adults with cancer in Peru.
Methods
Study design, population and sample
We performed a secondary analysis of data from a prospective cohort including older adults (60 years and older) of retired military men diagnosed with cancer and receiving palliative treatment. The participants were evaluated and enrolled from September 2012 to February 2013 and were followed until 2015, in the Geriatrics Service of the Naval Medical Center of Peru (CEMENA), located in Callao, Peru. We have developed previous articles ( 21 , 22 , 23 ) with the present database evaluating geriatric syndromes and their risk of adverse outcomes. The initial evaluation included 1,178 eligible older adults among which 121 were subsequently excluded due to a Mini Mental State Examination (MMSE) score less than or equal to 23, 81 for having a diagnosis of dementia, nine due to a diagnosis without curative intent, four because they discontinued their treatment, four due to incomplete medical records, six due to loss to follow-up, and 31 did not agree to participate in the study (Figure 1 ). For this secondary analysis we excluded participants with incomplete data on our variables of interest. The calculation of statistical power was based on a previous study by Yang et al. ( 24 ), considering a hazard ratio (HR) of 2.08, a proportion of deaths with risk of sarcopenia of 29% and a proportion of deaths without the risk of sarcopenia of 17.6%. Thus, we obtained a statistical power of 98% for a sample of 922 older adults included. Figure 1 Sample selection flowchart

Procedures
In the original study, older adults were invited to participate in the study during outpatient evaluation after confirmation of the diagnosis of cancer. After voluntarily agreeing to participate in the study and signing the informed consent, the evaluating physician collected the sociodemographic variables, medical history, and functional and performance-based measures, including the SARC-F and SARC-CalF scores.
Variables
Outcome variable: mortality
The outcome variable was all-cause mortality, which we defined by the patient's vital status at the end of follow-up. We obtained information on mortality from the CEMENA Epidemiological Surveillance Office registry.
Exposure variables: SARC-F and SARC-CalF scores
SARC-F is a questionnaire that is a quick and simple diagnostic tool to assess the risk of sarcopenia. It includes five components: aid for walking, falls, getting up from a chair, climbing stairs, and strength. The scores for each item range from zero to two, obtaining a total score between zero to 10 points. Thus, a score greater than or equal to four was defined as risk of sarcopenia ( 25 ).
SARC-CalF is a modified SARC-F, which adds calf circumference measurement, improving the diagnostic performance of the instrument for the evaluation of the risk of sarcopenia. This scale defines calf circumference in men greater than 34 cm with a score of 0 points and less than or equal to 34 with a score of 10. The total score with the previously mentioned components plus the calf circumference can be of up to 20, with a score of 11 to 20 being defined as an indicator of risk of sarcopenia ( 26 , 27 ).
Other variables
Sociodemographic characteristics
We included age (60–70, ≥71 years) and marital status (single, married/cohabitant, divorced/separated, widowed). These variables were collected by self-reporting.
Medical and personal history
We evaluated the following comorbidities: hypertension, type 2 diabetes mellitus, chronic obstructive pulmonary disease, chronic kidney disease, osteoporosis, dyslipidemia, urinary incontinence (assessed using the Edmonton Frailty Scale ( 28 )), sedentary lifestyle (defined by a score less than or equal to 64 on the Physical Activity Scale for the Elderly (PASE) ( 29 , 30 ) and overweight or obesity (defined by a body mass index greater than or equal to 25 kg/m 2 and 30 kg/m 2 , respectively). We collected data from the patient's clinical history and generated a variable that included the previously described comorbidities (0, 1, ≥2). Likewise, polypharmacy, defined as the consumption of five or more prescribed medications ( 31 ), use of health services, defined as at least one admission to the CEMENA hospitalization or emergency service and smoking habit (yes, no) were collected by self-reporting.
Functional evaluation
We used the Barthel index to assess dependence of basic activities of daily living (ABVD), considering a score less than 100 as positive ( 32 ). Likewise, we evaluated exhaustion with three questions: a) Do you feel full of energy? (yes, no); b) Do you feel that everything you do requires effort? (yes, no); c) Do you feel that you can no longer move forward? (yes, no). These questions evaluated how the older adult felt in the last two weeks and a score greater than or equal to two was considered as positive ( 33 ). Dynapenia was defined as grip strength less than 26 kg ( 10 ). We evaluated self-reported weight loss using the Edmonton Frailty Scale (yes, no) ( 28 ).
Statistical analysis
We performed the statistical analysis using the statistical package Stata v16.0 (StataCorp, Tx). The descriptive analysis was carried out using absolute and relative frequencies for the qualitative variables. Bivariate analysis between covariates of interest and all-cause mortality was performed using the Pearson's Chi-square test. We performed Cox proportional hazards modeling to assess the role of the SARC-F and SARC-CalF scores as risk factors for sarcopenia and mortality. We developed a crude model for each exposure variable and three models adjusted for potential confounders as described in the literature ( 11 , 24 ). In addition, we elaborated a supplementary Cox regression model including both SARC-F and SARC-CalF. We estimated crude HR (cHR) and adjusted HR (aHR), with their respective 95% confidence intervals (95%CI).
Similarly, a receiving operating characteristic curve analysis was performed to evaluate the diagnostic performance of SARC-F and SARC-CalF as predictors of mortality in in the study sample of older males with cancer. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, percentage of correctly classified, Youden index and area under the curve (AUC) of both exposure variables. We performed the diagnostic performance analysis considering cut-off points for SARC-F (greater than or equal to four) ( 25 ) and SARC-CalF (greater than or equal to 11) ( 26 , 27 ) described in previous studies to define risk of sarcopenia.
Ethical aspects
The primary study was approved by the Institutional Research Ethics Committee of CEMENA (Memorandum No. CEI-CMN-134-2009). The older male adults provided signed informed consent prior to enrollment in the primary study. For this secondary analysis, we did not carry out additional evaluations and the integrity or confidentiality of the participants was not compromised.
