Introduction
Frailty is widely recognized as the most challenging manifestations of population ageing.1 It is characterized by an increased vulnerability to poor resolution of homeostasis following stressors and results from a progressive decline across multiple physiological systems.2,3 Studies have shown that the prevalence of frailty among adults aged 50 and older is approximately 12%.4 Age-stratified meta-analyses conducted in Chinese communities revealed that the overall prevalence of frailty was 6% for individuals aged 65–74, 15% for those aged 75–84, and 25% for those aged 85 and older.5 Given its high prevalence and substantial impact on health, frailty has increasingly been acknowledged as a critical issue in aging populations.
Frailty has emerged as a significant global health and economic burden for both clinical practice and public health.6 It is associated with an increased risk of various adverse outcomes, including falls, cardiovascular disease, hospitalization, nursing home admission, poor surgical outcomes, disability, and mortality.7–14 These negative outcomes not only impact individuals but also impose substantial burdens on healthcare systems. Identifying populations at higher risk for frailty is crucial in clinical practice to prevent these adverse outcomes. This is especially pertinent in the context of cardiovascular diseases (CVD), which share a complex relationship with frailty.15–17
Frailty was associated with a 2-fold increase in mortality among patients with CVD. Epidemiological studies have consistently demonstrated that frailty is closely related to a wide range of CVD conditions, including stable CVD, subclinical CVD, heart failure, and outcomes following surgical and transcatheter interventions.15–17 For instance, the NHATS study demonstrated that both pre-frailty and physical frailty phenotypes were significantly associated with increased mortality and the development of major adverse cardiovascular events (MACE) over a 6-year follow-up period.18 These findings underscore the importance of elucidating the mechanisms underlying the relationship between frailty and CVD, particularly the role of inflammatory processes.
Substantial evidence indicates that chronic inflammation significantly contributes to the development of frailty.19–23 In recent years, researchers have endeavored to identify reliable biomarkers to assess inflammation and its impact on frailty. Systemic inflammatory response index (SIRI), a recently introduced composite marker that integrates neutrophil, monocyte, and lymphocyte counts, has garnered attention.24 SIRI reflects the balance among these cell types and has been proved to effectively represent both local immune responses and systemic inflammation.24 Notably, SIRI has demonstrated prognostic value in various conditions, including several cancers,25–29 aneurismal subarachnoid hemorrhage,30 acute ischemic stroke,31 and has been correlated with the severity of coronary artery disease32 as well as cardiovascular death and all-cause mortality.33 Despite its potential, the relationship between SIRI and frailty remains underexplored, particularly in disease-specific populations.
However, limited research has specifically examined the association between SIRI and frailty in disease-specific study populations. This gap highlights the necessity for targeted studies, particularly among older adults with CVD, who are at high risk for both frailty and adverse cardiovascular outcomes. Thus, this study aims to explore the association between inflammation markers, with a focus on SIRI, and frailty in older patients with CVD. Additionally, we developed a nomogram to predict the risk of frailty in this population, aiming to enhance the management of frail CVD patients, reduce hospitalizations and mortality, and improve quality of life. By addressing this critical research gap, our findings may inform more effective clinical strategies for managing frailty in high-risk populations.
Material and Methods
Study Design
This study aimed to explore the association between the SIRI and frailty in older patients with CVD and to develop a nomogram for estimating the risk of frailty in this population. Clinical and laboratory features, including inflammation markers such SIRI, were compared between patients with and without frailty to identify potential predictors. Selected significant variables associated with frailty were subsequently incorporated into the nomogram to estimate the probabilities of frailty.
Study Population
This study included 234 patients treated in the inpatient department of Geriatrics from Peking University Third Hospital. The clinical data and blood samples were collected from May 28th 2023 to November 15th 2023. The trial is being carried out in compliance with the ethical principles of the Declaration of Helsinki. All patients have signed the informed consent forms, and the study has been approved by the institutional review board. All personal information was encrypted. For the analysis of different measurements, the sample size varied according to the number of missing data.
We selected patients ≥ 60 years with CVD, which included ischemic heart disease (IHD), stroke, heart failure, peripheral and aortic arterial disease, arrhythmias, and valvular diseases.34 Patients with acute infections, autoimmune diseases, diseases of the hematopoietic system, malignancies and severe liver or kidney dysfunction were excluded.
Markers of Systemic Inflammation
The markers of systemic inflammation were obtained from routine blood tests at admission. The calculation formula is as follows:
Frailty status was assessed using the FRAIL scale. The FRAIL Scale was a simple and rapid screening tool for frailty, consisting of five dimensions, each assessed through a straightforward question or evaluation. Individuals who met three or more of the following five components were classified as frail: fatigue, resistance, ambulation, illness, and weight loss35 (Box 1).
