The Importance of HDL-Cholesterol and Fat-Free Percentage as Protective Markers in Risk Factor Hierarchy for Patients with Metabolic Syndrome

This research focused on establishing a hierarchy concerning the influence of various biological markers and body composition parameters on preventing, diagnosing and managing Metabolic Syndrome (MetS). Our cross-sectional cohort study included 104 subjects without any atherosclerotic antecedent pathology, organized in two groups (with and without MetS). All participants underwent clinical and anthropometric measurements, DEXA investigation and blood tests for all MetS criteria, together with adiponectin, leptin, insulin, uric acid and CRP. Based on mathematical logic, we calculated a normalized sensitivity score to compare the predictive power of biomarkers and parameters associated with MetS, upon the prevalence of MetS. Patients with MetS report higher levels of uric acid (p = 0.02), CRP (p = 0.012) and lower levels of adiponectin (p = 0.025) than patients without MetS. The top three biological markers with the highest predictive power of the prevalence of the disease are HDL, insulin, and adiponectin:leptin ratio, and the top three body composition parameters are trunk fat-free percentage, waist-height ratio and trunk fat percentage. Their high sensitivity scores differentiate them from all the other markers analysed in the study. Our findings report relevant scores for estimating the importance of cardiometabolic risks in the prevalence of MetS. The high rank of protective markers, HDL and trunk fat-free percentage, suggest that positive effects have a stronger association with the prevalence of MetS, than negative ones do. Therefore, this risk stratification study provides important support for prevention and management programs regarding MetS

Artificial Neural Network Models for Accurate Predictions of Fat-Free and Fat Masses, Using Easy-to-Measure Anthropometric Parameters

Abdominal fat and fat-free masses report a close association with cardiometabolic risks, therefore this specific body compartment presents more interest than whole-body masses. This research aimed to develop accurate algorithms that predict body masses and specifically trunk fat and fat-free masses from easy to measure parameters in any setting. The study included 104 apparently healthy subjects, but with a higher-than-normal percent of adiposity or waist circumference. Multiple linear regression (MLR) and artificial neural network (ANN) models were built for predicting abdominal fat and fat-free masses in patients with relatively low cardiometabolic risks. The data were divided into training, validation and test sets, and this process was repeated 20 times per each model to reduce the bias of data division on model accuracy. The best performance models used a maximum number of five anthropometric inputs, with higher R2 values for ANN models than for MLR models (R2 = 0.96–0.98 vs. R2 = 0.80–0.94, p = 0.006). The root mean square error (RMSE) for all predicted parameters was significantly lower for ANN models than for MLR models, suggesting a higher accuracy for ANN models. From all body masses predicted, trunk fat mass and fat-free mass registered the best performance with ANN, allowing a possible error of 1.84 kg for predicting the correct trunk fat mass and 1.48 kg for predicting the correct trunk fat-free mass. The developed algorithms represent cost-effective prediction tools for the most relevant adipose and lean tissues involved in the physiopathology of cardiometabolic risks

Artificial Neural Network Models for Accurate Predictions of Fat-Free and Fat Masses, Using Easy-to-Measure Anthropometric Parameters

Abdominal fat and fat-free masses report a close association with cardiometabolic risks, therefore this specific body compartment presents more interest than whole-body masses. This research aimed to develop accurate algorithms that predict body masses and specifically trunk fat and fat-free masses from easy to measure parameters in any setting. The study included 104 apparently healthy subjects, but with a higher-than-normal percent of adiposity or waist circumference. Multiple linear regression (MLR) and artificial neural network (ANN) models were built for predicting abdominal fat and fat-free masses in patients with relatively low cardiometabolic risks. The data were divided into training, validation and test sets, and this process was repeated 20 times per each model to reduce the bias of data division on model accuracy. The best performance models used a maximum number of five anthropometric inputs, with higher R2 values for ANN models than for MLR models (R2 = 0.96–0.98 vs. R2 = 0.80–0.94, p = 0.006). The root mean square error (RMSE) for all predicted parameters was significantly lower for ANN models than for MLR models, suggesting a higher accuracy for ANN models. From all body masses predicted, trunk fat mass and fat-free mass registered the best performance with ANN, allowing a possible error of 1.84 kg for predicting the correct trunk fat mass and 1.48 kg for predicting the correct trunk fat-free mass. The developed algorithms represent cost-effective prediction tools for the most relevant adipose and lean tissues involved in the physiopathology of cardiometabolic risks

Metabolic Phenotypes—The Game Changer in Quality of Life of Obese Patients?