Results
General characteristics of the sample and bivariate analysis according to mortality
We included 922 older men with cancer in the analysis, with a mean follow-up of 589 days. Of these, 43.1% (n=397) were between 60 and 70 years old, 55.6% (n=513) were married or cohabiting, 59.2% (n=546) had two or more comorbidities, and 20% (n= 184) had a smoking habit. In addition, 27% (n=249) had functional dependence for ABVD, 55.4% (n=511) reported polypharmacy, and 46.8% (n=431) reported weight loss, while 41.1% (n=379) and 48.7% (n=449) presented exhaustion and dynapenia, respectively.
In addition, according to the SARC-F and SARC-CalF scores, 21.5% (n=198) and 45.7% (n=421) were at risk of sarcopenia, respectively, while the incidence of mortality was 22.9% (n=211). We found that 62.1% (n=123) of older adults considered at risk of sarcopenia by the SARC-F score died during follow-up, while only 12.2% (n=88) of those without risk of sarcopenia died. On the other hand, 47% (n=198) of the group at risk of sarcopenia, defined by SARC-CalF, died during follow-up, while only 2.6% (n=13) of those without risk of sarcopenia died. There were statistically significant differences between the study covariates and mortality, except for age groups (Table 1 ). Table 1 Descriptive and bivariate analysis of the sample characteristics according to all-cause mortality (n=922) Variables n % Mortality after 2 years of follow-up No 77.1% (n=711) Yes 22.9% (n=211) P value Age 0.067 60–70 years 397 43.1 318 (80.1) 79 (19.9) ≥71 years 525 56.9 393 (74.9) 132 (25.1) Marital status <0.001 Single 179 19.4 161 (89.9) 18 (10.1) Married/cohabiting 513 55.6 484 (94.3) 29 (5.7) Divorced/separated 109 11.8 26 (23.9) 83 (76.1) Widowed 121 13.1 40 (33.1) 81 (66.9) Comorbidities <0.001 0 68 7.4 26 (38.2) 42 (61.8) 1 308 33.4 244 (79.2) 64 (20.8) ≥2 546 59.2 441 (80.8) 105 (19.2) Smoking habit <0.001 No 738 80.0 650 (88.1) 88 (11.9) Yes 184 20.0 61 (33.2) 123 (66.8) Functional dependence for ADL 1 <0.001 No 673 73.0 603 (89.6) 70 (10.4) Yes 249 27.0 108 (43.4) 141 (56.6) Polypharmacy <0.001 No 411 44.6 345 (83.9) 66 (16.1) Yes 511 55.4 366 (71.6) 145 (28.4) Use of health services <0.001 No 477 51.7 405 (84.9) 72 (15.1) Yes 445 48.3 306 (68.8) 139 (31.2) Self-reported weight loss <0.001 No 491 53.3 389 (79.2) 102 (20.8) Yes 431 46.8 322 (74.7) 109 (25.3) Exhaustion <0.001 No 543 58.9 449 (82.7) 94 (17.3) Yes 379 41.1 262 (69.1) 117 (30.9) Dynapenia <0.001 No 473 51.3 403 (85.2) 70 (14.8) Yes 449 48.7 308 (68.6) 141 (31.4) Gait speed <0.001 Normal 541 58.7 469 (86.7) 72 (13.3) Slow 379 41.3 240 (63.3) 139 (36.7) SARC-F <0.001 No risk of sarcopenia 724 78.5 636 (87.8) 88 (12.2) Risk of sarcopenia 198 21.5 75 (37.9) 123 (62.1) SARC-CalF <0.001 No risk of sarcopenia 501 54.3 488 (97.4) 13 (2.6) Risk of sarcopenia 421 45.7 223 (53.0) 198 (47.0) 1. Activities of daily living.
Table 1
Descriptive and bivariate analysis of the sample characteristics according to all-cause mortality (n=922)
1. Activities of daily living.
| Variables | n | % | Mortality after 2 years of follow-up | ||
| No 77.1% (n=711) | Yes 22.9% (n=211) | P value | |||
| Age | 0.067 | ||||
| 60–70 years | 397 | 43.1 | 318 (80.1) | 79 (19.9) | |
| ≥71 years | 525 | 56.9 | 393 (74.9) | 132 (25.1) | |
| Marital status | <0.001 | ||||
| Single | 179 | 19.4 | 161 (89.9) | 18 (10.1) | |
| Married/cohabiting | 513 | 55.6 | 484 (94.3) | 29 (5.7) | |
| Divorced/separated | 109 | 11.8 | 26 (23.9) | 83 (76.1) | |
| Widowed | 121 | 13.1 | 40 (33.1) | 81 (66.9) | |
| Comorbidities | <0.001 | ||||
| 0 | 68 | 7.4 | 26 (38.2) | 42 (61.8) | |
| 1 | 308 | 33.4 | 244 (79.2) | 64 (20.8) | |
| ≥2 | 546 | 59.2 | 441 (80.8) | 105 (19.2) | |
| Smoking habit | <0.001 | ||||
| No | 738 | 80.0 | 650 (88.1) | 88 (11.9) | |
| Yes | 184 | 20.0 | 61 (33.2) | 123 (66.8) | |
| Functional dependence for ADL 1 | <0.001 | ||||
| No | 673 | 73.0 | 603 (89.6) | 70 (10.4) | |
| Yes | 249 | 27.0 | 108 (43.4) | 141 (56.6) | |
| Polypharmacy | <0.001 | ||||
| No | 411 | 44.6 | 345 (83.9) | 66 (16.1) | |
| Yes | 511 | 55.4 | 366 (71.6) | 145 (28.4) | |
| Use of health services | <0.001 | ||||
| No | 477 | 51.7 | 405 (84.9) | 72 (15.1) | |
| Yes | 445 | 48.3 | 306 (68.8) | 139 (31.2) | |
| Self-reported weight loss | <0.001 | ||||
| No | 491 | 53.3 | 389 (79.2) | 102 (20.8) | |
| Yes | 431 | 46.8 | 322 (74.7) | 109 (25.3) | |
| Exhaustion | <0.001 | ||||
| No | 543 | 58.9 | 449 (82.7) | 94 (17.3) | |
