Type 2 diabetes mellitus (T2DM) is a systemic disease characterized by hyperglycemia, hyperlipidemia, and organismic insulin resistance. This pathological shift in both circulating fuel levels and energy substrate utilization by central and peripheral tissues contributes to mitochondrial dysfunction across organ systems. The mitochondrion lies at the intersection of critical cellular pathways such as energy substrate metabolism, reactive oxygen species (ROS) generation, and apoptosis. It is the disequilibrium of these processes in T2DM that results in downstream deficits in vital functions, including hepatocyte metabolism, cardiac output, skeletal muscle contraction, β-cell insulin production, and neuronal health. Although mitochondria are known to be susceptible to a variety of genetic and environmental insults, the accumulation of mitochondrial DNA (mtDNA) mutations and mtDNA copy number depletion is helping to explain the prevalence of mitochondrial-related diseases such as T2DM. Recent work has uncovered novel mitochondrial biology implicated in disease progressions such as mtDNA heteroplasmy, noncoding RNA (ncRNA), epigenetic modification of the mitochondrial genome, and epitranscriptomic regulation of the mtDNA-encoded mitochondrial transcriptome. The goal of this review is to highlight mitochondrial dysfunction observed throughout major organ systems in the context of T2DM and to present new ideas for future research directions based on novel experimental and technological innovations in mitochondrial biology. Finally, the field of mitochondria-targeted therapeutics is discussed, with an emphasis on novel therapeutic strategies to restore mitochondrial homeostasis in the setting of T2DM.

Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development. Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classification (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classification and Regression Tree (CART) models with tenfold cross validation. Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~ 84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P = 0.003) and CpG29 (chr10:58385324, P = 0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classification sets. Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discovery.

The integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic capabilities, yet the black-box nature of deep learning models poses significant challenges for clinical adoption. Current explainable AI (XAI) approaches, including SHAP, LIME, and Grad-CAM, predominantly focus on post hoc explanations that may inadvertently undermine clinical decision-making by providing misleading confidence in AI outputs. This paper presents a systematic review and meta-analysis of 67 studies (covering 23 radiology, 19 pathology, and 25 ophthalmology applications) evaluating XAI fidelity, stability, and performance trade-offs across medical imaging modalities. Our meta-analysis of 847 initially identified studies reveals that LIME achieves superior fidelity (0.81, 95% CI: 0.78–0.84) compared to SHAP (0.38, 95% CI: 0.35–0.41) and Grad-CAM (0.54, 95% CI: 0.51–0.57) across all modalities. Post hoc explanations demonstrated poor stability under noise perturbation, with SHAP showing 53% degradation in ophthalmology applications (ρ = 0.42 at 10% noise) compared to 11% in radiology (ρ = 0.89). We demonstrate a consistent 5–7% AUC performance penalty for interpretable models but identify modality-specific stability patterns suggesting that tailored XAI approaches are necessary. Based on these empirical findings, we propose a comprehensive three-pillar accountability framework that prioritizes transparency in model development, interpretability in architecture design, and a cautious deployment of post hoc explanations with explicit uncertainty quantification. This approach offers a pathway toward genuinely accountable AI systems that enhance rather than compromise clinical decision-making quality and patient safety.

Nanomaterial production is expanding as new industrial and consumer applications are introduced. Nevertheless, the impacts of exposure to these compounds are not fully realized. The present study was designed to determine whether gestational nano-sized titanium dioxide exposure impacts cardiac and metabolic function of developing progeny. Pregnant Sprague-Dawley rats were exposed to nano-aerosols (~10 mg/m³, 130- to 150-nm count median aerodynamic diameter) for 7–8 nonconsecutive days, beginning at gestational day 5–6. Physiological and bioenergetic effects on heart function and cardiomyocytes across three time points, fetal (gestational day 20), neonatal (4–10 days), and young adult (6–12 wk), were evaluated. Functional analysis utilizing echocardiography, speckle-tracking based strain, and cardiomyocyte contractility, coupled with mitochondrial energetics, revealed effects of nano-exposure. Maternal exposed progeny demonstrated a decrease in E- and A-wave velocities, with a 15% higher E-to-A ratio than controls. Myocytes isolated from exposed animals exhibited ~30% decrease in total contractility, departure velocity, and area of contraction. Bioenergetic analysis revealed a significant increase in proton leak across all ages, accompanied by decreases in metabolic function, including basal respiration, maximal respiration, and spare capacity. Finally, electron transport chain complex I and IV activities were negatively impacted in the exposed group, which may be linked to a metabolic shift. Molecular data suggest that an increase in fatty acid metabolism, uncoupling, and cellular stress proteins may be associated with functional deficits of the heart. In conclusion, gestational nano-exposure significantly impairs the functional capabilities of the heart through cardiomyocyte impairment, which is associated with mitochondrial dysfunction. NEW & NOTEWORTHY Cardiac function is evaluated, for the first time, in progeny following maternal nanomaterial inhalation. The findings indicate that exposure to nano-sized titanium dioxide (nano-TiO₂) during gestation negatively impacts cardiac function and mitochondrial respiration and bioenergetics. We conclude that maternal nano-TiO₂ inhalation contributes to adverse cardiovascular health effects, lasting into adulthood. Listen to this article's corresponding podcast at https://ajpheart.podbean.com/e/gestational-nanomaterial-exposure-and-cardiac-dysfunction/ .

Type 2 diabetes mellitus is a major risk factor for cardiovascular disease and mortality. Uncontrolled type 2 diabetes mellitus results in a systemic milieu of increased circulating glucose and fatty acids. The development of insulin resistance in cardiac tissue decreases cellular glucose import and enhances mitochondrial fatty acid uptake. While triacylglycerol and cytotoxic lipid species begin to accumulate in the cardiomyocyte, the energy substrate utilization ratio of free fatty acids to glucose changes to almost entirely free fatty acids. Accumulating evidence suggests a role of miRNA in mediating this metabolic transition. Energy substrate metabolism, apoptosis, and the production and response to excess reactive oxygen species are regulated by miRNA expression. The current momentum for understanding the dynamics of miRNA expression is limited by a lack of understanding of how miRNA expression is controlled. While miRNAs are important regulators in both normal and pathological states, an additional layer of complexity is added when regulation of miRNA regulators is considered. miRNA expression is known to be regulated through a number of mechanisms, which include, but are not limited to, epigenetics, exosomal transport, processing, and posttranscriptional sequestration. The purpose of this review is to outline how mitochondrial processes are regulated by miRNAs in the diabetic heart. Furthermore, we will highlight the regulatory mechanisms, such as epigenetics, exosomal transport, miRNA processing, and posttranslational sequestration, that participate as regulators of miRNA expression. Additionally, current and future treatment strategies targeting dysfunctional mitochondrial processes in the diseased myocardium, as well as emerging miRNA-based therapies, will be summarized.