Association of multidimensional biomarkers of immune, nutritional, inflammatory, and metabolic status with postoperative outcomes in older hip fracture patients: a systematic review and meta-analysis
Aug 2026· Frontiers in Nutrition· Vol 13· 0 citations· 113 references
Abstract
The high mortality and disability rates following hip fractures in the elderly have attracted widespread attention. Underlying inflammatory, immunological, nutritional, and metabolic imbalances are major contributing factors. However, the prognostic value of multidimensional composite biomarkers encompassing these dimensions remains unclear. This systematic review aimed to evaluate the prognostic value of these composite biomarkers for postoperative outcomes in elderly patients with hip fractures.
A systematic search was conducted in PubMed, Web of Science, Embase, Scopus, the Cochrane Library, ProQuest, and
ClinicalTrials.gov
, with a cutoff date of 7 January 2026, to identify eligible cohort studies. Data were analyzed using random-effects models for both continuous and cutoff-based categorical variables. Sensitivity analyses were conducted using the modified Knapp-Hartung method with an
ad hoc
variance correction and a Bayesian hierarchical model. The quality of the evidence was assessed using the GRADE criteria.
A meta-analysis was conducted, incorporating 46 cohort studies involving a total of 28,605 patients. The results showed that baseline immune-inflammatory, nutritional, and metabolic imbalances are associated with the risk of adverse postoperative outcomes. For short-term outcomes, among binary biomarkers, a low prognostic nutritional index (OR = 3.29, 95% CI: 2.22–4.88), a high neutrophil-to-lymphocyte ratio (OR = 5.17, 95% CI: 2.99–8.92), and a high glucose-to-albumin ratio (OR = 2.31, 95% CI: 1.86–2.88) constituted the core risk prognostic indicators. In the analysis of continuous variables, the prognostic nutritional index (OR = 1.09, 95% CI: 1.04–1.15), the C-reactive protein to albumin ratio (OR = 1.33, 95% CI: 1.19–1.47), and the neutrophil-to-lymphocyte ratio (OR = 1.08, 95% CI: 1.03–1.13) were also associated with an increased short-term risk. Among binary biomarkers evaluating the prognosis of medium-to-long-term adverse outcomes, in addition to the C-reactive protein to albumin ratio (OR = 3.19, 95% CI: 2.20–4.62) and the systemic immune-inflammation index (HR = 2.00, 95% CI: 1.37–2.91), the geriatric nutritional risk index (OR = 2.95, 95% CI: 1.94–4.48) also demonstrated medium-to-long-term prognostic value.
Overall, composite biomarkers of immunity, inflammation, nutrition, and metabolism demonstrate prognostic associations with short-term and medium-to-long-term adverse outcomes in older patients with hip fractures, with core prognostic indicators predominantly supported by moderate- to high-certainty evidence. These findings support the use of these multidimensional indicators as accessible and reliable assessment tools to assist clinicians in personalized perioperative risk screening and precision management.
Major adverse cardio- and cerebrovascular events (MACCE) are critical determinants of clinical outcomes in older patients. However, the relationship between nutritional status and the prognosis of cardiovascular and cerebrovascular diseases remains underexplored. This meta-analysis investigates the association of commonly used nutritional scores with MACCE in older adults. A systematic search of the Cochrane Library, Embase, PubMed, and Web of Science was conducted up to November 28, 2025, to identify clinical studies examining nutritional scores and MACCE in older adults. The primary outcome was the risk of MACCE. Meta-analyses were performed using RStudio to pool hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs) to quantify the association between various nutritional scores and MACCE. Our meta-analysis included 28 studies comprising 80,860 individuals. Data were stratified based on derivation from univariate or multivariate Cox regression. Pooled results from multivariate-adjusted data revealed that malnutrition, as assessed by the geriatric nutritional risk index (GNRI: HR [95% CI]: 1.38 [1.22, 1.55]), the prognostic nutritional index (PNI: 1.34 [1.02, 1.76]), and the nutritional risk index (NRI: 1.37 [1.08, 1.73]), was significantly associated with an elevated risk of MACCE. Furthermore, increasing scores on the controlling nutritional status (CONUT) index (1.19 [1.12, 1.26]) and decreasing scores on the GNRI (0.95 [0.93, 0.98]) and PNI (0.95 [0.93, 0.97]) were each significantly correlated with a higher risk of MACCE. In conclusion, nutritional scores demonstrate a significant association with the risk of MACCE and its secondary outcomes in the older population.