| Yes | 379 | 41.1 | 262 (69.1) | 117 (30.9) | |
| Dynapenia | <0.001 | ||||
| No | 473 | 51.3 | 403 (85.2) | 70 (14.8) | |
| Yes | 449 | 48.7 | 308 (68.6) | 141 (31.4) | |
| Gait speed | <0.001 | ||||
| Normal | 541 | 58.7 | 469 (86.7) | 72 (13.3) | |
| Slow | 379 | 41.3 | 240 (63.3) | 139 (36.7) | |
| SARC-F | <0.001 | ||||
| No risk of sarcopenia | 724 | 78.5 | 636 (87.8) | 88 (12.2) | |
| Risk of sarcopenia | 198 | 21.5 | 75 (37.9) | 123 (62.1) | |
| SARC-CalF | <0.001 | ||||
| No risk of sarcopenia | 501 | 54.3 | 488 (97.4) | 13 (2.6) | |
| Risk of sarcopenia | 421 | 45.7 | 223 (53.0) | 198 (47.0) | |
Frequency of cancer types according to risk of sarcopenia and mortality
We found that the most frequent types of cancer in the group at risk of sarcopenia according to SARC-F were lungs and airways (40.9%), liver and bile ducts (21.7%), lymphomas and leukemias (12.1%), multiple myeloma (11.1%) and colorectal (8.6%). In addition, in the group at risk of sarcopenia by SARC-CalF we found lungs and airways (25.6%), liver and bile ducts (23.0%), multiple myeloma (15.7%), lymphomas and leukemia (15.4%) and colorectal (10.4%) were the most frequent types of cancer. Likewise, the most frequent types of cancer in the group that died were lungs and airways (26.1%), liver and bile ducts (23.2%), lymphomas and leukemias (16.6%), multiple myeloma (16.1%) and colorectal (9.0%) (Table 2 ). Table 2 Frequency of cancer types according to variables of interest Type of cancer Risk of sarcopenia by SARC-F (n=198) Risk of sarcopenia by SARC-CalF (n=421) Mortality n=211) n % n % n % Lungs and airways 81 40.9 108 25.6 55 26.1 Liver and bile ducts 43 21.7 97 23.0 49 23.2 Lymphomas and leukemias 24 12.1 65 15.4 35 16.6 Multiple myeloma 22 11.1 66 15.7 34 16.1 Stomach 10 5.1 29 6.9 18 8.5 Colorectal 17 8.6 44 10.4 19 9.0 Prostate 0 0 4 1.0 1 0.5 Skin 1 0.5 4 1.0 0 0 Urogenital/Others 0 0 4 1.0 0 0
Table 2
Frequency of cancer types according to variables of interest
| Type of cancer | Risk of sarcopenia by SARC-F (n=198) | Risk of sarcopenia by SARC-CalF (n=421) | Mortality n=211) | |||
| n | % | n | % | n | % | |
| Lungs and airways | 81 | 40.9 | 108 | 25.6 | 55 | 26.1 |
| Liver and bile ducts | 43 | 21.7 | 97 | 23.0 | 49 | 23.2 |
| Lymphomas and leukemias | 24 | 12.1 | 65 | 15.4 | 35 | 16.6 |
| Multiple myeloma | 22 | 11.1 | 66 | 15.7 | 34 | 16.1 |
| Stomach | 10 | 5.1 | 29 | 6.9 | 18 | 8.5 |
| Colorectal | 17 | 8.6 | 44 | 10.4 | 19 | 9.0 |
| Prostate | 0 | 0 | 4 | 1.0 | 1 | 0.5 |
| Skin | 1 | 0.5 | 4 | 1.0 | 0 | 0 |
| Urogenital/Others | 0 | 0 | 4 | 1.0 | 0 | 0 |
SARC-F and SARC-CalF scores as risk factors for mortality
In the crude Cox regression model, the risk of sarcopenia measured by SARC-F (cHR=4.31; 95%CI: 2.12–5.23) and SARC-CalF (cHR=2.98; 95%CI: 1.14–6.91) increased the risk of death in older men with cancer. Likewise, this association was maintained in the adjusted models (models 1, 2 and 3). In model 3, the association persisted between the risk of sarcopenia measured by SARC-F (aHR=2.51; 95%CI: 1.40–2.77) and SARC-CalF (aHR=2.04; 95%CI: 1.55–4.02) and a greater risk of mortality, respectively (Table 3 ). In addition, in the supplementary Cox regression model, we found that after including SARC-CalF as a confounder, SARC-F remained associated with an increased risk of mortality (aHR=2.11; 95%CI: 1.34–2.44). Likewise, after including SARC-F as a confounder, SARC-CalF remained associated but with a greater magnitude (aHR=3.98; 95%CI: 2.28–6.56) (Table S1). Table 3 Cox regression models to evaluate the role of SARC-F and SARC-CalF scores as risk factors for mortality in the study sample (n=922) Variables Crude Model 1 Model 2 Model 3 cHR 1 (95%CI) 2 aHR 2 (95%CI) 3 aHR 2 (95%CI) 3 aHR 2 (95%CI) 3 SARC-F (≥4) No risk of sarcopenia Reference Reference Reference Reference Risk of sarcopenia 4.31 (2.12–5.23) 3.73 (1.32–4.75) 2.34 (1.14–1.94) 2.51 (1.40–2.77) SARC-CalF (≥11) No risk of sarcopenia Reference Reference Reference Reference Risk of sarcopenia 2.98 (1.14–6.91) 2.76 (1.12–6.55) 1.34 (1.04–1.93) 2.04 (1.55–4.02) 1. Crude Hazard Ratio; 2. Adjusted Hazard Ratio; 3. 95% Confidence Interval; Model 1: Adjusted for age and marital status; Model 2: adjusted for age, marital status, comorbidities, smoking habit, and use of health services; Model 3: adjusted for age, marital status, comorbidities, smoking habit, use of health services, polypharmacy, self-reported weight loss, exhaustion and cancer types.
