Lessons Learned From Blue Zones, Lifestyle Medicine Pillars and Beyond: An Update on the Contributions of Behavior and Genetics to Wellbeing and Longevity

Blue Zones are regions of the world that have a higher number of individuals who live longer than the expected average. The current paper revisits principles previously identified to be common in Blue Zones and to be contributing to longevity (move naturally, eat wisely, improve resilience to stress, get adequate sleep, keep strong family ties, stimulate strong community support, respect for the planet and having a purpose in life'), compares these to the 6 pillars of Lifestyle Medicine (healthy eating, exercising, avoidance of smoking and other risky substances, stress management, restorative sleep, and forming and maintaining relationships) and reviews new studies investigating the association between behavioral factors and longevity. In addition to the role of behavior, the review also discusses the important role of genetics and emphasizes the importance of conducting further research to understand how behavioral and genetic factors may affect molecular pathways with consequent effects on wellbeing and longevity.

GH Therapy in Chronic Heart Failure: A Systematic Review and Meta-analysis of Randomized Controlled Trials

CONTEXT

Guideline-directed medical therapy of heart failure (HF) primarily targets neurohormonal activation. However, GH has emerged as a potential treatment for the multiple hormonal deficiency syndrome, which is associated with worse outcomes in HF.

OBJECTIVE

This study evaluates the efficacy and safety of GH therapy in HF.

DATA SOURCES

A systematic search was conducted in PubMed, Cochrane Library, and ClinicalTrials.gov, according to PRISMA guidelines.

STUDY SELECTION

Randomized, placebo-controlled trials studying GH therapy in adult HF patients were included. Of the 1184 initially identified records, 17 studies (1.4%) met the inclusion criteria.

DATA EXTRACTION

Two independent authors conducted the search, with any disagreements resolved by a third author. Study quality was assessed using predefined criteria, including randomization, blinding, and the presence of a placebo group.

DATA SYNTHESIS

A random-effects model was applied due to heterogeneity across studies. GH therapy significantly improved left ventricular ejection fraction (+3.34%; 95% CI, 1.09-5.59; P = .0037), peak oxygen consumption (+2.84 mL/kg/min; 95% CI, 1.32-4.36; P = .0002), and New York Heart Association class (-0.44; 95% CI, -0.08 to -0.81; P = .023). GH therapy also reduced the composite of death, worsening HF or ventricular tachycardia by 41% (RR = .59; 95% CI, 0.39-0.90; P = .013). Subgroup analyses indicated that patients with ischemic cardiomyopathy, baseline ejection fraction ≥30%, and longer treatment duration experienced greater benefits.

CONCLUSION

GH therapy demonstrated improvements in cardiac function, exercise capacity, and HF symptoms, along with a statistically significant trend toward improvements in hard endpoints. Event-driven trials are needed to validate these findings.

Adipokines and Cardiometabolic Heart Failure with Preserved Ejection Fraction: A State-of-the-Art Review

BACKGROUND

Cardiometabolic heart failure with preserved ejection fraction (HFpEF) is largely driven by obesity-related factors, including adipokines and bioactive peptides primarily secreted by the adipose tissue, such as leptin, adiponectin, and resistin. These molecules link metabolic dysregulation to cardiovascular dysfunction, influencing HFpEF progression and patient outcomes Methods: A comprehensive literature search was conducted in PubMed up to 20 November 2024, using keywords and MeSH terms, such as "HFpEF", "adipokines", "leptin", "adiponectin", and "resistin", yielding 723 results. Boolean operators refined the search, and reference lists of key studies were reviewed. After screening for duplicates and irrelevant studies, 103 articles were included, providing data on adipokines' roles in HFpEF pathophysiology, biomarkers, and therapeutic implications.

