Body mass index (BMI) and blood biomarkers are not enough to predict cardiovascular disease risk. Apolipoprotein B was identified to be associated with cardiovascular disease (CVD) progression. The Dual-energy X-ray Absorption (DXA) results could be considered as a predictor for cardiovascular disease in a more refined way based on fat distribution. The prediction of CVD risk by simple indicators still cannot meet clinical needs. The association of ApoB with specific fat depot features remains to be explored to better co-predict cardiovascular disease risk. An amount of 5997 adults from National Health and Nutrition Examination Survey (NHANES) were enrolled. Their demographic information, baseline clinical condition, blood examination, and DXA physical examination data were collected. Multivariate regression was used to assess the correlation between ApoB and site-specific fat characteristics through different adjusted models. Smooth curve fittings and threshold analysis were used to discover the turning points with 95% confidence intervals. ApoB is positively correlated with arms percent fat, legs percent fat, trunk percent fat, android percent fat, gynoid percent fat, arm circumference and waist circumference after adjustment with covariates for age, gender, race, hypertension, diabetes, hyperlipidemia, coronary heart disease, smoking status and vigorous work activity. The smooth curve fitting and threshold analysis also showed that depot-specific fat had lower turning points of ApoB in both males and females within the normal reference range of ApoB. Meanwhile, females have a lower increase in ApoB per 1% total percent fat and android percent fat than males before the turning points, while females have a higher growth of ApoB per 1% gynoid percent fat than males. The combined specific fat-depot DXA and ApoB analysis could indicate the risk of CVD in advance of lipid biomarkers or DXA alone.

Rationale: Intravenous leiomyomatosis (IVL) and uterine leiomyosarcoma (ULMS) share overlapping imaging features, leading to frequent preoperative misdiagnosis. Accurate differentiation is critical for appropriate surgical management. Patient concerns: Case 1 is a 48-year-old woman with intermittent vaginal bleeding. Case 2 is a 52-year-old woman with exertional dyspnea for 8 months. Diagnoses: Preoperative computed tomography venography suggested IVL in both cases. Final pathology showed case 1 for low-grade endometrial stromal sarcoma with vascular invasion and case 2 for ULMS. Interventions: Case 1 under hysterectomy, bilateral salpingoo-opherectomy, ureteral replantation, tumor resection. Case 2 under resection of uterus, adnexa, right kidney, and intravascular tumor. Outcomes: Both cases showed a fine postoperative condition with no severe adverse reactions. Lessons: Intraoperative pathology is essential for distinguishing ULMS from IVL when imaging is inconclusive.

Atherosclerosis remains a major cause of cardiovascular morbidity and mortality. Growing evidence suggests that microbiota–host interactions contribute to atherosclerosis through systemic and local mechanisms. This review summarizes current evidence on gut microbiota, microbial metabolites, and plaque-associated microbial signals, and discusses their potential therapeutic implications. Gut dysbiosis may contribute to atherosclerosis through multiple metabolite-dependent, immune, vascular, and metabolic pathways. Recent multi-omics and experimental studies further indicate that microbiota influence vascular biology beyond compositional changes alone. In parallel, plaque-associated microbial signals, potentially originating from the oral cavity or gut, may contribute to local inflammation, oxidative stress, necrotic core expansion, and plaque vulnerability. We further discuss a putative gut–oral–intraplaque axis, a potential local microbial metabolite microenvironment within plaques, and possible cross-kingdom interactions between gut fungi and bacteria. Together, these findings support an integrated framework in which microbiota may shape atherosclerosis through interconnected systemic and local pathways, with potential implications for biomarker development and microbiota-targeted therapies.