Lipids in Health and Disease最新文献

Type 2 diabetes mellitus (T2DM) is a globally prevalent metabolic disorder, and cardiovascular disease (CVD) is a significant cause of mortality and morbidity in diabetic individuals. In addition to hyperglycemia, lipid abnormalities associated with T2DM play a crucial role in the development of CVD complications. Diabetic dyslipidemia is characterized by elevated levels of triglyceride (TG)-rich lipoproteins and small dense low-density lipoprotein (LDL) particles, reduced high-density lipoprotein (HDL) cholesterol, and impaired HDL function. Angiopoietin protein-like 3 (ANGPTL3) is a liver-derived protein that plays a crucial role in regulating plasma lipoprotein metabolism by inhibiting lipoprotein lipase and influencing lipid levels. Inhibiting ANGPTL3 has shown promising effects in promoting HDL-mediated cholesterol reverse transport and reducing the levels of TG-rich lipoproteins and LDL cholesterol. Here, we explore the potential of ANGPTL3 as a therapeutic target for lipid management in T2DM patients.

Objective: The study aims to investigate the associations of triglyceride glucose (TyG), TyG combined with body mass index (TyG-BMI), metabolic score for insulin resistance (METS-IR), the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C), and the risk of mild cognitive impairment (MCI) in individuals without diabetes aged 45 and above.

Methods: The most recent data in this study were from a cohort study, which sourced samples from the China Health and Retirement Longitudinal Study, spanning 2011 to 2018. The four indices' associations with MCI risk were analyzed using logistic regression. The predictive capacity was measured using the receiver operating characteristic (ROC) curve.

Results: Over a 7-year follow-up, 1,261 individuals (31.34%) of the 4,027 participants developed MCI. Logistic regression analysis revealed significant associations between these surrogate indices and MCI. The findings for the highest quartile compared with the lowest quartile were as follows: TyG: 1.24 (95% CI: 1.02, 1.49); TyG-BMI: 1.38 (95% CI: 1.13, 1.68); METS-IR: 1.50 (95% CI: 1.09, 2.06); TG/HDL-C: 1.34 (95% CI: 1.10, 1.64). ROC analysis revealed that TyG, TyG-BMI, TG/HDL-C, and METS-IR demonstrated excellent discriminatory power for MCI, with area under the curve (AUC) values of 0.82 (95% CI: 0.80, 0.83), 0.82 (95% CI: 0.80, 0.83), 0.83 (95% CI: 0.80, 0.84), and 0.83 (95% CI: 0.80, 0.84), respectively. The four indices showed stronger ability to predict MCI risk in females compared to males.

Conclusion: Elevated levels of four indices are positively correlated with MCI risk. TyG-BMI and METS-IR demonstrate stronger capabilities in identifying MCI across both male and female populations. This suggests that early intervention in patients with elevated IR surrogate indices may help reduce the MCI.

Background: Inflammatory responses and lipid metabolism make vital impacts on the development of gallstones. This study investigated the relationship between gallstone disease (GSD) and the neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) in American patients with gallstones.

Methods: The data analyzed were sourced from the 2017-2020 National Health and Nutrition Examination Survey (NHANES) and comprised of participants with complete data on GSD and NHR. The relationship between NHR and GSD was evaluated with weighted multivariable logistic regression analysis. Additionally, subset analyses, interaction tests, smoothed curve fitting, and threshold effect analyses were conducted.

Results: Among the 7894 participants analyzed in this study, the prevalence of GSD was 10.98%, and the average NHR value was 3.41 ± 0.06. The fully adjusted multivariable logistic regression results demonstrated an obvious positive association between NHR and the likelihood of GSD (OR = 1.09, 95% CI: 1.01, 1.16; P = 0.0197). Consistency of this association was confirmed through subset analyses and interaction tests across various subgroups, including those categorized by smoking status and asthma. Furthermore, smoothed curve fitting and threshold effect analyses revealed a nonlinear relationship with a threshold of 2.86.

Conclusions: NHR shows a positive relationship to an increased likelihood of GSD among Americans. It can act as an easy and cost-effective tool for the early detection and management of individuals at risk for GSD.

Background: This study was aimed at investigating the correlation between the occurrence of stroke and relative fat mass (RFM), a novel metric for determining total body fat.

Methods: This cross-sectional study employed the National Health and Nutrition Examination Survey (NHANES) dataset, which encompassed the years 2005 to 2018 to assess the independent relationship between RFM and stroke. Moreover, multinomial logistic regression, subgroup analysis, smooth curve fitting, and interaction testing were also used.