RESULTS

Both preclinical and clinical studies have demonstrated that adipokines play a role in modulating cardiovascular function, thereby contributing to the development of cardiometabolic HFpEF. Leptin promotes myocardial hypertrophy, fibrosis, endothelial dysfunction, and inflammation, though contradictory evidence suggests potential cardioprotective roles in subgroups like obese African American women. Adiponectin generally offers protective effects but presents a paradox, where elevated levels may correlate with worse outcomes, which may reflect either a compensatory response to cardiac dysfunction or a maladaptive state characterized by adiponectin resistance. Resistin is associated with increased cardiovascular risk through pro-inflammatory and pro-fibrotic effects, though its role in HFpEF requires further clarification. Other adipokines, like retinol-binding protein 4 and omentin-1, have emerged as potential contributors. Despite growing insights, clinical translation remains limited, underscoring a significant gap between experimental evidence and therapeutic application.

CONCLUSIONS

Future research should focus on targeted interventions that modulate adipokine pathways to potentially improve HFpEF outcomes. Innovative treatment strategies addressing underlying metabolic disturbances and adipokine dysregulation are essential for advancing the management of this challenging condition.

Ghrelin: An Emerging Therapy for Heart Failure

OBJECTIVE

Ghrelin is emerging as a promising therapeutic option for heart failure (HF) due to its potent inotropic, anabolic, and cardioprotective properties. This review aims to critically examine the available clinical evidence on ghrelin therapy in HF, while also incorporating key findings from preclinical studies that support its therapeutic potential.

METHODS

A comprehensive search was conducted in PubMed and the Cochrane Library up to September 15, 2024, using the keywords "heart failure" and "ghrelin." From 247 identified records, four randomized controlled trials, one open-label trial, one observational study, and key preclinical studies were included. Two independent authors performed the screening and quality assessment, with any discrepancies resolved through consensus.

RESULTS

Clinical trials investigating ghrelin's acute effects in HF patients have demonstrated significant improvements in cardiac output, ranging from 15% to 30%. Moreover, one study showed that a 3-week course of ghrelin therapy significantly increased maximal oxygen consumption, lean body mass, and grip strength in HF patients. Preclinical studies further support these clinical findings, highlighting additional benefits of ghrelin, including modulation of the autonomic nervous system, promotion of vasodilation, enhancement of endothelial function, prevention of myocardial remodeling, reduction of arrhythmogenic risk, and increased muscle mass in HF models.

CONCLUSIONS

Ghrelin is a promising therapeutic option for HF, particularly as an inotropic agent with multifaceted benefits, including autonomic nervous system modulation, anabolic effects, and metabolic regulation. However, further trials are required to confirm its long-term efficacy and safety and assess whether its benefits can translate into reductions in hard clinical endpoints.

Increase in cardioprotective SUR2A does not alter heart rate and heart rate regulation by physical activity and diurnal rhythm

OBJECTIVES

SUR2A is an ABC protein serving as a regulatory subunit of ATP-sensitive (KATP) channels. An increase in SUR2A levels is cardioprotective and it is a potential therapeutic strategy against ischaemic heart disease, heart failure and other diseases. However, whether overexpression of this protein has any adverse effects is yet to be fully understood. Here, we examined the heart rate and the heart rate diurnal variation in mice overexpressing SUR2A (SUR2A+) and their littermate controls (WT) using ECG telemetry that was continuously recorded for 14 days (days 8-23 post-radiotransmitter implantation).

METHODS

Using SigmaPlot 14.0 and Microsoft Excel, Area Under the Curve (AUC) for each parameter was calculated and plotted in a graph.

RESULTS

Both WT and SUR2A+ mice were more physically active during nights and there were no significant differences between two phenotypes. Physical activity was associated with increased heart rate in both phenotypes, but there were no differences in heart rate between phenotypes irrespective of physical activity or time of the day. A diurnal heart rate variation was preserved in the SUR2A+ mice. As area under the curve (AUC) analysis has the potential to reveal differences that are invisible with other statistical methods, we compared AUC of heart rate in SUR2A+ and WT mice. This analysis did not yield anything different from traditional analysis.

