Hasil untuk "Diseases of the circulatory (Cardiovascular) system"

Quyen Thuy Nguyen, An Viet Tran, Bao The Nguyen et al.

BackgroundApolipoprotein B (APOB) rs676210 polymorphism has been associated with altered lipid metabolism and cardiovascular risk in various populations; however, data from Vietnamese populations remain limited. ObjectiveThis study aimed to investigate the association of the APOB rs676210 variant with lipid profiles among Vietnamese individuals newly diagnosed with elevated low-density lipoprotein cholesterol (LDL-C). MethodsA cross-sectional study was conducted among 69 Vietnamese adults newly diagnosed with elevated LDL-C (≥130 mg/dL) at a tertiary hospital in Southern Vietnam. Participants were genotyped for APOB rs676210 using real-time polymerase chain reaction (PCR) with allele-specific probes. Lipid profile components, including LDL-C, high-density lipoprotein cholesterol (HDL-C), non–HDL-C, and ApoB, were compared across genotype groups (AA vs GA/GG) and alleles (A vs G). Statistical analyses involved t tests, chi-square tests, and multivariable linear regression adjusted for age, sex, the BMI, and diabetes. P<.05 was considered statistically significant. ResultsOf the 69 participants, 32 (46.4%) carried the AA genotype, while 37 (53.6%) carried the GA or the GG genotype. The AA genotype was associated with significantly higher LDL-C (mean 5.19, SD 0.95, vs mean 4.37, SD 0.97, mmol/L; P<.001), non–HDL-C (mean 5.94, SD 1.08, vs mean 5.31, SD 1.22 mmol/L; P=.03), and ApoB (mean 149.5, SD 26.3, vs mean 136.9, SD 15.2, mg/dL; P=.02) and lower HDL-C (mean 1.26, SD 0.31, vs mean 1.44, SD 0.39, mmol/L; P=.03) compared to the GA/GG genotype. Allele-based analysis showed that carriers of the A allele (98/138, 71%) also had higher LDL-C (mean 4.91, SD 1.02, vs mean 4.36, SD 0.97, mmol/L; P=.004) and ApoB (mean 145.6, SD 23.2, vs mean 135.9, SD 16.0, mg/dL; P=.02) than G allele carriers (40/138, 29%). These associations remained significant after multivariate adjustment. ConclusionsAPOB rs676210 polymorphism is associated with significant differences in lipid profiles among Vietnamese adults with elevated LDL-C. Specifically, the A allele and the AA genotype confer a more atherogenic profile, suggesting potential utility as a genetic marker in lipid screening and personalized cardiovascular risk management in this population.

Niranjana Arun Menon, Iqra Farooq, Yulong Li et al.

Cardiovascular disease (CVD) prediction remains a tremendous challenge due to its multifactorial etiology and global burden of morbidity and mortality. Despite the growing availability of genomic and electrophysiological data, extracting biologically meaningful insights from such high-dimensional, noisy, and sparsely annotated datasets remains a non-trivial task. Recently, LLMs has been applied effectively to predict structural variations in biological sequences. In this work, we explore the potential of fine-tuned LLMs to predict cardiac diseases and SNPs potentially leading to CVD risk using genetic markers derived from high-throughput genomic profiling. We investigate the effect of genetic patterns associated with cardiac conditions and evaluate how LLMs can learn latent biological relationships from structured and semi-structured genomic data obtained by mapping genetic aspects that are inherited from the family tree. By framing the problem as a Chain of Thought (CoT) reasoning task, the models are prompted to generate disease labels and articulate informed clinical deductions across diverse patient profiles and phenotypes. The findings highlight the promise of LLMs in contributing to early detection, risk assessment, and ultimately, the advancement of personalized medicine in cardiac care.

