Association of nutritional status and comorbidity with long-term survival among community-dwelling older males
BMC Geriatrics volume 23, Article number: 697 (2023)
Estimates of survival in the older can be of benefit in various facets, particularly in medical and individual decision-making. We aim to validate the value of a combination of nutrition status evaluation and comorbidity assessment in predicting long-term survival among community-dwelling older.
The Charlson Comorbidity Index (CCI) was applied for comprehensive evaluation of comorbidities. Participants were classified into CCI score ≤ 2 and ≥ 3 subgroups. Nutritional status was assessed by using Mini Nutritional Assessment-Short Form (MNA-SF) and Geriatric Nutritional Risk Index (GNRI) evaluations. Mortality rates and survival curves over a 5-year period were compared among subgroups classified by CCI and/or MNA-SF/GNRI evaluations.
A total of 1033 elderly male participants were enrolled in this study, with an average age of 79.44 ± 8.61 years. 108 deceased participants (10.5%) were identified during a follow-up of 5 years. Cox proportional hazards regression analysis showed that age, CCI, MNA-SF and GNRI were independent predictors of 5-year all-cause death in this cohort. Compared to those with normal nutrition status and CCI ≤ 2, the subgroup at risk of malnutrition and CCI ≥ 3 had a significantly higher 5-year all-cause mortality rate (HR = 4.671; 95% CI:2.613–8.351 for MNA-SF and HR = 7.268; 95% CI:3.401–15.530 for GNRI; P < 0.001 for both). Receiver operating characteristic curve analysis demonstrated that a combination of either MNA-SF or GNRI with CCI had significantly better performance than CCI, MNA-SF or GNRI alone in predicting all-cause death.
The combination of nutritional assessment (MNA-SF or GNRI) with CCI can significantly improve the predictive accuracy of long-term mortality outcomes among community-dwelling older males.
Old people have complex clinical identities and needs, that presented with more comorbidities, cognitive and functional impairments and higher mortality rates than young counterparts [1, 2]. Estimating mortality rates and expected survival among older individuals is valuable for individual decision-making, such as end-of-life decisions or treatment benefits consideration by patients and their family members, which helps to determine the level of care required for those with a fair chance of survival . Age itself is ultimately an important risk factor for death in older adults . Besides, factors related to old age, but not age per se, are reported to be predictive of mortality. These factors included socio-demographic background , lifestyles [6, 7], dietary factors , life satisfaction , metabolic health , comorbidities  and geriatric syndromes , etc.
Comorbidity affects outcomes of the older. The Charlson Comorbidity Index (CCI) is a simple and widely used index for the assessment of comorbidities, and it is the most commonly used and studied for predicting mortality . However, its role to predict long-term clinical outcomes in elderly patients is currently controversial [12,13,14,15]. Criticisms include the lack of consideration of disease severity and functional impairment associated with different diseases, as well as the omission of nutritional and social assessments . Similar to comorbidities, nutritional status are confirmed by numerous studies to have a strong association with long-term mortality in the elderly, and it is believed that good nutritional status is significantly correlated with better prognosis [17,18,19]. Poor nutritional status weakens the body’s immune system and increases the susceptibility to infection-related diseases. While comorbidity and nutritional status are distinct conditions, they are closely related [20,21,22]. Therefore, a combination of comorbidity assessment with nutritional evaluation may be more effective for predicting mortality. Lee S et al.  combined CCI with Geriatric Nutritional Risk Index (GNRI), and found that the GNRI significantly improved long term prognostic predictive accuracy when added to CCI in elderly diffuse large B cell lymphoma (DLBCL) patients. However, the significance of combining CCI with nutritional assessment in predicting long-term mortality among the general elderly population remains unclear.
In this study of a community-dwelling older cohort, we evaluated the nutritional status of enrolled subjects by using Mini Nutritional Assessment-Short Form (MNA-SF) and GNRI, as well as their comorbidities by the CCI score. The associations of comorbidities and malnutrition/nutritional risk with 5-year all-cause mortality were investigated.
Materials and methods
This is a retrospective study which is conducted with a cohort of community-dwelling older adults who underwent annual health assessments at the Chinese PLA General Hospital. Detailed clinical data, including survival outcomes, were recorded. We enrolled people with health records between January 2013 and December 2015. This study included male subjects only due to the shorter life expectancy for males compared to females (Available at https://population.un.org/wpp/). Inclusion criteria include male, age 65 to 95 with medical records and relevant laboratory test results. Accidental deaths not caused by diseases were excluded from the study. The Ethics Committee of the General Hospital of Chinese PLA approved this study (Ethics Approval Number: S2020-330-01). As a retrospective statistical analysis based on electronic health records, no individual patients were directly contacted for data collection, and all clinical data involving human participants were treated as confidential and de-identified. The review of medical records by the Ethics Committee of PLA General Hospital was approved, and individual consent for this retrospective analysis was waived.