End Points and Other Covariate Data
Demographic and clinical characteristics, laboratory data and lifestyles were collected.
The demographic information (sex, age, body mass index, smoking habits), medical history (comorbidity: hypertension, diabetes and chronic kidney disease) were compiled from the electronic medical record system. Comorbidities were assessed using the age-adjusted Charlson comorbidity index (aCCI). At admission, the Barthel Index was used to assess activities of daily living (ADL).
Laboratory data included white blood cells count (WBC), hemoglobin (HB), platelets (PLT), mean platelet volume (MPV), serum albumin (ALB), prealbumin, lactate dehydrogenase (LDH), serum creatinine (Cr), estimated glomerular filtration rate (eGFR), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FBG), ultra-sensitive C-reactive protein (US-CRP), brain natriuretic peptide (BNP) and D-Dimer levels.
Statistical Analysis
Descriptive statistical analyses were performed to compare characteristics between patients with or without frailty. Continuous variables with a normal distribution were presented as mean ± standard deviation, and the differences were analyzed by T-test. Non-normally distributed measurement data were presented as the median (lower and upper quartiles), and intergroup comparisons were analyzed by Mann–Whitney-U test. Categorical variables were presented as absolute numbers (%), group differences were analyzed by Chi-square tests or Fisher’s test.
Stratification analysis was used to explore the influencing factors of SIRI. Stratification was based on the median value of every category. Items which were affected by frailty but independent of SIRI were included in the Multivariate binary logistic regression.
Multivariate binary logistic regression was used to estimate the association of frailty with systemic inflammation. The data was presented using odds ratios (ORs) with 95% CI.
We constructed a nomogram for frailty according to the variables screened. Multivariate binary logistic regression was used to select variables for inclusion in the nomogram. Using the nomogram, the probabilities of frailty were estimated. We used concordance area under the time‐dependent receiver operating characteristic curve (time‐dependent AUC) calculated by bootstrapping to evaluate discriminative ability. Specially, AUC values greater than 0.7 was considered reasonable.
The two-tailed P value of <0.05 was considered significant. All analyses were conducted with SPSS 22.0 and R (version 4.2.3).
Results
Population Characteristics
Table 1 shows patient characteristics, which has been categorized based on the frailty parameters. In our study, a total of 234 individuals were included and analyzed in cross-sectional setting. Of all the patients, 53.42% were males and the mean (±SD) age was 76.28 years (±9.62), mean score on the FRAIL scale was 1.99 (±1.42), with 50% having diabetes, 17.95% having chronic kidney diseases, 67.95% having hypertension, and 29.49% having a history of smoking. In our study, the majority of patients presented with a spectrum of severities of ischemic heart disease, heart failure, and degenerative valvular heart disease, while a smaller proportion of patients also had comorbid conditions such as atrial fibrillation and lower extremity arterial stenosis.
Enlarge this image.Table 1 Patient Characteristics Between the Frailty Group and the Non-Frailty Group
According to the results of FRAIL scale, 98 cases were assigned to the frailty group and 136 to the non-frailty group. Patients without frailty were younger, more likely to have a normal kidney function and good blood pressure control. Frail patients had lower body mass index (BMI) and aADL score but higher aCCI score than non-frail patients. Frail patients exhibited higher WBC, LDH, Cr, BNP and D-Dimer levels. In contrast, lower HB, ALB, eGFR, TG and LDL-C levels were seen in frail patients (P < 0.05). In addition, no significant differences were found in sex, diabetes, history of smoking, PLT and US-CRP between the two groups (P > 0.05).
Association of the Markers of Systemic Inflammation with Frailty
Table 2 shows the association of the markers of systemic inflammation with frailty.
Enlarge this image.Table 2 Markers of Systemic Inflammation Between the Frailty Group and the Non-Frailty Group
Frail patients exhibited high systemic inflammation, with higher SIRI, NLR, dNLR, CLR, AISI, AIP, PNI and TyG levels than non-frail patients.
Stratification analysis revealed that SIRI was positively associated with age, aCCI score, WBC, US-CRP, BNP and D-Dimer levels. Male patients with chronic kidney disease and a history of smoking showed elevated SIRI. SIRI was negatively associated with aADL score and ALB. Furthermore, SIRI was independent of diabetes, hypertension, BMI, HB, PLT, eGFR and LDL-C (all P < 0.05, Table 3).
Influencing Factors Related to Frailty in CVD Patients by Logistic Regression Analysis
The occurrence of frailty served as the dependent variable in our study, while factors such as gender, age, hypertension, BMI, HB, eGFR, LDL-C, and SIRI were included in multivariate binary logistic regression analysis. It was found that age, LDL-C and SIRI were three independent risk factors leading to frailty in CVD patients (Table 4).
