RESUMO
PURPOSE: Frailty, type 2 diabetes (T2D) and dyslipidemia are highly prevalent in middle-aged and elderly populations. However, evidence on the longitudinal association of frailty with T2D and dyslipidemia is limited. The aim of our study was to explore the cross-sectional and longitudinal effects of frailty levels on T2D and dyslipidemia in combination with phenotypic frailty and frailty index (FI). MATERIALS AND METHODS: Multivariate logistic regression model was used to explore the association of frailty status with T2D and dyslipidemia. Area under curve (AUC) of the receiver operating characteristic curve (ROC) to estimate the predictive values of phenotypic frailty and frailty index for T2D and dyslipidemia. In addition, depressive symptom was used as a mediating variable to examine whether it mediates the association between frailty and T2D or dyslipidemia. RESULTS: 10,203 and 9587 participants were chosen for the longitudinal association analysis of frailty with T2D and dyslipidemia. Frailty was associated with T2D (phenotypic frailty: OR=1.50, 95 %CI=1.03, 2.17; FI: OR=1.17, 95 %CI=1.08, 1.26) and dyslipidemia (phenotypic frailty: OR=1.56, 95 %CI=1.16, 2.10; FI: OR=1.17, 95 %CI=1.10, 1.25). Phenotypic frailty and frailty index significantly improved the risk discrimination of T2D and dyslipidemia (p<0.05). Depressive symptoms played a mediating role in the association between frailty and long-term T2D or dyslipidemia (p<0.05). CONCLUSION: Frailty had adverse effects on type 2 diabetes and dyslipidemia, with depressive symptoms acting as the mediator.
Assuntos
Diabetes Mellitus Tipo 2 , Dislipidemias , Fragilidade , Idoso , Humanos , Pessoa de Meia-Idade , Fragilidade/complicações , Fragilidade/epidemiologia , Fragilidade/diagnóstico , Estudos Longitudinais , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/epidemiologia , Diabetes Mellitus Tipo 2/diagnóstico , Idoso Fragilizado , Estudos Transversais , Estudos de Coortes , Dislipidemias/epidemiologia , China/epidemiologiaRESUMO
This study focuses on recent advances in proteomics and provides an up-to-date use of this technology in identifying cardiovascular disease (CVD) biomarkers. A total of eight electronic databases (PubMed, EMBASE, Web of Science, Cochrane Library, Wanfang, Vip, Sinomed, and CNKI) were searched and five were used for integrative analysis of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic ratio (DOR) and 1 secondary indicator area under the curve (AUC). This systematic review and integrative analysis summarized potential biomarkers previously identified by proteomics. The integrative analysis suggested that proteomics technology had high clinical value in CVD diagnosis. The findings provided new possible directions for the prevention or diagnosis of CVD.