CONCLUSIONS

We conclude that increased SUR2A levels are not associated with changes in physical activity, heart rate and/or circadian rhythm influence on the heart rate. This lack of adverse effects supports a notion that manipulation with SUR2A levels is a promising cardioprotective strategy.

Growth Hormone and Heart Failure: Implications for Patient Stratification, Prognosis, and Precision Medicine

Heart failure (HF) remains a major cause of morbidity and mortality worldwide. While standard treatments primarily target neurohormonal pathways, emerging evidence highlights the significant role of hormonal deficiencies, such as impaired growth hormone (GH) signaling, in HF progression and outcomes. GH is crucial for cardiovascular and skeletal muscle function, and its deficiency has been associated with worse prognosis. This review synthesizes recent findings from randomized controlled trials (RCTs) to explore how GH can contribute to personalized care and improve patient stratification in HF. A comprehensive literature review was conducted using PubMed up to 10 October 2024. Search terms included "growth hormone" combined with "heart failure", "HFrEF", "HFpEF", and "HFmrEF." Only placebo-controlled RCTs published in English and involving human subjects were considered. Data on study design, participant characteristics, GH dosing, and key clinical outcomes were systematically extracted and analyzed. Several trials demonstrated that GH therapy can transiently improve left ventricular ejection fraction (LVEF), exercise capacity, and reduce inflammatory markers. For example, one study has reported an increase in LVEF from 32 ± 3.8% to 43.8 ± 4.6% (p = 0.002), following three months of GH therapy in post-MI HF patients. However, benefits diminished after discontinuation. Additional studies have observed sustained improvements in peak oxygen consumption and LVEF over four years, with an additional trend towards hard endpoint improvement. Conversely, some studies showed no significant impact on cardiac function, highlighting heterogeneity in outcomes. As a result, GH therapy holds promise for improving cardiac and functional parameters in HF patients, but evidence remains mixed. Larger, long-term RCTs are needed to confirm its efficacy and safety. Precision medicine approaches and biomarker-driven strategies may optimize patient outcomes and guide clinical practice.

Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management-A State-of-the-Art Narrative Review

The global rise in obesity underscores the need for effective weight management strategies that address individual metabolic and hormonal variability, moving beyond the simplistic "calories in, calories out" model. Body types-ectomorph, mesomorph, and endomorph-provide a framework for understanding the differences in fat storage, muscle development, and energy expenditure, as each type responds uniquely to caloric intake and exercise. Variability in weight outcomes is influenced by factors such as genetic polymorphisms and epigenetic changes in hormonal signaling pathways and metabolic processes, as well as lifestyle factors, including nutrition, exercise, sleep, and stress. These factors impact the magnitude of lipogenesis and myofibrillar protein synthesis during overfeeding, as well as the extent of lipolysis and muscle proteolysis during caloric restriction, through complex mechanisms that involve changes in the resting metabolic rate, metabolic pathways, and hormonal profiles. Precision approaches, such as nutrigenomics, indirect calorimetry, and artificial-intelligence-based strategies, can potentially leverage these insights to create individualized weight management strategies aligned with each person's unique metabolic profile. By addressing these personalized factors, precision nutrition offers a promising pathway to sustainable and effective weight management outcomes. The main objective of this review is to examine the metabolic and hormonal adaptations driving variability in weight management outcomes and explore how precision nutrition can address these challenges through individualized strategies.

