Magdalini Kreouzi

Obesity in elderly individuals is associated with increased levels of inflammatory biomarkers, indicating a state of chronic low-grade inflammation, which has been recently termed as adipaging. Several studies have demonstrated this relationship: overweight and obese middle-aged and elderly individuals show elevated levels of inflammatory markers like CXCL-16, IL-6, and adipokines compared to normal weight counterparts. These markers positively correlate with anthropometric parameters indicating increased cardiovascular risk. C-reactive protein (CRP) and fibrinogen levels increase progressively with higher obesity classes in the general population, including the elderly. For instance, CRP levels nearly double with each increase in weight class compared to normal weight individuals. Additionally, the presence of obesity-related comorbidities like hypertension or diabetes further elevates these inflammatory markers. In conclusion, obesity in the elderly is characterized by elevated levels of various inflammatory biomarkers, reflecting a state of chronic low-grade inflammation. This inflammatory state may contribute to the development of obesity-related comorbidities. The clarification of the complementary or independent role of these biomarkers in aging and obesity could lead to targeted therapeutic interventions in this vulnerable population group.

Obesity in elderly individuals is associated with increased levels of inflammatory biomarkers, indicating a state of chronic low-grade inflammation, which has been recently termed as inflammaging and adipaging. Several studies have demonstrated this relationship: Overweight and obese middle-aged and elderly individuals show elevated levels of inflammatory markers like CXCL-16, IL-6, and adipokines compared to normal-weight counterparts. These markers positively correlate with anthropometric parameters indicating increased cardiovascular risk. C-reactive protein (CRP) and fibrinogen levels increase progressively with higher obesity classes in the general population, including the elderly . For instance, CRP levels nearly double with each increase in weight class compared to normal weight individuals. Additionally, the presence of obesity-related comorbidities like hypertension or diabetes further elevates these inflammatory markers. In conclusion, obesity in the elderly is characterized by elevated levels of various inflammatory biomarkers, reflecting a state of chronic low-grade inflammation. This inflammatory state may contribute to the development of obesity-related comorbidities. The clarification of the complementary or independent role of these biomarkers in aging and obesity could lead to targeted therapeutic interventions in this vulnerable population group.

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, 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.

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.

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.

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.

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.