Nomogram Construction and Validation
We constructed a nomogram for frailty based on age, LDL-C and SIRI. Figure 1 shows an example of using the nomogram to predict frailty probability of a given patient. The total score was determined based on the individual scores calculated using the nomogram. The model was internally verified using 100 repetitions of bootstrap sample corrections. The AUC was 0.897 (95% CI = 0.854–0.940), indicating favorable discrimination by the nomogram (Figure 2). The calibration curves of the nomogram showed high consistencies between the predicted and observed frailty probability (Figure 3). In summary, the nomogram for frailty had considerable discriminative and calibrating abilities.
Discussion
Frailty is a prevalent condition among older adults, marked by reductions in physiological reserve and function across various systems due to aging, which diminishes their capacity to manage daily demands or respond to acute stressors. Clinical manifestations of frailty are frequently linked to increased risks for adverse outcomes including falls, incident disability, hospitalization, and mortality.36 In our regression analysis, we found that advanced age, high blood pressure, and high SIRI levels all contributed to frailty, while male, higher levels of BMI, hemoglobin, kidney function, and blood lipid delayed the occurrence of frailty. In older patients, high BMI and blood lipid levels tend to indicate better nutrition status and a lower risk of malnutrition,37 and thus are inversely associated with frailty. In our study, high level of inflammation markers was found to be associated with increased frailty incidence in older patients with CVD. Moreover, we developed and validated a nomogram to predict risk of frailty in older patients with CVD, which could assist clinicians in evaluating prognosis in such population. It was demonstrated that age, LDL-C and SIRI were significant predictors for frailty in older patients with CVD.
It has been confirmed in previous studies that inflammation markers were associated with the risk of frailty.38,39 It was widely recognized that c-reactive protein, interleukin-6 and tumor necrosis factor α were associated with frailty.1 In patients with maintenance hemodialysis (MHD), NLR and PLR were of a certain diagnostic value for frailty. MHD patients with frailty had high NLR and PLR levels and an unfavorable prognosis.40 A prospective study demonstrated a positive association between SII, NLR and PLR level with frailty. The frailty phenotype partially mediated the association between systemic inflammation and osteoporosis and fracture risks.41 The correlation between frailty and inflammation markers, in particular NLR were also observed in older patients with CHD and cancer.42–44 The relationship between inflammation and frailty involves several key mechanisms: 1. Cytokine imbalance, with elevated pro-inflammatory cytokines (eg, IL-6, TNF-α, CRP)45,46 and reduced anti-inflammatory cytokines (eg, IL-10), causes systemic inflammation and tissue damage.47 2. Chronic inflammation increased reactive oxygen species (ROS), accelerating cell damage and frailty.48,49 3. Inflammatory pathways (eg, NF-κB, MAPK) promote muscle wasting by enhancing protein breakdown and inhibiting synthesis, impairing muscle function.48,50–52 4. Endocrine dysregulation disrupts hormonal balance (eg, HPA axis, IGF-1),53–55 reducing anabolic activity and tissue repair. 5. Immune dysfunction, including immunosenescence and inflammasome activation, perpetuate chronic inflammation, further exacerbating tissue damage and frailty.56 6. Cellular senescence promotes the secretion of pro-inflammatory factors (SASP), impairing tissue regeneration.57,58 7. Neuroinflammation contributes to cognitive decline and neurodegeneration, which are often associated with frailty.59–61 8. Vascular dysfunction reduces blood flow and nutrient delivery to tissues.62,63 9. Age-related gut microbiota increase gut permeability, triggering systemic inflammation.56 10. Inflammatory cytokines (eg, IL-6, TNF-α) stimulate osteoclast activity, leading to osteoporosis and increased fracture risk, further contributing to frailty.64
Our results highlight the potential utility of SIRI for identifying frailty in CVD patients. Neutrophils, one of the primary members of the innate immune system, are the traditional index to reflect inflammatory status. Circulatory monocytes, which can migrate out of the bloodstream and become tissue macrophages, were involved in immune defense and the damage-repairment process. While lymphocytes play a key role in adaptive immune responses.65 SIRI comprises three components from hemograms, including neutrophils, monocytes, and lymphocytes. SIRI demonstrates strong associations with various diseases, including cardiovascular conditions,33 cancers,66,67 and frailty,39 due to its reliance on standard blood tests. It may serve as a more sensitive and representative inflammatory biomarker compared to single parameters, as it incorporates both the NLR and MLR.33 SIRI exhibits distinct advantages over other inflammation parameters in predicting outcomes for diseases like CVD and cancers. In patients with acute coronary syndrome who underwent PCI, SIRI outperformed four other lymphocyte-based inflammatory indices including PLR, NLR, MLR and SII in predicting MACE combining SIRI with the Global Registry of Acute Coronary Events (GRACE) risk score improved the accuracy of MACE prediction.68 Elevated SIRI, but not SII, was independently associated with an increased risk for myocardial infarction.69 Furthermore, SIRI showed superior predictive power for stroke prognosis compared to NLR, PLR, LMR, and RDW.70 In obese populations, SIRI significantly outperformed SII in predicting both all-cause and CVD mortality.71 Elevated SIRI was independently associated with an increased risk of CVD in diabetic patients with high BMI, demonstrating greater clinical value than hs-CRP.72 Moreover, SIRI predicted survival in patients with pancreatic cancer receiving gemcitabine chemotherapy better than NLR, LMR, tumor stage or CA 19–9 levels.24 Preoperative SIRI clearly predicted the efficacy and prognosis of neoadjuvant chemoradiotherapy in patients with rectal cancer. Compared to NLR and SII, only the SIRI was found to be an independent prognostic factor for overall survival and disease-free survival.73 However, SIRI has several limitations, such as the lack of standardized cut-off values,67 potential susceptibility to external influences like infections or medications, and unclear biological mechanisms. Additionally, it cannot differentiate specific sources of inflammation.
