Da Yin

Previous studies indicated that obesity defined by BMI accelerate frailty, but this effect weakens when stratified by metabolic status. The Weight-adjusted Waist Index (WWI), a better indicator of central obesity, may provide a more accurate measurement. The primary aim of this study was to estimate the impact of WWI on frailty progression and compare its effects across different metabolic statuses. We used data from 10,440 participants aged 45 and older from the China Health and Retirement Longitudinal Study (CHARLS).The Frailty Index (FI) was derived from 32 health deficits. K-means clustering identified three trajectories for WWI or BMI: stable low, stable moderate, and stable high. Longitudinal associations were assessed using Accelerated Failure Time and linear mixed models. Each Standard deviation (SD) increment in WWI was linked to a faster onset of frailty [Time ratio (TR) = 0.899; 95% CI 0.872 to 0.926; p < 0.001] and accelerated FI progression (β = 0.186/year; 95% CI 0.152 to 0.220/year; p < 0.001). For BMI, each SD increment was associated with a shorter time to frailty onset (TR = 0.943; 95% CI 0.917 to 0.970; p < 0.001), which was positively correlated with the accelerated FI, but this estimate is imprecise. Smooth curve fitting revealed a dose–response relationship between WWI and FI and a U-shaped relationship between BMI and FI. In WWI trajectories, stable moderate, and stable high groups presented shorter frailty onset time and accelerated FI progression. For BMI trajectories, only the stable high group was associated with frailty progression in participants without baseline underweight. Stratified analysis showed that the association between WWI and FI progression remained consistent across different metabolic statuses, while the association between BMI and FI progression was weakened in all subgroups. WWI accelerates the progression of frailty,and remains consistent across different metabolic statuses, unlike BMI. This indicates that WWI may better capture obesity-related frailty risk, emphasizing the role of central obesity in frailty assessment.

This study attempts to compare the predictive effects of several prediction models on obstructive coronary artery disease (OCAD) in young patients (30–50 years old), with a view to providing a new evaluation tool for the prediction of premature coronary artery disease (PCAD). A total of 532 hospitalized patients aged 30–50 were included in the study.All of them underwent coronary computed tomography angiography (CCTA) for suspected symptoms of coronary heart disease.Coronary artery calcium score (CACS) combined with traditional risk factors and pre-test probability models are the prediction models to be compared in this study.The PTP model was selected from the upgraded Diamond-Forrester model (UDFM) and the Duke clinical score (DCS). All patients included in the study were aged 30–50 years. Among them, women accounted for 24.4%, and 355 patients (66.7%) had a CACS of 0. OCAD was diagnosed in 43 patients (8.1%). The CACS combined with traditional risk factors to predict the OCAD area under the curve of receiver operating characteristic (ROC) (AUC = 0.794,p < 0.001) was greater than the PTP models (AUCUDFM=0.6977,p < 0.001;AUCDCS=0.6214,p < 0.001). By calculating the net reclassification index (NRI) and the integrated discrimination index (IDI), the ability to predict the risk of OCAD using the CACS combined with traditional risk factors was improved compared with the PTP models (NRI&IDI > 0,p < 0.05). The predictive value of CACS combined with traditional risk factors for OCAD in young patients is better than the PTP models.