Clinical data collection
We reviewed the medical records of all subjects, and collected clinical data at enrollment, including age, height, weight, diagnosis, dietary status, ability to perform daily activities, and mental and psychological status. Mortality data were collected. The time from the initial assessment to death or the last day of follow-up was obtained. Blood test results, including hemoglobin, serum total protein, serum albumin, serum creatinine, lipoprotein, triglyceride, and cholesterol levels, were collected using the medical record management system at the time of enrollment. Blood routine test is performed by XN3000 automatic blood analyzer (Sysmex XN3000, Sysmex Corporation, Kobe, Japan). Biochemical indicators were determined by an electrochemiluminescence immunoassay (Cobas e601, Roche Diagnostics Ltd., Switzerland). Reagents were supplied by equipment manufacturers.
CCI, MNA-SF and GNRI scores
The CCI scale  was used to calculate the comorbidities of the subjects at enrollment. MNA-SF  and GNRI  scales were used to evaluate the nutritional status of the subjects. The MNA-SF has three classifications: 0–7 points: malnourished; 8–11 points: at risk of malnutrition; or 12–14 points: well-nourished . Participants were subgrouped according to MNA-SF score, into “well-nourished” and “at risk/malnourished”, the latter included those who were at risk of malnutrition and malnourished. The GNRI was calculated from body weight (BW) and serum albumin using the following formula: 14.89 × albumin (g/dl) + 41.7 × (BW/ideal BW). BW/ideal BW was defined as 1 when the patient’s BW exceeded the ideal BW. All patients were categorized into the following four groups according to the GNRI value: no risk (> 98), low risk (92–98), moderate risk (82 to < 92) and major risk (< 82) . Subjects were further divided based on GNRI value, into “no risk” and “nutrition-related risk” subgroups, the latter encompassed low risk, moderate risk and major risk subjects.
The endpoint of the study was death, and the 5-year mortality was defined as the interval from the subjects’ enrollment to the date of all-cause death or the end of the 5-year follow-up period. Continuous variables were expressed as x ± s for variables of normal distribution and median (interquartile range) for variables of skewness distribution. Analysis of variance F test was used for comparison between groups, and Mann-Whitney U test was used for comparison between groups of samples with uneven variance. Categorical data are expressed as numbers and percentages, and the groups were compared using the chi-squared test. Diagnostic performance was assessed by multivariate receiver operating characteristic (ROC) analysis. The area under the receiver operating characteristic curve (AUROC) of the MNA-SF + CCI and GNRI + CCI were compared to the AUROC of the CCI or MNA-SF/GNRI alone using DeLong’s method. The optimal cut-off values of the CCI were identified by ROC analysis using Youden’s index. Survival curves for each group were estimated using the Kaplan–Meier curves and compared by the log-rank test. Cox proportional hazards regression was used to analyze the correlation between variables and 5-year all-cause mortality risk. Two-sided P value < 0.05 was considered as statistically significant. GraphPad Prism 7.0 (GraphPad Software, La Jolla, CA, USA,) and SPSS 22.0 (IBM Corp., Armonk, NY, USA) were used for statistical analysis. R-language was used to analyze the additive interaction and multiplicative interaction between CCI and nutritional status. The evaluation indexes of additive interaction included relative excess risk of interaction(RERI),attributable proportion of interaction (API), synergyindex (S). If there is no additive interaction between the two risk factors, then the confidence interval for RERI and API should contain 0, and the confidence interval for S should contain 1.
A total of 1221 male subjects (≥ 65 years) were available in this study. Among them, 82 participants were lost to follow-up, and 106 participants had incomplete outcome data. Finally, a total of 1033 elderly men aged 79.44 ± 8.61 years were enrolled. The characteristics of the subjects at the time of enrollment are summarized in Table 1. The median of CCI score of the 1033 study participants was 2 (range, 0–9). 342 (33.1%) subjects presented with CCI score ≥ 3. According to MNA-SF score, 838 (81.1%) subjects were indicated at normal nutrition (well-nourished) in this cohort, while the left 195 (18.9%) subjects were at risk/malnourished. Based on the GNRI score, we identified 945 (95.7%) cases who were at no nutrition-related risk and 88 (4.3%) cases who were at nutrition-related risk. Considering the possibility of attrition bias, we also investigated the relationship between single study variables (CCI, MNA-SF,GNRI) and loss to follow-up. The results showed that these main study variables did not show significant differences between the enrolled and the lost population (Supplementary Table 1).
Follow-up and survival analysis
During a follow-up of 5 years, all-cause mortality was ascertained and 108 deceased participants (10.5%) were identified. A comparison of baseline characteristics between the survival and dead groups were performed. Univariable predictors of mortality included age, serum total protein, albumin, hemoglobin, BUN/albumin (BAR), CCI, MNA-SF and GNRI (Table 1). When conducting multivariate Cox proportional hazards analysis, we merged certain anthropometric parameters or composite indicators that reflect organ function. These parameters included age, serum lipids (total cholesterol and triglycerides), kidney function (creatinine), liver enzymes indicating hepatocyte damage (alanine aminotransferase), enzymes indicating cholestasis (total bilirubin), blood glucose, uric acid, serum iron, nutritional status (MNA-SF and GNRI), comorbidities (CCI) and BUN/albumin (BAR). The results of the multivariate analysis showed that age, CCI and nutritional status were independent predictors of 5-year all-cause mortality. Malnutrition (at risk/malnourished versus no nutrition-related risk) was associated with significantly increased risk for mortality as assessed by MNA-SF (HR = 0.859; 95%CI:0.742–0.995; P = 0.043) (Table 2, Model 1) or GNRI (HR = 0.981; 95%CI:0.964–0.998; P = 0.033) (Table 2, Model 2).