There are various methods for assessing frailty, with common assessment tools including: Fried Frailty Phenotype, Frailty Index, FRAIL Scale, Edmonton Frailty Scale and Clinical Frailty Scale, etc.74,75 The FRAIL Scale was a simple and efficient tool for rapid frailty screening, particularly useful in resource-limited clinical and community settings.76 Its advantages include simplicity, no need for special equipment, reliance on patient self-reporting, and quick completion, making it ideal for large-scale screening and epidemiological studies. The FRAIL Scale was widely applicable and easy to implement, especially in busy or resource-constrained environments. Our study investigated the association between SIRI and frailty in older patients with CVD, introducing a novel nomogram-based method to estimate risk of frailty. This tool can be integrated into routine clinical visits for ongoing frailty risk monitoring. The risk calculations facilitate the identification of high-risk patients, enabling targeted interventions. Interventions are customized according to frailty risk levels: low-risk patients receive lifestyle modifications and education; moderate-to-high risk patients undergo comprehensive geriatric assessments, individualized care plans, supervised exercise programs, medication optimization such as statin therapy, and nutritional support. Greater awareness should be raised among older CVD patients with elevated SIRI levels to encourage early interventions aimed at reducing the incidence of frailty.
The limitations were also presented in our study. First, considering the broad spectrum of cardiovascular diseases, there was potential heterogeneity among the CVD patients included in our study, which may influence the findings to some extent. Second, in our study, frailty status was assessed using the FRAIL scale in which most of measures were relatively subjective. For more precise assessments, it may be necessary to combine it with other tools like the Fried Frailty Phenotype or Frailty Index to achieve a comprehensive evaluation of frailty. Third, though we adjusted for several confounding factors, we could not eliminate other potential factors for frailty such as medication use, nutritional status, and cognitive function. These factors often interact and significantly contribute to frailty in older adults. Forth, since this was a cross-sectional study, we only captured inflammation markers at one point, thus we were unable to assess the relationship between frailty and changes in these markers. Longitudinal studies in the future would further illustrate possible relationship.
Conclusions
Our findings indicate that that elevated levels of inflammation markers are significantly associated with an increased risk of frailty, thereby supporting the role of inflammation in the development of frailty. Further research is warranted to fully elucidate the underlying mechanisms involved. Moreover, integrating frailty assessment into routine clinical cardiovascular practice is crucial for enhancing cardiovascular risk prediction in older adults. By employing a nomogram based on age, LDL-C, and SIRI, we were able to predict the risk of frailty in older patients with cardiovascular disease. Further research with larger and more populations with diverse CVD subtypes is warranted to confirm these findings and further explore the clinical utility of SIRI and the nomogram in frailty prediction and management.
Data Sharing Statement
All data relevant to the study are included in the article.
Ethics Approval and Informed Consent
All patients have signed the informed consent forms, and the study has been approved by Peking University Third Hospital Medical Science Research Ethics Committee (No. 696-02).
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; SH, XL, and CC took part in drafting, YS, HC, ZG and DL revising or critically reviewing the article; all authors took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Funding
This work was supported by Beijing Research Ward Excellence Program, BRWEP (BRWEP2024W014090208), Research Project of Peking University Third Hospital in State Key Laboratory of Vascular Homeostasis and Remodeling (Peking University, No.2024-VHR-SY-11).
Disclosure
All authors declare no competing interests in this work.
1. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752–762. doi:10.1016/s0140-6736(12)62167-9
2. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol a Biol Sci Med Sci. 2001;56(3):M146–156. doi:10.1093/gerona/56.3.m146
3. Walston J, Hadley EC, Ferrucci L, et al. Research agenda for frailty in older adults: toward a better understanding of physiology and etiology: summary from the American Geriatrics Society/National Institute on Aging Research Conference on Frailty in Older Adults. J Am Geriatr Soc. 2006;54(6):991–1001. doi:10.1111/j.1532-5415.2006.00745.x
4. O’Caoimh R, Sezgin D, O’Donovan MR, et al. Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. Age Ageing. 2021;50(1):96–104. doi:10.1093/ageing/afaa219
5. He B, Ma Y, Wang C, et al. Prevalence and Risk Factors for Frailty among Community-Dwelling Older People in China: a Systematic Review and Meta-Analysis. J Nutr Health Aging. 2019;23(5):442–450. doi:10.1007/s12603-019-1179-9
6. Hoogendijk EO, Afilalo J, Ensrud KE, et al. Frailty: implications for clinical practice and public health. Lancet. 2019;394(10206):1365–1375. doi:10.1016/s0140-6736(19)31786-6
7. Bromfield SG, Ngameni C-A, Colantonio LD, et al. Blood Pressure, Antihypertensive Polypharmacy, Frailty, and Risk for Serious Fall Injuries Among Older Treated Adults With Hypertension. Hypertension. 2017;70(2):259–266. doi:10.1161/hypertensionaha.116.09390
8. Sergi G, Veronese N, Fontana L, et al. Pre-frailty and risk of cardiovascular disease in elderly men and women: the Pro.V.A. study. J Am Coll Cardiol. 2015;65(10):976–983. doi:10.1016/j.jacc.2014.12.040
9. Ferrante LE, Pisani MA, Murphy TE, et al. The Association of Frailty With Post-ICU Disability, Nursing Home Admission, and Mortality: a Longitudinal Study. Chest. 2018;153(6):1378–1386. doi:10.1016/j.chest.2018.03.007
10. Veronesi F, Borsari V, Martini L, et al. The Impact of Frailty on Spine Surgery: systematic Review on 10 years Clinical Studies. Aging Dis. 2021;12(2):625–645. doi:10.14336/ad.2020.0904
11. Middleton R, Poveda JL, Orfila Pernas F, et al. Mortality, Falls, and Fracture Risk Are Positively Associated With Frailty: a SIDIAP Cohort Study of 890 000 Patients. J Gerontol a Biol Sci Med Sci. 2022;77(1):148–154. doi:10.1093/gerona/glab102
12. Liu HX, Ding G, Yu WJ, et al. Association between frailty and incident risk of disability in community-dwelling elder people: evidence from a meta-analysis. Public Health. 2019;175:90–100. doi:10.1016/j.puhe.2019.06.010
13. Hanlon P, Nicholl BI, Jani BD, et al. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. Lancet Public Health. 2018;3(7):e323–e332. doi:10.1016/s2468-2667(18)30091-4
14. Aguayo GA, Vaillant MT, Donneau A-F, et al. Comparative analysis of the association between 35 frailty scores and cardiovascular events, cancer, and total mortality in an elderly general population in England: an observational study. PLoS Med. 2018;15(3):e1002543. doi:10.1371/journal.pmed.1002543
15. Afilalo J, Alexander KP, Mack MJ, et al. Frailty assessment in the cardiovascular care of older adults. J Am Coll Cardiol. 2014;63(8):747–762. doi:10.1016/j.jacc.2013.09.070
16. Alegre O, Formiga F, López-Palop R, et al. An Easy Assessment of Frailty at Baseline Independently Predicts Prognosis in Very Elderly Patients With Acute Coronary Syndromes. J Am Med Dir Assoc. 2018;19(4):296–303. doi:10.1016/j.jamda.2017.10.007
17. Murali-Krishnan R, Iqbal J, Rowe R, et al. Impact of frailty on outcomes after percutaneous coronary intervention: a prospective cohort study. Open Heart. 2015;2(1):e000294. doi:10.1136/openhrt-2015-000294
18. Damluji AA, Chung S-E, Xue Q-L, et al. Frailty and cardiovascular outcomes in the National Health and Aging Trends Study. Eur Heart J. 2021;42(37):3856–3865. doi:10.1093/eurheartj/ehab468
19. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease and frailty. Nat Rev Cardiol. 2018;15:505–522. doi:10.1038/s41569-018-0064-2
20. Chen X, Mao G, Leng SX. Frailty syndrome: an overview. Clin Interv Aging. 2014;9:433–441. doi:10.2147/cia.S45300