Background: This study aimed to introduce a novel optical coherence tomography-derived fractional flow reserve (FFR) computational approach and assess the diagnostic performance of the algorithm for assessing physiological function. Methods: The fusion of coronary optical coherence tomography and angiography was used to generate a novel FFR algorithm (AccuFFRoct) to evaluate functional ischemia of coronary stenosis. In the current study, a total of 34 consecutive patients were included, and AccuFFRoct was used to calculate the FFR for these patients. With the wire-measured FFR as the reference standard, we evaluated the performance of our approach by accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Per vessel accuracy, sensitivity, specificity, PPV, and NPV for AccuFFRoct in identifying hemodynamically significant coronary stenosis were 93.8%, 94.7%, 92.3%, 94.7%, and 92.3%, respectively, were found. Good correlation (Pearson’s correlation coefficient r = 0.80, p < 0.001) between AccuFFRoct and FFR was observed. The Bland-Altman analysis showed a mean difference value of –0.037 (limits of agreement: –0.189 to 0.115). The area under the receiver-operating characteristic curve (AUC) of AccuFFRoct in identifying physiologically significant stenosis was 0.94, which was higher than the minimum lumen area (MLA, AUC = 0.91) and significantly higher than the diameter stenosis (%DS, AUC = 0.78). Conclusions: This clinical study shows the efficiency and accuracy of AccuFFRoct for clinical implementation when using invasive FFR measurement as a reference. It could provide important insights into coronary imaging superior to current methods based on the degree of coronary artery stenosis. Background: This study aimed to introduce a novel optical coherence tomography-derived fractional flow reserve (FFR) computational approach and assess the diagnostic performance of the algorithm for assessing physiological function. Methods: The fusion of coronary optical coherence tomography and angiography was used to generate a novel FFR algorithm (AccuFFRoct) to evaluate functional ischemia of coronary stenosis. In the current study, a total of 34 consecutive patients were included, and AccuFFRoct was used to calculate the FFR for these patients. With the wire-measured FFR as the reference standard, we evaluated the performance of our approach by accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Per vessel accuracy, sensitivity, specificity, PPV, and NPV for AccuFFRoct in identifying hemodynamically significant coronary stenosis were 93.8%, 94.7%, 92.3%, 94.7%, and 92.3%, respectively, were found. Good correlation (Pearson’s correlation coefficient r = 0.80, p < 0.001) between AccuFFRoct and FFR was observed. The Bland-Altman analysis showed a mean difference value of –0.037 (limits of agreement: –0.189 to 0.115). The area under the receiver-operating characteristic curve (AUC) of AccuFFRoct in identifying physiologically significant stenosis was 0.94, which was higher than the minimum lumen area (MLA, AUC = 0.91) and significantly higher than the diameter stenosis (%DS, AUC = 0.78). Conclusions: This clinical study shows the efficiency and accuracy of AccuFFRoct for clinical implementation when using invasive FFR measurement as a reference. It could provide important insights into coronary imaging superior to current methods based on the degree of coronary artery stenosis.

Background: The addition of coronary artery calcium score (CACS) to prediction models has been verified to improve performance. Machine learning (ML) algorithms become important medical tools in an era of precision medicine, However, combined utility by CACS and ML algorithms in hypertensive patients to forecast obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CCTA) is rare. Methods: This retrospective study was composed of 1,273 individuals with hypertension and without a history of CAD, who underwent dual-source computed tomography evaluation. We applied five ML algorithms, coupled with clinical factors, imaging parameters, and CACS to construct predictive models. Moreover, 80% individuals were randomly taken as a training set on which 5-fold cross-validation was done and the remaining 20% were regarded as a validation set. Results: 16.7% (212 out of 1,273) of hypertensive patients had obstructive CAD. Extreme Gradient Boosting (XGBoost) posted the biggest area under the receiver operator characteristic curve (AUC) of 0.83 in five ML algorithms. Continuous net reclassification improvement (NRI) was 0.55 (95% CI (0.39–0.71), p < 0.001), and integrated discrimination improvement (IDI) was 0.04 (95% CI (0.01–0. 07), p = 0.0048) when the XGBoost model was compared with traditional Models. In the subgroup analysis stratified by hypertension levels, XGBoost still had excellent performance. Conclusion: The ML model incorporating clinical features and CACS may accurately forecast the presence of obstructive CAD on CCTA among hypertensive patients. XGBoost is superior to other ML algorithms.