Table 3
Cox regression models to evaluate the role of SARC-F and SARC-CalF scores as risk factors for mortality in the study sample (n=922)
1. Crude Hazard Ratio; 2. Adjusted Hazard Ratio; 3. 95% Confidence Interval; Model 1: Adjusted for age and marital status; Model 2: adjusted for age, marital status, comorbidities, smoking habit, and use of health services; Model 3: adjusted for age, marital status, comorbidities, smoking habit, use of health services, polypharmacy, self-reported weight loss, exhaustion and cancer types.
| Variables | Crude | Model 1 | Model 2 | Model 3 |
| cHR 1 (95%CI) 2 | aHR 2 (95%CI) 3 | aHR 2 (95%CI) 3 | aHR 2 (95%CI) 3 | |
| SARC-F (≥4) | ||||
| No risk of sarcopenia | Reference | Reference | Reference | Reference |
| Risk of sarcopenia | 4.31 (2.12–5.23) | 3.73 (1.32–4.75) | 2.34 (1.14–1.94) | 2.51 (1.40–2.77) |
| SARC-CalF (≥11) | ||||
| No risk of sarcopenia | Reference | Reference | Reference | Reference |
| Risk of sarcopenia | 2.98 (1.14–6.91) | 2.76 (1.12–6.55) | 1.34 (1.04–1.93) | 2.04 (1.55–4.02) |
Diagnostic performance analysis of SARC-F and SARC-CalF scores as predictors of mortality
We performed a diagnostic performance analysis for both tools, finding a sensitivity of 58% (95%CI: 0.51–0.65) and a specificity of 89% (95%CI: 0.86–0.92) for the prediction of mortality for SARC-F. On the other hand, SARC-CalF had a sensitivity of 94% (95%CI: 0.90–0.97) and a specificity of 69% (95%CI: 0.65–0.72). In addition, the percentage of correct classification was 82% (95%CI: 0.80–0.85) and 74% (95%CI: 0.71–0.77) for SARC-F and SARC-CalF, respectively. Finally, the AUC calculated for the prediction of mortality was 0.71 (95%CI: 0.68–0.75) for SARC-F and 0.80 (95%CI: 0.78–0.82) for SARC-CalF (Table 4 ). Table 4 Diagnostic performance analysis of SARC-F and SARC-CalF scores related to the incidence of mortality. Variables Sensitivity (95%CI) 1 Specificity (95%CI) 1 PPV 1 (95%CI) 1 NPV 3 (95%CI) 1 Correctly classified Youden Index AUC 4 (95%CI) SARC-F (≥4) 0.58 (0.51–0.65) 0.89 (0.86–0.92) 0.62 (0.56–0.68) 0.88 (0.86–0.89) 0.82 (0.80–0.85) 0.48 0.71 (0.68–0.75) SARC-CalF (≥11) 0.94 (0.90–0.97) 0.69 (0.65–0.72) 0.47 (0.44–0.50) 0.97 (0.96–0.98) 0.74 (0.71–0.77) 0.62 0.80 (0.78–0.82) 1. 95% Confidence Interval; 2. Positive predictive value; 3. Negative predictive value; 4. Area under the curve.
Table 4
Diagnostic performance analysis of SARC-F and SARC-CalF scores related to the incidence of mortality.
1. 95% Confidence Interval; 2. Positive predictive value; 3. Negative predictive value; 4. Area under the curve.
| Variables | Sensitivity (95%CI) 1 | Specificity (95%CI) 1 | PPV 1 (95%CI) 1 | NPV 3 (95%CI) 1 | Correctly classified | Youden Index | AUC 4 (95%CI) |
| SARC-F (≥4) | 0.58 (0.51–0.65) | 0.89 (0.86–0.92) | 0.62 (0.56–0.68) | 0.88 (0.86–0.89) | 0.82 (0.80–0.85) | 0.48 | 0.71 (0.68–0.75) |
| SARC-CalF (≥11) | 0.94 (0.90–0.97) | 0.69 (0.65–0.72) | 0.47 (0.44–0.50) | 0.97 (0.96–0.98) | 0.74 (0.71–0.77) | 0.62 | 0.80 (0.78–0.82) |
Discussion
Main results
We found that approximately two out of 10 participants were at risk of sarcopenia defined by SARC-F and five out of 10 by SARC-CalF. In addition, two out of 10 died during follow-up. We found that SARC-F and SARC-CalF scores were associated with an increased risk of mortality, regardless of sociodemographic characteristics, history, and functional assessment. The latter tool presented the best diagnostic performance. Both scales are useful and rapid instruments to identify groups at higher risk of death.
Comparison with previous studies
We found only one study in older Chinese adults that evaluated the same association of interest, but this did not include cancer patients ( 24 ). This comparison of instruments was also evaluated in another Chinese study, but the incidence of mortality was not evaluated ( 11 ). Studies in the United States ( 13 ) and Brazil ( 14 ) evaluated the role of the SARC-F score as a risk factor for mortality, while previous studies in India ( 34 ), and Brazil ( 35 ) evaluated SARC-F in adults from 30 years and older adults with cancer. Likewise, the role of the SARC-CalF score as a risk factor for mortality was evaluated in a study in Brazil ( 36 ); however, this study included older adults without cancer. On the other hand, another study evaluated the role of the SARC-CalF score as a risk factor for mortality in adults with cancer in Brazil, but included participants from the age of 20, with only 52.9% being older adults ( 26 ).
We evaluated the risk of sarcopenia only in older men, while previous studies included both sexes, thus, reporting a higher frequency of risk of sarcopenia in men compared to women ( 11 , 13 ). This is important as a previous study described that adults with cancer and at risk of sarcopenia were more likely to die after major surgery, especially gastrointestinal operations ( 14 ). In addition, the risk of sarcopenia in older men may increase in the short term after receiving chemotherapy, radiotherapy or chemo-radiotherapy with curative intent ( 34 ), as well as predict lower survival ( 13 ).
