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PubMed2026

Prognostic Value of Frailty in Aortic Surgery: A Systematic Review and Meta-Analysis Comparing Frailty Assessment Tools.

BACKGROUND: Frailty is increasingly recognized as an important determinant of outcomes after aortic vascular surgery, but assessment methods vary substantially and the optimal tool for risk stratification remains uncertain. This systematic review and meta-analysis evaluated the prognostic value of preoperative frailty and compared the predictive performance of different frailty instruments in aortic surgery. METHODS: PubMed, Embase, and Cochrane Library were searched from inception to April 27, 2026. Eligible studies included patients undergoing open, endovascular, or hybrid aortic procedures involving abdominal, thoracic, thoracoabdominal, arch, and proximal aortic diseases, including aneurysms and dissections, assessed frailty preoperatively, and reported postoperative outcomes. RESULTS: Thirty studies comprising 419,459 patients were included. Frailty was associated with higher early mortality (odds ratio [OR] 2.20; 95% confidence interval [CI] 1.54-3.14) and late mortality (hazard ratio 2.18; 95% CI 1.64-2.90). Frail patients also had increased risks of major complications (OR 2.52; 95% CI 1.22-5.19), acute kidney injury (OR 1.64; 95% CI 1.34-2.02), and nonhome discharge (OR 5.50; 95% CI 3.05-9.92). Associations were consistent across surgical approaches and aortic segments. Judgment-based or phenotype-like tools yielded higher effect estimates than deficit-accumulation indices, although differences were not statistically significant; among index-based tools, Modified Frailty Index (mFI)-11 outperformed mFI-5. CONCLUSION: Preoperative frailty strongly predicts mortality, morbidity, and loss of functional independence after open, endovascular, and hybrid aortic surgery across different aortic segments and pathologies, including aneurysmal and dissecting aortic disease. Routine frailty assessment may improve risk stratification and perioperative decision-making.

Humans↗

PubMed2027

Effectiveness of high-dose versus standard-dose influenza vaccines against hospitalisation according to frailty risk: a prespecified analysis of the randomised trial DANFLU-2.

BACKGROUND: Frailty is a major risk factor for influenza-related complications and can influence vaccine effectiveness. We aimed to assess the relative vaccine effectiveness (rVE) of high-dose (HD-IIV) versus standard-dose inactivated influenza vaccine (SD-IIV) in older adults aged 65 years or older according to frailty risk. METHODS: This study was a prespecified analysis of DANFLU-2, an open-label, individually randomised trial, conducted in Denmark during three consecutive influenza seasons (2022-23, 2023-24, and 2024-25). Adults aged 65 years or older were randomised (1:1) to the HD-IIV or SD-IIV group. The primary endpoint was hospitalisation for influenza or pneumonia. Frailty was defined according to the validated Hospital Frailty Risk Score (HFRS) based on ICD-10 codes within 10 years before randomisation. Participants were stratified into three HFRS categories, namely low (<5 points), intermediate (5-15 points), and high (>15 points) frailty risk. The rVE of HD-IIV versus SD-IIV against the primary endpoint was assessed across prespecified HFRS categories and treating HFRS as a continuous variable. Pearson's chi-square test was used to compare safety events across frailty risk groups and randomisation groups. FINDINGS: Among 332 438 randomised participants (mean age 73·7 years [SD 5·8]; 161 538 [48·6%] were female), 276 173 (83·1%) had low frailty risk, 52 395 (15·8%) had intermediate frailty risk, and 3861 (1·2%) had high frailty risk. The primary endpoint of hospitalisation for influenza or pneumonia occurred in 1424 (0·5%) of 276 173 participants with low frailty risk, 761 (1·5%) of 52 395 with intermediate frailty risk, and 163 (4·2%) of 3861 with high frailty risk (relative risk [RR] for intermediate vs low frailty risk 2·8 [95% CI 2·6-3·1]; RR for high vs low frailty risk 8·2 [7·0-9·6]). HFRS as a continuous variable significantly modified the effect of HD-IIV versus SD-IIV against the primary endpoint with higher rVE estimates with increasing HFRS (pinteraction=0·020). The rVE was 0·2% (95% CI -10·8 to 10·2) among those with low frailty risk, 13·1% (-0·4 to 24·8) among those with intermediate frailty risk, and 19·9% (-10·3 to 42·1) among those with high frailty risk. No significant interaction was observed when HFRS was assessed according to the prespecified categorical frailty groups (pinteraction=0·17). The proportion of participants with at least one serious adverse event increased across frailty risk groups (13 366 [4·8%] of 275 795 for low frailty risk, 5475 [10·5%] of 52 315 for intermediate frailty risk, and 777 [20·2%] of 3850 for high frailty risk; p<0·0001), with similar proportions of serious adverse events in the HD-IIV and SD-IIV groups for each frailty risk group. INTERPRETATION: Among adults aged 65 years or older in Denmark, frailty risk might modify the effects of HD-IIV versus SD-IIV against hospitalisation for influenza or pneumonia, with higher rVE estimates with increasing frailty risk. These findings might support considering high-dose influenza vaccines for frail older adults. However, effect modification was not evident when frailty was assessed using prespecified categorical subgroups, and subgroup-specific estimates were imprecise, with 95% CIs crossing the null. These results should be considered exploratory, warranting further investigation. FUNDING: The DANFLU-2 trial was funded by Sanofi.