Tariq M Khan, Toufique Ahmed Soomro, Imran Razzak

Retinal imaging has emerged as a powerful, non-invasive modality for detecting and quantifying biomarkers of systemic diseases-ranging from diabetes and hypertension to Alzheimer's disease and cardiovascular disorders but current insights remain dispersed across platforms and specialties. Recent technological advances in optical coherence tomography (OCT/OCTA) and adaptive optics (AO) now deliver ultra-high-resolution scans (down to 5 μm ) with superior contrast and spatial integration, allowing early identification of microvascular abnormalities and neurodegenerative changes. At the same time, AI-driven and machine learning (ML) algorithms have revolutionized the analysis of large-scale retinal datasets, increasing sensitivity and specificity; for example, deep learning models achieve > 90 \% sensitivity for diabetic retinopathy and AUC = 0.89 for the prediction of cardiovascular risk from fundus photographs. The proliferation of mobile health technologies and telemedicine platforms further extends access, reduces costs, and facilitates community-based screening and longitudinal monitoring. Despite these breakthroughs, translation into routine practice is hindered by heterogeneous imaging protocols, limited external validation of AI models, and integration challenges within clinical workflows. In this review, we systematically synthesize the latest OCT/OCT and AO developments, AI/ML approaches, and mHealth/Tele-ophthalmology initiatives and quantify their diagnostic performance across disease domains. Finally, we propose a roadmap for multicenter protocol standardization, prospective validation trials, and seamless incorporation of retinal screening into primary and specialty care pathways-paving the way for precision prevention, early intervention, and ongoing treatment of life-threatening systemic diseases.

Abdelrhman Gaber, Hassan Abd-Eltawab, John Elgallab et al.

Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enhanced federated learning (FL) framework that integrates both parametric models (logistic regression, SVM, neural networks) and non-parametric models (Random Forest, XGBoost) for coronary heart disease risk prediction. To address key FL challenges, we propose: (1) tree-subset sampling that reduces Random Forest communication overhead by 70%, (2) XGBoost-based feature extraction enabling lightweight federated ensembles, and (3) federated SMOTE synchronization for resolving cross-institutional class imbalance. Evaluated on the Framingham dataset (4,238 records), FedCVD++ achieves state-of-the-art results: federated XGBoost (F1 = 0.80) surpasses its centralized counterpart (F1 = 0.78), and federated Random Forest (F1 = 0.81) matches non-federated performance. Additionally, our communication-efficient strategies reduce bandwidth consumption by 3.2X while preserving 95% accuracy. Compared to existing FL frameworks, FedCVD++ delivers up to 15% higher F1-scores and superior scalability for multi-institutional deployment. This work represents the first practical integration of non-parametric models into federated healthcare systems, providing a privacy-preserving solution validated under real-world clinical constraints.

Christoph Balada, Aida Romano-Martinez, Vincent ten Cate et al.

In this study, hypertension is utilized as an indicator of individual vascular damage. This damage can be identified through machine learning techniques, providing an early risk marker for potential major cardiovascular events and offering valuable insights into the overall arterial condition of individual patients. To this end, the VideoMAE deep learning model, originally developed for video classification, was adapted by finetuning for application in the domain of ultrasound imaging. The model was trained and tested using a dataset comprising over 31,000 carotid sonography videos sourced from the Gutenberg Health Study (15,010 participants), one of the largest prospective population health studies. This adaptation facilitates the classification of individuals as hypertensive or non-hypertensive (75.7% validation accuracy), functioning as a proxy for detecting visual arterial damage. We demonstrate that our machine learning model effectively captures visual features that provide valuable insights into an individual's overall cardiovascular health.

Houda Belhad, Asmae Bourbia, Salma Boughanja

Chronic diseases, such as cardiovascular disease, diabetes, chronic kidney disease, and thyroid disorders, are the leading causes of premature mortality worldwide. Early detection and intervention are crucial for improving patient outcomes, yet traditional diagnostic methods often fail due to the complex nature of these conditions. This study explores the application of machine learning (ML) and deep learning (DL) techniques to predict chronic disease and thyroid disorders. We used a variety of models, including Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT), Neural Networks (NN), Decision Trees (DT) and Native Bayes (NB), to analyze and predict disease outcomes. Our methodology involved comprehensive data pre-processing, including handling missing values, categorical encoding, and feature aggregation, followed by model training and evaluation. Performance metrics such ad precision, recall, accuracy, F1-score, and Area Under the Curve (AUC) were used to assess the effectiveness of each model. The results demonstrated that ensemble methods like Random Forest and Gradient Boosted Trees consistently outperformed. Neutral Networks also showed superior performance, particularly in capturing complex data patterns. The findings highlight the potential of ML and DL in revolutionizing chronic disease prediction, enabling early diagnosis and personalized treatment strategies. However, challenges such as data quality, model interpretability, and the need for advanced computational techniques in healthcare to improve patient outcomes and reduce the burden of chronic diseases. This study was conducted as part of Big Data class project under the supervision of our professors Mr. Abderrahmane EZ-ZAHOUT and Mr. Abdessamad ESSAIDI.