21. Yao X, Li H, Leng SX. Inflammation and immune system alterations in frailty. Clin Geriatr Med. 2011;27(1):79–87. doi:10.1016/j.cger.2010.08.002
22. Cybularz M, Wydra S, Berndt K, et al. Frailty is associated with chronic inflammation and pro-inflammatory monocyte subpopulations. Exp Gerontol. 2021;149:111317. doi:10.1016/j.exger.2021.111317
23. Zhang H, Hao M, Hu Z, et al. Association of immunity markers with the risk of incident frailty: the Rugao longitudinal aging study. Immun Ageing. 2022;19(1):1. doi:10.1186/s12979-021-00257-6
24. Qi Q, Zhuang L, Shen Y, et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158–2167. doi:10.1002/cncr.30057
25. Chen Z, Wang K, Lu H, et al. Systemic inflammation response index predicts prognosis in patients with clear cell renal cell carcinoma: a propensity score-matched analysis. Cancer Manag Res. 2019;11:909–919. doi:10.2147/cmar.S186976
26. Geng Y, Zhu D, Wu C, et al. A novel systemic inflammation response index (SIRI) for predicting postoperative survival of patients with esophageal squamous cell carcinoma. Int Immunopharmacol. 2018;65:503–510. doi:10.1016/j.intimp.2018.10.002
27. Li S, Lan X, Gao H, et al. Systemic Inflammation Response Index (SIRI), cancer stem cells and survival of localised gastric adenocarcinoma after curative resection. J Cancer Res Clin Oncol. 2017;143:2455–2468. doi:10.1007/s00432-017-2506-3
28. Pacheco-Barcia V, Mondéjar Solís R, France T, et al. A systemic inflammation response index (SIRI) correlates with survival and predicts oncological outcome for mFOLFIRINOX therapy in metastatic pancreatic cancer. Pancreatology. 2020;20(2):254–264. doi:10.1016/j.pan.2019.12.010
29. Xie H, Wei B, Shen H, et al. BRAF mutation in papillary thyroid carcinoma (PTC) and its association with clinicopathological features and systemic inflammation response index (SIRI). Am J Transl Res. 2018;10(8):2726–2736.
30. Zhang P, Li Y, Zhang H, et al. Prognostic value of the systemic inflammation response index in patients with aneurismal subarachnoid hemorrhage and a Nomogram model construction. Br J Neurosurg. 2023;37(6):1560–1566. doi:10.1080/02688697.2020.1831438
31. Zhou Y, Zhang Y, Cui M, Zhang Y, Shang X. Prognostic value of the systemic inflammation response index in patients with acute ischemic stroke. Brain Behav. 2022;12(6):e2619. doi:10.1002/brb3.2619
32. Dziedzic EA, Gąsior JS, Tuzimek A, et al. Investigation of the Associations of Novel Inflammatory Biomarkers-Systemic Inflammatory Index (SII) and Systemic Inflammatory Response Index (SIRI)-With the Severity of Coronary Artery Disease and Acute Coronary Syndrome Occurrence. Int J mol Sci. 2022;23(17):9553. doi:10.3390/ijms23179553
33. Xia Y, et al. Systemic Immune Inflammation Index (SII), System Inflammation Response Index (SIRI) and Risk of All-Cause Mortality and Cardiovascular Mortality: a 20-Year Follow-Up Cohort Study of 42,875 US Adults. J Clin Med. 2023;12:31128. doi:10.3390/jcm12031128
34. Mensah GA, Fuster V, Murray CJL, Roth GA. Global Burden of Cardiovascular Diseases and Risks, 1990–2022. J Am Coll Cardiol. 2023;82(25):2350–2473. doi:10.1016/j.jacc.2023.11.007
35. Dong L, Qiao X, Tian X, et al. Cross-Cultural Adaptation and Validation of the FRAIL Scale in Chinese Community-Dwelling Older Adults. J Am Med Dir Assoc. 2018;19(1):12–17. doi:10.1016/j.jamda.2017.06.011
36. Xue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1–15. doi:10.1016/j.cger.2010.08.009
37. Zhang Z, Pereira SL, Luo M, Matheson EM. Evaluation of Blood Biomarkers Associated with Risk of Malnutrition in Older Adults: a Systematic Review and Meta-Analysis. Nutrients. 2017;9(8):829. doi:10.3390/nu9080829
38. Tang Y, Zhai Y, Song W, et al. Association between complete blood count-derived inflammatory markers and the risk of frailty and mortality in middle-aged and older adults. Front Public Health. 2024;12:1427546. doi:10.3389/fpubh.2024.1427546
39. Zhang H, Liu X, Wang X, Jiang Y. Association of two novel systemic inflammatory biomarkers and frailty based on NHANES 2007–2018. Front Public Health. 2024;12:1377408. doi:10.3389/fpubh.2024.1377408