On the other hand, SARC-CalF demonstrated a higher sensitivity and better diagnostic performance but a lower specificity than SARC-F for screening for the risk of sarcopenia. This finding is similar to that reported in a study conducted in China, which however, only included adults from 18 years of age with advanced cancer ( 11 ).
Interpretation of results
Among the most common changes that accompany aging, the decrease in muscle strength and mass can lead to adverse health outcomes and decreased survival ( 8 , 37 ). Muscle tissue changes as the person ages, producing cell reduction, decreased volume of the sarcoplasmic reticulum and calcium pumping, disorganization of the sarcomere spaces, less excitability in the muscle of the plasma membrane, greater storage of fat around and inside muscle cells, reduced time and force of muscle contraction, fewer motor neurons, and a reduced ability to regenerate nerve tissue ( 8 ). In addition, the gradual decline in testosterone concentrations leads to decreased muscle mass and muscle protein synthesis ( 38 ).
Sarcopenia is the pathological decline in the function and structure of muscle mass, being affected by aging, due to changes in muscle structures such as a decrease in the number and size of type 1 and 2 fibers, together with a loss of motor units. This leads to disorganization of the Z lines and myofilaments, in addition to an accumulation of lipofuscin inside these fibers ( 8 , 39 , 40 ). Sarcopenia is commonly accompanied by disability, functional impairment, decreased quality of life, and an increased risk of death ( 41 ).
One of the most common causes of sarcopenia is neoplastic diseases ( 8 ). The high prevalence of sarcopenia in older adults with cancer, in addition to the age-related risk, represents an even greater threat to this population, making them especially vulnerable. Therefore, after receiving treatments such as systemic chemotherapy, radiotherapy and surgery, they can develop lower tolerance to treatment, a higher risk of toxicity to chemotherapy treatments, a greater number of postoperative complications, a lower survival and, finally, higher mortality rates independently of age, sex, type and stage of cancer ( 41 , 42 ).
Clinical relevance of the findings
According to estimates by the World Health Organization (WHO) in Latin America and the Caribbean (LAC) there were more than 900 thousand new cases of cancer in older adults during 2020, being the fourth continent with the highest incidence of cancer in the older age group, behind Oceania, North America and Europe. Furthermore, more than 500,000 cancer deaths were recorded in 2020 in older Latin American adults alone ( 43 ).
It should be noted that while low- and middle-income countries only represent 51.8% of all older adults with cancer worldwide, they account for 65% of all deaths ( 43 ). In addition, cancer is the second leading cause of mortality in the Americas with 1.4 million deaths in 2018 and with an expected increase to 2.1 million by 2030 ( 44 , 45 ). This is due to demographic aging ( 46 ) and the high prevalence of cancer related to infectious diseases ( 47 ). Consequently, oncological pathology is an important and growing health challenge that requires preventive interventions, especially in the elderly.
Although efforts have been made to address this problem through universal insurance, there is still a significant gap in access to health services due to long waiting times, out-of-pocket costs, geographical and cultural barriers in LAC ( 48 ). Likewise, there is a limited supply of specialized care and oncology drugs, exacerbated by the COVID-19 pandemic, a context that overshadows predictions of cancer mortality in LAC ( 49 ).
Due to its pathophysiological correlation (including high levels of energy consumption due to inflammation, malnutrition and decreased physical activity) ( 8 ), the prevalence of sarcopenia associated with cachexia in older cancer patients is high and varies from 16% to 71% according to the type of cancer. There are numerous reports that identify the presence of sarcopenia as a poor prognostic factor for survival independent of cancer location ( 50 , 51 , 52 , 53 , 54 ). However, definitive diagnosis of sarcopenia requires the use of medical imaging technologies such as DXA, magnetic resonance imaging or computed tomography ( 9 ), which are difficult to access in developing countries and, therefore, little applied in the first level of care.
We found that both SARC-F and SARC-CalF have prognostic value (they have an AUC greater than 0.5), however, by themselves they are not good prognostic markers. It is necessary to develop future studies including both scores into pre-existing models or designing new ones considering them. In this sense, the use of rapid and low-cost diagnostic tools such as SARC-F and SARC-CalF is important for low- and middle-income countries such as those located in LAC due to their applicability in daily clinical practice, without requiring specialized centers, and good performance for the identification of individuals with a higher risk of death.
Limitations and strengths
This study has several limitations: 1) The 2-year follow-up time could have limited the recording of a greater number of events; 2) Only male participants were included, which means that the association in females may vary; 3) The study population was exclusively made up of retired sailors, and thus, the results of the study cannot be extrapolated to the general population; 4) We did not obtain a record of the dose of antineoplastic treatment received by the participants; 5) Data of relevant variables such as urea and creatinine for diagnosing renal failure were not available, which would serve to identify patients with a higher risk of adverse effects to antineoplastic treatment inducing a higher risk of mortality; 6) BMI, albumin and other variables related to nutrition in older adults with cancer were not available, since this is an important topic, we consider it as a limitation. Despite these limitations, this study evaluated the role of SARC-F and SARC-CalF scores as risk factors for mortality in older Peruvian adults with cancer. In addition, their usefulness as low-cost and easily accessible diagnostic tools allow the identification of groups of older adults with cancer at increased risk of mortality due to increased risk of sarcopenia.
Conclusions
The risk of sarcopenia assessed by SARC-F and SARC-CalF was associated with a higher risk of mortality in older men with cancer. To our knowledge, this is the first study in Latin America that evaluates this association. However, more studies are required to determine the association between the risk of sarcopenia and mortality in older adults of both sexes and with a longer follow-up time.