Shosaku Nomura MD, Takehito Taniura MD, Jun Ichikawa MD et al.

We measured high mobility group box 1 protein (HMGB1) and platelet-derived microparticles (PDMP) in blood samples from patients with untreated type 2 diabetes mellitus (T2DM). We examined the effects of a combination of sodium/glucose cotransporter 2 (SGLT2) inhibitors and dipeptidyl peptidase-4 (DPP-4) inhibitors. Multiple regression analysis of HMGB1 was conducted on data from 252 patients in our previously reported T2DM-related clinical study. The results revealed significant correlations between HMGB1 and PDMP, soluble CD40 ligand, plasminogen activator inhibitor-1, and soluble E-selectin in multivariate analysis. Based on the HMGB1 levels before treatment with combination, 46 T2DM patients in the study were classified into two groups, high and low. The high HMGB1 group showed a significantly lower adiponectin level and higher PDMP production than the low HMGB1 group. T2DM risk significantly and positively correlated with HMGB1 and PDMPs. HMGB1-induced PDMP production was simulated in vitro using healthy platelets. Furthermore, The combination of a SGLT2 inhibitor and a DPP-4 inhibitor significantly reduced HMGB1 and PDMP levels. These results suggest that in addition to abnormal glucose metabolism, HMGB1-dependent PDMP production and the resulting development of atherosclerosis are also a concern in patients with T2DM.

Seyed Saeed Tamehri Zadeh, Michael D. Shapiro

Dyslipidemia, characterized by abnormal lipid levels in the blood, significantly escalates the risk of atherosclerotic cardiovascular disease and requires effective treatment strategies. While existing therapies can be effective, long-term adherence is often challenging. There has been an interest in developing enduring and more efficient solutions. In this context, gene editing, particularly clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) technology, emerges as a groundbreaking approach, offering potential long-term control of dyslipidemia by directly modifying gene expression. This review delves into the mechanistic insights of various gene-editing tools. We comprehensively analyze various pre-clinical and clinical studies, evaluating the safety, efficacy, and therapeutic implications of gene editing in dyslipidemia management. Key genetic targets, such as low-density lipoprotein receptor (LDLR), proprotein convertase subtilisin/kexin type 9 (PCSK9), angiopoietin-like protein 3 (ANGPTL3), apolipoprotein C3 (APOC3), and lipoprotein (a) (Lp(a)), known for their pivotal roles in lipid metabolism, are scrutinized. The paper highlights the promising outcomes of gene editing in achieving sustained lipid homeostasis, discusses the challenges and ethical considerations in genome editing, and envisions the future of gene therapy in revolutionizing dyslipidemia treatment and cardiovascular risk reduction.

Balla Y, Hashi AS, Osman AA et al.

