Physical performance strongly predicts all-cause mortality risk in a real-world population of older diabetic patients: machine learning approach for mortality risk stratification

Pre-fracture functional status and 30-day recovery predict 5-year survival in patients with hip fracture: findings from a prospective real-world study

Disability overcomes mortality burden in older adults with hip fracture, expanding unhealthy lifespan. Building comprehensive assessment, pre-fracture functional status and 30-day post-surgical recovery are the most powerful predictors of 5-years survival. A tool supporting estimation of long-term survival may optimize the appropriate delivery of targeted interventions.

BACKGROUND

Older people with hip fractures are highly heterogeneous patients, impacting health and economic systems. The availability of tools to estimate survival may help optimize patients' outcomes and treatment management decisions.

METHODS

A prospective observational study was conducted on older patients with hip fractures who received baseline and 30-day comprehensive assessment from discharge, focusing on functional status based on Basic Activity of Daily Living (BADL). The primary outcome was to identify predictors of 5-year survival and develop nomograms to be adopted at admission or 30 days after discharge.

RESULT

Among 231 hip fracture patients, 5-year survival was 38.3% in men and 61.9% in women; women experienced a 1.8 higher likelihood of survival than men. Pre-fracture functional status predicted mortality as a function of age. At hospital admission, pre-fracture BADL level was a protective factor (HR 0.742; 95% CI 0.668-0.825), while male gender (HR 1.840; 95% CI 1.192-2.841), age (HR 1.070; 95% CI 1.037-1.105), and multimorbidity (HR 1.096; 95% CI 1.007-1.193) were independent mortality risk factors. At the 30-day follow-up visit, the BADL recovery gap was an independent predictor of 5-year survival (HR 1.439; 95% CI 1.158-1.789), in addition to male gender (HR 1.773; 95% CI 1.146-2.744), age (HR 1.046; 95% CI 1.010-1.083), and pre-fracture BADL (HR 0.621; 95% CI 0.528-0.730), while comorbidity disappeared (HR 1.083; 95% CI 0.994-1.179).

CONCLUSION

More than half of hip fracture patients are still alive 5 years after surgical repair. Pre-fracture functional status and a 30-day functional recovery gap are the main predictors of survival. Nomograms may help to define prognosis and suitable interventions.

Glucagon-like peptide-1: a new potential regulator for mesenchymal stem cells in the treatment of type 2 diabetes mellitus and its complication

Glucagon-like peptide-1 is an enteric proinsulin hormone secreted by intestinal L-cells that orchestrates insulin secretion in a glucose-dependent manner. Renowned for preserving pancreatic β-cell mass, glucagon-like peptide-1 facilitates β-cell proliferation and inhibits apoptosis, while concurrently suppressing glucagon secretion from pancreatic α-cells, thereby exerting hypoglycemic effects.Recent in vitro and in vivo studies have clarified the benefits of combination therapy with glucagon-like peptide-1 and stem cells in Type 2 diabetes mellitus. Glucagon-like peptide-1 enhances the repair of type 2 diabetes mellitus-afflicted tissues and organs by modulating sourced mesenchymal stem cell differentiation, proliferation, apoptosis, and migration. Importantly, glucagon-like peptide-1 overcomes the detrimental effects of the diabetic microenvironment on transplanted mesenchymal stem cells by increasing the number of localized cells in stem cell therapy and the unstable efficacy of stem cell therapy.This review elucidates the molecular and cellular mechanisms through which glucagon-like peptide-1 regulates mesenchymal stem cells in the type 2 diabetes mellitus context and discuss its therapeutic prospects for type 2 diabetes mellitus and its associated complications, proposing a novel and comprehensive treatment paradigm.

Prognostic effects of glycaemic variability on diastolic heart failure and type 2 diabetes mellitus: insights and 1-year mortality machine learning prediction model

BACKGROUND

Diastolic heart failure (DHF) and type 2 diabetes mellitus (T2DM) often coexist, causing increased mortality rates. Glycaemic variability (GV) exacerbates cardiovascular complications, but its impact on outcomes in patients with DHF and T2DM remains unclear. This study examined the relationships between GV with mortality outcomes, and developed a machine learning (ML) model for long-term mortality in these patients.

METHODS

Patients with DHF and T2DM were included from the Medical Information Mart for Intensive Care IV, with admissions (2008-2019) as primary analysis cohort and admissions (2020-2022) as external validation cohort. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to evaluate the associations of GV with 90-day, 1-year, and 3-year all-cause mortality. The primary analysis cohort was split into training and internal validation cohorts, then developing ML models for predicting 1-year all-cause mortality in training cohort, which were validated using the internal and external validation cohorts.

RESULTS

2,128 patients with DHF and T2DM were included in primary analysis cohort (meidian age 71.0years [IQR: 62.0-79.0]; 46.9% male), 498 patients with DHF and T2DM were included in the external validation cohort (meidian age 75.0years [IQR: 67.0-81.0]; 54.0% male). Multivariate Cox proportional hazards models showed that high GV tertiles were associated with higher risk of 90-day (T2: HR 1.45, 95%CI 1.09-1.93; T3: HR 1.96, 95%CI 1.48-2.60), 1-year (T2: HR 1.25, 95%CI 1.02-1.53; T3: HR 1.54, 95%CI 1.26-1.89), and 3-year (T2: HR 1.31, 95%CI: 1.10-1.56; T3: HR 1.48, 95%CI 1.23-1.77) all-cause mortality, compared with lowest GV tertile. Chronic kidney disease, creatinine, potassium, haemoglobin, and white blood cell were identified as mediators of GV and 1-year all-cause mortality. Additionally, GV and other clinical features were pre-selected to construct ML models. The random forest model performed best, with AUC (0.770) and G-mean (0.591) in internal validation, with AUC (0.753) and G-mean (0.599) in external validation.

CONCLUSION

GV was determined as an independent risk factor for short-term and long-term all-cause mortality in patients with DHF and T2DM, with a potential intervention threshold around 25.0%. The ML model incorporating GV demonstrated strong predictive performance for 1-year all-cause mortality, highlighting its importance in early risk stratification management of these patients.

Intra-Individual Variations in How Insulin Sensitivity Responds to Long-Term Exercise: Predictions by Machine Learning Based on Large-Scale Serum Proteomics

Physical activity is effective for preventing and treating type 2 diabetes, but some individuals do not achieve metabolic benefits from exercise ("non-responders"). We investigated non-responders in terms of insulin sensitivity changes following a 12-week supervised strength and endurance exercise program. We used a hyperinsulinaemic euglycaemic clamp to measure insulin sensitivity among 26 men aged 40-65, categorizing them into non-responders or responders based on their insulin sensitivity change scores. The exercise regimen included VO2max, muscle strength, whole-body MRI scans, muscle and fat biopsies, and serum samples. mRNA sequencing was performed on biopsies and Olink proteomics on serum samples. Non-responders showed more visceral and intramuscular fat and signs of dyslipidaemia and low-grade inflammation at baseline and did not improve in insulin sensitivity following exercise, although they showed gains in VO2max and muscle strength. Impaired IL6-JAK-STAT3 signalling in non-responders was suggested by serum proteomics analysis, and a baseline serum proteomic machine learning (ML) algorithm predicted insulin sensitivity responses with high accuracy, validated across two independent exercise cohorts. The ML model identified 30 serum proteins that could forecast exercise-induced insulin sensitivity changes.