Purpose: High triglyceride glucose (TyG) index level is one of the risks for cardiovascular events. The purpose of this research was to examine the correlation of the triglyceride glucose (TyG) index levels with plaque characteristics and calcification types determined by intravascular ultrasound (IVUS) in acute coronary syndrome (ACS) patients. Methods: A total of 234 acute coronary syndromes (ACS) participants who completed intravascular ultrasound (IVUS) and coronary angiography (CAG) were finally enrolled. Results: Logistic regression analysis manifested that the TyG index was independently correlated with the occurrence of coronary calcification, minimum lumen area (MLA) ≤ 4.0 mm², plaque burden (PB) > 70%, and spotty calcification. Taking the lowest group as a reference, the risk of coronary calcification (OR, 2.57; 95%CI, 1.04–6.35; p = 0.040), MLA ≤ 4.0 mm² (OR, 7.32; 95%CI, 2.67–20.01; p < 0.001), PB > 70% (OR, 2.68; 95%CI, 1.04–6.91; p = 0.041), and spotty calcification (OR, 1.48; 95%CI, 0.59–3.71; p = 0.407) was higher in the highest TyG index group. TyG index was converted into a dichotomous variable or a continuous variable for analysis, and we found that a similar result was observed. In addition, optimal predictive models consisting of clinical variables and the TyG index distinctly improved the ability to predict the prevalence of coronary calcification and MLA ≤ 4.0 mm² (p < 0.05). Conclusion: The TyG index may serve as a potential predictor for calcification patterns and plaque vulnerability.

Background: Hypertension is a leading risk of coronary artery disease (CAD). Triglyceride glucose index (TyG) is a surrogate of insulin resistance (IR). Few studies explore the association between TyG and the incidence of obstructive CAD (OCAD) in hypertensive patients. Methods: We retrospectively screened 1841 hypertensive subjects who were free of a history of CAD and underwent coronary computed tomography angiography (CCTA) because of chest pain. TyG index was calculated as ln (fasting TG [mg/dL] * fasting glucose [mg/dL]/2). The outcome of this research was OCAD, which was defined as the presence of diameter stenosis ≥ 50% in any of the four major epicardial coronary arteries detected on CCTA. Results: A total of 310 (16.8%) patients developed obstructive CAD. The restricted cubic spline (RCS) analysis showed a J-shaped relationship between TyG and OCAD and the OR for OCAD increased as the TyG rose over 8.61 (OR perSD) 1.64, 95% CI 1.13–2.54, p = 0.008). After full adjustments for confounding covariates, patients with TyG index in tertile 3 (T3) had 2.12 times (95% CI 1.80 to 3.81) and in T2 had 2.01 times (95% CI 1.40 to 2.88) as high as the risk of OCAD compared with patients in T1 (p for trend = 0.001). When regarding TyG as a continuous variable, 1-SD increase elevated 49% (OR (95%CI), 1.49 (1.30–1.74)) risk of obstructive CAD (p = 0.007). This positive effect was still consistent across the subgroups (p for interaction > 0.05). Conclusion: TyG index was associated with the incidence of obstructive CAD in hypertensive patients.

Objective: We investigated the predictive value of clinical factors combined with coronary artery calcium (CAC) score based on a machine learning method for obstructive coronary heart disease (CAD) on coronary computed tomography angiography (CCTA) in individuals with atypical chest pain. Methods: The study included data from 1,906 individuals undergoing CCTA and CAC scanning because of atypical chest pain and without evidence for the previous CAD. A total of 63 variables including traditional cardiovascular risk factors, CAC score, laboratory results, and imaging parameters were used to build the Random forests (RF) model. Among all the participants, 70% were randomly selected to train the models on which fivefold cross-validation was done and the remaining 30% were regarded as a validation set. The prediction performance of the RF model was compared with two traditional logistic regression (LR) models. Results: The incidence of obstructive CAD was 16.4%. The area under the receiver operator characteristic (ROC) for obstructive CAD of the RF model was 0.841 (95% CI 0.820–0.860), the CACS model was 0.746 (95% CI 0.722–0.769), and the clinical model was 0.810 (95% CI 0.788–0.831). The RF model was significantly superior to the other two models (p < 0.05). Furthermore, the calibration curve and Hosmer–Lemeshow test showed that the RF model had good classification performance (p = 0.556). CAC score, age, glucose, homocysteine, and neutrophil were the top five important variables in the RF model. Conclusion: RF model was superior to the traditional models in the prediction of obstructive CAD. In clinical practice, the RF model may improve risk stratification and optimize individual management.