Yassir Balla,1 Abdullahi Said Hashi,2 Ahmed Adam Osman,3,4 Mohamed Sheikh Hassan,5 Eren Mutlu3 1Department of Internal Medicine, Somali-Sudanese Specialized hospital, Mogadishu, Somalia; 2Department of Anesthesiology and Reanimation, Mogadishu Somalia Turkish Training and Research Hospital, Mogadishu, Somalia; 3Department of Radiology, Mogadishu Somalia Turkish Training and Research Hospital, Mogadishu, Somalia; 4Faculty of Medicine and Surgery, University of Somalia, Mogadishu, Somalia; 5Department of Neurology, Mogadishu Somalia Turkish Training and Research Hospital, Mogadishu, SomaliaCorrespondence: Ahmed Adam Osman, Department of Radiology, Mogadishu Somalia Turkish Training and Research Hospital, Mogadishu, Somalia, Email fahadyare41@gmail.comAbstract: Cerebral sinovenous thrombosis (CSVT) encompasses a spectrum of disorders involving thrombosis of the cerebral venous system. As shown by previous epidemiological studies, the prevalence of cerebral sinovenous thrombosis is 4– 7 cases per million people. Nephrotic syndrome was very rarely associated with thrombosis cerebral veins or sinuses. Hypercoagulability and thrombotic complications in nephrotic syndrome are most commonly seen in deep veins of the lower extremities and renal veins. Our case highlights a unique scenario in which cerebral sinovenous thrombosis was the initial presentation of nephrotic syndrome in a patient that was not an important past medical or surgical problem. The patient was brought to the emergency department with severe headache, vomiting, altered mental status, and generalized body swelling. Laboratory results showed proteinuria, hypoalbuminemia and hyperlipidemia. Non-contrast brain CT demonstrated hemorrhagic venous infarct associated with vasogenic edema. A subsequent brain MR venogram demonstrated occlusion of superior sagittal and right transverse sinuses. She was managed with low molecular weight heparin and intervenous corticosteroids then shifted to rivaroxaban and oral steroids, respectively, which resulted in massive clinical improvement and resolution of thrombus.Keywords: nephrotic syndrome, cerebral venous sinus thrombosis, MR venography

Huixin Zhan, Zijun Zhang

Clinical variant classification of pathogenic versus benign genetic variants remains a challenge in clinical genetics. Recently, the proposition of genomic foundation models has improved the generic variant effect prediction (VEP) accuracy via weakly-supervised or unsupervised training. However, these VEPs are not disease-specific, limiting their adaptation at the point of care. To address this problem, we propose DYNA: Disease-specificity fine-tuning via a Siamese neural network broadly applicable to all genomic foundation models for more effective variant effect predictions in disease-specific contexts. We evaluate DYNA in two distinct disease-relevant tasks. For coding VEPs, we focus on various cardiovascular diseases, where gene-disease relationships of loss-of-function vs. gain-of-function dictate disease-specific VEP. For non-coding VEPs, we apply DYNA to an essential post-transcriptional regulatory axis of RNA splicing, the most common non-coding pathogenic mechanism in established clinical VEP guidelines. In both cases, DYNA fine-tunes various pre-trained genomic foundation models on small, rare variant sets. The DYNA fine-tuned models show superior performance in the held-out rare variant testing set and are further replicated in large, clinically-relevant variant annotations in ClinVAR. Thus, DYNA offers a potent disease-specific variant effect prediction method, excelling in intra-gene generalization and generalization to unseen genetic variants, making it particularly valuable for disease associations and clinical applicability.

Lang Cao, Jimeng Sun, Adam Cross

Rare diseases affect millions worldwide but often face limited research focus due to their low prevalence. This results in prolonged diagnoses and a lack of approved therapies. Recent advancements in Large Language Models (LLMs) have shown promise in automating the extraction of medical information, offering potential to improve medical diagnosis and management. However, most LLMs lack professional medical knowledge, especially concerning rare diseases, and struggle to handle the latest rare disease information. They also cannot effectively manage rare disease data and are not directly suitable for diagnosis and management tasks. Our objective is to create an end-to-end system called AutoRD, which automates the extraction of information from medical texts about rare diseases, focusing on entities and their relations. AutoRD integrates up-to-date structured knowledge and demonstrates superior performance in rare disease extraction tasks. We conduct various experiments to evaluate AutoRD's performance, aiming to surpass common LLMs and traditional methods.

