| Background:Population aging,or the percentage of older adults in a country’s population,is increasing globally,including in China.In 2020,China’s population of 65+-year-olds was 18.7%;this is estimated to rise to 30%by 2050.Population aging is accompanied by an increased prevalence of undernutrition,or those suffering from generally poor nutritional status.Several age-related pathophysiological and psychosocial factors,as well as protein and nutrient intake and drug use,shape dietary habits,leading to specific nutritional deficits.Among the older adults admitted to hospital,undernutrition is a common and serious nutritional condition,which often deteriorates further during hospitalization.The prevalence of hospital undernutrition is estimated to range from 20%to 50%of inpatients.Undernutrition’s negative impact on health substantially increases the risk of frailty,owing in part to decreased protein intake that accelerates the declines in muscle mass.Frailty is a biological syndrome characterized by deteriorating functions across a broad spectrum of physiological symptoms,accompanied by increased vulnerability to stressors.Therefore,early detection of frailty in older inpatients with undernutrition is essential to prevent adverse clinical outcomes.However,most of the frailty instruments have been developed by researchers outside of China;the current frailty assessment tools are being developed for specific populations and on the basis of biological causality.Thus,there is an urgent need to develop a frailty prediction model based on Chinese older inpatients with undernutrition.Objectives:(1)To develop a frailty prediction model for older inpatients with undernutrition in China;(2)To apply the model in a clinical setting,identify high-risk patients,and explore the portability and generalizability of the model;(3)To explore the biomarkers that may be associated with the diagnosis of frailty,and to provide objective measurement indicators for the diagnostic criteria of frailty.Methods:The current study comprised three parts.Study 1:We identified the factors associated with frailty among 2842 older inpatients with undemutrition;developed a frailty prediction model and conducted internal verification;and evaluated the discrimination,calibration,and clinical validity of the model.Study 2:We conducted a cohort study among 232 older inpatients with undernutrition,and to carry out the temporal validation and geographical validation of the model.Study 3:We conducted bioinformatics analysis based on GEO and Genecards to explore blood biomarkers that may be associated with frailty;the collected blood samples were tested using ELISA technology,and the correlations with frailty were discussed.Results:Study 1:The frailty prediction model developed for older inpatients with undernutrition identified 11 risk factors,including clinical and functional markers,mental and psychological factors,and nutritional status indicators.The incidence of frailty was 13.54%in the derivation cohort and 13.43%in the validation cohort.The model’s C-statistic after modeling with the original data was 0.733(95%Confidence Interval[0.698-0.698]),while the adjusted C-statistic with Bootstrapping was 0.694(95%CI[0.659-0.659]).The area under the receiver operating characteristic curve was 0.71.The calibration slope(0.806)of the model showed good consistency between the risk of frailty at the 30-day follow-up and the actual observed frailty risk.To further validate the model,we applied it in Hubei Province with the validation cohort.The model’s C-statistic was 0.645(95%CI[0.575-0.71]);the calibration slope of the frailty prediction model showed good consistency,and the calibration slope was as high as 0.879.The clinical usefulness of the model was described using decision curve analysis(DCA).For probability thresholds between 5%and 45%,the new frailty prediction model showed a positive net benefit.Study 2:We prospectively collected blood samples from 268 older inpatients in non-intensive departments,who served as the external validation cohort 2;this group’s incidence of frailty was 15.09%(35/232).The model’s C-statistic was 0.743(95%CI[0.649-0.891])and the calibration slope was 0.856.Study 3:a bioinformatics analysis based on the GEO and GeneCards database revealed five potential biomarkers,namely,Myostatin(MSTN),and MSTN inhibitory binding protein decorin(Decorin,DCN),Irisin,leptin(LEP),and adenylate-activated protein kinase(AMP-activated protein kinase,AMPK).The five cytokines were measured using ELISA Kits technology.The results indicated that the levels of LEP,AMPK,and MSTN in the frail patients were significantly different from those in the non-frail group(P<0.05).The difference in DCN levels between the different groups was nonsignificant(P=0.287).The results of a correlation analysis showed that LEP and Irisin were negatively correlated with frailty.The results of a multiple linear regression analysis suggested that among the 15 potential frailty-related blood biomarkers,LEP and MSTN had stronger effects on frailty than the other three myokines.Conclusions:This study developed the first frailty prediction model for older inpatients in China with undernutrition,and identified 11 risk factors;indicators comprised functional markers,blood biological markers,clinical markers,and psychological factors.The internal verification of the model indicates that it has good discrimination,calibration,and clinical benefits.Furthermore,the results of both temporal and geographical validation suggest that the model has strong portability and generalizability.The results of a bioinformatics analysis combined with ELISA technology,which were conducted to explore potential blood biomarkers related to frailty,showed that LEP,DCN,MSTN,AMPK,and Irisin may be associated with frailty.The current study integrates nursing research into a multidisciplinary research approach,and illustrates the substantial value in multidisciplinary crossover for improving the quality of nursing research and enriching nursing studies. |
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