Background: The present study aimed to investigate the association of obesity phenotypes and quality of life (QoL) scales and their relationship with fat mass (FM) parameters. Methods: This study categorized 104 subjects into 4 obesity phenotypes based on BMI and metabolic syndrome status: metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), metabolically healthy non-obese (MHNO), and metabolically unhealthy non-obese (MUNO). Body composition was measured by dual-energy X-ray absorptiometry (DEXA) and metabolic profile was characterized by blood samples. All subjects completed the SF-36 item Short Form Health Survey Questionnaire. Results: Comparing the four obesity phenotypes, significant results were reported for Bodily Pain between MHNO/MUNO (p = 0.034), for Vitality between MHO/MUO (p = 0.024), and for Mental Component Score between MHO/MUO (p = 0.026) and MUO/MUNO (p = 0.003). A more thorough inside-groups analysis yielded a positive and moderate to high correlation between FM parameters and QoL scales in MHO and MHNO, while a negative and weak to moderate correlation was observed in MUO and MUNO. Conclusion: This study reported an inverse U-shaped relationship between FM and QoL in obesity phenotypes, suggesting that metabolic status is a key factor involved in modulating QoL and therefore challenging the idea of obesity as a main driver of low QoL. We recommend the inclusion of FM percentage in the definition of obesity phenotypes in future research, to better evaluate QoL of obesity phenotypes

Metabolic Phenotypes—The Game Changer in Quality of Life of Obese Patients?

Background: The present study aimed to investigate the association of obesity phenotypes and quality of life (QoL) scales and their relationship with fat mass (FM) parameters. Methods: This study categorized 104 subjects into 4 obesity phenotypes based on BMI and metabolic syndrome status: metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), metabolically healthy non-obese (MHNO), and metabolically unhealthy non-obese (MUNO). Body composition was measured by dual-energy X-ray absorptiometry (DEXA) and metabolic profile was characterized by blood samples. All subjects completed the SF-36 item Short Form Health Survey Questionnaire. Results: Comparing the four obesity phenotypes, significant results were reported for Bodily Pain between MHNO/MUNO (p = 0.034), for Vitality between MHO/MUO (p = 0.024), and for Mental Component Score between MHO/MUO (p = 0.026) and MUO/MUNO (p = 0.003). A more thorough inside-groups analysis yielded a positive and moderate to high correlation between FM parameters and QoL scales in MHO and MHNO, while a negative and weak to moderate correlation was observed in MUO and MUNO. Conclusion: This study reported an inverse U-shaped relationship between FM and QoL in obesity phenotypes, suggesting that metabolic status is a key factor involved in modulating QoL and therefore challenging the idea of obesity as a main driver of low QoL. We recommend the inclusion of FM percentage in the definition of obesity phenotypes in future research, to better evaluate QoL of obesity phenotypes

The Importance of HDL-Cholesterol and Fat-Free Percentage as Protective Markers in Risk Factor Hierarchy for Patients with Metabolic Syndrome

This research focused on establishing a hierarchy concerning the influence of various biological markers and body composition parameters on preventing, diagnosing and managing Metabolic Syndrome (MetS). Our cross-sectional cohort study included 104 subjects without any atherosclerotic antecedent pathology, organized in two groups (with and without MetS). All participants underwent clinical and anthropometric measurements, DEXA investigation and blood tests for all MetS criteria, together with adiponectin, leptin, insulin, uric acid and CRP. Based on mathematical logic, we calculated a normalized sensitivity score to compare the predictive power of biomarkers and parameters associated with MetS, upon the prevalence of MetS. Patients with MetS report higher levels of uric acid (p = 0.02), CRP (p = 0.012) and lower levels of adiponectin (p = 0.025) than patients without MetS. The top three biological markers with the highest predictive power of the prevalence of the disease are HDL, insulin, and adiponectin:leptin ratio, and the top three body composition parameters are trunk fat-free percentage, waist-height ratio and trunk fat percentage. Their high sensitivity scores differentiate them from all the other markers analysed in the study. Our findings report relevant scores for estimating the importance of cardiometabolic risks in the prevalence of MetS. The high rank of protective markers, HDL and trunk fat-free percentage, suggest that positive effects have a stronger association with the prevalence of MetS, than negative ones do. Therefore, this risk stratification study provides important support for prevention and management programs regarding MetS

The Effect of Vitamin Supplementation on Subclinical Atherosclerosis in Patients without Manifest Cardiovascular Diseases: Never-ending Hope or Underestimated Effect?