Journal Article↗

PubMed2026

Identifying potential drug targets for physical and cognitive frailty: an integrative analysis of CHARLS cohort, mendelian randomization, and gene colocalization.

With the aging of the population, frailty has become a common syndrome that severely affects the quality of life of older adults. This study aims to analyze the correlation between cognition and frailty, physical activity and frailty, and elucidate the potential pharmacological targets of cognitive frailty and physical frailty.We conducted logistic regression analyses using data from the China Health and Retirement Longitudinal Study (CHARLS) to examine the associations between total cognition and frailty, physical activity and frailty. Furthermore, summary-data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (TSMR) were employed to explore potential pharmacological targets for frailty. Genes associated with physical frailty and cognitive frailty were identified, followed by analysis via colocalization analysis, phenome-wide association studies (PheWAS), and DsigDB drug prediction. Cross-sectional analysis of CHARLs revealed that total cognition(OR 0.93, 95% CI 0.92-0.95) and middle physical activity(OR 0.95, 95% CI 0.92-0.97) were negatively correlated with frailty. SMR identified 41 drug genes associated with frailty, and subsequent TSMR validation and co-localization analysis showed that 11 candidate genes exhibited strong colocalization (PP.H4 > 0.8). GRPEL 1, PABPC 4, and WBP 2NL were ultimately identified as potential drug targets associated with physical frailty, while LANCL1, LRPPRC, FADS1, and WBP2NL were identified as potential drug targets associated with cognitive frailty. Phenome-wide association analysis(PheWAS) did not reveal any significant associations between these genes and other phenotypes at the genome-wide significance threshold. Laudanosine, 25-hydroxycholesterol, and hexadecanal emerged as the top three candidate compounds for therapeutic intervention. We identified potential drug targets for physical frailty and cognitive frailty through comprehensive analysis and elucidated drugs associated with potentially relevant genetic markers, thereby laying the foundation for a deeper understanding of the mechanisms of frailty.

Humans↗

PubMed2025

Finerenone According to Frailty in Heart Failure: A Prespecified Analysis of the FINEARTS-HF Randomized Clinical Trial.

IMPORTANCE: Patients with frailty are often perceived to have a less favorable benefit-risk profile for novel therapies and therefore may be less likely to receive these. OBJECTIVE: To examine the efficacy and safety of finerenone, compared with placebo, according to frailty status in patients with heart failure (HF) and mildly reduced ejection fraction (HFmrEF) or with HF and preserved ejection fraction (HFpEF). DESIGN, SETTING, AND PARTICIPANTS: This was a prespecified secondary analysis of a phase 3 randomized clinical trial, the Finerenone Trial to Investigate Efficacy and Safety Superior to Placebo in Patients With Heart Failure (FINEARTS-HF), conducted across 653 sites in 37 countries. Patients with HF with New York Heart Association functional class II through IV, a left ventricular ejection fraction of 40% or higher, evidence of structural heart disease, and elevated natriuretic peptide levels were randomized between September 2020 and January 2023. Data analysis was conducted from October 1 to November 30, 2024. INTERVENTION: Addition of once-daily finerenone or placebo to usual therapy. MAIN OUTCOMES AND MEASURES: The primary outcome was a composite of cardiovascular death and total worsening HF events. Frailty was measured using the Rockwood cumulative deficit approach. RESULTS: Of the 6001 patients randomized in FINEARTS-HF, a frailty index (FI) was calculable in 5952 patients (mean [SD] age, 72.0 [9.6] years; 3241 [54.4%] male). In total, 1588 patients (26.7%) had class I frailty (FI ≤0.210 [not frail]), 2141 (36.0%) had class II frailty (FI 0.211-0.310 [more frail]), and 2223 (37.3%) had class III frailty (FI ≥0.311 [most frail]). Compared with patients with class I frailty, those with class II and III frailty had a higher risk of the primary outcome (unadjusted rate ratio [RR], 1.88 [95% CI, 1.54-2.28] for class II and 3.86 [95% CI, 3.22-4.64] for class III). The effect of finerenone on the primary outcome did not vary significantly by frailty class (class I: RR, 1.07 [95% CI, 0.77-1.49]; class II: RR, 0.66 [95% CI, 0.52-0.83]; class III: RR, 0.91 [95% CI, 0.76-1.07]; P for interaction = .77). Frailty class did not modify the effects of finerenone on the components of the primary outcome, all-cause death, or improvement in the Kansas City Cardiomyopathy Questionnaire total symptom score. The effects of finerenone, compared with placebo, on experiencing hypotension, elevated creatinine level, hyperkalemia, or hypokalemia did not differ by frailty class. CONCLUSIONS AND RELEVANCE: In FINEARTS-HF, finerenone reduced the risk of total worsening HF events and cardiovascular death, and it improved symptoms; these effects were not modified by frailty status. In addition, the effects of finerenone on experiencing hypotension, elevated creatinine level, hyperkalemia, or hypokalemia did not differ by frailty status. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04435626.