Antibiotic Resistance in the Elderly: Mechanisms, Risk Factors, and Solutions

Antibiotic resistance presents a critical challenge in healthcare, particularly among the elderly, where multidrug-resistant organisms (MDROs) contribute to increased morbidity, mortality, and healthcare costs. This review focuses on the mechanisms underlying resistance in key bacterial pathogens and highlights how aging-related factors like immunosenescence, frailty, and multimorbidity increase the burden of infections from MDROs in this population. Novel strategies to mitigate resistance include the development of next-generation antibiotics like teixobactin and cefiderocol, innovative therapies such as bacteriophage therapy and antivirulence treatments, and the implementation of antimicrobial stewardship programs to optimize antibiotic use. Furthermore, advanced molecular diagnostic techniques, including nucleic acid amplification tests and next-generation sequencing, allow for faster and more precise identification of resistant pathogens. Vaccine development, particularly through innovative approaches like multi-epitope vaccines and nanoparticle-based platforms, holds promise in preventing MDRO infections among the elderly. The role of machine learning (ML) in predicting resistance patterns and aiding in vaccine and antibiotic development is also explored, offering promising solutions for personalized treatment and prevention strategies in the elderly. By integrating cutting-edge diagnostics, therapeutic innovations, and ML-based approaches, this review underscores the importance of multidisciplinary efforts to address the global challenge of antibiotic resistance in aging populations.

Efficacy and safety of dupilumab in adult moderate-to-severe atopic dermatitis: An update narrative literature review

Background: Adult atopic dermatitis (AD) is defined as a continuum of childhood AD or the development of the disease in adulthood, accounting for 7.7–59.7% of adult AD cases varying in severity and manifestations. The symptomatology of moderate-to-severe adult AD may significantly impact the overall health and quality of life of the patient. The “classic” topical treatments used in mild-to-moderate cases, such as emollients and topical corticosteroids, are usually not adequate to control the symptoms of most of the patients with moderate-to-severe disease. For many years these patients were managed with systemic corticosteroids and immunomodulators, leading to substantial side effects with questionable efficacy. The introduction of dupilumab, the first biologic agent approved by the Food and Drug Administration for use in adult moderate-to-severe AD, has commenced a new era in the management of AD. This narrative literature review addresses the question of how patients with moderate-to-severe AD may achieve a recession or improvement in the overall progression of the disease with the use of dupilumab in both an efficient and safe way. Material and Methods: A search in the PubMed, Embase, and Cochrane databases was conducted using the following combination of MeSH terms: “dupilumab” AND “atopic” (“dermatitis” OR “eczema”). The searches were limited to RCTs written in the English language published before January 25, 2021. The literature used included phase II and III RCTs examining the efficacy and/or safety of dupilumab compared to placebo or other treatments in adults with moderate-to-severe AD. Moderate-to-severe AD was defined by an IGA score of 3 (moderate) or 4 (severe) and EASI 16 or higher at screening and baseline. Additionally, we searched the website clinicaltrials.gov for any unpublished or ongoing RCTs. The search was done independently by two authors in all databases and followed by the exclusion of duplicates. Results: Upon reviewing all randomized controlled trials, dupilumab was found to be an effective and safe option for managing adult moderate-to-severe AD with long-term therapeutic effects. Conclusion: The best results for maintaining long-term disease recession were achieved with the combination of dupilumab and topical corticosteroids.

Deep Learning for Melanoma Detection: A Deep Learning Approach to Differentiating Malignant Melanoma from Benign Melanocytic Nevi

Background/Objectives: Melanoma, an aggressive form of skin cancer, accounts for a significant proportion of skin-cancer-related deaths worldwide. Early and accurate differentiation between melanoma and benign melanocytic nevi is critical for improving survival rates but remains challenging because of diagnostic variability. Convolutional neural networks (CNNs) have shown promise in automating melanoma detection with accuracy comparable to expert dermatologists. This study evaluates and compares the performance of four CNN architectures-DenseNet121, ResNet50V2, NASNetMobile, and MobileNetV2-for the binary classification of dermoscopic images. Methods: A dataset of 8825 dermoscopic images from DermNet was standardized and divided into training (80%), validation (10%), and testing (10%) subsets. Image augmentation techniques were applied to enhance model generalizability. The CNN architectures were pre-trained on ImageNet and customized for binary classification. Models were trained using the Adam optimizer and evaluated based on accuracy, area under the receiver operating characteristic curve (AUC-ROC), inference time, and model size. The statistical significance of the differences was assessed using McNemar's test. Results: DenseNet121 achieved the highest accuracy (92.30%) and an AUC of 0.951, while ResNet50V2 recorded the highest AUC (0.957). MobileNetV2 combined efficiency with competitive performance, achieving a 92.19% accuracy, the smallest model size (9.89 MB), and the fastest inference time (23.46 ms). NASNetMobile, despite its compact size, had a slower inference time (108.67 ms), and slightly lower accuracy (90.94%). Performance differences among the models were statistically significant (p < 0.0001). Conclusions: DenseNet121 demonstrated a superior diagnostic performance, while MobileNetV2 provided the most efficient solution for deployment in resource-constrained settings. The CNNs show substantial potential for improving melanoma detection in clinical and mobile applications.