S. Yogeshpriya

Aashish Cheruvu

While deep-learning based recommender systems utilizing collaborative filtering have been commonly used for recommendation in other domains, their application in the medical domain have been limited. In addition to modeling user-item interactions, we show that deep-learning based recommender systems can be used to model subject-disease code interactions. Two novel applications of deep learning-based recommender systems using Neural Collaborative Filtering (NCF) and Deep Hybrid Filtering (DHF) were utilized for disease diagnosis based on known past patient comorbidities. Two datasets, one incorporating all subject-disease code pairs present in the MIMIC-III database, and the other incorporating the top 50 most commonly occurring diseases, were used for prediction. Accuracy and Hit Ratio@10 were utilized as metrics to estimate model performance. The performance of the NCF model making use of the reduced "top 50" ICD-9 code dataset was found to be lower (accuracy of ~80% and hit ratio@10 of 35%) as compared to the performance of the NCF model trained on all ICD-9 codes (accuracy of ~90% and hit ratio@10 of ~80%). Reasons for the superior performance of the sparser dataset with all ICD codes can be mainly attributed to the higher volume of data and the robustness of deep-learning based recommender systems with modeling sparse data. Additionally, results from the DHF models reflect better performance than the NCF models, with a better accuracy of 94.4% and hit ratio@10 of 85.36%, reflecting the importance of the incorporation of clinical note information. Additionally, compared to literature reports utilizing primarily natural language processing-based predictions for the task of ICD-9 code co-occurrence, the novel deep learning-based recommender systems approach performed better. Overall, the deep learning-based recommender systems have shown promise in predicting disease comorbidity.

Mengjie Du, Xiang Fang, Jie Li

This technical report describes ChinaTelecom system for Track 1 (closed) of the VoxCeleb2023 Speaker Recognition Challenge (VoxSRC 2023). Our system consists of several ResNet variants trained only on VoxCeleb2, which were fused for better performance later. Score calibration was also applied for each variant and the fused system. The final submission achieved minDCF of 0.1066 and EER of 1.980%.

Matteo Bertini, Daniela Mele, Francesco Vitali et al.

Background: A 61-year-old male with Steinert Dystrophy and prior history of cardiac implantable device complained of highly symptomatic right atrial tachycardia. Unresponsive to pharmacological therapy. Methods: The patient underwent catheter ablation procedure aided by high-density mapping. Results: Ablation procedure was succesful. Conclusions: This unique case report highlights the role of high-density mapping in the identification of critical isthmus and management of macro-reentrant tachycardia in complex situations such as the presence of multiple leads in the chamber.

Carolina T. Macedo, Carolina T. Macedo, Carolina T. Macedo et al.

AimPrevious studies showed that granulocyte-colony stimulating factor (G-CSF) improved heart function in a mice model of Chronic Chagas Cardiomyopathy (CCC). Herein, we report the interim results of the safety and efficacy of G-CSF therapy vs. placebo in adults with Chagas cardiomyopathy.MethodsPatients with CCC, New York Heart Association (NYHA) functional class II to IV and left ventricular ejection fraction (LVEF) 50% or below were included. A randomization list using blocks of 2 and 4 and an allocation rate of 1:1 was generated by R software which was stratified by functional class. Double blinding was done to both arms and assessors were masked to allocations. All patients received standard heart failure treatment for 2 months before 1:1 randomization to either the G-CSF (10 mcg/kg/day subcutaneously) or placebo group (1 mL of 0.9% saline subcutaneously). The primary endpoint was either maintenance or improvement of NYHA class from baseline to 6–12 months after treatment, and intention-to-treat analysis was used.ResultsWe screened 535 patients with CCC in Salvador, Brazil, of whom 37 were randomized. Overall, baseline characteristics were well-balanced between groups. Most patients had NYHA class II heart failure (86.4%); low mean LVEF was 32 ± 7% in the G-CSF group and 33 ± 10% in the placebo group. Frequency of primary endpoint was 78% (95% CI 0.60–0.97) vs. 66% (95% CI 0.40–0.86), p = 0.47, at 6 months and 68% (95% CI 0.43–0.87) vs. 72% (95% CI 0.46–0.90), p = 0.80, at 12 months in placebo and G-CSF groups, respectively. G-CSF treatment was safe, without any related serious adverse events. There was no difference in mortality between both arms, with five deaths (18.5%) in treatment vs. four (12.5%) in the placebo arm. Exploratory analysis demonstrated that the maximum rate of oxygen consumption during exercise (VO2 max) showed an improving trend in the G-CSF group.ConclusionG-CSF therapy was safe and well-tolerated in 12 months of follow-up. Although prevention of symptom progression could not be demonstrated in the present study, our results support further investigation of G-CSF therapy in Chagas cardiomyopathy patients.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT02154269].

Mattias Brunström, Krister Lindmark, Helena Norberg et al.