40. Wang J, Huang L, Xu M, et al. Study on the Clinical Implications of NLR and PLR for Diagnosing Frailty in Maintenance Hemodialysis Patients and Their Correlations with Patient Prognosis. J Healthc Eng. 2022;2022:1267200. doi:10.1155/2022/1267200
41. Di D, Zhou H, Cui Z, et al. Frailty phenotype as mediator between systemic inflammation and osteoporosis and fracture risks: a prospective study. J Cachexia, Sarcopenia Muscle. 2024;15(3):897–906. doi:10.1002/jcsm.13447
42. Hou P, Xue H-P, Mao X-E, et al. Inflammation markers are associated with frailty in elderly patients with coronary heart disease. Aging (Albany NY). 2018;10(10):2636–2645. doi:10.18632/aging.101575
43. Nishijima TF, Deal AM, Williams GR, et al. Frailty and inflammatory markers in older adults with cancer. Aging (Albany NY). 2017;9(3):650–664. doi:10.18632/aging.101162
44. Giri S, Dahal S, Bal S, et al. Pre-treatment neutrophil to lymphocyte ratio as a biomarker of frailty and predictor of survival among older adults with multiple myeloma. J Geriatr Oncol. 2022;13(4):486–492. doi:10.1016/j.jgo.2021.12.004
45. Soysal P, Stubbs B, Lucato P, et al. Inflammation and frailty in the elderly: a systematic review and meta-analysis. Ageing Res Rev. 2016;31:1–8. doi:10.1016/j.arr.2016.08.006
46. Collerton J, Martin-Ruiz C, Davies K, et al. Frailty and the role of inflammation, immunosenescence and cellular ageing in the very old: cross-sectional findings from the Newcastle 85+ Study. Mech Ageing Dev. 2012;133(6):456–466. doi:10.1016/j.mad.2012.05.005
47. Lukyanova SO, Artemyeva OV, Strazhesko ID, et al. Expression of TLR2, IL-1β, and IL-10 Genes as a Possible Factor of Successful or Pathological Aging in Nonagenarians. Bull Exp Biol Med. 2024;176(4):505–508. doi:10.1007/s10517-024-06056-5
48. Bar-Shai M, Carmeli E, Reznick AZ. The role of NF-kappaB in protein breakdown in immobilization, aging, and exercise: from basic processes to promotion of health. Ann N Y Acad Sci. 2005;1057(1):431–447. doi:10.1196/annals.1356.034
49. Liguori I, Russo G, Curcio F, et al. Oxidative stress, aging, and diseases. Clin Interv Aging. 2018;13:757–772. doi:10.2147/cia.S158513
50. Kramer HF, Goodyear LJE. MAPK, and NF-kappaB signaling in skeletal muscle. J Appl Physiol. 2007;103(1):388–395. doi:10.1152/japplphysiol.00085.2007
51. Moaddel R, Fabbri E, Khadeer MA, et al. Plasma Biomarkers of Poor Muscle Quality in Older Men and Women from the Baltimore Longitudinal Study of Aging. J Gerontol a Biol Sci Med Sci. 2016;71(10):1266–1272. doi:10.1093/gerona/glw046
52. Zhang Q, Wang Z, Song M, et al. Effects of systemic inflammation and frailty on survival in elderly cancer patients: results from the INSCOC study. Front Immunol. 2023;14:936904. doi:10.3389/fimmu.2023.936904
53. Barbieri M, Ferrucci L, Ragno E, et al. Chronic inflammation and the effect of IGF-I on muscle strength and power in older persons. Am J Physiol Endocrinol Metab. 2003;284(3):E481–487. doi:10.1152/ajpendo.00319.2002
54. Sartori R, Romanello V, Sandri M. Mechanisms of muscle atrophy and hypertrophy: implications in health and disease. Nat Commun. 2021;12(1):330. doi:10.1038/s41467-020-20123-1
55. Lamberts SW. The endocrinology of aging and the brain. Arch Neurol. 2002;59(11):1709–1711. doi:10.1001/archneur.59.11.1709
56. Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018;14(10):576–590. doi:10.1038/s41574-018-0059-4
57. Hernandez-Segura A, de Jong TV, Melov S, et al. Unmasking Transcriptional Heterogeneity in Senescent Cells. Curr Biol. 2017;27(17):2652–2660.e2654. doi:10.1016/j.cub.2017.07.033
58. Roger L, Tomas F, Gire V. Mechanisms and Regulation of Cellular Senescence. Int J mol Sci. 2021;22(23). doi:10.3390/ijms222313173
59. Luo XG, Ding JQ, Chen SD. Microglia in the aging brain: relevance to neurodegeneration. Mol Neurodegener. 2010;5(1):12. doi:10.1186/1750-1326-5-12
60. Cunningham C, Wilcockson DC, Campion S, Lunnon K, Perry VH. Central and systemic endotoxin challenges exacerbate the local inflammatory response and increase neuronal death during chronic neurodegeneration. J Neurosci. 2005;25(40):9275–9284. doi:10.1523/jneurosci.2614-05.2005