Humans↗

PubMed2025

Multi-Polygenic prediction of Frailty and its Trajectories highlights Chronic Pain, Rheumatoid Arthritis, and Educational Attainment pathways.

Frailty is a complex ageing-related trait with a growing evidence base for genetic influence. While a single polygenic score (PGS) for frailty has shown predictive value, few studies have examined the joint effect of multiple genetic risks. This study used a multi-polygenic score (MPS) approach to evaluate the combined and relative contributions of 26 PGSs to frailty, measured via the Frailty Index (FI), in two UK cohorts aged 65 and older: the English Longitudinal Study of Ageing (ELSA) and the Lothian Birth Cohort 1936 (LBC1936). Using elastic net regression with repeated cross-validation, we identified chronic pain and depressive symptoms PGSs as the strongest risk predictors of cross-sectional frailty status, while educational attainment, parental longevity, and rheumatoid arthritis PGSs were protective. Compared to single PGS models, MPS models provided improved prediction of frailty levels, explaining up to 4.7% of variance in frailty status - an improvement over the best single PGS (2.5%). To assess whether PGSs also predicted longitudinal frailty progression, we applied generalized additive mixed models (GAMMs) to model age-related trajectories. In ELSA, five PGSs (chronic pain, depressive symptoms, rheumatoid arthritis, educational attainment, and parental death) significantly interacted with age, influencing the rate of frailty change. In LBC1936, consistent though weaker effects were observed for chronic pain and education PGSs. These findings show that polygenic liability shapes both frailty levels and trajectories in later life. Our results support the use of multi-trait genomic models to improve risk prediction and understanding of frailty's complex aetiology.

Journal Article↗

PubMed2026

Effectiveness of Multidomain Cardiac Rehabilitation After Myocardial Infarction by Patient Frailty: Prespecified Subgroup Analysis of the PIpELINe Trial.

BACKGROUND: Frailty is common among older patients surviving myocardial infarction, is associated with adverse outcomes, and is often perceived as a barrier to cardiac rehabilitation (CR). The aim of this study is to determine whether frailty influences prognosis after myocardial infarction, and whether frailty modifies the clinical benefit of multidomain CR. METHODS: We performed a prespecified subgroup analysis of the PIpELINe (Physical Activity Intervention in Elderly Patients With Myocardial Infarction) randomized clinical trial conducted in Italy, which enrolled 512 patients aged ≥65 years recovering from myocardial infarction and randomized them in a 2:1 ratio to CR or usual care. Frailty was assessed using the Fried Frailty Phenotype, and patients were categorized as nonfrail (robust) or prefrail/frail. Time-to-event outcomes were analyzed using Kaplan-Meier estimates and Cox proportional hazards models, including treatment-by-frailty interaction terms to evaluate effect modification of the multidomain CR. The primary outcome was a composite of cardiovascular death or unplanned hospitalization for cardiovascular causes within 1 year after randomization. RESULTS: Overall, 350 patients (68.4%) were classified as prefrail/frail, of whom 232 were randomized to intervention arm (66%). Frail patients were older (median age, 80 [75-85] years) and more frequently female (41.7% versus 24.7%). Compared with robust patients, prefrail/frail patients had a higher risk of the primary outcome (16 [9.9%] versus 62 [17.7%]; hazard ratio, 1.59 [95% CI, 0.89-2.82]; adjusted P=0.117). Among prefrail/frail patients, assignment to multidomain CR was associated with a lower risk of the primary outcome compared with usual care (hazard ratio, 0.57 [95% CI, 0.34-0.94]; P=0.028), with no statistically significant interaction in the treatment effect on the primary end point (P=0.57). CONCLUSIONS: Among older patients recovering from myocardial infarction, frailty is associated with worse prognosis but does not diminish the benefit of multidomain CR. These findings support the use of frailty assessment to guide rather than limit access to CR. REGISTRATION: ClinicalTrials.gov; Unique identifier: NCT04183465.