Comprehensive Peri-Operative Risk Assessment and Management of Geriatric Patients

Background: As the population ages, the prevalence of surgical interventions in individuals aged 65+ continues to increase. This poses unique challenges due to the higher incidence of comorbidities, polypharmacy, and frailty in the elderly population, which result in high peri-operative risks. Traditional preoperative risk assessment tools often fail to accurately predict post-operative outcomes in the elderly, overlooking the complex interplay of factors that contribute to risk in the elderly. Methods: A literature review was conducted, focusing on the predictive value of CGA for postoperative prognosis and the implementation of perioperative interventions. Results: Evidence shows that CGA is a superior predictive tool compared to traditional models, as it more accurately identifies elderly patients at higher risk of complications such as postoperative delirium, infections, and prolonged hospital stays. CGA includes assessments of frailty, sarcopenia, nutritional status, cognitive function, mental health, and functional status, which are crucial in predicting post-operative outcomes. Studies demonstrate that CGA can also guide personalized perioperative care, including nutritional support, physical training, and mental health interventions, leading to improved surgical outcomes and reduced functional decline. Conclusions: The CGA provides a more holistic approach to perioperative risk assessment in elderly patients, addressing the limitations of traditional tools. CGA can help guide surgical decisions (e.g., curative or palliative) and select the profiles of patients that will benefit from perioperative interventions to improve their prognosis and prevent functional decline.

Immunosenescence: How Aging Increases Susceptibility to Bacterial Infections and Virulence Factors

The process of aging leads to a progressive decline in the immune system function, known as immunosenescence, which compromises both innate and adaptive responses. This includes impairments in phagocytosis and decreased production, activation, and function of T- and B-lymphocytes, among other effects. Bacteria exploit immunosenescence by using various virulence factors to evade the host's defenses, leading to severe and often life-threatening infections. This manuscript explores the complex relationship between immunosenescence and bacterial virulence, focusing on the underlying mechanisms that increase vulnerability to bacterial infections in the elderly. Additionally, it discusses how machine learning methods can provide accurate modeling of interactions between the weakened immune system and bacterial virulence mechanisms, guiding the development of personalized interventions. The development of vaccines, novel antibiotics, and antivirulence therapies for multidrug-resistant bacteria, as well as the investigation of potential immune-boosting therapies, are promising strategies in this field. Future research should focus on how machine learning approaches can be integrated with immunological, microbiological, and clinical data to develop personalized interventions that improve outcomes for bacterial infections in the growing elderly population.

Barriers to Implementing Effective Healthcare Practices for the Aging Population: Approaches to Identification and Management

The aging population presents a growing challenge to healthcare systems, necessitating urgent adaptations to meet the complex needs of older adults. Existing healthcare models often lack integration and fail to provide patient-centered care, leading to fragmented services, suboptimal outcomes, increased hospitalizations, and escalating healthcare costs. This narrative review aims to systematically identify and categorize the key barriers to effective healthcare implementation for the elderly, evaluate current healthcare models and their limitations, and explore evidence-based strategies to improve care delivery. A comprehensive literature search was conducted in PubMed, MEDLINE, Scopus, and Web of Science for studies published from 2000 to October 2024. The identified barriers span multiple domains, including patient-related challenges such as low health literacy and socioeconomic disparities, disease-specific factors like frailty and multimorbidity, provider-related constraints such as inadequate geriatric training, and system-wide deficiencies in primary care infrastructure and policy support. To address these challenges, this review explores emerging solutions, including risk stratification tools, integrated healthcare models, digital health innovations, and artificial intelligence-driven interventions. By providing a structured analysis of barriers and solutions, this review aims to inform policy and healthcare practices that enhance elderly care, reduce hospital readmissions, and optimize resource utilization in aging populations.