Prognostic stratification based on CCI and nutritional status
The ROC analysis showed that the optimal cut-off value of CCI was 2.5 (sensitivity 58.3%, specificity 69.8%). Participants were then divided into two subgroups for further analysis: CCI ≤ 2 (low CCI score) and CCI ≥ 3 (high CCI score). Based on the combination of CCI and MNA-SF, all subjects were classified into four subgroups: CCI ≤ 2 with normal nutrition (L-NN); CCI ≤ 2 with at risk/malnourished (L-ARM); CCI ≥ 3 with normal nutrition (H-NN); CCI ≥ 3 with at risk/malnourished (H-ARM). The 5-year mortality rates of these groups were 5.3%, 12.3%, 16.9%, and 23.5%, respectively. Similarly, according to the combination of CCI and GNRI, all subjects were divided into four groups: CCI ≤ 2 with no nutrition-related risk (L-NNR); CCI ≤ 2 with nutrition-related risk (L-NR); CCI ≥ 3 with No nutrition-related risk (H-NNR); CCI ≥ 3 with nutrition-related risk (H-NR). The 5-year mortality rates for each group were 6.4%, 25.0%, 17.4%, and 36.4%, respectively.
In the Cox proportional hazards regression analyses, L-NN and L-NNR were used as the reference group. Compared with subgroup in normal nutrition status and CCI ≤ 2, the 5-year all-cause mortality rate was significantly increased in those at risk of malnutrition and CCI ≥ 3 (HR = 4.671; 95% CI:2.613–8.351 for MNA-SF and HR = 7.268; 95% CI:3.401–15.530 for GNRI; P < 0.001 for both) (Table 3). The Kaplan-Meier curves (Fig. 1) indicate significant differences in survival between the CCI ≤ 2 with normal nutrition group and the CCI ≥ 3 with malnutrition group. Considering the possible interaction between CCI and nutritional status on long-term survival, the additive and multiplicative models were applied to analyze the interaction effect of them. The results showed that although the risk of malnutrition increased with higher comorbidities scores (Supplementary Table 2), there was no additive interaction and multiplicative interaction between CCI and MNA-SF/GNRI (Supplementary Tables 3 and Supplementary Table 4).
Predictive value of CCI and nutritional status in all-cause mortality
We performed ROC curve analysis to compare the predictive accuracy of different measures. As shown in Fig. 2, when compared with single index, the combination of CCI and MNA-SF showed significantly better performance (AUC, 0.716; 95% CI: 0.687–0.743) than CCI alone (AUC, 0.695; 95% CI: 0.666–0.723), and MNA-SF alone (AUC, 0.594; 95% CI: 0.563–0.624) in predicting all-cause death (all DeLong’ test P for difference in AUC < 0.05) (Fig. 2a). The Z value for each pairwise AUC was 2.666(CCI vs MNA-SF),2.081(CCI vs CCI + MNA-SF),3.897(MNA-SF vs CCI + MNA-SF). The standard error for each pairwise AUC was 0.0379(CCI vs MNA-SF),0.00998(CCI vs CCI + MNA-SF),0.0313(MNA-SF vs CCI + MNA-SF) (Fig. 2a). Similarly, the combination of CCI and GNRI showed significantly better performance (AUC, 0.740; 95% CI: 0.712–0.767) than CCI alone (AUC, 0.688; 95% CI: 0.658–0.717), and GNRI alone (AUC, 0.664; 95% CI: 0.634–0.694) (all DeLong’ test P for difference in AUC < 0.005), although there is no difference in the prediction accuracy between CCI and GNRI (P = 0.5421) (Fig. 2b). The Z value for each pairwise AUC was 0.610(CCI vs GNRI),2.825(CCI vs CCI + GNRI),2.952(GNRI vs CCI + GNRI).The standard error for each pairwise AUC was 0.0389(CCI vs GNRI),0.0184(CCI vs CCI + GNRI),0.0256(GNRI vs CCI + GNRI) (Fig. 2b). When the combination of CCI and GNRI is compared with the combination of CCI and MNA-SF, there is no obvious difference in their performance (P = 0.0647) (Fig. 2c). The Z value was 1.847 and the standard error was 0.0174 for CCI + GNRI vs CCI + MNA-SF (Fig. 2c).
Numerous studies have shown that the mortality of community-dwelling older adults is influenced to multiple factors, including comorbidities , community activities , cognitive function , nutritional status [31, 32], and others. In this study, the results of Cox proportional risk regression indicated that age, CCI, MNA-SF or GNRI were independent factors affecting the 5-year all-cause mortality of the elderly. While chronological age cannot be changed, we can make a difference in geriatric practice by controlling comorbidities and adjusting nutrition to benefit the older. The association of comorbidities with mortality in the elderly were consistently demonstrated in many studies [33,34,35]. CCI has been widely used to predict short-term clinical outcomes and been validated in various populations [36,37,38,39]. Some studies also found that CCI could help to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit, trauma, and cancer patients [40,41,42,43,44]. In our study, the CCI score proved to be an independent predictor of 5-year mortality (HR = 1.269, 95%CI: 1.109–1.452, P = 0.001), suggesting that CCI has predictive value for long-term mortality in the general elderly population. However, the role of CCI for long-term mortality prediction in the older is still controversial. Gianluca Testa et al. found that CCI does not predict long-term mortality in elderly patients with chronic heart failure , while Frenkel et al. indicated that the CCI independently predicts 3-month, 1-year, and 5-year mortality in acutely ill hospitalized elderly adults . The fact that CCI did not include risk factors, such as functional assessments, social and nutritional status, might decrease its predictive value. Therefore, some studies have attempted to combine CCI with other indicators to compensate for its deficiency.