Low-density lipoprotein cholesterol (LDL-C) is a traditional and important risk factor for atherosclerotic cardiovascular disease (CVD). Recently, lipoprotein (a) (lp(a)) attracts considerable attention as a residual risk factor for CVD. However, the roles of lp(a) in acute coronary syndrome (ACS) patients with well-controlled LDL-C (≤1.8mmol/L) after percutaneous coronary intervention (PCI) remain unclear. Current study results demonstrated that occurrence of major adverse cardiovascular events (MACE) and recurrent myocardial infarction (MI) increased with the Lp(a) increasing in patients with LDL-C≤1.8mmol/L at 1-month follow-up. In relatively low-risk patients presented with ACS and underwent PCI (LDL-C ≤1.8mmol/L at 1-month follow-up), lp(a) is still independently related to adverse prognosis. Further researches of targeted therapy against lp(a) are warranted.

To evaluate the diagnostic efficacy of CCTA + plain scan for ruptured plaques, with optical coherence tomography (OCT) as the reference, and to provide preliminary analysis of influential factors. Patients who underwent CCTA and OCT were retrospectively enrolled. The diagnostic standards for ruptured plaque on CCTA + plain scan were ulcer or intra-plaque dye penetration on CCTA, and a careful review of images from the plain scans to ensure areas of them were not calcification. The diagnosis of ruptured plaque was made by OCT. Total 65 patients with 71 plaques were included. There were 40 OCT-confirmed ruptured plaques in 38 patients and 31 OCT-confirmed non-ruptured plaques in 27 patients. CCTA + plain scan identified 27 ruptured plaques in 27 patients and 28 non-ruptured plaques in 24 patients. With OCT as the gold standard, the per-patient sensitivity, specificity, positive and negative predictive values, and accuracy of CCTA + plain scan for diagnosing ruptured plaque were 71%, 89%, 90%, 69%, and 78%, and there was good agreement (Kappa = 0.70) between CCTA + plain scan and OCT. Among 13 false negative ruptured plaques, 2 had calcifications close to the rupture, and the cavity depth in the remaining 11 was 0.46 ± 0.17 mm, versus 0.98 ± 0.26 mm in 27 true positive ruptured plaques (P < 0.01). CCTA + plain scan may identify morphological features of ruptured plaques. The cavity depth of the ruptured plaques and calcification at the rupture site seem major factors influencing the diagnostic accuracy for plaque rupture. Future perspective studied are needed to confirm these preliminary findings.

Objectives: This study aimed to investigate the association of serum alkaline phosphatase (ALP) with calcification patterns and plaque morphology detected by intravascular ultrasound (IVUS) in acute coronary syndrome (ACS) patients.Background: ALP has been shown to predict vascular calcification and long‐term cardiovascular events. However, the relationship between ALP and vascular calcification patterns or plaque morphology remains unclear.Methods: In total, 328 ACS patients who underwent IVUS examinations were screened from January 2017 to December 2018; among them, 234 eligible participants were grouped according to the tertiles of ALP levels (80 IU/L). Demographic data and IVUS parameters were documented and analyzed.Results: After adjusting for potential confounders, independent associations were observed between ALP and the presence of coronary calcification, spotty calcification, minimum lumen area (MLA) ≤ 4.0 mm², and plaque burden (PB) > 70%. Compared with the lowest ALP tertile group, the highest ALP group had higher risks of calcification (odds ratio [OR], 2.85; 95% confidence interval [95%CI], 1.38–5.90; p = .005), spotty calcification (OR, 1.86; 95%CI, 1.09–3.84; p = .012), MLA≤4.0 mm² (OR, 3.32; 95%CI, 1.51–7.28; p = .003), and PB > 70% (OR, 4.59; 95%CI, 1.83–11.50; p = .001). Similar results were found when ALP was analyzed as a continuous variable or a category variate according to the cut‐off value determined by the receiver operating characteristic curve analysis. Furthermore, the model including clinical factors and ALP significantly improved the predictive power for coronary calcification, spotty calcification, MLA≤4.0 mm², and PB > 70%.Conclusion: Our findings suggest that ALP may be a potential predictive biomarker for calcification and plaque vulnerability.