Aim: To investigate the role of neutrophil-to-lymphocyte ratio(NLR) and platelet-to-lymphocyte(PLR) in predicting the risk of in-hospital mortality in elderly acute myocardial infarction(AMI) patients. Methods: This study was a single-center, retrospective and observational study. From December 2015 to December 2021, a total of 1550 elderly patients (age ≥ 60 years) with AMI with complete clinical history data were enrolled in the Second Hospital of Dalian Medical University. Routine blood tests were performed on admission, and NLR and PLR were calculated based on neutrophil, platelet, and lymphocyte counts. Outcome was defined as all-cause mortality during hospitalization. Cox regression and restricted spline cubic(RCS) models were used to evaluate the association of NLR and in-hospital mortality risk and the association of PLR with in-hospital mortality risk, respectively. Results: (1) A total of 132 (8.5%) patients died during hospitalization. From the results of blood routine, the white blood cell, neutrophil, NLR and PLR in the death group were higher than those in the non-death group, while the lymphocyte was lower than that in the non-death group, and the difference was statistically significant (P < 0.05). (2) The results of receiver operating characteristic(ROC) curves analysis showed that the predictive ability of NLR (AUC = 0.790) for in-hospital death was better than that of PLR (AUC = 0.637). (3) Multivariate Cox proportional regression hazard models showed that high NLR was associated with the risk of in-hospital mortality in elderly AMI patients (HR = 3.091, 95% CI 2.097– 4.557, P < 0.001), while high PLR was not. (4) RCS models showed a nonlinear dose-response relationship between NLR and in-hospital death (P for nonlinear = 0.0007). Conclusion: High NLR (> 6.69) is associated with the risk of in-hospital mortality in elderly patients with AMI and can be an independent predictor of poor short-term prognosis in elderly patients with AMI.

Background: Atherogenic Index of plasma (AIP) is closely related to metabolic abnormalities. But as of now, there is no definitive conclusion on the dose-response relationship pattern between AIP and metabolic associated fatty liver disease (MAFLD).Objective: The objective of this study was to provide a fresh insight for understanding the intrinsic link between AIP and the prevalence of MAFLD by exploring the dose-response pattern between AIP and MAFLD.Methods: A total of 9254 participants received the survey and 1090 participants were finally included according to the screening criteria. To evaluate the association between AIP and the prevalence of MAFLD based on weighted multivariate logistic regression. Sensitivity analysis of the association between AIP and MAFLD was performed using propensity score matching (PSM). Restrictive cubic splines (RCS) were used to identify patterns of dose-response relationships between AIP and MAFLD, and receiver operator characteristic (ROC) curves were used to evaluate the predictive ability of AIP and traditional lipid parameters for MAFLD.Results: In this study, a total of 563 participants were found to have MAFLD. The results of weighted multivariate logistic regression analysis demonstrated that, after adjusting for sex and age, participants in the highest quartile (Q4) of AIP had a significantly increased risk of developing MAFLD compared to those in the lowest quartile (Q1) (Model 2: OR = 9.03, 95% CI 4.75–17.17). A similar trend was observed in the fully adjusted model (Model 3: OR = 3.85, 95% CI 1.55–9.52). The RCS analysis revealed a linear dose-response association between AIP and MAFLD(P for crude non-linearity = 0.087). This association remained significant after accounting for potential confounding variables(P for adjusted non-linearity = 0.663). The ROC curve results suggest that AIP performs better than traditional lipid indicators in predicting MAFLD (AUC = 0.732, 95%CI 0.705–0.758).Conclusion: A linear dose-response relationship exists between AIP and MAFLD, suggesting that as AIP increases, so does the risk of developing MAFLD.