Malnourished older adults are at high risk of mortality. Nutritional status has been reported as a predictor of complications and outcomes of various entities [45,46,47]. MNA-SF  and GNRI  are commonly used nutritional screening tools for the elderly. Both of them are helpful to predict the long-term prognosis of elderly patients and a poor nutritional status is associated with an elevated risk of all-cause mortality [48,49,50,51]. MNA-SF involves subjective questions and GNRI relies on objective indicators, making them complementary in assessing nutritional status. Therefore, we used these two methods to assess nutritional status in the same population to compare the difference of these two nutritional assessment methods in predicting long-term mortality in the elderly. As expected, we found that both MNA-SF and GNRI were independent predictors of 5-year mortality in this cohort, in which the elderly who exhibited lower MNA-SF or GNRI score had a higher risk of death within 5 years. It has been reported that MNA-SF has a greater tendency to classify patients as malnourished than GNRI does [52, 53]. This was corroborated by the results in this study, where the diagnostic rates of (being at risk of) malnutrition for MNA-SF and GNRI were 18.9% and 4.3%, respectively.
To our knowledge, the present study is the first to combine nutritional screening tools and CCI to predict long-term survival in community-dwelling older adults. One of the significant highlights of this study is that all subjects were divided into four groups based on their malnutrition risk and CCI scores. The 5-year mortality risk was significantly higher for older adults with CCI ≥ 3 and poor nutrition (both MNA-SF and GNRI) compared to those with CCI ≤ 2 and normal nutrition. This suggests a promising role of this combination of evaluation in identifying high risk individuals with poor long-term outcomes in community-dwelling older adults, and consequencely, effective interventions on these subgroups will improve their outcomes. Besides CCI scores, other indicators combined with nutritional status can also predict mortality outcomes in older adults. A cohort study in Singapore involving 2804 community-dwelling adults discovered that poor nutrition combined with prefrailty/frailty was associated with substantially increased prevalence and incidence of poor functional and mortality outcomes . Handgrip strength, an objective marker of frailty, has been shown to independently predict adverse health outcomes and mortality in older populations and different clinical settings . In the future, we expect that more easy-to-get indicators will be reported for predicting mortality in the elderly population.
It should be noted that although there is a significant relationship in the hazard ratio between higher CCI score and mortality in older adults , the optimal cut-off value for CCI is still unclear. In our study, based on ROC analysis, we defined high-risk comorbidities as having a CCI of ≥ 3 and low-risk comorbidities as having a CCI of ≤ 2, which is consistent with previous studies [21, 56]. Nonetheless, further research is needed to determine the optimal cut-off value for CCI.
There are several limitations to consider in this study. Firstly, this was a retrospective, single-center study involving community-dwelling older adults. External validation through cohort studies conducted in different settings is necessary to confirm the robustness of our findings. Secondly, although the CCI, MNA-SF and GNRI are continuous variables, they were transformed into categorical variables when combined. Therefore, further clarification is needed to determine the optimal cut-off values for CCI, MNA-SF and GNRI. Thirdly, the study population consisted of elderly male living in the communities with high-level healthcare services, which may not fully represent the general elderly population. However, given the average life expectancy in China being 74.7 years for males and 80.5 years for females , studing factors related to the long-term survival in elderly male subgroups can still be meaningful. Lastly, more detailed information about prognostic outcomes might be provided if periodical assessment of CCI and nutrition scores were obtained.
In summary, our findings suggest that CCI, MNA-SF and GNRI are independent factors affecting 5-year all-cause mortality in community-dwelling older males. Combining nutritional assessment (using either MNA-SF or GNRI) with CCI significantly improves the predictive accuracy of long-term mortality outcomes in this cohort. Interventional studies that investigate the improvement of nutritional status could potentially lead to favorable mortality outcomes in the elderly. Multi-centered, large scale and prospective studies are needed to validate this conclusion.
The combination of nutritional assessment (MNA-SF or GNRI) with CCI can significantly improve the predictive accuracy of long-term mortality outcomes among community-dwelling older males.
The datasets generated for this research are available from the corresponding author on reasonable request.
area under the receiver operating characteristic curve
blood urea nitrogen
charlson comorbidity index
diffuse large B cell lymphoma
geriatric nutritional risk index
mini nutritional assessment-short form
receiver operating characteristic
Singal BM, Hedges JR, Rousseau EW, Sanders AB, Berstein E, McNamara RM, Hogan TM. Geriatric patient emergency visits. Part I: Comparison of visits by geriatric and younger patients. Ann Emerg Med. 1992;21(7):802-7. https://doi.org/10.1016/s0196-0644(05)81025-x. PubMed PMID: 1610036.
