Lipids in Health and Disease最新文献

Familial dyslipidemia (FD), particularly familial hypercholesterolemia (FH), is a major contributor to premature cardiovascular disease (CVD), especially in regions with high consanguinity and underutilized genetic screening, such as Egypt. This study aimed to assess clinical, biochemical, and genetic factors that differentiate FD patients with and without CVD, and to develop a composite risk score for individualized stratification. A cross-sectional study was conducted on 60 Egyptian patients aged 15-25 years with genetically confirmed FD, equally divided based on CVD status. All participants underwent detailed clinical assessment, lipid profiling, and targeted next-generation sequencing of LDLR, APOB, and PCSK9 genes. Missense variants were evaluated using SIFT, PolyPhen-2, CADD, and ΔΔG stability scores, and classified according to ACMG criteria. Compared to non-CVD patients, those with CVD had significantly higher triglyceride levels (median: 356.5 vs. 236.5 mg/dL; p < 0.001) and a higher frequency of heterozygous pathogenic LDLR variants (30.0% vs. 3.3%; p = 0.006), while homozygous variants were more common in non-CVD patients (26.7% vs. 0%; p = 0.002). Deleterious missense variants were notably more frequent in the CVD group (56.7% vs. 10.0%; p < 0.001). A 10-variable composite risk score integrating clinical, lipid, and bioinformatic predictors effectively distinguished high- and moderate-risk cases (AUC = 0.742; p = 0.022), with 89.5% sensitivity and 81.8% negative predictive value. The study highlights the importance of combining clinical and genomic data for early risk stratification and introduces a pragmatic tool for identifying high-risk youth in resource-limited, consanguineous populations.

Background: preeclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality worldwide. While metabolic and inflammatory factors are increasingly recognized in its pathogenesis, the clinical utility of composite biomarkers remains underexplored. This study aimed to investigate the association between the C-reactive protein-triglyceride-glucose (CRP-TG-glucose) index (CTGI), a novel marker of metabolic-inflammation stress, and the risk of preeclampsia.

Methods: This retrospective cohort study included 11,916 pregnant women, of whom 486 developed preeclampsia. Maternal baseline characteristics were compared between the PE and non-PE groups. Logistic regression analyses were conducted to identify factors associated with PE. The relationship between CTGI and PE risk was further explored using quartile stratification, restricted cubic spline regression, and threshold effect analyses. Subgroup analyses were also performed to assess interaction effects across maternal and obstetric variables.

Results: Women with PE had significantly higher maternal age, body mass index (BMI), in vitro fertilization (IVF) conception, multifetal pregnancies, and elevated CTGI levels compared to non-PE counterparts (all P < .001). Multivariate logistic regression identified CTGI as an independent risk factor for PE (adjusted OR, 1.78; 95% CI, 1.51-2.09; P < .001), alongside BMI, maternal age, IVF, and multifetal gestation. A dose-response relationship was observed across CTGI quartiles, with the highest quartile showing a markedly increased PE risk (adjusted OR, 2.06; 95% CI, 1.52-2.81). Restricted cubic spline models and threshold analysis revealed a nonlinear association with a significant inflection point at CTGI = 2.244. Above this threshold, the risk of PE rose sharply (OR, 3.93; 95% CI, 2.09-7.39; P < .001). Subgroup analyses demonstrated consistent associations across maternal age, BMI, parity, plurality, and IVF status, without significant interaction.

Conclusions: Elevated CTGI in early pregnancy is independently and nonlinearly associated with an increased risk of preeclampsia, particularly above a critical threshold of 2.244. These findings underscore the potential clinical value of CTGI as an early risk stratification biomarker for PE, enabling timely intervention in high-risk pregnancies.

Background: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide. Lipid biomarkers, including direct low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein B (ApoB), and apolipoprotein A1 (ApoA1), are essential tools for cardiovascular risk assessment. Monitoring patient-derived median values over time may provide insights into population health and analytical performance. This study provides a descriptive analysis of population-level lipid results spanning nearly two decades. While trends in patient medians may support quality assurance, these data do not constitute a validated approach to risk prediction or definitive analytical monitoring due to the absence of outcome and treatment information.

Methods: We retrospectively analyzed routine clinical laboratory data from Uppsala University Hospital, Sweden, covering January 2006-December 2024. A total of 890,948 LDL-C, 867,446 HDL-C, 64,787 ApoB, and 65,500 ApoA1 results were included. Measurements were performed on Abbott Architect systems until 2021, after which assays were transferred to Roche Cobas Pro platforms. Statistical analyses included trend evaluation, variability assessment, and seasonal pattern analysis.

