Si, Bing

The advent of high-throughput sequencing technologies has revolutionized cardiovascular genetics, generating vast amounts of data. A major challenge in the genomic era is the efficient and accurate identification of causative genetic variants associated with cardiovascular disease. Artificial intelligence (AI)-driven computational approaches offer a powerful solution by enabling the automation of variant classification, improving consistency and reproducibility, and enhancing predictive accuracy. These methods may guide not only variant classification, variant effect prediction, and clinical prioritization, but also open the door to data-driven precision medicine, enabling more accurate and individualized diagnoses and targeted therapeutic strategies tailored to each patient’s unique genetic and clinical profile. This state-of-the-art review provides a structured overview of AI methodologies applied to cardiovascular genomics, beginning with rule-based expert systems that encode standardized guidelines for consistent variant interpretation. Next, we examine machine learning approaches capable of identifying complex patterns in annotated multimodal clinical and multi-omic datasets. The role of deep learning algorithms is highlighted for their ability to extract features from high-dimensional, unstructured data relevant to cardiovascular disease. In addition, the potential of generative AI is explored, including applications in synthetic data generation, variant impact prediction, and automated summarization of biomedical literature. Despite advances, several challenges remain, including data heterogeneity, the need for explainable AI models to elucidate the decision mechanisms, and the complexity linked to the integration of AI-based tools into clinical workflows. Addressing these issues requires interdisciplinary collaboration among clinicians, geneticists, data scientists, and bioinformaticians to ensure the effective translation of AI-generated insights into clinical practice. This review aims to provide a comprehensive perspective on the evolving role of AI in cardiovascular genomics and its implications for advancing precision medicine.

Abstract Background Normal weight obesity (NWO) has been linked to metabolic syndrome (MetS), cardiovascular disease (CVD) and mortality, but has not been comprehensively studied in the Hispanic population. Purpose Our study aims to examine the CVD risk factors associated with NWO and to compare the development of CVD risk factors between NWO and normal weight non-obese (NWNO) Hispanic adults over 6 years. Methods This is a prospective study using data from the Hispanic Community Health Study (HCHS). Interviews and clinical assessments were collected from 2008–2011 (visit 1) to 2014–2017 (visit 2). NWO was defined as a body mass index (BMI) between 18.5–24.9 kg/m2 and a body fat percentage of ≥25 % in men and ≥35% in women, estimated using the Tanita scale. We identified 1,000 HCHS participants as having a normal weight at visit 1 and not missing data on covariates of interest including hypertension (HTN), hyperlipidemia (HLD), MetS, and insulin resistance (Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) ≥2.5). Participants were categorized into NWO (n=104) or NWNO (n=896) groups. Univariate logistic regression models were used to estimate the odds ratio (OR) and 95% confidence intervals (CI) of the association between health risk factors and NWO. Multivariate logistic regression models were used to estimate the odds of developing CVD risk factors over 6 years, using propensity score matching. Results The prevalence of NWO among our sample of Hispanic adults was 10.4% and the 6-year incidence of NWO at visit 2 was 3.5% (n=31) from NWNO participants at visit 1. Higher BMI (OR=5.19; 95% CI=3.15–9.27, p<0.001) and a higher mean overnight heart rate (OR=1.34; 95% CI=1.01–1.79, p=0.043) were statistically significantly associated with NWO at visit 1. Furthermore, HTN (OR=2.88; 95% CI=1.31–6.71, p=0.011), HLD (OR=2.12; 95% CI=1.21–3.77, p=0.009), and MetS (OR=3.48; 95% CI=1.62–8.09, p=0.002) were statistically significantly associated with NWO at visit 1. Participants with NWO at visit 1 had higher odds of developing insulin resistance (OR=1.79; 95% CI=1.07–3.04, p=0.029) and MetS (OR=2.03; 95% CI=1.15–3.66, p=0.016) at visit 2 compared to NWNO participants at visit 1, after age and sex matching. Conclusion Among Hispanic adults, the prevalence of NWO was associated with a higher BMI, higher mean overnight heart rate, treated HTN, HLD, and MetS. Hispanic adults with NWO were more likely to develop insulin resistance and MetS over 6 years compared to NWNO Hispanic adults. Overall, NWO among Hispanic adults was associated with CVD risk factors which places this population at a higher risk for CVD morbidity and mortality.

Background The current subclassification of migraine is according to headache frequency and aura status. The variability in migraine symptoms, disease course, and response to treatment suggest the presence of additional heterogeneity or subclasses within migraine. Objective The study objective was to subclassify migraine via a data‐driven approach, identifying latent factors by jointly exploiting multiple sets of brain structural features obtained via magnetic resonance imaging (MRI). Methods Migraineurs ( n = 66) and healthy controls ( n = 54) had brain MRI measurements of cortical thickness, cortical surface area, and volumes for 68 regions. A multimodality factor mixture model was used to subclassify MRIs and to determine the brain structural factors that most contributed to the subclassification. Clinical characteristics of subjects in each subgroup were compared. Results Automated MRI classification divided the subjects into two subgroups. Migraineurs in subgroup #1 had more severe allodynia symptoms during migraines (6.1 ± 5.3 vs. 3.6 ± 3.2, P = .03), more years with migraine (19.2 ± 11.3 years vs 13 ± 8.3 years, P = .01), and higher Migraine Disability Assessment (MIDAS) scores (25 ± 22.9 vs 15.7 ± 12.2, P = .04). There were not differences in headache frequency or migraine aura status between the two subgroups. Conclusions Data‐driven subclassification of brain MRIs based upon structural measurements identified two subgroups. Amongst migraineurs, the subgroups differed in allodynia symptom severity, years with migraine, and migraine‐related disability. Since allodynia is associated with this imaging‐based subclassification of migraine and prior publications suggest that allodynia impacts migraine treatment response and disease prognosis, future migraine diagnostic criteria could consider allodynia when defining migraine subgroups.