Association of physical activity with fractures in kidney transplant recipients: A Korean nationwide cohort study.

Kidney transplant recipients are at high risk for fractures, primarily due to post-transplant bone disease. This retrospective cohort study analyzed data from the Korean National Health Insurance Service, including 10 083 kidney transplant recipients examined from 2009 to 2017. We assessed fracture incidence, emphasizing vertebral and hip fractures, and the association of physical activity and traditional risk factors with fracture risk. Kidney transplant recipients were categorized into three groups according to physical activity levels: non-activity, metabolic equivalent of task (MET) 1-499, and MET ≥500. Physical activity was associated with a decreased risk of all types of fractures: any (MET 1-499: adjusted hazard ratio (aHR) .75; 95% confidence interval (CI) .62-.92, MET ≥500: aHR .84; 95% CI .70-1.00), vertebral (MET 1-499: aHR .69; 95% CI .49-.98, MET ≥500: aHR .67; 95% CI .49-.91), and hip (MET 1-499: aHR .43; 95% CI .23-.81) fractures. Additionally, older age, female sex, and diabetes were associated with an increased fracture risk. The assessment of physical activity and traditional risk factors could improve fracture risk prediction. Our findings emphasize the need for further research to establish optimal physical activity recommendations for fracture prevention in kidney transplant recipients.

Platelets as Potential Non-Traditional Cardiovascular Risk Factor-Analysis Performed in Healthy Donors.

Abnormal lipid profile, increased glucose level, and elevated body weight are traditional cardiometabolic risk factors; however, the role of platelets in the development of cardiovascular disease (CVD) is increasingly being highlighted. The aim of this study was to select platelet-related parameters (non-genetic molecular and routine laboratory measurements) that may be associated with increased cardiovascular risk among healthy populations. We evaluated the level of platelet indices, platelet-based inflammatory markers, platelet reactivity parameters, and platelet reactive oxygen species (ROS) generation in relation to selected cardiometabolic risk factors. We noted the association between total cholesterol and LDL cholesterol with platelet aggregation and platelet ROS generation. We found the relationship between triglycerides, glucose, and body mass index with the relatively new multi-inflammatory indices (MII-1 and MII-3). Moreover, we noticed that the mean platelet volume-to-lymphocyte ratio in healthy subjects is not a good source of information about platelets and inflammation. We also highlighted that platelet-to-HDL-cholesterol ratio may be a promising prognostic cardiometabolic indicator. The association between platelet-related (especially molecular) and cardiometabolic parameters requires further research. However, the goal of this study was to shed light on the consideration of platelets as a non-traditional cardiovascular risk factor and a crucial element in identifying individuals at high-risk of developing CVD in the future.

Relationship between serum high sensitivity C-reactive protein with angiographic severity of coronary artery disease and traditional cardiovascular risk factors.

Serum high-sensitivity C-reactive protein (hs-CRP) is predictive of coronary artery disease (CAD). The aim of this study was to examine the possible association of hs-CRP with presence and severity of CAD and traditional CAD risk factors. This case-control study was carried out on 2,346 individuals from September 2011 to May 2013. Of these 1,187 had evidence of coronary disease, and were subject to coronary angiography, and the remainder were healthy controls (n = 1,159). Characteristics were determined using standard laboratory techniques and serum Hs-CRP levels were estimated using enzyme-linked immunosorbent assay (ELISA) kits, and severity of CAD was assessed according to the score of obstruction in coronary artery. Serum hs-CRP levels were higher in those with severe coronary disease, who had stenosis ≥ 50% stenosis of at least one coronary artery (all p < 0.001 vs. individuals in healthy control), and correlated significantly with the score for coronary artery disease (all p < 0.01). After adjustment for conventional risk factors, regression analysis revealed that smoking habits, fasting blood glucose, total cholesterol, high-density lipoprotein, hs-CRP, blood pressure, anxiety, dietary intake of vitamin E, and cholesterol remained as independent determinants for angiographic severity of CAD. The area under the receiving operating characteristic (ROC) curve for serum hs-CRP was 0.869 (CI 95% 0.721-0.872, p < 0.001). The optimal values for the cut-off point was a serum hs-CRP of 2.78 mg/l (sensitivity 80.20%, specificity 85%) to predict severity of CAD. Increased serum hs-CRP levels are significantly associated with angiographic severity of CAD, suggesting its value as a biomarkers for predicting CAD.

Plasma metabolites associated with type 2 diabetes in a Swedish population: a case-control study nested in a prospective cohort.