Heart failure (HF) is one of the most common clinical syndromes and a leading cause of morbidity and mortality in the world, affecting 64 million people. The Systemic Immune-Inflammation Index (SII) is a new composite biomarker from standard blood tests that also reflects the imbalance between inflammation and immune response, thrombosis, and adaptive immunity, and reflects the multifactorial immune imbalance in cardiovascular diseases. This systematic review and diagnostic accuracy meta-analysis assessed the prognostic value of SII in adult patients with HF while clarifying its role in clinical decision-making. A comprehensive systematic search of the literature was conducted in the electronic databases PubMed, Web of Science, Scopus, and Embase from their inception until August 2025. Six studies were included in the systematic analysis. The current analysis showed a significant association (HR ≤ 1.64, 95% CI: 1.21, 2.22, p < 0.0001), with high heterogeneity. The sensitivity was 73% (95% CI: 0.62â"0.82), and the specificity was 70% (95% CI: 0.57â"0.81). The diagnostic odds ratio (DOR) was 6.34 (95% CI: 2.95â"13.6). The likelihood ratio for a positive test was 2.45 (95 % CI: 1.59-3.76) and for a negative test 0.387 (95 % CI: 0.259-0.578). The false positive rate (FPR) was 0.297 (95%CI 0.191â"0.43). To summarize, the current evidence indicated only moderate accuracy, limiting its utility as a sole predictor of mortality. Its greatest value might be in combination with existing risk models, and further large, prospective studies are needed to validate its clinical utility and refine its application.
Neyaf Nouh, Alyazid Alanazi, Abdullah Aldakhil et al.· International Journal of Med...· 0 citations
Background Frailty is highly prevalent among patients with diabetes and is associated with an increased risk of disability, hospitalization, and mortality. Although several frailty prediction models have been developed for this population, their methodological quality, predictive performance, and clinical applicability remain unclear. This systematic review and meta‐analysis was therefore conducted to comprehensively evaluate existing prediction models for frailty in patients with diabetes. Methods PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, China Biology Medicine Database (CBM), and China Science and Technology Journal Database (VIP) were searched for eligible prediction model studies from database inception to April 28, 2026. Two reviewers independently screened studies, extracted data, and assessed methodological quality using the CHARMS checklist and the PROBAST tool. Random‐effects or fixed‐effect models were applied according to heterogeneity. Meta‐analyses of pooled model discrimination (area under the curve [AUC]) and common predictors were performed using RevMan 5.4 and MedCalc 23.6.1 software. Results Of the 3492 identified articles, 19 studies were included, comprising a total of 36 prediction models. Sample sizes ranged from 152 to 1436 participants, and the AUC values varied from 0.703 to 0.975. The random forest model demonstrated the highest discriminative performance (AUC = 0.975). Frequently identified predictors included age, depression, activities of daily living (ADL), nutritional status, duration of diabetes, physical activity, polypharmacy, glycated hemoglobin (HbA1c), cognitive function, and marital status. All studies were judged to have a high risk of bias due to insufficient reporting of participants, predictors, outcomes, and analytical methods, although their overall applicability was considered high. Conclusion Existing frailty prediction models for patients with diabetes demonstrated good overall predictive performance and potential clinical utility. Nevertheless, substantial methodological limitations and a high risk of bias were identified across all included studies. Future model development should emphasize methodological rigor, external validation, and transparent reporting to improve reliability and facilitate clinical implementation.
Qing Chen, Meiling Yang, Mengmeng Chen et al.· Journal of Diabetes Research· 0 citations
BackgroundClinical frailty is an emerging health problem and a risk factor for venous disease and its adverse outcomes. This review and meta-analysis will determine the association of frailty in the development of venous diseases and related adverse outcomes.Materials and methodsA literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Searches were done in PubMed and Embase, from 1 January 2007 to 1 January 2025. Articles that were not related to frailty and venous disease were excluded. References from review papers were further screened following the same inclusion and exclusion criteria. R studio was used for the meta-analysis of the studies on non-postoperative venous thromboembolism (VTE) and pulmonary embolism (PE).ResultsA total of 1338 articles were retrieved, and 72 papers, involving 6,407,901 patients, were included in the final review. Sixty-one studies assessed frailty and postoperative complications, and 65% reported that frail patients had a higher risk of postoperative VTE (p < 0.05). Spinal, neurosurgical, abdominal, and orthopaedic patients exhibited the highest rates across surgical specialties. Meta-analysis of six studies demonstrated that frailty was associated with an increased risk of non-postoperative VTE (OR 1.91, 95% CI 1.15-3.18, p = 0.012). Three papers showed that high frailty risk was associated with greater odds of mortality in patients with PE (Pooled OR 9.95, 95% CI 6.32-15.67, p < 0.0001). Of the remaining two studies, one found that frail patients were more likely to have ulcer recurrence than non-frail patients (p < 0.001). The other reported that frail patients had a higher risk of varicose veins than non-frail patients (p = 0.01).ConclusionClinical frailty is associated with an increased risk of VTE and adverse outcomes in venous disease, although the certainty of evidence is limited by substantial heterogeneity and methodological differences among studies.