The correlation between the triglyceride-glucose (TyG) index and mortality in the general population remains controversial, with inconsistent conclusions emerging from different studies. This study aims to investigate whether there is an association between the TyG index and mortality in the general population in the United States, and to explore whether a new index combining the TyG index with systemic inflammation indicators can better predict all-cause and cardiovascular mortality risks in the general population than using the TyG index alone. Calculate the systemic inflammation indicators and TyG index for each participant based on their complete blood count, as well as their triglyceride and glucose levels in a fasting state. TyG-inflammation indices were obtained by multiplying the TyG index with systemic inflammation indicators (TyG-NLR, TyG-MLR, TyG-lgPLR, TyG-lgSII, and TyG-SIRI). Based on the weighted Cox proportional hazards model, assess whether the TyG and TyG-Inflammation indices are associated with mortality risk in the general population. Restricted cubic splines (RCS) are used to clarify the dose-response relationship between the TyG and TyG-Inflammation indices and mortality, and to visualize the results. Time-dependent receiver operating characteristic (ROC) curves are used to evaluate the accuracy of the TyG and TyG-Inflammation indices in predicting adverse outcomes. This study included 17,118 participants. Over a median follow-up period of 125 months, 2595 patients died. The TyG index was not found to be related to mortality after adjusting for potentially confounding factors. However, the TyG-inflammation indices in the highest quartile (Q4), except for TyG-lgPLR, were significantly associated with both all-cause and cardiovascular mortality, compared to those in the lowest quartile (Q1). Among them, TyG-MLR and TyG-lgSII showed the strongest correlations with all-cause mortality and cardiovascular mortality. Specifically, compared to their respective lowest quartiles (Q1), participants in the highest quartile (Q4) of TyG-MLR had a 48% increased risk of all-cause mortality (95% CI: 1.23–1.77, P for trend < 0.0001), while participants in the highest quartile (Q4) of TyG-lgSII had a 92% increased risk of cardiovascular mortality (95% CI: 1.31–2.81, P for trend < 0.001). Time-dependent ROC curve analysis showed that the TyG-MLR had the highest accuracy in predicting long-term mortality outcomes. The TyG-Inflammation indices constructed based on TyG and systemic inflammation indicators are closely related to mortality in the general population and can better predict the risk of adverse outcomes. However, no association between TyG and mortality in the general population was found.

Background: Inflammatory scores are known to reflect the systemic inflammatory burden. Despite this, the association between the inflammatory score and the risk of all-cause and cardiovascular mortality in patients with metabolic syndrome (MetS) remains poorly understood. To address this gap in the literature, this study investigated this potential association between these two factors.Methods: A total of 3401 patients with MetS from the National Health and Nutrition Examination Survey (1999–2010) were enrolled. Survival status and cause of death were obtained by linking data from the National Death Index (NDI). The inflammatory score was calculated based on the sum of the Z-scores for white blood cell (WBC) count and C-reactive protein (CRP) at baseline. The patients were divided into inflammatory score quartiles. Cox proportional hazards regression was used to determine the association between inflammatory score and mortality. Restricted cubic splines (RCS) were used to explore the dose-response relationship between inflammatory score and mortality. Stratified analyses and interaction tests were conducted according to sex, age, body mass index (BMI), alcohol consumption, smoking status, hypertension, diabetes, and stroke status.Results: After a mean follow-up of 145.9 months, 1039 all-cause deaths and 295 cardiovascular deaths were recorded. The results of multivariate Cox regression analysis showed that compared to the lowest quartile (Q1), patients in the highest quartile (Q4) had a 1.74-fold increased risk of all-cause mortality (Model 3: HR = 1.74, 95%CI 1.30–2.32, P < 0.001) and a 1.87-fold increased risk of cardiovascular mortality (Model 3: HR = 1.87, 95%CI 1.12–3.13, P = 0.020). There was a ‘J’-shaped nonlinear relationship between the inflammatory score and all-cause mortality (P for nonlinearity = 0.001), and a marginally significant ‘J’-shaped relationship with cardiovascular mortality (P for nonlinearity = 0.057). The threshold points of the inflammatory score for adverse outcomes were - 0.643 and - 0.621, respectively.Conclusion: The inflammatory score is independently associated with increased all-cause and cardiovascular mortality in patients with MetS, and risk stratification of these patients using inflammatory scores may provide specific therapeutic strategies to improve their prognosis.

Aim: Inflammatory response is closely associated with poor prognosis in elderly patients with acute myocardial infarction (AMI). The aim of this study was to develop an easy-to-use predictive model based on medical history data at admission, systemic immune inflammatory index (SII), and systemic inflammatory response index (SIRI) to predict the risk of in-hospital mortality in elderly patients with AMI. Methods: We enrolled 1550 elderly AMI patients (aged ≥ 60 years) with complete medical history data and randomized them 5:5 to the training and validation cohorts. Univariate and multivariate logistic regression analyses were used to screen risk factors associated with outcome events (in-hospital death) and to establish a nomogram. The discrimination, calibration, and clinical application value of nomogram were evaluated based on receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. Results: The results of multivariate logistic regression showed that age, body mass index (BMI), previous stroke, diabetes, SII, and SIRI were associated with in-hospital death, and these indicators will be included in the final prediction model, which can be obtained by asking the patient’s medical history and blood routine examination in the early stage of admission and can improve the utilization rate of the prediction model. The areas under the ROC curve for the training and validation cohorts nomogram were 0.824 (95% CI 0.796 to 0.851) and 0.809 (95% CI 0.780 to 0.836), respectively. Calibration curves and DCA showed that nomogram could better predict the risk of in-hospital mortality in elderly patients with AMI. Conclusion: The nomogram constructed by combining SII, SIRI, and partial medical history data (age, BMI, previous stroke, and diabetes) at admission has a good predictive effect on the risk of in-hospital death in elderly patients with AMI.