AIMS/HYPOTHESIS: The aims of the present work were to identify plasma metabolites that predict future type 2 diabetes, to investigate the changes in identified metabolites among individuals who later did or did not develop type 2 diabetes over time, and to assess the extent to which inclusion of predictive metabolites could improve risk prediction. METHODS: We established a nested case-control study within the Swedish prospective population-based Västerbotten Intervention Programme cohort. Using untargeted liquid chromatography-MS metabolomics, we analysed plasma samples from 503 case-control pairs at baseline (a median time of 7 years prior to diagnosis) and samples from a subset of 187 case-control pairs at 10 years of follow-up. Discriminative metabolites between cases and controls at baseline were optimally selected using a multivariate data analysis pipeline adapted for large-scale metabolomics. Conditional logistic regression was used to assess associations between discriminative metabolites and future type 2 diabetes, adjusting for several known risk factors. Reproducibility of identified metabolites was estimated by intra-class correlation over the 10 year period among the subset of healthy participants; their systematic changes over time in relation to diagnosis among those who developed type 2 diabetes were investigated using mixed models. Risk prediction performance of models made from different predictors was evaluated using area under the receiver operating characteristic curve, discrimination improvement index and net reclassification index. RESULTS: We identified 46 predictive plasma metabolites of type 2 diabetes. Among novel findings, phosphatidylcholines (PCs) containing odd-chain fatty acids (C19:1 and C17:0) and 2-hydroxyethanesulfonate were associated with the likelihood of developing type 2 diabetes; we also confirmed previously identified predictive biomarkers. Identified metabolites strongly correlated with insulin resistance and/or beta cell dysfunction. Of 46 identified metabolites, 26 showed intermediate to high reproducibility among healthy individuals. Moreover, PCs with odd-chain fatty acids, branched-chain amino acids, 3-methyl-2-oxovaleric acid and glutamate changed over time along with disease progression among diabetes cases. Importantly, we found that a combination of five of the most robustly predictive metabolites significantly improved risk prediction if added to models with an a priori defined set of traditional risk factors, but only a marginal improvement was achieved when using models based on optimally selected traditional risk factors. CONCLUSIONS/INTERPRETATION: Predictive metabolites may improve understanding of the pathophysiology of type 2 diabetes and reflect disease progression, but they provide limited incremental value in risk prediction beyond optimal use of traditional risk factors.

Association of Smoking Status With Mortality and Hospitalization in Hemodialysis Patients.

RATIONALE & OBJECTIVE: The relationship between tobacco smoking and comorbid condition outcomes in hemodialysis (HD) patients is not well understood. This study examined the association of tobacco smoking status with hospitalization and mortality in HD patients. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: Adult HD patients at 2,223 US dialysis centers with HD vintage of 30 days or less who completed a tobacco smoking status survey as part of standard care between April 2013 and June 2015. PREDICTOR: Tobacco smoking category: never smoked, currently living with smoker, former smoker, moderate smoker (<1 pack per day), or heavy smoker (≥1 pack per day). OUTCOMES: Death and hospital admissions within 2 years of the tobacco smoking survey. ANALYTICAL APPROACH: Kaplan-Meier analysis and Cox proportional hazards regression for time to death; cumulative incidence function and Cox proportional hazards regression for time to first hospitalization; negative-binomial regression for number of hospitalizations. RESULTS: Of 22,230 patients studied, 13% were active smokers. Mortality probabilities increased with greater exposure to smoking (17%, 22%, 23%, and 27% for never, moderate, former, and heavy smokers, respectively; P<0.001), as did incidence rates for first hospitalization (23%, 27%, 27%, and 30%, respectively; P<0.001). Compared to never smoked, heavy smokers had the highest mortality rate (HR for heavy smokers, 1.41 [95% CI, 1.18-1.69]; HR for moderate smokers, 1.39 [95% CI, 1.24-1.55]; HR for former smokers, 1.19 [95% CI, 1.11-1.28]). Living with a smoker was not associated with mortality (HR, 0.93; 95% CI, 0.72-1.22). HRs for first hospitalization followed similar patterns. The incidence rate of mortality for active smokers with diabetes was 173.7/1,000 patient-years and 103.5/1,000 patient-years for those who never smoked (incidence rate ratio, 1.68; P<0.001). LIMITATIONS: Self-reported survey without detailed history of smoking/cessation. CONCLUSIONS: Risks for death and hospitalization are elevated among HD patients who smoke, being highest among younger individuals and those with diabetes. Second-hand smoke was not associated with poor clinical outcomes.