Aminzadeh F, Dalziel WB. Older adults in the emergency department: a systematic review of patterns of use, adverse outcomes, and effectiveness of interventions. Ann Emerg Med. 2002;39(3):238–47. https://doi.org/10.1067/mem.2002.121523. PubMed PMID: 11867975.
Frenkel WJ, Jongerius EJ, Mandjes-van Uitert MJ, van Munster BC, de Rooij SE. Validation of the Charlson Comorbidity Index in acutely hospitalized elderly adults: a prospective cohort study. J Am Geriatr Soc. 2014;62(2):342–6. https://doi.org/10.1111/jgs.12635. PubMed PMID: 24521366.
DALYs GBD, Collaborators H. Global, regional, and national disability-adjusted life-years (DALYs) for 359 Diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet. 2018;392(10159):1859–922. https://doi.org/10.1016/S0140-6736(18)32335-3. PubMed PMID: 30415748; PMCID: PMC6252083.
Meara ER, Richards S, Cutler DM. The gap gets bigger: changes in mortality and life expectancy, by education, 1981–2000. Health Aff (Millwood). 2008;27(2):350–60. https://doi.org/10.1377/hlthaff.27.2.350. PubMed PMID: 18332489; PMCID: PMC2366041.
Gellert C, Schottker B, Brenner H. Smoking and all-cause mortality in older people: systematic review and meta-analysis. Arch Intern Med. 2012;172(11):837–44. https://doi.org/10.1001/archinternmed.2012.1397. PubMed PMID: 22688992.
Li Y, Pan A, Wang DD, Liu X, Dhana K, Franco OH, Kaptoge S, Di Angelantonio E, Stampfer M, Willett WC, Hu FB. Impact of healthy lifestyle factors on life expectancies in the US Population. Circulation. 2018;138(4):345–55. PubMed PMID: 29712712; PMCID: PMC6207481.
Schwingshackl L, Schwedhelm C, Hoffmann G, Lampousi AM, Knuppel S, Iqbal K, Bechthold A, Schlesinger S, Boeing H. Food groups and risk of all-cause mortality: a systematic review and meta-analysis of prospective studies. Am J Clin Nutr. 2017;105(6):1462–73. https://doi.org/10.3945/ajcn.117.153148. PubMed PMID: 28446499.
Boehm JK, Winning A, Segerstrom S, Kubzansky LD. Variability modifies life satisfaction’s Association with Mortality Risk in older adults. Psychol Sci. 2015;26(7):1063–70. https://doi.org/10.1177/0956797615581491. PubMed PMID: 26048888; PMCID: PMC4544695.
Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309(1):71–82. https://doi.org/10.1001/jama.2012.113905. PubMed PMID: 23280227; PMCID: PMC4855514.
Huang CC, Lee JD, Yang DC, Shih HI, Sun CY, Chang CM. Associations between geriatric syndromes and Mortality in Community-Dwelling Elderly: results of a National Longitudinal Study in Taiwan. J Am Med Dir Assoc. 2017;18(3):246–51. https://doi.org/10.1016/j.jamda.2016.09.017. PubMed PMID: 27838338.
Sharabiani MT, Aylin P, Bottle A. Systematic review of comorbidity indices for administrative data. Med Care. 2012;50(12):1109–18. https://doi.org/10.1097/MLR.0b013e31825f64d0. PubMed PMID: 22929993.
Bravo G, Dubois MF, Hebert R, De Wals P, Messier L. A prospective evaluation of the Charlson Comorbidity Index for use in long-term care patients. J Am Geriatr Soc. 2002;50(4):740-5. https://doi.org/10.1046/j.1532-5415.2002.50172.x. PubMed PMID: 11982678.
Testa G, Cacciatore F, Galizia G, Della-Morte D, Mazzella F, Russo S, Ferrara N, Rengo F, Abete P. Charlson Comorbidity Index does not predict long-term mortality in elderly subjects with chronic Heart Failure. Age Ageing. 2009;38(6):734–40. https://doi.org/10.1093/ageing/afp165. PubMed PMID: 19755712.
Sinvani L, Kuriakose R, Tariq S, Kozikowski A, Patel V, Smilios C, Akerman M, Qiu G, Makhnevich A, Cohen J, Wolf-Klein G, Pekmezaris R. Using Charlson Comorbidity Index to Predict Short-Term Clinical outcomes in hospitalized older adults. J Healthc Qual. 2019;41(3):146–53. doi: 10.1097/JHQ.0000000000000153. PubMed PMID: 31094947.
Chan TC, Luk JK, Chu LW, Chan FH. Validation study of Charlson Comorbidity Index in predicting mortality in Chinese older adults. Geriatr Gerontol Int. 2014;14(2):452–7. https://doi.org/10.1111/ggi.12129. PubMed PMID: 24020396.
Efthymiou A, Hersberger L, Reber E, Schonenberger KA, Kagi-Braun N, Tribolet P, Mueller B, Schuetz P, Stanga Z, group Es. Nutritional risk is a predictor for long-term mortality: 5-Year follow-up of the EFFORT trial. Clin Nutr. 2021;40(4):1546–54. https://doi.org/10.1016/j.clnu.2021.02.032. PubMed PMID: 33743290.