61. Buchman AS, Boyle PA, Wilson RS, Tang Y, Bennett DA. Frailty is associated with incident Alzheimer’s disease and cognitive decline in the elderly. Psychosom Med. 2007;69(5):483–489. doi:10.1097/psy.0b013e318068de1d
62. Wang L, Cheng CK, Yi M, Lui KO, Huang Y. Targeting endothelial dysfunction and inflammation. J mol Cell Cardiol. 2022;168:58–67. doi:10.1016/j.yjmcc.2022.04.011
63. Ridker PM, Everett BM, Thuren T, et al. Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease. N Engl J Med. 2017;377(12):1119–1131. doi:10.1056/NEJMoa1707914
64. Yokota K. Inflammation and osteoclasts. Nihon Rinsho Meneki Gakkai Kaishi. 2017;40(5):367–376. doi:10.2177/jsci.40.367
65. Bonilla FA, Oettgen HC. Adaptive immunity. J Allergy Clin Immunol. 2010;125(2 Suppl 2):S33–40. doi:10.1016/j.jaci.2009.09.017
66. Shen H, Zuo F. Prognostic role of systemic inflammation response index (SIRI) in patients with pancreatic cancer: a meta-analysis. Front Oncol. 2024;14:1465279. doi:10.3389/fonc.2024.1465279
67. Zhang S, Cheng T. Prognostic and clinicopathological value of systemic inflammation response index (SIRI) in patients with breast cancer: a meta-analysis. Ann Med. 2024;56:2337729. doi:10.1080/07853890.2024.2337729
68. Li Q, Ma X, Shao Q, et al. Prognostic Impact of Multiple Lymphocyte-Based Inflammatory Indices in Acute Coronary Syndrome Patients. Front Cardiovasc Med. 2022;9:811790. doi:10.3389/fcvm.2022.811790
69. Jin Z, Wu Q, Chen S, et al. The Associations of Two Novel Inflammation Indexes, SII and SIRI with the Risks for Cardiovascular Diseases and All-Cause Mortality: a Ten-Year Follow-Up Study in 85,154 Individuals. J Inflamm Res. 2021;14:131–140. doi:10.2147/jir.S283835
70. Zhang Y, Xing Z, Zhou K, Jiang S. The Predictive Role of Systemic Inflammation Response Index (SIRI) in the Prognosis of Stroke Patients. Clin Interv Aging. 2021;16:1997–2007. doi:10.2147/cia.S339221
71. Kong F, Huang J, Xu C, et al. System inflammation response index: a novel inflammatory indicator to predict all-cause and cardiovascular disease mortality in the obese population. Diabetol Metab Syndr. 2023;15(1):195. doi:10.1186/s13098-023-01178-8
72. Lin K, Lan Y, Wang A, Yan Y, Ge J. The association between a novel inflammatory biomarker, systemic inflammatory response index and the risk of diabetic cardiovascular complications. Nutr, Metab Cardiovasc Dis. 2023;33(7):1389–1397. doi:10.1016/j.numecd.2023.03.013
73. Ding Y, et al. Predictive effect of the systemic inflammation response index (SIRI) on the efficacy and prognosis of neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer. BMC Surg. 2024;24:89. doi:10.1186/s12893-024-02384-5
74. Dent E, Martin FC, Bergman H, et al. Management of frailty: opportunities, challenges, and future directions. Lancet. 2019;394(10206):1376–1386. doi:10.1016/s0140-6736(19)31785-4
75. Faller JW, Pereira DDN, de Souza S, et al. Instruments for the detection of frailty syndrome in older adults: a systematic review. PLoS One. 2019;14(4):e0216166. doi:10.1371/journal.pone.0216166
76. Morley JE, Vellas B, van Kan GA, et al. Frailty consensus: a call to action. J Am Med Dir Assoc. 2013;14(6):392–397. doi:10.1016/j.jamda.2013.03.022
Suiyuan Hu,1,* Xuan Lai,1,* Yanyan Shi,2,* Haodi Chai,3,4 Zhijun Guo,5 Dongyang Liu,3,4,6 Cheng Cui3,4
1Geriatrics Department, Peking University Third Hospital, Beijing, People’s Republic of China; 2Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, People’s Republic of China; 3Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People’s Republic of China; 4Center of Clinical Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, People’s Republic of China; 5Department of Gastroenterology, Peking University Third Hospital, Beijing, People’s Republic of China; 6State Key Laboratory of Vascular Homeostasis and Remodeling, Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Cheng Cui; Dongyang Liu, Email [email protected]; [email protected]
© 2025. This work is licensed under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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; Guo, Z; Liu, D; Cui, C