Humans↗

PubMed2025

Causal relationship between frailty and diabetes subtypes: A bidirectional Mendelian randomization study.

Frailty and diabetes mellitus (DM) are closely linked, but their causal relationship remains unclear. This study aims to determine the bidirectional causal relationship between frailty and different DM subtypes using Mendelian randomization (MR). We performed a 2-sample MR analysis using summary statistics from large-scale genome-wide association studies. The inverse-variance weighting method was the primary analytical approach, with MR-Egger regression and weighted median methods for sensitivity analysis. Horizontal pleiotropy and heterogeneity were assessed using MR-PRESSO and Cochran Q test. Genetically predicted frailty was significantly associated with an increased risk of type 2 diabetes (T2DM) and gestational diabetes (GDM) (odds ratio [OR] = 2.142, 95% confidence interval [CI]: 1.751-2.621, P < .001; OR = 2.280, 95% CI: 1.368-3.800, P = .002), but no causal relationship was observed for type 1 diabetes or glycemic traits (P > .05). Conversely, genetically predicted type 1 diabetes, T2DM, GDM, and postprandial glucose levels (2-hour post-load glucose) increased the risk of frailty (OR = 1.026, 95% CI: 1.014-1.038, P < .001; OR = 1.046, 95% CI: 1.033-1.058, P < .001; OR = 1.068, 95% CI: 1.040-1.096, P < .001; OR = 1.095, 95% CI: 1.049-1.144, P < .001). Sensitivity analyses confirmed the robustness of these findings. This study provides genetic evidence supporting a bidirectional causal relationship between frailty and diabetes, particularly T2DM and GDM. These findings highlight the need for early frailty screening in diabetic patients and better metabolic management in frail populations.

Humans↗

PubMed2026

Beyond multidimensionality: a systematic review of recurrent frailty archetypes in community-dwelling older adults.

BACKGROUND: Frailty is a clinically heterogeneous geriatric syndrome commonly summarised using physical or multidomain severity scores. Whether person-centred analyses identify recurring within-frailty configurations has not been systematically examined in community-dwelling older adults. METHODS: We searched PubMed, Embase, MEDLINE, and CINAHL (January 2000-November 2025) for cross-sectional studies using latent class, latent profile, or analogous clustering methods to derive frailty subgroups. Quality was assessed using the AHRQ checklist and a purpose-built appraisal of person-centred model reporting. Study-derived classes were mapped in duplicate to a structured archetype framework developed through comparison of class-defining features across studies. RESULTS: Fourteen reports representing 12 independent datasets from eight countries were included. Six configurations were identified: minimally impaired reference, mobility-physical, nutritional-metabolic, cognitive-predominant, combined cognitive-physical, and psychosocial/mood-predominant. Convergence was measurement-dependent. The reference and mobility-physical configurations recurred across physical-only and multidomain indicator sets, while the combined cognitive-physical configuration appeared across several multidomain frameworks but required cognition to be measured. The remaining configurations emerged only when their defining domains were included. Evidence of prognostic value beyond aggregate frailty severity came from one deficit-index study. Collapsing shared-provenance reports and excluding the boundary-eligible study did not alter recurrence; excluding the Croatian dataset left five configurations recurrent, with the cognitive-predominant configuration supported by one independent dataset. CONCLUSIONS: Person-centred analyses identify recurring within-frailty configurations, but their apparent stability is partly measurement-dependent. A five-configuration core persisted after exclusion of the Croatian dataset, whereas the cognitive-predominant configuration remained weakly replicated. Harmonised indicators and rigorous external validation are needed before clinical application.

Humans↗

PubMed2026

Validation and refinement of a biomarker panel for frailty assessment and prediction of muscle weakness in older adults.