References
1 Siegel RL Miller KD Fuchs HE Jemal A Cancer Statistics, 2021 CA: A Cancer Journal for Clinicians 71 1 2021 7 33 33433946 10.3322/caac.21654
2 Hashim D Carioli G Malvezzi M Bertuccio P Waxman S Negri E Cancer mortality in the oldest old: a global overview Aging (Albany NY) 12 17 2020 16744 16758 10.18632/aging.103503 32882684 10.18632/aging.103503 PMC7521488
3 Pilleron S Sarfati D Janssen-Heijnen M Vignat J Ferlay J Bray F Global cancer incidence in older adults, 2012 and 2035: A population-based study International Journal of Cancer 144 1 2019 49 58 PubMed PMID: 29978474 10.1002/ijc.31664 29978474 10.1002/ijc.31664
4 He W Goodkind D Kowal P An Aging World: 2015. U.S. Census Bureau, International Population Reports 2016 U.S. Government Publishing Office Washington, DC Vol. P95/16-1
5 Zhang X Tang T Pang L Sharma SV Li R Nyitray AG Malnutrition and overall survival in older adults with cancer: A systematic review and meta-analysis Journal of Geriatric Oncology 10 6 2019 874 883 PubMed PMID: 30917937 10.1016/j.jgo.2019.03.002 30917937 10.1016/j.jgo.2019.03.002
6 Bleve A, Motta F, Durante B, Pandolfo C, Selmi C, Sica A. Immunosenescence, Inflammaging, and Frailty: Role of Myeloid Cells in Age-Related Diseases. Clin Rev Allergy Immunol. 2022;1–22; doi: 10.1007/s12016-021-08909-7 . PMC8760106 35031957
7 Peterson SJ Mozer M Differentiating Sarcopenia and Cachexia Among Patients With Cancer Nutrition in Clinical Practice 32 1 2017 30 39 PubMed PMID: 28124947 10.1177/0884533616680354 28124947 10.1177/0884533616680354
8 Colloca G Capua BD Bellieni A Cesari M Marzetti E Valentini V Muscoloskeletal aging, sarcopenia and cancer Journal of Geriatric Oncology 10 3 2019 504 509 PubMed PMID: 30551959 10.1016/j.jgo.2018.11.007 30551959 10.1016/j.jgo.2018.11.007
9 Cruz-Jentoft AJ Sayer AA Sarcopenia The Lancet 393 10191 2019 2636 2646 10.1016/S0140-6736(19)31138-9 10.1016/S0140-6736(19)31138-9 31171417
10 Cruz-Jentoft AJ Bahat G Bauer J Boirie Y Bruyère O Cederholm T Sarcopenia: revised European consensus on definition and diagnosis Age Ageing 48 1 2019 16 31 PubMed PMID: 30312372 10.1093/ageing/afy169 30312372 10.1093/ageing/afy169 PMC6322506
11 Fu X Tian Z Thapa S Sun H Wen S Xiong H Comparing SARC-F with SARC-CalF for screening sarcopenia in advanced cancer patients Clinical Nutrition 39 11 2020 3337 3345 PubMed PMID: 32143888 10.1016/j.clnu.2020.02.020 32143888 10.1016/j.clnu.2020.02.020
12 Mo Y-H Zhong J Dong X Su Y-D Deng W-Y Yao X-M Comparison of Three Screening Methods for Sarcopenia in Community-Dwelling Older Persons Journal of the American Medical Directors Association 22 4 2021 746 750 PubMed PMID: 32669238 , e1 10.1016/j.jamda.2020.05.041 32669238 10.1016/j.jamda.2020.05.041
13 Williams GR Al-Obaidi M Dai C Bhatia S Giri S SARC-F for screening of sarcopenia among older adults with cancer Cancer 127 9 2021 1469 1475 PubMed PMID: 33369894 10.1002/cncr.33395 33369894 10.1002/cncr.33395 PMC8085056
14 Behne TEG Dock-Nasimento DB Sierra JC Rodrigues HHNP Palauro ML Andreo FO Association between preoperative potential sarcopenia and survival of cancer patients undergoing major surgical procedures Rev Col Bras Cir 47 2020 e20202528 PubMed PMID: 32901706 10.1590/0100-6991e-20202528 32901706 10.1590/0100-6991e-20202528
15 Mainardi LG Borges TC Gomes TLN Pichard C Laviano A Pimentel GD Association of SARC-F and dissociation of SARC-F + calf circumference with comorbidities in older hospitalized cancer patients Experimental Gerontology 148 2021 111315 PubMed PMID: 33741454 10.1016/j.exger.2021.111315 33741454 10.1016/j.exger.2021.111315
16 Landi F Calvani R Cesari M Tosato M Martone AM Ortolani E Sarcopenia: An Overview on Current Definitions, Diagnosis and Treatment Curr Protein Pept Sci 19 7 2018 633 638 PubMed PMID: 28595526 10.2174/1389203718666170607113459 28595526 10.2174/1389203718666170607113459
17 Anjanappa M Corden M Green A Roberts D Hoskin P McWilliam A Sarcopenia in cancer: Risking more than muscle loss Tech Innov Patient Support Radiat Oncol 16 2020 50 57 PubMed PMID: 33385074 , PMCID 7769854 10.1016/j.tipsro.2020.10.001 33385074 10.1016/j.tipsro.2020.10.001 PMC7769854
18 Messina C Albano D Gitto S Tofanelli L Bazzocchi A Ulivieri FM Body composition with dual energy X-ray absorptiometry: from basics to new tools Quant Imaging Med Surg 10 8 2020 1687 1698 PubMed PMID: 32742961 , PMCID 7378094 10.21037/qims.2020.03.02 32742961 10.21037/qims.2020.03.02 PMC7378094
19 Lera L Albala C Ángel B Sánchez H Picrin Y Hormazabal MJ Predicción de la masa muscular apendicular esquelética basado en mediciones antropométricas en Adultos Mayores Chilenos Nutrición Hospitalaria 29 3 2014 611 617 PubMed PMID: 24559006 24559006 10.3305/nh.2014.29.3.7062
20 Huicho L Canseco FD Lema C Miranda JJ Lescano AG Incentivos para atraer y retener personal de salud de zonas rurales del Perú: un estudio cualitativo Cad Saude Publica 28 4 2012 729 739 PubMed PMID: 22488318 , PMCID 4074081 10.1590/S0102-311X2012000400012 22488318 10.1590/s0102-311x2012000400012 PMC4074081
21 Runzer-Colmenares FM Urrunaga-Pastor D Aguirre LG Reategui-Rivera CM Parodi JF Taype-Rondan A Frailty and vulnerability as predictors of radiotoxicity in older adults: A longitudinal study in Peru Medicina Clínica (English Edition) 149 8 2017 325 330 10.1016/j.medcle.2017.09.005 10.1016/j.medcli.2017.02.022 28400051