Micronutrients, especially vitamins, play an important role in the evolution of cardiovascular diseases (CVD). It has been speculated that additional intake of vitamins may reduce the CVD burden by acting on the inflammatory and oxidative response starting from early stages of atherosclerosis, when the vascular impairment might still be reversible or, at least, slowed down. The current review assesses the role of major vitamins on subclinical atherosclerosis process and the potential clinical implications in patients without CVD. We have comprehensively examined the literature data for the major vitamins: A, B group, C, D, and E, respectively. Most data are based on vitamin E, D and C supplementation, while vitamins A and B have been scarcely examined for the subclinical atherosclerosis action. Though the fundamental premise was optimistic, the up-to-date trials with vitamin supplementation revealed divergent results on subclinical atherosclerosis improvement, both in healthy subjects and patients with CVD, while the long-term effect seems minimal. Thus, there are no conclusive data on the prevention and progression of atherosclerosis based on vitamin supplementation. However, given their enormous potential, future trials are certainly needed for a more tailored CVD prevention focusing on early stages as subclinical atherosclerosis

Circulating Biomarkers for Laboratory Diagnostics of Atherosclerosis—Literature Review

Atherosclerosis is still considered a disease burden with long-term damaging processes towards the cardiovascular system. Evaluation of atherosclerotic stages requires the use of independent markers such as those already considered traditional, that remain the main therapeutic target for patients with atherosclerosis, together with emerging biomarkers. The challenge is finding models of predictive markers that are particularly tailored to detect and evaluate the evolution of incipient vascular lesions. Important advances have been made in this field, resulting in a more comprehensible and stronger linkage between the lipidic profile and the continuous inflammatory process. In this paper, we analysed the most recent data from the literature studying the molecular mechanisms of biomarkers and their involvement in the cascade of events that occur in the pathophysiology of atherosclerosis

Nanomaterial-Based Drug Targeted Therapy for Cardiovascular Diseases: Ischemic Heart Failure and Atherosclerosis

Cardiovascular diseases (CVDs) represent the most important epidemic of our century, with more than 37 million patients globally. Furthermore, CVDs are associated with high morbidity and mortality, and also increased hospitalization rates and poor quality of life. Out of the plethora of conditions that can lead to CVDs, atherosclerosis and ischemic heart disease are responsible for more than 2/3 of the cases that end in severe heart failure and finally death. Current therapy strategies for CVDs focus mostly on symptomatic benefits and have a moderate impact on the underlying physiopathological mechanisms. Modern therapies try to approach different physiopathological pathways such as reduction of inflammation, macrophage regulation, inhibition of apoptosis, stem-cell differentiation and cellular regeneration. Recent technological advances make possible the development of several nanoparticles used not only for the diagnosis of cardiovascular diseases, but also for targeted drug delivery. Due to their high specificity, nanocarriers can deliver molecules with poor pharmacokinetics and dynamics such as: peptides, proteins, polynucleotides, genes and even stem cells. In this review we focused on the applications of nanoparticles in the diagnosis and treatment of ischemic heart failure and atherosclerosis

A Rising Star of the Multimarker Panel: Growth Differentiation Factor-15 Levels Are an Independent Predictor of Mortality in Acute Heart Failure Patients Admitted to an Emergency Clinical Hospital from Eastern Europe

(1) Background: Acute heart failure (HF) represents one of the most common yet extremely severe presentations in emergency services worldwide, requiring prompt diagnosis, followed by an adequate therapeutic approach, and a thorough risk stratification. Natriuretic peptides (NPs) are currently the most widely implemented biomarkers in acute HF, but due to their lack of specificity, they are mainly used as ruling-out criteria. Growth differentiation factor-15 (GDF-15) is a novel molecule expressing different pathophysiological pathways in HF, such as fibrosis, remodeling, and oxidative stress. It is also considered a very promising predictor of mortality and poor outcome. In this study, we aimed to investigate the GDF-15’s expression and particularities in patients with acute HF, focusing mainly on its role as a prognosis biomarker, either per se or as part of a multimarker panel. (2) Methods: This unicentric prospective study included a total of 173 subjects, divided into 2 subgroups: 120 patients presented in emergency with acute HF, while 53 were ambulatory-evaluated controls with chronic HF. At admission, all patients were evaluated according to standard clinical echocardiography and laboratory panel, including the assessment of GDF-15. (3) Results: The levels of GDF-15 were significantly higher in patients with acute HF, compared to controls [596 (305–904) vs. 216 (139–305) ng/L, p < 0.01]. GDF-15 also exhibited an adequate diagnostic performance in acute HF, expressed as an area under the curve (AUC) of 0.883 [confidence interval (CI) 95%: 0.828–0.938], similar to that of NT-proBNP (AUC: 0.976, CI 95%: 0.952–1.000), or troponin (AUC: 0.839, CI 95%: 0.733–0.944). High concentrations of GDF-15 were significantly correlated with mortality risk. In a multivariate regression model, GDF-15 was the most important predictor of a poor outcome, superior to NT-proBNP or troponin. (4) Conclusions: GDF-15 proved to be a reliable tool in the multimarker assessment of patients with acute HF. Compared to the gold standard NT-proBNP, GDF-15 presented a similar diagnostic performance, doubled by a significantly superior prognostic value, making it worth being included in a standardized multimarker panel