Frailty is a complex geriatric syndrome characterized by age-related declines in physiological function and cognitive reserve. To promote early prevention and intervention, minimally invasive and objective biomarkers that can detect frailty progression are required. We aimed to identify biomarkers associated with frailty progression and to elucidate their relevance to the Japanese version of the Cardiovascular Health Study (J-CHS) criteria, consist of five components (unintentional weight loss, self-reported exhaustion, muscle weakness, slow walking speed, and low physical activity). A total of 168 individuals (61 robust, 25 pre-frail, and 82 frail) enrolled in the NCGG (National Center for Geriatrics and Gerontology) Biobank were analyzed. Clinical information, blood-test data, aging-related factors, and gene-expression data were integrated for the analysis. First, linear regression identified one clinical factor, five aging-related factors, and 251 gene-expression factors associated with frailty. Subsequent logistic regression analyses examining each J-CHS components highlighted six candidate biomarkers. Cross-validation further suggested that three of these biomarkers-SMI, apelin, and GDF15-may represent potential biomarkers. Finally, retrospective and prospective analyses further demonstrated that those biomarkers were predictive of future muscle weakness, yielding a concordance index of 0.70. In conclusion, we validated and refined a biomarker panel consisting of SMI, apelin, and GDF15 that is associated with frailty, particularly muscle weakness (a major J-CHS component). These biomarkers may be useful for frailty assessment. Longitudinal analyses further suggested that they may be associated with the future development of muscle weakness in initially robust older adults, although validation in larger prospective cohorts is warranted.

Journal Article↗

PubMed2025

Gut metagenome and plasma metabolome profiles in older adults suggest pyruvate metabolism as a link between sleep quality and frailty.

Poor sleep quality is associated with increased frailty in older adults, but the role of the gut microbiome in this relationship remains unclear. Here, gut metagenome and plasma metabolome were profiled in 1,225 individuals aged 62-96 years. Poor sleep quality was associated with reduced abundances of potential probiotics such as Faecalibacterium prausnitzii and elevated abundances of pathobionts. A gut microbiome sleep quality index (GMSI) was developed to quantify microbial balance related to better sleep quality; higher GMSI scores were inversely associated with frailty and related clinical traits. Pyruvate metabolism emerged as a key microbial pathway linking sleep quality to frailty, with features such as F. prausnitzii abundance and microbial pyridoxal 5'-phosphate biosynthesis implicated in this connection. These findings deepen our understanding of microbiome-metabolome pathways related to sleep quality and frailty in aging and provide a valuable resource for future longitudinal and interventional studies.

Humans↗

PubMed2026

Unraveling 'F' factor: towards a genetic-clinical framework for the musculoskeletal-heart crosstalk in metabolic aging.

BACKGROUND: The rising co-occurrence of cardiometabolic diseases and musculoskeletal degeneration poses a critical challenge to healthy aging, yet the shared biological mechanisms underlying this multimorbidity remain poorly defined. This study aimed to establish an integrative clinical-genetic framework to elucidate the common frailty factor, the 'F' factor, that captures the systemic vulnerability linking cardiometabolic multimorbidity (CMM) and musculoskeletal aging. METHODS: Utilizing the prospective China Health and Retirement Longitudinal Study (CHARLS) cohort, we developed and validated novel Frailty-Integrated Indices for CMM risk prediction, evaluated with machine learning models interpreted via SHapley Additive exPlanations (SHAP). Independently, we applied genomic structural equation modeling (Genomic-SEM) to integrate genome-wide association data from six traits-coronary artery disease, type 2 diabetes, hypertension, bone mineral density, frailty, and telomere length-to model a shared latent genetic factor ('F' factor). This was followed by multivariate GWAS, fine-mapping, transcriptome-wide association study (TWAS), gene-based analysis, and functional annotation to prioritize causal genes, pathways, and cell types. RESULTS: Clinically, several Frailty-Integrated Indices significantly improved CMM risk prediction, with the optimal model achieving an AUC of 0.727. Genetically, we modeled a significant shared latent genetic factor ('F' factor), pinpointing novel risk loci and implicating key genes such as APOE and SLC22A3. These genes were enriched in pathways including cellular senescence and cholesterol metabolism and showed specific expression patterns in developmental brain stages and across multi-organ endothelial cells. CONCLUSION: Our findings provide converging evidence for Musculoskeletal‑Heart crosstalk of metabolic aging and inferred the 'F' factor as a genetic correlate of a transdiagnostic state, which links genetic predisposition to metabolic dysregulation, and systemic functional decline. This work provides a multi-level biological characterization of multimorbidity liability, informing early-risk detection and preventive strategies for complex aging-related comorbidities.

Humans↗

PubMed2026

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study.

BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. OBJECTIVE: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. METHODS: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants' homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features-such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns-will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and F1-score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. RESULTS: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. CONCLUSIONS: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.

Humans↗

PubMed2026

Proteomics-driven discovery of intervention windows and risk subtypes in osteoporosis: A prospective cohort study.

Given the limited feasibility of population-wide bone mineral density screening and the infrequency of long-term monitoring in healthy individuals, identifying the window for early intervention and the populations to be prioritized for screening is critical. This study aimed to identify intervention windows for osteoporosis and to determine potential high-risk subtypes within the healthy population. Based on proteomic data from 41,408 healthy adults, we conducted the DE-SWAN method to identify change peaks in plasma protein during the pre-diagnostic osteoporosis phase, and employed finite Gaussian mixture model-based clustering to delineate high-risk subtypes of osteoporosis. We identified 122 protein biomarkers significantly associated with osteoporosis risk throughout the follow-up period. Importantly, we identified two critical peaks occurring approximately 10 and 6 years before diagnosis, with the former enriched in immune-related pathways and the latter prominently involving responses to retinoic acid and glucocorticoids. Furthermore, one high-risk subtype for osteoporosis was identified in both males and females, termed the Frailty and Obesity Subtype. This subtype is characterized by a high degree of frailty and obesity, accompanied by a significantly elevated risk of both osteoporosis and fractures. Finally, we developed a predictive model comprising 10 proteins for identifying high-risk subtypes of osteoporosis, which demonstrated better performance than the traditional risk factor model (AUC: 0.743 vs. 0.680). Our findings demonstrate that proteomic profiling can reveal early molecular changes and identify high-risk subtypes years before clinical onset, providing a foundation for screening and precision prevention of osteoporosis.

Proteomics↗

PubMed2026

Adolescent depression as a systemic multimorbidity catalyst: integrated genetic and metabolic pathway analysis.

BACKGROUND: Although adolescent depression has been linked to individual chronic conditions, its broader role in shaping multimorbidity risk remains understudied. METHODS: A total of 87,562 UK Biobank participants were included, of whom 18,851 had documented adolescent depression. Cox proportional hazards models were applied to evaluate associations between adolescent depression and 24 chronic diseases, followed by stratified analyses by sex and age. Two-sample Mendelian randomization (MR) was then conducted to infer causality for diseases showing significant associations. Genomic colocalization analyses were performed using relevant GWAS data to identify shared causal variants. Mediation analyses were performed to detect possible mediating factors, including the frailty index, KDM biological age acceleration, allostatic load and 30 circulating biomarkers. RESULTS: Adolescent depression was associated with elevated risk for 12 chronic diseases, with strongest associations for hypothyroidism (HR = 1.29 [1.18-1.42]), diabetes (HR = 1.25 [1.13-1.38]) and chronic obstructive pulmonary disease (COPD) (HR = 1.74 [1.50-2.01]). Risks were notably higher among females and younger adults. MR confirmed likely causal relationships for hypothyroidism (OR = 1.45 [1.03-2.05]), diabetes (OR = 1.01 [1.01-1.02]) and COPD (OR = 1.04 [1.02-1.06]). Genomic colocalization revealed a shared genetic signal at the CDSN/PSORS1C1 locus between adolescent depression and hypothyroidism. Mediation analyses revealed disease-specific pathways: creatinine for hypothyroidism, testosterone for diabetes, KDM biological ageing for COPD and frailty index across all three conditions. CONCLUSIONS: Adolescent depression confers systemic vulnerability through genetic and metabolic mechanisms, with amplified risks in females and individuals aged ≤55 years. These findings support early, integrated interventions to mitigate long-term multimorbidity.

Humans↗

PubMed2026

Beyond Glycaemia: Fear of Hypoglycaemia, Cognition and Functional Mobility After Advanced Hybrid Closed-Loop Therapy in Older Adults With Type 1 Diabetes: A Prespecified Secondary Analysis of a Randomised, Single-Centre Study.