22 Runzer-Colmenares FM Urrunaga-Pastor D Roca-Moscoso MA De Noriega J Rosas-Carrasco O Parodi JF Frailty and Vulnerability as Predictors of Chemotherapy Toxicity in Older Adults: A Longitudinal Study in Peru J Nutr Health Aging 24 9 2020 966 972 PubMed PMID: 33155622 10.1007/s12603-020-1504-3 33155622 10.1007/s12603-020-1404-6
23 Runzer-Colmenares F Chambergo-Michilot D Espinoza-Gutiérrez G Corcuera-Ciudad R Patiño-Villena A Paima-Olivari R Type 2 Diabetes mellitus and chemotherapy toxicity in older adults with prostate cancer Rev Habanera Ciencias Médicas 18 1 2019 74 87
24 Yang M Jiang J Zeng Y Tang H Sarcopenia for predicting mortality among elderly nursing home residents: SARC-F versus SARC-CalF Medicine 98 7 2019 e14546 PubMed PMID: 30762799 , PMCID 6407983 10.1097/MD.0000000000014546 30762799 10.1097/MD.0000000000014546 PMC6407983
25 Malmstrom TK Morley JE SARC-F: A Simple Questionnaire to Rapidly Diagnose Sarcopenia Journal of the American Medical Directors Association 14 8 2013 531 532 PubMed PMID: 23810110 10.1016/j.jamda.2013.05.018 23810110 10.1016/j.jamda.2013.05.018
26 Souza V d Ribeiro T d S Marques R d A Petarli GB Pereira TSS Rocha JLM SARC-CalF-assessed risk of sarcopenia and associated factors in cancer patients Nutr Hosp 37 6 2020 1173 1178 PubMed PMID: 32960621 32960621 10.20960/nh.03158
27 Barbosa-Silva TG Menezes AMB Bielemann RM Malmstrom TK Gonzalez MC Enhancing SARC-F: Improving Sarcopenia Screening in the Clinical Practice Journal of the American Medical Directors Association 17 12 2016 1136 1141 PubMed PMID: 27650212 10.1016/j.jamda.2016.08.004 27650212 10.1016/j.jamda.2016.08.004
28 Rolfson DB Majumdar SR Tsuyuki RT Tahir A Rockwood K Validity and reliability of the Edmonton Frail Scale Age Ageing 35 5 2006 526 529 PubMed PMID: 16757522 , PMCID 5955195 10.1093/ageing/afl041 16757522 10.1093/ageing/afl041 PMC5955195
29 Theou O Brothers TD Mitnitski A Rockwood K Operationalization of Frailty Using Eight Commonly Used Scales and Comparison of Their Ability to Predict All-Cause Mortality Journal of the American Geriatrics Society 61 9 2013 1537 1551 PubMed PMID: 24028357 10.1111/jgs.12420 24028357 10.1111/jgs.12420
30 Menant JC Weber F Lo J Sturnieks DL Close JC Sachdev PS Strength measures are better than muscle mass measures in predicting health-related outcomes in older people: time to abandon the term sarcopenia? Osteoporos Int 28 1 2017 59 70 PubMed PMID: 27394415 10.1007/s00198-016-3691-7 27394415 10.1007/s00198-016-3691-7
31 Viktil KK Blix HS Moger TA Reikvam A Polypharmacy as commonly defined is an indicator of limited value in the assessment of drug-related problems Br J Clin Pharmacol 63 2 2007 187 195 PubMed PMID: 16939529 10.1111/j.1365-2125.2006.02744.x 16939529 10.1111/j.1365-2125.2006.02744.x PMC2000563
32 Wade DT Collin C The Barthel ADL Index: a standard measure of physical disability Int Disabil Stud 10 2 1988 64 67 PubMed PMID: 3042746 10.3109/09638288809164105 3042746 10.3109/09638288809164105
33 Fried LP Tangen CM Walston J Newman AB Hirsch C Gottdiener J Frailty in Older Adults: Evidence for a Phenotype The Journals of Gerontology: Series A 56 3 2001 M146 57 10.1093/gerona/56.3.M146 10.1093/gerona/56.3.m146 11253156
34 Chauhan NS Samuel SR Meenar N Saxena PP Keogh JWL Sarcopenia in male patients with head and neck cancer receiving chemoradiotherapy: a longitudinal pilot study PeerJ 8 2020 e8617 PubMed PMID: 32149024 , PMCID 7049254 10.7717/peerj.8617 32149024 10.7717/peerj.8617 PMC7049254
35 Gomes TLN Borges TC Pichard C Pimentel GD Correlation Between SARC-F Score and Ultrasound-Measured Thigh Muscle Thickness in Older Hospitalized Cancer Patients J Nutr Health Aging 24 10 2020 1128 1130 PubMed PMID: 33244572 10.1007/s12603-020-1524-z 33244572 10.1007/s12603-020-1524-z
36 Rodrigues FW Burgel CF Brito JE Baumgardt E de Araújo BE Silva FM SARC-CalF tool has no significant prognostic value in hospitalized patients: A prospective cohort study Nutrition in Clinical Practice 36 5 2021 1072 1079 PubMed PMID: 34245469 10.1002/ncp.10675 34245469 10.1002/ncp.10675
37 Marzetti E Calvani R Tosato M Cesari M Di Bari M Cherubini A Physical activity and exercise as countermeasures to physical frailty and sarcopenia Aging Clin Exp Res 29 1 2017 35 42 PubMed PMID: 28181204 10.1007/s40520-016-0705-4 10.1007/s40520-016-0705-4 28181204
38 Tenover JS Matsumoto AM Clifton DK Bremner WJ Age-Related Alterations in the Circadian Rhythms of Pulsatile Luteinizing Hormone and Testosterone Secretion in Healthy Men Journal of Gerontology 43 6 1988 M163 9 PubMed PMID: 3183306 10.1093/geronj/43.6.M163 3183306 10.1093/geronj/43.6.m163
39 Rosenberg IH Sarcopenia: Origins and Clinical Relevance The Journal of Nutrition 127 5 1997 990S 991S PubMed PMID: 9164280 10.1093/jn/127.5.990S 9164280 10.1093/jn/127.5.990S
40 Kamel HK Sarcopenia and Aging Nutrition Reviews 61 5 2003 157 167 PubMed PMID: 12822704 10.1301/nr.2003.may.157-167 12822704 10.1301/nr.2003.may.157-167
41 Williams GR Rier HN McDonald A Shachar SS Sarcopenia & Aging in Cancer J Geriatr Oncol 10 3 2019 374 377 PubMed PMID: 30343999 10.1016/j.jgo.2018.10.009 30343999 10.1016/j.jgo.2018.10.009 PMC6472991
42 Kazemi-Bajestani SMR Mazurak VC Baracos V Computed tomography-defined muscle and fat wasting are associated with cancer clinical outcomes Seminars in Cell & Developmental Biology 54 2016 2 10 10.1016/j.semcdb.2015.09.001 26343952 10.1016/j.semcdb.2015.09.001
43 Cancer today [Internet]. World Health Organization. [cited 2021 Dec 21]. Available from: http://gco.iarc.fr/today/home .