Results: Women had modestly higher LDL-C and HDL-C levels compared to men, while ApoB values were similar between sexes. ApoA1 was notably higher in women. Over the 19-year period, median LDL-C declined from 3.18 to 2.62 mmol/L, consistent with improved lipid management. HDL-C remained stable (1.36-1.45 mmol/L), while ApoB and ApoA1 concentrations showed minimal change. Variability was highest for LDL-C (median CV 6.4%) and lowest for ApoA1 (median CV 2.6%). Seasonal variation was negligible across all analytes. Testing volumes increased substantially for LDL-C and HDL-C, whereas ApoB and ApoA1 requests peaked around 2010 and later declined.

Conclusions: Long-term monitoring of median patient values demonstrates declining LDL-C, stable HDL-C, and consistent ApoB/ApoA1 ratios with minimal seasonal effects. These findings highlight the potential utility of patient medians as supplementary quality indicators and for population-level lipid surveillance.

Objective: To explore if the phospholipid composition in the small airway lining fluid differed between a group of tunnel construction workers exposed to respirable crystalline silica (RCS) and a reference group.

Methods: In total, 19 healthy, non-smoking workers under exposure to RCS and 21 unexposed referents from the same construction site were included. The participants underwent a health examination including lung function measurements and collection of exhaled particles (PEx) using the Particles in exhaled air (PExA) method. Analysis of PEx included determination of lipids. In total, 95 lipid species, primarily phospholipids, were determined. Non-parametric analyses (Wilcoxon rank-sum test and quantile regression), principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used for data analysis.

Results: A difference in mol% of phospholipids between the RCS exposed tunnel construction workers and unexposed referents was observed. On lipid class level there was a higher mol% of sphingomyelin (SM) species among exposed workers compared to referents. Regarding single phospholipid species, higher mol % of phosphatidylcholine (PC) and phosphatidylglycerol (PG) species containing diacyl chains with 18:2 (linoleic acid) and 20:4 (arachidonic acid) fatty acid components were observed in the exposed group. Additionally, lower mol% of saturated PC species were observed among the exposed.

Conclusions: Differences in phospholipid composition in the small airway lining fluid between workers exposed to RCS and a reference group were observed. This indicates a possible impact of RCS exposure on phospholipids in the small airways. However, whether these are linked to health effects is currently not known.

Epicardial adipose tissue, a metabolically active visceral fat depot surrounding the heart, is increasingly implicated as a key contributor to cardiovascular disease in patients with chronic kidney disease. This review explores how chronic kidney disease induced metabolic dysregulation, chronic inflammation and uremic toxin accumulation lead to epicardial adipose tissue dysfunction. Pathologically altered epicardial adipose tissue promotes atherosclerosis, myocardial fibrosis and arrhythmogenesis through paracrine signaling, oxidative stress and direct cellular interactions. Advanced imaging modalities now enable precise epicardial adipose tissue quantification, revealing its correlation with cardiovascular outcomes in chronic kidney disease. Epicardial adipose tissue is expected to become a new target for predicting and assessing cardiovascular risk, and for interventions to reduce cardiovascular risk in chronic kidney disease patients, thereby providing a new direciovascular risk, and for interventions to reduce cardiovascular risk in chronic kidney disease patients, thereby providing a new direction for future risk management and therapeutic strategies.

Background: Acute basilar artery occlusion (BAO) represents an uncommon but highly devastating form of ischemic stroke, frequently resulting in severe disability or death, despite advances in endovascular treatment (EVT). Although insulin resistance (IR) has been connected to poorer neurological outcomes after stroke, evidence regarding the predictive usefulness of non-insulin-based IR indices in BAO patients receiving EVT is still limited.

Methods: In this study, 266 BAO patients who received EVT between 2012 and 2024 were analyzed. Three IR indices were calculated at admission: the triglyceride-glucose (TyG) index, the metabolic score for insulin resistance (METS-IR), and the ratio of triglyceride to high-density lipoprotein cholesterol (TG/HDL-C). The primary endpoint included a 90-day unfavorable outcome (modified Rankin scale score: 4-6), and the secondary endpoints included mortality and symptomatic intracranial hemorrhage (sICH). Associations were examined by using multivariable regression, and discriminative ability was quantified via receiver operating characteristic analysis.

