Background: Klotho deficiency is a significant predictor of cardiovascular disease (CVD)-related mortality and morbidity. However, research assessing the association between klotho and individual risk factors of CVD is limited. This study aimed to explore the association between circulating serum klotho levels and risk factors for CVD in adults. Methods: We used the 2007–2016 National Health and Nutrition Examination Survey and included 13,154 participants for whom serum klotho levels were available. Body mass index (BMI), exercise, smoking status, alcohol consumption, hypertension, dyslipidemia, serum lipid parameters, and blood pressure were considered as CVD risk factors. Results: Circulating klotho levels were negatively associated with being overweight (beta coefficient: − 22.609, p = 0.0025), obesity (beta coefficient: − 23.716, p = 0.0011), current smoking (beta coefficient: − 46.412, p < 0.0001), and alcohol consumption (beta coefficient: − 51.194, p < 0.0001). There was a positive association between serum klotho levels and no history of dyslipidemia (beta coefficient: 15.474, p = 0.0053). Serum klotho levels were significantly decreased by a unit increase in triglycerides (beta coefficient: − 0.117, p = 0.0006) and total cholesterol (beta coefficient: − 0.249, p = 0.0002). There was a significant non-linear relationship between serum klotho levels, triglycerides, and total cholesterol. Conclusions: Lower serum klotho levels are associated with certain CVD risk factors, including high BMI, smoking, alcohol consumption, and lipid parameters (triglycerides and total cholesterol). This study suggests that the soluble klotho level may be a potential marker for CVD risk.

(1) Background: Previous studies have suggested the association between lipid profiles and cognitive function in older adults. However, they generated inconsistent results. We aim to determine the relationship between lipid profiles and cognitive performance in older adults. (2) Methods: We used the 2011–2014 National Health and Nutrition Examination Survey. This study included 2215 participants who were aged more than 60 years old and free of coronary heart disease or stroke. Lipid profiles included total cholesterol (TC), low density lipid cholesterol (LDL), high density lipid cholesterol (HDL), and triglyceride (TG). Cognitive function was assessed using the digit symbol substitution test (DSST). (3) Results: Positive correlations of DSST were observed with TC (r = 0.111; p < 0.0001), HDL (r = 0.127; p < 0.0001), and LDL (r = 0.107; p = 0.0005). However, there was no significant relationship between TG and DSST. A one-unit increase in HDL was associated with an increase in DSST score (beta coefficient: 0.036; p = 0.018); but the association was not significant for LDL, TG, and TC. In the categorical analysis, the high HDL group had a higher DSST score than the low HDL group (beta = 3.113; p < 0.0001) and the low TG group was more likely to show a lower DSST score than the high TC group (beta = −1.837; p = 0.0461). However, LDL and TC showed no statistically significant associations. Moreover, HDL was only associated with a 0.701 times increased risk of cognitive impairment (95% CI = 0.523–0.938) in the logistic regression analysis. (4) Conclusions: Higher blood concentrations of HDL levels were positively associated with DSST scores in older adults. We suggest that the high levels of HDL may be a protective factor against cognitive impairment.

We investigated predictions from ¹⁸F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent ¹⁸F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians’ assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (p < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians.

We investigated the prognostic significance of radiomic features from ¹⁸F-FDG PET/CT to predict overall survival (OS) in patients with stage III NSCLC undergoing neoadjuvant chemoradiation therapy followed by surgery. We enrolled 300 patients with stage III NSCLC who underwent PET/CT at the initial work-up (PET1) and after neoadjuvant concurrent chemoradiotherapy (PET2). Radiomic primary tumor features were subjected to LASSO regression to select the most useful prognostic features of OS. The prognostic significance of the LASSO score and conventional PET parameters was assessed by Cox proportional hazards regression analysis. In conventional PET parameters, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of each PET1 and PET2 were significantly associated with OS. In addition, both the PET1-LASSO score and the PET2-LASSO score were significantly associated with OS. In multivariate Cox regression analysis, only the PET2-LASSO score was an independently significant factor for OS. The LASSO score showed better predictive performance for OS regarding the time-dependent receiver operating characteristic curve and decision curve analysis than conventional PET parameters. Radiomic features from PET/CT were an independent prognostic factor for the estimation of OS in stage III NSCLC. The newly developed LASSO score using radiomic features showed better prognostic results for individualized OS estimation than conventional PET parameters.