BACKGROUND: Evidence on psychological, cognitive and functional outcomes of advanced diabetes technologies in older adults with long-standing type 1 diabetes (T1D) remains limited. We evaluated whether initiation of advanced hybrid closed-loop (AHCL) therapy was associated with changes in fear of hypoglycaemia, diabetes distress, psychological well-being, cognition, frailty-related measures and mobility-related function in adults aged ≥ 65 years with T1D. METHODS: This prespecified, exploratory secondary analysis was conducted within a single-centre, open-label, randomised, controlled, parallel-group trial including adults aged ≥ 65 years with long-standing T1D. Participants were randomly assigned (1:1) to initiate AHCL therapy using the MiniMed 780G system or to continue standard diabetes treatment. The secondary outcomes included WHO-5, the 17-item Diabetes Distress Scale (DDS), Hypoglycemia Fear Survey-II (HFS-II), Montreal Cognitive Assessment, Digit Symbol Substitution Test, Fried frailty phenotype and performance-based functional measures. No formal sample-size calculation was performed for these secondary outcomes. RESULTS: Thirty-one participants were randomised and 29 completed 12 months of follow-up and were included in the treatment-effect analyses. In the baseline-adjusted primary analysis, AHCL therapy was associated with a lower HFS-II score than standard treatment (adjusted mean difference -18.9; 95% CI: -32.4 to -5.4; nominal p = 0.008), although this finding did not remain statistically significant after Holm correction (adjusted p = 0.104) or in an exploratory model additionally adjusted for sex (difference -13.6; 95% CI: -32.2 to 5.0; p = 0.145). Diabetes distress, psychological well-being, global cognition and processing speed did not differ between groups. In sex-adjusted sensitivity analyses, the between-group differences remained statistically significant for 6-min walk distance (92.6 m; 95% CI: 36.8 to 148.3; p = 0.002) and Timed Up and Go performance (-2.27 s; 95% CI: -4.28 to -0.27; p = 0.028), but not for gait speed (0.27 m/s; 95% CI: -0.05 to 0.59; p = 0.099). At 12 months, 12 of 14 AHCL participants were robust and 2 were pre-frail; in the control group, 11 of 15 were robust and 4 were pre-frail. No participant was classified as frail at follow-up. CONCLUSIONS: In this small, selected cohort, AHCL therapy was associated with a nominally lower fear-of-hypoglycaemia score and better performance on selected mobility-related tests over 12 months. The fear-of-hypoglycaemia finding did not remain statistically significant after correction for multiple comparisons or additional adjustment for sex. Six-minute walk distance and Timed Up and Go remained statistically significant in the exploratory sex-adjusted sensitivity analyses, whereas the gait-speed difference did not. No measurable between-group deterioration in global cognition or processing speed was observed. These exploratory findings require confirmation in larger studies with balanced representation by sex and direct measurement of physical activity. These findings also support a person-centred clinical message: older age alone should not be regarded as a barrier to AHCL when treatment is introduced with individualised education and appropriate ongoing support.

Humans↗

PubMed2026

Investigation of the Causal Association Between Biological Aging Indicators and Vascular Disease Through Two-Sample Mendelian Randomization Analysis.

ObjectiveThis study used two-sample Mendelian Randomization to investigate the causal link between multiple biological aging indicators and vascular disease.MethodsSummary genetic data was obtained from genome-wide association studies (GWAS) focusing on aging-related exposures and various vascular disease outcomes. The exposures included granulocyte proportions, PAI-1 (plasminogen activator inhibitor-1), telomere lengths, and the Frailty Index. The primary analysis employed the Inverse Variance Weighted (IVW) method to estimate causal relationships, supported by MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran's Q test, MR-Egger regression, leave-one-out test, and the MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) test, were conducted to evaluate heterogeneity and pleiotropy.ResultsThe analysis revealed distinct pathways after sensitivity adjustments. A higher genetically predicted Frailty Index was associated with an increased risk of abdominal aortic aneurysm (OR=2.5935, 95% CI: 1.3936-4.8268, P=0.0026, false discovery rate (FDR)=0.0475), atherosclerosis excluding cerebral and coronary sclerosis (OR=2.0262, 95% CI:1.5179-2.705, P=1.66×10-6, FDR=1×10-4), and arterial thromboembolic events (OR = 4.0306, 95% CI: 1.7133-9.4818, P = 0.0014, FDR = 0.0337). Conversely, longer telomere length demonstrated a strong, specific protective effect against abdominal aortic aneurysm (OR=0.5008, 95% CI:0.4111-0.6100, P=6.42×10-12, FDR=9.25×10-10), indicating that shorter telomere length is associated with an increased risk of AAA. Furthermore, a lower granulocyte proportion was causally linked to an increased risk of thoracic aortic aneurysm (OR=0.0181, 95% CI: 0.0014-0.2376, P=0.0023, FDR=0.0465).ConclusionThis study identifies three genetic pathways linking biological aging to vascular disease, offering new molecular targets for its prevention and treatment.