Results: In 266 patients, 149 (56.0%) demonstrated unfavorable outcomes, and 77 (28.9%) died within 90 days. Higher TyG values showed a robust association with unfavorable outcomes, mortality, and sICH; moreover, it provided the strongest predictive ability for functional outcomes (area under the curve comparisons, P < 0.05). METS-IR was associated with mortality and demonstrated a predictive ability comparable to that of TyG but weaker for functional outcome. TG/HDL-C was not significantly related to any outcome.

Conclusions: Among BAO patients who underwent EVT, the TyG index could strongly predict unfavorable functional recovery and mortality, whereas the METS-IR was mainly associated with mortality. These findings indicate that TyG may help in refining risk stratification after EVT, whereas METS-IR appears to be less informative.

Background: High-altitude hypoxia is closely linked to dysregulated lipid metabolism, particularly elevated triglyceride (TG) levels, which increase cardiovascular and metabolic risks. This study proposes an interpretable deep learning model to predict TG levels in high-altitude migrants based on clinically accessible indicators.

Methods: Data were collected from low-altitude residents (n = 96) and high-altitude migrants (n = 388). An Uncertainty-driven Gated Feature Selection Network (UGFS-Net) was developed for TG prediction, incorporating an uncertainty-driven sample re-weighting and hard example mining strategy. The model was trained via modern optimization techniques and stratified partitioning by the TG distribution. Performance was evaluated using accuracy and calibration metrics, and interpretability was assessed via SHapley Additive exPlanations (SHAP). Five benchmark machine learning models with PCA or LASSO dimensionality reduction were used for comparison.

Results: The UGFS-Net demonstrated a notable performance gain through uncertainty estimation, yielding an increase in R² from 0.7294 to 0.8776 for TG levels prediction under high-altitude. Predicted uncertainty showed significant correlations with errors and effectively distinguished low- from high-reliability samples, with strong calibration (bin-wise r = 0.9164). SHAP analysis highlighted that lipid metabolism, glucose metabolism, and the erythrocyte system collectively form a network that drives TG alterations in high-altitude environments and the Pearson correlation coefficient between gated the attention weights and SHAP importance scores was 0.9093. UGFS-Net consistently outperformed conventional machine learning models.

Conclusions: This study developed UGFS-Net, an interpretable deep learning model that accurately predicts triglyceride levels in high-altitude migrants (R² = 0.8776) and provides well-calibrated uncertainty estimates, with identified key biomarkers offering clinical insights.

Introduction: Noncommunicable diseases, primarily cardiovascular diseases (CVDs), are an emerging cause of morbidity in sub-Saharan Africa. Dyslipidaemia, a key modifiable CVD risk factor, is increasing but remains underdiagnosed and undertreated, particularly among people living with HIV (PLHIV), where HIV-related chronic inflammation, antiretroviral therapy (ART), and sociodemographic factors may contribute. This study assessed the prevalence, correlates of dyslipidaemia, and estimated 10-year CVD risk among PLHIV and people without HIV (PWoH) adults in Rwanda.

Methods: We analysed baseline data from 1,546 adults (1,234 PLHIV and 312 PWoH) enrolled in the NCOHIRWA Cohort from 12 Rwandan health facilities. Data were collected via standardised World Health Organisation (STEP) questionnaires. Dyslipidaemia was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria, and 10-year CVD risk was calculated via the Framingham risk score. For group comparisons, chi-square tests were used. Multivariate logistic regression identified independent predictors of dyslipidaemia, with adjusted odds ratios and 95% confidence intervals reported.

Results: Overall, 979 participants (61.9%) had dyslipidaemia, with comparable prevalence rates among PLHIV (744/1234; 60.29%) and PWoH participants (205/312; 65.06%). The PWoH participants had higher LDL-C levels (59 (18.9%) vs. 120 (9.7%), p < 0.0001). Independent predictors of dyslipidaemia included female sex, older age, obesity, and widowhood. PLHIV had lower odds of having elevated total cholesterol (aOR = 0.68, 0.49-0.95). Based on the estimated 10-year CVD risk, 3.95% of the participants had high and 21.47% had very high 10-year CVD, respectively, which was concentrated among older, widowed women with low education levels.

Conclusion: Dyslipidaemia and elevated CVD risk are highly prevalent among the study participants, with disparities based on HIV status, sex, and social vulnerability. Routine lipid screening and integrated HIV-CVD care, particularly for high-risk subgroups such as older women, are essential for reducing the long-term CVD burden.