Skin Microbiota: Mediator of Interactions Between Metabolic Disorders and Cutaneous Health and Disease

Metabolic disorders, including type 2 diabetes mellitus (T2DM), obesity, and metabolic syndrome, are systemic conditions that profoundly impact the skin microbiota, a dynamic community of bacteria, fungi, viruses, and mites essential for cutaneous health. Dysbiosis caused by metabolic dysfunction contributes to skin barrier disruption, immune dysregulation, and increased susceptibility to inflammatory skin diseases, including psoriasis, atopic dermatitis, and acne. For instance, hyperglycemia in T2DM leads to the formation of advanced glycation end products (AGEs), which bind to the receptor for AGEs (RAGE) on keratinocytes and immune cells, promoting oxidative stress and inflammation while facilitating Staphylococcus aureus colonization in atopic dermatitis. Similarly, obesity-induced dysregulation of sebaceous lipid composition increases saturated fatty acids, favoring pathogenic strains of Cutibacterium acnes, which produce inflammatory metabolites that exacerbate acne. Advances in metabolomics and microbiome sequencing have unveiled critical biomarkers, such as short-chain fatty acids and microbial signatures, predictive of therapeutic outcomes. For example, elevated butyrate levels in psoriasis have been associated with reduced Th17-mediated inflammation, while the presence of specific Lactobacillus strains has shown potential to modulate immune tolerance in atopic dermatitis. Furthermore, machine learning models are increasingly used to integrate multi-omics data, enabling personalized interventions. Emerging therapies, such as probiotics and postbiotics, aim to restore microbial diversity, while phage therapy selectively targets pathogenic bacteria like Staphylococcus aureus without disrupting beneficial flora. Clinical trials have demonstrated significant reductions in inflammatory lesions and improved quality-of-life metrics in patients receiving these microbiota-targeted treatments. This review synthesizes current evidence on the bidirectional interplay between metabolic disorders and skin microbiota, highlighting therapeutic implications and future directions. By addressing systemic metabolic dysfunction and microbiota-mediated pathways, precision strategies are paving the way for improved patient outcomes in dermatologic care.

Sarcopenia and Cardiogeriatrics: The Links Between Skeletal Muscle Decline and Cardiovascular Aging

Sarcopenia, an age-related decline in skeletal muscle mass, strength, and function, is increasingly recognized as a significant condition in the aging population, particularly among those with cardiovascular diseases (CVD). This review provides a comprehensive synthesis of the interplay between sarcopenia and cardiogeriatrics, emphasizing shared mechanisms such as chronic low-grade inflammation (inflammaging), hormonal dysregulation, oxidative stress, and physical inactivity. Despite advancements in diagnostic frameworks, such as the EWGSOP2 and AWGS definitions, variability in criteria and assessment methods continues to challenge standardization. Key diagnostic tools include dual-energy X-ray absorptiometry (DXA) and bioimpedance analysis (BIA) for muscle mass, alongside functional measures such as grip strength and gait speed. The review highlights the bidirectional relationship between sarcopenia and cardiovascular conditions such as heart failure, aortic stenosis, and atherosclerotic cardiovascular disease, which exacerbate each other through complex pathophysiological mechanisms. Emerging therapeutic strategies targeting the mTOR pathway, NAD+ metabolism, and senescence-related processes offer promise in mitigating sarcopenia's progression. Additionally, integrated interventions combining resistance training, nutritional optimization, and novel anti-aging therapies hold significant potential for improving outcomes. This paper underscores critical gaps in the evidence, including the need for longitudinal studies to establish causality and the validation of advanced therapeutic approaches in clinical settings. Future research should leverage multi-omics technologies and machine learning to identify biomarkers and personalize interventions. Addressing these challenges is essential to reducing sarcopenia's burden and enhancing the quality of life for elderly individuals with comorbid cardiovascular conditions. This synthesis aims to guide future research and promote effective, individualized management strategies.