Zhang X, Zhang X, Zhu Y, Tao J, Zhang Z, Zhang Y, Wang Y, Ke Y, Ren C, Xu J. Predictive value of Nutritional Risk Screening 2002 and Mini Nutritional Assessment Short Form in Mortality in Chinese hospitalized geriatric patients. Clin Interv Aging. 2020;15:441–9. https://doi.org/10.2147/CIA.S244910. PubMed PMID: 32256059; PMCID: PMC7093094.
Lai KY, Wu TH, Liu CS, Lin CH, Lin CC, Lai MM, Lin WY. Body mass index and albumin levels are prognostic factors for long-term survival in elders with limited performance status. Aging. 2020;12(2):1104–13. https://doi.org/10.18632/aging.102642. PubMed PMID: 31945744; PMCID: PMC7053589.
Kubo Y, Tanaka K, Yamasaki M, Yamashita K, Makino T, Saito T, Yamamoto K, Takahashi T, Kurokawa Y, Motoori M, Kimura Y, Nakajima K, Eguchi H, Doki Y. Influences of the Charlson Comorbidity Index and Nutrition Status on Prognosis after Esophageal Cancer Surgery. Ann Surg Oncol. 2021;28(12):7173–82. https://doi.org/10.1245/s10434-021-09779-1. PubMed PMID: 33835302.
Iwai N, Dohi O, Naito Y, Inada Y, Fukui A, Takayama S, Ogita K, Terasaki K, Nakano T, Ueda T, Okayama T, Yoshida N, Katada K, Kamada K, Uchiyama K, Ishikawa T, Handa O, Takagi T, Konishi H, Yagi N, Itoh Y. Impact of the Charlson comorbidity index and prognostic nutritional index on prognosis in patients with early gastric cancer after endoscopic submucosal dissection. Dig Endosc. 2018;30(5):616–23. https://doi.org/10.1111/den.13051. PubMed PMID: 29532961.
Chen B, Liu W, Chen Y, She Q, Li M, Zhao H, Zhao W, Peng Z, Wu J. Effect of Poor Nutritional Status and comorbidities on the occurrence and outcome of Pneumonia in Elderly adults. Front Med (Lausanne). 2021;8:719530. PubMed PMID: 34712677; PMCID: PMC8547609.
Lee S, Fujita K, Morishita T, Negoro E, Oiwa K, Tsukasaki H, Yamamura O, Ueda T, Yamauchi T. Prognostic utility of a geriatric nutritional risk index in combination with a comorbidity index in elderly patients with diffuse large B cell Lymphoma. Br J Haematol. 2021;192(1):100–9. https://doi.org/10.1111/bjh.16743. PubMed PMID: 32410224.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. https://doi.org/10.1016/0021-9681(87. )90171-8. PubMed PMID: 3558716.
Rubenstein LZ, Harker JO, Salva A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci. 2001;56(6):M366–72. https://doi.org/10.1093/gerona/56.6.m366. PubMed PMID: 11382797.
Bouillanne O, Morineau G, Dupont C, Coulombel I, Vincent JP, Nicolis I, Benazeth S, Cynober L, Aussel C. Geriatric nutritional risk index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82(4):777–83. https://doi.org/10.1093/ajcn/82.4.777. PubMed PMID: 16210706.
Kaiser MJ, Bauer JM, Ramsch C, Uter W, Guigoz Y, Cederholm T, Thomas DR, Anthony P, Charlton KE, Maggio M, Tsai AC, Grathwohl D, Vellas B, Sieber CC. Group MN-I. validation of the Mini Nutritional Assessment short-form (MNA-SF): a practical tool for identification of nutritional status. J Nutr Health Aging. 2009;13(9):782–8. https://doi.org/10.1007/s12603-009-0214-7. PubMed PMID: 19812868.
St John PD, Tyas SL, Menec V, Tate R. Multimorbidity, disability, and mortality in community-dwelling older adults. Can Fam Physician. 2014;60(5):e272–80. PubMed PMID: 24829022; PMCID: PMC4020665.
Okura M, Ogita M, Yamamoto M, Nakai T, Numata T, Arai H. Community activities predict disability and mortality in community-dwelling older adults. Geriatr Gerontol Int. 2018;18(7):1114–24. https://doi.org/10.1111/ggi.13315. PubMed PMID: 29603568.
Lv X, Li W, Ma Y, Chen H, Zeng Y, Yu X, Hofman A, Wang H. Cognitive decline and mortality among community-dwelling Chinese older people. BMC Med. 2019;17(1):63. https://doi.org/10.1186/s12916-019-1295-8. PubMed PMID: 30871536.
Cardoso AS, Xavier MO, Dos Santos Costa C, Tomasi E, Cesar JA, Gonzalez MC, Domingues MR, Barbosa-Silva TG, Bielemann RM. Body mass index and mortality among community-dwelling elderly of Southern Brazil. Prev Med. 2020;139:106173. https://doi.org/10.1016/j.ypmed.2020.106173. PubMed PMID: 32592797.