C. Ho, Y. Chan, G. Cheung· Phlebology· 0 citations
While traditional circulating biomarkers have demonstrated prognostic value in general lung cancer patients, their short-term predictive value in critical care settings remains unclear. This study aims to systematically evaluate the association between elevated multiple circulating biomarkers and short-term mortality in critically ill lung cancer patients.
This study searched databases including PubMed, Embase, Web of Science, Cochrane Library, and CNKI from inception to February 2026. Literatures that enrolled patients with severe lung cancer, reported data on the association between biomarkers and short-term mortality, and provided relative risks (RR) or convertible data were included. Meta-analysis was conducted using R software with the Hartung-Knapp-Sidik-Jonkman (HKSJ) method for random effects models and robust variance estimation (RVE) to handle correlated effect sizes from the same study. Heterogeneity was assessed, and subgroup analyses, meta-regression, and sensitivity analyses were performed to evaluate pooled effect sizes and publication bias.
This study included 9 literatures, extracting 19 studies covering 4,436 critically ill lung cancer patients. The primary meta-analysis using RVE showed that elevated biomarkers were significantly associated with short-term mortality (pooled RR = 1.62, 95% CI: 1.09-2.41, P = 0.022). Among these, 17 studies showed positive associations, with pooled RR = 1.93 (95% CI: 1.50-2.48, P < 0.001). Subgroup analysis revealed significant differences in effect sizes across different outcomes, biomarker types, and geographical regions (all P < 0.01 between subgroups), with the inflammatory biomarker subgroup showing the highest effect size (RR = 2.44). Subgroup analysis by critical illness definition showed that the effect was significant in both ICU-admitted patients (RR = 1.38, 95% CI: 1.07-1.78) and patients with advanced/terminal disease (RR = 2.45, 95% CI: 1.66-3.62). Meta-regression analysis indicated that outcome measures (90-day mortality) and patient region (North America) were significant factors influencing heterogeneity. Sensitivity analysis confirmed the robustness of results.
Multi-biomarker detection provides important reference for risk stratification and treatment decision-making in critically ill lung cancer patients. Future clinical practice could combine traditional critical care scoring systems to establish individualized prognostic prediction models based on multiple biomarkers.
Congcong Li, Jing Miao, Liqing Gao et al.· Frontiers in Oncology· 0 citations
Osteoporosis (OP) is a common comorbidity in patients with chronic obstructive pulmonary disease (COPD), but reported prevalence estimates vary substantially across studies, and the magnitudes of associations between OP and potentially modifiable factors remain uncertain. This systematic review and meta-analysis aimed to provide an updated estimate of the prevalence of OP among patients with COPD, explore potential sources of heterogeneity, and synthesise adjusted associations with selected clinical factors.
EMBASE, CINAHL, Scopus, PubMed, and the Cochrane Library were searched from inception to 5 February 2026. Only observational studies published in English involving adults with objectively diagnosed COPD and reporting OP prevalence or associated factors were included. Two reviewers independently conducted study selection, data extraction, and quality assessment using the Newcastle–Ottawa Scale or the AHRQ assessment tool. Random-effects models were used for all quantitative syntheses. Risk-factor analyses were based exclusively on adjusted ORs. Subgroup analyses, leave-one-out sensitivity analyses, and univariable meta-regression were used to explore heterogeneity.
Eighty-seven studies involving 1,453,879 patients with COPD were included. The pooled prevalence of OP was 33.8% (95% CI 31.4%–36.1%), with extremely high between-study heterogeneity (I²=99.7%). Prevalence estimates varied geographically, ranging from 28.7% in Europe and 28.8% in Africa to 42.3% in North America. The methodological quality score was significantly associated with variation in prevalence estimates in univariable meta-regression (
P
< 0.001), although substantial residual heterogeneity remained. Underweight was associated with higher odds of OP (OR = 3.91, 95% CI 1.36–11.24). Corticosteroid exposure was also associated with OP (OR = 2.85, 95% CI 1.45–5.59). In an exploratory analysis based on two studies involving 172 participants, cumulative systemic corticosteroid exposure exceeding 1,000 mg prednisone-equivalent was associated with higher odds of OP (OR = 7.36, 95% CI 1.70–31.98). Higher FEV₁% predicted was associated with lower odds of OP (OR = 0.97, 95% CI 0.96–0.98). No statistically significant associations were identified for age ≥ 65 years or overweight status.
OP affects approximately one-third of patients with COPD across the included studies. Underweight, corticosteroid exposure, and reduced lung function were associated with OP, but the findings should be interpreted cautiously because of extreme residual heterogeneity, differences in covariate adjustment, and limited evidence available for some subgroup analyses. Standardised diagnostic methods and better reporting of clinical, treatment-related, and sociodemographic characteristics are needed to improve future risk estimation.
CRD420261381268.
Chunhui Yu, Xuejiao Teng, Weilong Du et al.· BMC Public Health· 0 citations