Aims The aim of this study was to investigate a real-world heart failure (HF) cohort regarding (1) prevalence of known diabetes mellitus (DM), undiagnosed DM and pre-diabetes, (2) if hf treatment differs depending on glycaemic status and (3) if treatment of DM differs depending on HF phenotype.Methods All patients who had received a diagnosis of HF at Umeå University Hospital between 2010 and 2019 were identified and data were extracted from patient files according to a prespecified protocol containing parameters for clinical characteristics, including echocardiogram results, comorbidities, fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) values. Patients’ HF phenotype was determined using the latest available echocardiogram. The number of patients with previous DM diagnosis was assessed. Patients without a previous diagnosis of DM were classified as non-DM, pre-diabetes or probable DM according to FPG and HbA1c levels using WHO criteria.Results In total, 2326 patients (59% male, mean age 76±13 years) with HF and at least one echocardiogram were assessed. Of these, 617 (27%) patients had a previous diagnosis of DM. Of the 1709 patients without a previous diagnosis of DM, 1092 (67%) patients had either an FPG or HbA1c recorded, of which 441 (41%) met criteria for pre-diabetes and 97 (9%) met criteria for probable diabetes, corresponding to 19% and 4% of the entire cohort, respectively. Patients with HF and diabetes were more often treated with diuretics and beta blockers compared with non-DM patients (64% vs 42%, p&lt;0.001 and 88% vs 83%, p&lt;0.001, respectively). There was no difference in DM treatment between HF phenotypes.Conclusions DM and pre-diabetes are common in this HF population with 50% of patients having either known DM, probable DM or pre-diabetes. Patients with HF and DM are more often treated with common HF medications. HF phenotype did not affect choice of DM therapy.

Shahabedin Nabavi, Mohammad Hashemi, Mohsen Ebrahimi Moghaddam et al.

Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential basic quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment. This study examines the full cardiac coverage using a 3D convolutional model and then reduces the number of false predictions using an innovative salient region detection model. Salient regions are extracted from the short-axis cine CMR stacks using a three-step proposed algorithm. Combining the 3D CNN baseline model with the proposed salient region detection model provides a cascade detector that can reduce the number of false negatives of the baseline model. The results obtained on the images of over 6,200 participants of the UK Biobank population cohort study show the superiority of the proposed model over the previous state-of-the-art studies. The dataset is the largest regarding the number of participants to control the cardiac coverage. The accuracy of the baseline model in identifying the presence/absence of basal/apical slices is 96.25\% and 94.51\%, respectively, which increases to 96.88\% and 95.72\% after improving using the proposed salient region detection model. Using the salient region detection model by forcing the baseline model to focus on the most informative areas of the images can help the model correct misclassified samples' predictions. The proposed fully automated model's performance indicates that this model can be used in image quality control in population cohort datasets and also real-time post-imaging quality assessments.

ET Valeeva, GG Gimranova, E. Shaikhlislamova

Xiong Ding, Jinfeng Li, Ying Wu et al.

Background: No study has explored the modification effect of ideal cardiovascular health metrics (ICVHMs) on the association between famine exposure and risk of cardiovascular disease (CVD) so far. We aim to examine the effect of ICVHMs on the association between exposure to famine early in life and the risk of CVD in adulthood.Methods: A total of 61,527 participants free of CVD were included in this study from the Kailuan Study. All participants were divided into three groups, included nonexposed, fetal-exposed, and childhood-exposed groups. Cox regression was used to estimate the effect of famine exposure and ICVHMs on CVD risk.Results: After a median of 13.0 (12.7–13.2) years follow-up, 4,814 incident CVD cases were identified. Compared with nonexposed participants, the CVD risk increased in participants with fetal famine exposure (hazard ratio [HR]: 1.21; 95% CI: 1.07–1.37), but not in childhood famine-exposed participants. After stratifying by the number of ICVHMs, the increased CVD risk associated with fetal famine exposure was only observed in participants with less ICVHMs ( ≤ 2) (HR: 1.30; 95% CI: 1.11–1.52, P for interaction=0.008), but disappeared in those with three or more ICVHMs. The modified effect of ICVHMs was sex specific (P for sex interaction = 0.031).Conclusions: Exposing to famine in the fetal period could increase the risk of CVD in late life; however, ICVHMs might modify the effect of famine exposure on CVD risk, especially in men.