Wu CY, Hu HY, Huang N, Chou YC, Li CP, Chou YJ. Albumin levels and cause-specific mortality in community-dwelling older adults. Prev Med. 2018;112:145–51. https://doi.org/10.1016/j.ypmed.2018.04.015. PubMed PMID: 29649489.
Xing F, Luo R, Chen W, Zhou X. The risk-adjusted Charlson comorbidity index as a new predictor of one-year mortality rate in elderly Chinese patients who underwent hip fracture Surgery. Orthop Traumatol Surg Res. 2021;107(3):102860. https://doi.org/10.1016/j.otsr.2021.102860. PubMed PMID: 33609760.
Formiga F, Moreno-Gonzalez R, Chivite D, Franco J, Montero A, Corbella X. High comorbidity, measured by the Charlson Comorbidity Index, associates with higher 1-year mortality risks in elderly patients experiencing a first acute Heart Failure hospitalization. Aging Clin Exp Res. 2018;30(8):927–33. https://doi.org/10.1007/s40520-017-0853-1. PubMed PMID: 29124524.
Yousufuddin M, Shultz J, Doyle T, Rehman H, Murad MH. Incremental risk of long-term mortality with increased burden of comorbidity in hospitalized patients with Pneumonia. Eur J Intern Med. 2018;55:23–7. https://doi.org/10.1016/j.ejim.2018.05.003. PubMed PMID: 29754939.
Walter LC, Brand RJ, Counsell SR, Palmer RM, Landefeld CS, Fortinsky RH, Covinsky KE. Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization. JAMA. 2001;285(23):2987–94. https://doi.org/10.1001/jama.285.23.2987. PubMed PMID: 11410097.
Imam Z, Odish F, Gill I, O’Connor D, Armstrong J, Vanood A, Ibironke O, Hanna A, Ranski A, Halalau A. Older age and comorbidity are Independent mortality predictors in a large cohort of 1305 COVID-19 patients in Michigan, United States. J Intern Med. 2020;288(4):469–76. https://doi.org/10.1111/joim.13119. PubMed PMID: 32498135.
Dhakal P, Shostrom V, Al-Kadhimi ZS, Maness LJ, Gundabolu K, Bhatt VR. Usefulness of Charlson Comorbidity Index to predict early mortality and overall survival in older patients with Acute Myeloid Leukemia. Clin Lymphoma Myeloma Leuk. 2020;20(12):804–12e8. https://doi.org/10.1016/j.clml.2020.07.002. PubMed PMID: 32739312.
Kang HW, Kim SM, Kim WT, Yun SJ, Lee SC, Kim WJ, Hwang EC, Kang SH, Hong SH, Chung J, Kwon TG, Kim HH, Kwak C, Byun SS, Kim YJ, Group K. The age-adjusted Charlson comorbidity index as a predictor of overall survival of surgically treated non-metastatic clear cell renal cell carcinoma. J Cancer Res Clin Oncol. 2020;146(1):187–96. https://doi.org/10.1007/s00432-019-03042-7. PubMed PMID: 31606760.
Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson Comorbidity Index: a critical review of Clinimetric Properties. Psychother Psychosom. 2022;91(1):8–35. doi: 10.1159/000521288. PubMed PMID: 34991091.
Marti S, Munoz X, Rios J, Morell F, Ferrer J. Body weight and comorbidity predict mortality in COPD patients treated with oxygen therapy. Eur Respir J. 2006;27(4):689–96. https://doi.org/10.1183/09031936.06.00076405. PubMed PMID: 16585077.
Olsson T, Terent A, Lind L. Charlson Comorbidity Index can add prognostic information to Rapid Emergency Medicine Score as a predictor of long-term mortality. Eur J Emerg Med. 2005;12(5):220–4. https://doi.org/10.1097/00063110-200510000-00004. PubMed PMID: 16175058.
Torres OH, Munoz J, Ruiz D, Ris J, Gich I, Coma E, Gurgui M, Vazquez G. Outcome predictors of pneumonia in elderly patients: importance of functional assessment. J Am Geriatr Soc. 2004;52(10):1603-9. https://doi.org/10.1111/j.1532-5415.2004.52492.x. PubMed PMID: 15450034.
Birim O, Kappetein AP, Bogers AJ. Charlson comorbidity index as a predictor of long-term outcome after Surgery for nonsmall cell Lung cancer. Eur J Cardiothorac Surg. 2005;28(5):759–62. https://doi.org/10.1016/j.ejcts.2005.06.046. PubMed PMID: 16157485.
Lobato ZM, Almeida da Silva AC, Lima Ribeiro SM, Biella MM, Santos Silva Siqueira A, Correa de Toledo Ferraz Alves, Machado-Vieira T, Borges R, Oude Voshaar MK, Aprahamian RC. I. Nutritional Status and Adverse Outcomes in Older Depressed Inpatients: A Prospective Study. J Nutr Health Aging. 2021;25(7):889 – 94. https://doi.org/10.1007/s12603-021-1638-y. PubMed PMID: 34409967.
Torbahn G, Strauss T, Sieber CC, Kiesswetter E, Volkert D. Nutritional status according to the mini nutritional assessment (MNA)(R) as potential prognostic factor for health and treatment outcomes in patients with cancer - a systematic review. BMC Cancer. 2020;20(1):594. https://doi.org/10.1186/s12885-020-07052-4. PubMed PMID: 32586289.