Humans↗

PubMed2026

Exerkine dysregulation links visceral adiposity to skeletal muscle impairment in end-stage heart failure with reduced ejection fraction: proteomic evidence for a cardio-adipose-muscle axis.

BACKGROUND: Heart failure with reduced ejection fraction (HFrEF) is associated with profound alterations in body composition, skeletal muscle dysfunction, and impaired exercise capacity. Exerkines representing exercise-responsive signaling molecules released by skeletal muscle, adipose tissue, and other organs may mediate systemic metabolic communication between tissues. However, their role in advanced HFrEF and their relationship with adiposity and skeletal muscle characteristics remain poorly understood. METHODS: We studied 73 patients with end-stage HFrEF and 16 healthy controls. Body composition was assessed using computed tomography, including visceral (VAT), subcutaneous (SAT), and epicardial adipose tissue (EAT), as well as skeletal muscle quantity (psoas muscle index, PMI) and quality (psoas muscle density, PMD). Functional performance was evaluated using handgrip strength (HGT) and the 6-min walk test (6MWT). Circulating exerkines were quantified using the Olink technology. Associations between proteins and clinical variables were assessed using age- and creatinine-adjusted linear models with false discovery rate correction. RESULTS: Among patients with HFrEF, 36% were obese and 38% exhibited central obesity independent of BMI. Muscle strength and muscle quality were strongly associated with functional capacity. VAT correlated with muscle mass but not with muscle quality or performance. Compared with controls, HFrEF patients demonstrated elevated inflammatory and metabolic stress-related exerkines including CXCL8, CCL2, IL-6, TNF, IL-15, GDF15, FGF21, ANGPTL4, CTSB, DCN, and resistin. In contrast, proteins associated with muscle integrity and regenerative signaling (myostatin, BDNF, IL-7, SPARC) were significantly reduced. In HFrEF patients leptin strongly correlated with adiposity measures. Metabolic stress mediators (GDF15, IL-15, FGF21, CTSB) were inversely associated with muscle quality and functional performance, whereas myostatin positively correlated with muscle quality, strength, and exercise capacity. BDNF was inversely associated with frailty. CONCLUSIONS: Advanced HFrEF is characterized by a dysregulated exerkine network linking adiposity, skeletal muscle quality, and functional performance. Four biologically coherent axes were identified: a leptin-driven adiposity axis, a metabolic stress-muscle quality axis, a myostatin-related muscle function axis, and a neurotrophic frailty axis. These findings support the presence of a systemic cardio-adipose-muscle signaling network in end-stage HFrEF and identify candidate molecular mediators of sarcopenia and functional decline.

Humans↗

PubMed2026

Identifying biomarkers of accelerated ageing in cancer patients from routine clinical data.

INTRODUCTION: Cancer and ageing have a bidirectional relationship: age is the strongest risk factor for cancer, and cancer and treatments can accelerate ageing. Therefore, biological age can differ from chronological age; biomarkers are needed to stratify interventions to minimise accelerated ageing. METHODS: PhenoAge was calculated from routine blood test results of patients attending a Geriatric Oncology clinic. PhenoAgeAccel was the residual from a regression of PhenoAge against age. RESULTS: Data were available for 173 patients (62% male). Mean PhenoAge was higher than age (84.3 (12.6) vs 76.2 (7.24), p < 0.001), though the two were correlated (r = 0.579, p < 0.001). Unlike age, PhenoAge and PhenoAgeAccel were associated with one-year mortality (PhenoAge OR=1.083, 95% CI: 1.038-1.136; PhenoAgeAccel OR=1.096, 95% CI: 1.047-1.155). PhenoAge correlated with Clinical Frailty Score and Timed Up and Go (CFS: Rs=0.31, p < 0.001; TUG: Rs=0.25, p < 0.005); there were no correlations with age. PhenoAgeAccel correlated with the number of CGA interventions made (Rs=0.17, p < 0.05), unlike age and PhenoAge. Patients with diabetes mellitus had a higher PhenoAgeAccel compared to those without (3.40 vs -1.71, p = 0.002). In patients receiving systemic anti-cancer treatment, patients with PhenoAgeAccel calculated pre-treatment had less age acceleration than those with PhenoAgeAccel calculated post-treatment, both overall (2.18 vs -2.87; p = 0.048) and in matched samples (n = 21, 7.76 vs -2.87, p < 0.001). CONCLUSIONS: PhenoAgeAccel is a greater predictor of risk than chronological age in older people with cancer. This makes it a promising biomarker to stratify patients for holistic geriatric assessment, dose reductions, or future geroprotective measures which could be integrated within electronic healthcare record systems.

Humans↗