Forecasting Hospitalization Trends in the Greek Elderly Population

This study examines the forecasting of all-cause hospitalizations in the Greek elderly population until 2032, using historical data from 2001 to 2019. We employed two forecasting models: Autoregressive Integrated Moving Average (ARIMA) and Prophet model. The ARIMA model demonstrated a conservative approach, generating stable forecasts with narrower confidence intervals, making it suitable for identifying gradual trends. In contrast, the Prophet model, with its flexibility in trend capture, produced forecasts with broader confidence intervals, capturing potential sharp increases but with greater uncertainty. Our findings underscore that forecasting accuracy varies across age groups, with the highest precision observed in the 80+ age cohort, reflecting the more predictable healthcare utilization patterns of older populations. These insights emphasize the value of a multi-model approach in healthcare planning, particularly for accurately predicting trends within aging populations and efficiently allocating healthcare resources.

Predicting Body Fat Percentage from Simple Anthropometric Measurements: A Machine Learning Approach

Accurately assessing body fat percentage (BF%) is crucial for healthcare and fitness but is hindered by gold-standard methods that are costly and invasive. This study employs a dataset containing variables such as age, sex, Body Mass Index (BMI), and body circumferences, from individuals whose body fat percentage (BF%) was estimated via underwater weighing, to develop predictive machine learning models. Multiple regression techniques alongside a neural network were employed to compare model accuracies in estimating BF%. Ridge Regression emerged as the most effective model, demonstrating the highest R2 score. Notably, feature importance analysis using ElasticNet and SHAP revealed that abdominal circumference was the most significant predictor of BF%, challenging the adequacy of BMI as a measure of adiposity. These insights advocate for the broader adoption of circumference measurements in everyday practice to enhance the predictive accuracy of cost-effective and easily performed BF% estimation.

Semaglutide in heart failure and atherosclerotic cardiovascular disease: the current state-of-the-art

Cardiovascular disease mortality rates, which had steadily declined over decades, are now plateauing or reversing due to the global rise in type 2 diabetes mellitus (T2DM) and obesity. These cardiometabolic conditions contribute significantly to atherosclerotic cardiovascular disease (ASCVD), heart failure (HF), and chronic kidney disease. Among emerging pharmacological treatments, glucagon-like peptide-1 receptor agonists, particularly semaglutide, have shown benefits beyond diabetes and obesity management, including cardioprotective and renoprotective effects. This state-of-the-art review comprehensively analyzes current evidence from clinical trials, identifies critical insights, and outlines research directions regarding semaglutide use in HF, ASCVD, and diabetic nephropathy. In ASCVD, semaglutide has demonstrated significant reductions in major adverse cardiovascular events, supported by findings from meta-analyses of trials in patients with T2DM and the SELECT trial for patients without T2DM. In a prespecified analysis of the SELECT trial, semaglutide demonstrated significant reductions in cardiovascular mortality and HF hospitalizations for patients with HF and ASCVD. In HF with preserved ejection fraction and mildly reduced ejection fraction, semaglutide improved symptoms, physical function, natriuretic peptide levels, echocardiographic parameters, and HF hospitalizations, as shown in the STEP-HFpEF program and a pooled analysis of trials. Furthermore, evidence from the FLOW trial underscores semaglutide's renal and cardiovascular benefits in diabetic nephropathy, irrespective of body mass index. While these findings suggest semaglutide's efficacy in cardiorenal diseases, gaps in evidence remain, including the need for event-driven trials in HF populations without ASCVD and irrespective of obesity. Future research should address these gaps, which could potentially update guideline recommendations.