Tonet E, Campo G, Maietti E, Formiga F, Martinez-Selles M, Pavasini R, Biscaglia S, Serenelli M, Sanchis J, Diez-Villanueva P, Bugani G, Vitali F, Ruggiero R, Cimaglia P, Bernucci D, Volpato S, Ferrari R, Ariza-Sole A. Nutritional status and all-cause mortality in older adults with acute coronary syndrome. Clin Nutr. 2020;39(5):1572–9. https://doi.org/10.1016/j.clnu.2019.06.025. PubMed PMID: 31324416.
Nishi I, Seo Y, Hamada-Harimura Y, Yamamoto M, Ishizu T, Sugano A, Sato K, Sai S, Obara K, Suzuki S, Koike A, Aonuma K, Ieda M. Ibaraki Cardiovascular Assessment Study-Heart failure I. geriatric nutritional risk index predicts all-cause deaths in Heart Failure with preserved ejection fraction. ESC Heart Fail. 2019;6(2):396–405. https://doi.org/10.1002/ehf2.12405. PubMed PMID: 30706996.
Hao X, Li D, Zhang N. Geriatric nutritional risk index as a predictor for mortality: a meta-analysis of observational studies. Nutr Res. 2019;71:8–20. https://doi.org/10.1016/j.nutres.2019.07.005. PubMed PMID: 31708186.
Wei K, Nyunt MSZ, Gao Q, Wee SL, Ng TP. Long-term changes in nutritional status are associated with functional and mortality outcomes among community-living older adults. Nutrition. 2019;66:180–6. https://doi.org/10.1016/j.nut.2019.05.006. PubMed PMID: 31310959.
Motokawa K, Yasuda J, Mikami Y, Edahiro A, Morishita S, Shirobe M, Ohara Y, Nohara K, Hirano H, Watanabe Y. The Mini Nutritional Assessment-Short Form as a predictor of nursing home mortality in Japan: a 30-month longitudinal study. Arch Gerontol Geriatr. 2020;86:103954. https://doi.org/10.1016/j.archger.2019.103954. PubMed PMID: 31710866.
Baek MH, Heo YR. Evaluation of the efficacy of nutritional screening tools to predict Malnutrition in the elderly at a geriatric care hospital. Nutr Res Pract. 2015;9(6):637–43. https://doi.org/10.4162/nrp.2015.9.6.637. PubMed PMID: 26634053.
Saghafi-Asl M, Vaghef-Mehrabany E, Karamzad N, Daeiefarshbaf L, Kalejahi P, Asghari-Jafarabadi M. Geriatric nutritional risk index as a simple tool for assessment of Malnutrition among geriatrics in Northwest of Iran: comparison with mini nutritional assessment. Aging Clin Exp Res. 2018;30(9):1117–25. https://doi.org/10.1007/s40520-018-0892-2. PubMed PMID: 29340964.
Wei K, Nyunt MS, Gao Q, Wee SL, Yap KB, Ng TP. Association of Frailty and Malnutrition with Long-Term Functional and Mortality outcomes among Community-Dwelling older adults: results from the Singapore Longitudinal Aging Study 1. JAMA Netw Open. 2018;1(3):e180650. https://doi.org/10.1001/jamanetworkopen.2018.0650. PubMed PMID: 30646023.
Marano L, Carbone L, Poto GE, Gambelli M, Nguefack Noudem LL, Grassi G, Manasci F, Curreri G, Giuliani A, Piagnerelli R, Savelli V, Marrelli D, Roviello F, Boccardi V. Handgrip strength predicts length of hospital stay in an abdominal surgical setting: the role of frailty beyond age. Aging Clin Exp Res. 2022;34(4):811–7. https://doi.org/10.1007/s40520-022-02121-z. Epub 2022 Apr 7. PubMed PMID: 35389186.
Uemura M, Imataki O, Kawachi Y, Kawakami K, Hoshijima Y, Matsuoka A, Kadowaki N. Charlson comorbidity index predicts poor outcome in CML patients treated with tyrosine kinase inhibitor. Int J Hematol. 2016;104(5):621–7. https://doi.org/10.1007/s12185-016-2074-3. PubMed PMID: 27492732.
World. Health Organization. (2021). World health statistics 2021: monitoring health for the SDGs, sustainable development goals. World Health Organization. https://apps.who.int/iris/handle/10665/342703.
This study was supported by Healthcare Research Projects, China PLA (grant number: 18BJZ22 and 23BJZ50). The funder had no role in the study design, data collection, data analysis, data interpretation, or writing of the article.
The authors declare that they have no competing interests.
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki, and approved by The Ethics Committee of PLA General Hospital (approval number: S2020-330-01, date of approval 24 September 2020). Since this is a retrospective statistical analysis based on electronic health records collected as part of standard healthcare, without disclosing the patients’ identity, no individual patients were directly contacted for the data. The need of informed consent was waived by the Ethics Committee of PLA General Hospital.
Consent for publication
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
Hou, B., Lin, Y., Zhang, W. et al. Association of nutritional status and comorbidity with long-term survival among community-dwelling older males. BMC Geriatr 23, 697 (2023). https://doi.org/10.1186/s12877-023-04413-z