Lipidomics Profiling and Risk of Coronary Artery Disease in the BioHEART-CT Discovery Cohort.

The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (n = 580), with sCAD+ (n = 243) compared to sCAD- (n = 337). Through a machine learning approach, we built a lipid risk score (LRS) and compared the performance of the existing Framingham Risk Score (FRS) in predicting sCAD+. We obtained individual classifiability scores and determined Body Mass Index (BMI) as the modifying variable. FRS and LRS models achieved similar areas under the receiver operating characteristic curve (AUC) in predicting the validation cohort. LRS enhanced the prediction of sCAD+ in the healthy-weight group (BMI < 25 kg/m2), where FRS performed poorly and identified individuals at risk that FRS missed. Lipid features have strong potential as biomarkers to predict CAD plaque burden and can identify residual risk not captured by traditional risk factors/scores. LRS compliments FRS in prediction and has the most significant benefit in healthy-weight individuals.

Relative Estimate of Revised Cardiovascular Risk Combining Traditional and Non-traditional Image-based CV Markers: A Kerala Based Study.

BACKGROUND: Non-traditional image markers can improve the traditional cardiovascular risk estimation, is untested in Kerala based participants. OBJECTIVE: To identify the relationship between the 'Modified CV risk' categories with traditional and non-traditional image-based risk markers. The correlation and improvement in reclassification, achieved by pooling atherosclerotic non-traditional markers with Intermediate (≥7.5% and <20%) and High (≥20%) 10-year participants is evaluated. METHODS: The cross-sectional study with 594 participants has the ultrasound measurements recorded from the medical archives of clinical locations at Ernakulum district, Kerala. With carotid Intima-Media Thickness (cIMT) measurement, the Plaque (cP) complexity was computed using selected plaque characteristics to compute the carotid Total Plaque Risk Score (cTPRS) for superior risk tagging. Statistical analysis was done using RStudio, the classification accuracy was verified using the decision tree algorithm. RESULTS: The mean age of the participants was (58.14±10.05) years. The mean cIMT was (0.956±0.302) mm, with 65.6% plaque incidence. With 94.90% variability around its mean, the Multinomial Logistic Regression model identifies cIMT and cTPRS, age, diabetics, Familial Hypercholesterolemia (FH), Hypertension treatment, the presence of Rheumatoid Arthritis (RA), Chronic Kidney Disease (CKD) as significant (p<0.05). cIMT and cP were found significant for 'Intermediate High', 'High' and 'Very High' 'Modified CV risk' categories. However, age, diabetes, gender and use of hypertension treatment are significant for the 'Intermediate' 'Modified CV risk' category. The overall performance of the MLR model was 80.5%. The classification accuracy verified using the decision tree algorithm has 78.7% accuracy. CONCLUSION: The use of atherosclerotic markers shows a significant correlation suitable for a nextlevel reclassification of the traditional CV risk.

Association of FV G1691A Polymorphism but not A4070G With Coronary Artery Disease.

Coronary artery disease (CAD) is one of the chief causes of death in the world. Several hypotheses have been promoted as for the origin of the disease, among which are genetic predispositions and/or environmental factors. The aim of this study was to determine the effect of factor V (FV) gene polymorphisms (Leiden, G1691A [FVL] and HR2 A4070G) and to analyze their association with traditional risk factors in assessing the risk of CAD. Our study population included 200 Tunisian patients with symptomatic CAD and a control group of 300 participants matched for age and sex. All participants were genotyped for the FVL and HR2 polymorphisms. Multivariate logistic regression was applied to analyze independent factors associated with the risk of CAD. Our analysis showed that the FVL A allele frequency ( P < 10-3, odds ratio [OR] = 2.81, 95% confidence interval [CI] = 1.6-4.9) and GA genotype ( P < 10-3, OR = 4.03, 95% CI = 2.1-7.6) are significantly more prevalent among patients with CAD compared to those controls and may be predisposing to CAD. We further found that the FVL mutation is an independent risk factor whose effect is not modified by other factors (smoking, diabetes, hypertension, dyslipidemia, and a family history of CAD) in increasing the risk of the disease. However, analysis of FV HR2 variation does not show any statistically significant association with CAD. The FVL polymorphism may be an independent risk factor for CAD. However, further investigations on these polymorphisms and their possible synergisms with traditional risk factors for CAD could help to ascertain better predictability for CAD susceptibility.