44 Cancer — PAHO/WHO Pan American Health Organization [Internet]. [cited 2022 Mar 14]. Available from: https://www.paho.org/en/topics/cancer .
45 Cayon A. PAHO/WHO Country cancer profiles 2020 [Internet]. Pan American Health Organization/World Health Organization. 2020 [cited 2022 Mar 14]. Available from: https://www3.paho.org/hq/index.php?option=com_content&view=article&id=15716:country-cancer-profiles-2020&Itemid=72576&lang=en .
46 Lence JJ Camacho R Cáncer y transición demográfica en América Latina y el Caribe Revista Cubana de Salud Pública 32 3 2006
47 Stewart BW, Wild CP. World Cancer Report 2014. International Agency for Research on Cancer (IARC); 2014. 39432694
48 Báscolo E Houghton N Del Riego A Lógicas de transformación de los sistemas de salud en América Latina y resultados en acceso y cobertura de salud Rev Panam Salud Publica 42 2018 e126 PubMed PMID: 31093154 , PMCID 6385643 10.26633/RPSP.2018.126 31093154 10.26633/RPSP.2018.126 PMC6385643
49 Barrios CH Werutsky G Mohar A Ferrigno AS Müller BG Bychkovsky BL Cancer control in Latin America and the Caribbean: recent advances and opportunities to move forward The Lancet Oncology 22 11 2021 e474 87 PubMed PMID: 34735817 10.1016/S1470-2045(21)00492-7 34735817 10.1016/S1470-2045(21)00492-7
50 Dohzono S Sasaoka R Takamatsu K Hoshino M Nakamura H Low paravertebral muscle mass in patients with bone metastases from lung cancer is associated with poor prognosis Supportive Care in Cancer 28 1 2020 389 395 PubMed PMID: 31055666 10.1007/s00520-019-04843-9 31055666 10.1007/s00520-019-04843-9
51 Chargi N Bril SI Emmelot-Vonk MH de Bree R Sarcopenia is a prognostic factor for overall survival in elderly patients with head-and-neck cancer Eur Arch Otorhinolaryngol 276 5 2019 1475 1486 PubMed PMID: 30830300 , PMCID 6458984 10.1007/s00405-019-05361-4 30830300 10.1007/s00405-019-05361-4 PMC6458984
52 Kuwada K Kuroda S Kikuchi S Yoshida R Nishizaki M Kagawa S Clinical Impact of Sarcopenia on Gastric Cancer Anticancer Research 39 5 2019 2241 2249 PubMed PMID: 31092415 10.21873/anticanres.13340 31092415 10.21873/anticanres.13340
53 Villaseñor A Ballard-Barbash R Baumgartner K Baumgartner R Bernstein L McTiernan A Prevalence and prognostic effect of sarcopenia in breast cancer survivors: the HEAL Study J Cancer Surviv 6 4 2012 398 406 PubMed PMID: 23054848 , PMCID 3747827 10.1007/s11764-012-0234-x 23054848 10.1007/s11764-012-0234-x PMC3747827
54 Stene GB Helbostad JL Amundsen T Sørhaug S Hjelde H Kaasa S Changes in skeletal muscle mass during palliative chemotherapy in patients with advanced lung cancer Acta Oncologica 54 3 2015 340 348 PubMed PMID: 25225010 10.3109/0284186X.2014.953259 25225010 10.3109/0284186X.2014.953259
Funding
Self-funded.
Conflict of interests
The authors declare no conflict of interest.
Authors' contributions
All authors participated in this research and contributed to the final version of the manuscript. Kimi Ururi-Cupi, Fiorella Oliva-Zapata, Leslie Salazar-Talla, Sofia Cuba-Ruiz, Diego Urrunaga-Pastor, Fernando M. Runzer-Colmenares, Jose F. Parodi participated in concept design and supervising the study. Diego Urrunaga-Pastor and Fernando M. Runzer-Colmenares conducted the statistical analysis. In addition, all the authors participated in manuscript writing, editing, final revision and have read and agreed on the submitted manuscript.
Electronic Supplementary Material
Supplementary material is available for this article at https://doi.org/10.1007/s12603-022-1844-2 and is accessible for authorized users.
Table S1. Supplementary Cox regression model including both SARC-F and SARC-CalF scores as risk factors for mortality in the study sample (n=922).
Acknowledgments
We thank the staff of the Center for Research on Aging — Faculty of Medicine of the University of San Martin de Porres, Peru and the staff of the Geriatric Service of the Naval Medical Center of Peru for the logistical support provided. We thank the Universidad Científica del Sur and Donna Pringle for English editing support.