Results: This study included 35,842 participants and 1,267 (3.53%) of them were diagnosed with stroke. Fully adjusted Models showed that RFM was positively associated with stroke (odds ratio [OR] = 1.02; 95% confidence interval [CI] = 1.01-1.03). The odds of having a stroke in quartile 4 were significantly elevated by 44%, compared to quartile 1 (OR = 1.44,95%CI:1.09-1.90). In addition, a subgroup analysis also demonstrated that age and BMI significantly impacted the association between RFM and stroke (P for interaction<0.01).

Conclusions: Elevated RFM is associated with increased odds of stroke, suggesting that RFM may have potential value in the prevention and management of stroke.

Background: Abnormalities in lipid metabolism are common among adult asthmatics. However, the precise directionality linking asthma to blood lipid levels remains controversial. Our study aimed to evaluate the association between the Non-HDL to HDL Ratio (NHHR), a lipid composite index, and asthma prevalence among the adult population in the United States.

Methods: Utilizing adult participants' data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2009 to 2018, the study employed a multivariable logistic regression model, adjusting for covariables, to establish the relationship between NHHR levels and the prevalence of asthma. Furthermore, smoothing curve fitting and subgroup analyses were conducted to investigate the robustness of this association.

Results: This study included 26,023 adult individuals (mean age = 49.63 ± 17.66). In the fully adjusted model, a significant inverse association was observed between log-transformed NHHR values and asthma prevalence (OR = 0.85, 95% CI: 0.79-0.93). Subgroup analysis revealed that gender served as a modulator, altering the association between NHHR levels and asthma prevalence. A more pronounced negative association between lnNHHR and asthma prevalence was noted among male participants [(Male: OR = 0.78, 95% CI: 0.69-0.88) vs. (Female: OR = 0.92, 95% CI: 0.83-1.03), P for interaction = 0.0313].

Conclusions: Our study revealed an inverse association between NHHR levels and the prevalence of asthma in the US adult population, which is influenced by gender. NHHR measurement may be a potential tool for early identification and prediction of adult asthmatics in specific populations.

Background: Glycated Albumin (GA) and atherogenic index of plasma (AIP) are two important biomarkers that respectively reflect lipid and glucose levels. Previous research has revealed their roles in cardiovascular diseases (CVD) and diabetes. However, their combined predictive ability in forecasting cardiovascular events (CVE) after percutaneous coronary intervention (PCI) among postmenopausal acute coronary syndrome (ACS) patients remains insufficiently studied.

Methods: Based on the levels of AIP (AIP-L and AIP-H) and GA (GA-L and GA-H), four groups were used to categorize the patients. The CVE assessed included cardiac death, nonfatal myocardial infarction (MI) and nonfatal stroke. To evaluate the relationship between AIP, GA, and CVE, multivariate Cox regression analyses were performed.

Results: 98 patients (7.5%) experienced CVE during follow-up. AIP and GA were revealed as strong independent predictors of CVE through multivariate analysis (AIP: HR 3.324, 95%CI 1.732-6.365, P = 0.004; GA: HR 1.098, 95% CI 1.023-1.177, P = 0.009). In comparison to those in the initial group (AIP-L and GA-L), the fourth group (AIP-H and GA-H) of patients exhibited the greatest CVE risk (HR 2.929, 95% CI 1.206-5.117, P = 0.018). Derived from the model of baseline risk, the combination of AIP + GA significantly enhanced the AUC, meanwhile combining AIP and GA levels maximized prognostic accuracy in the baseline risk model.

Conclusions: This study found that the combined measurement of AIP and GA significantly enhanced the predictive capability for CVE following PCI in postmenopausal ACS patients. By integrating these two biomarkers, it became possible to more accurately identify high-risk individuals and provided clinicians with new predictive tools for postmenopausal ACS patients in risk assessment and management.

Background: Currently, studies on the formation mechanism for the enlargement of arachnoid granulation (AG) are lacking. The impact of dyslipidemia on the formation of multiple large arachnoid granulations (LAGs) was studied in this research.

Methods: The study included patients diagnosed with cerebral venous sinus stenosis (CVSS) related to LAG. The number of LAGs was assessed via high-resolution black blood magnetic resonance imaging. The relationships between blood lipids and multiple LAGs were explored to evaluate the effects of dyslipidemia on the formation of multiple LAGs.

Results: A total of 163 participants with a diagnosis of LAG were included. The levels of total cholesterol (TC) (P = 0.004) and low-density lipoprotein cholesterol (LDL-c) (P = 0.01) in the multiple LAGs group were greater than those in the non-multiple LAGs group. Multivariate logistic regression analysis revealed that TC (odds ratio (OR), 2.19; 95% confidence interval (CI), 1.26-3.80; P = 0.006) and LDL-c (OR, 2.18; 95% CI, 1.16-4.07; P = 0.02) were independently associated with multiple LAGs.

Conclusions: TC and LDL-c are independently related to multiple LAGs, indicating that dyslipidemia may be a potential cause of CVSS. Therefore, monitoring blood lipids may be necessary for patients with LAGs.

Background: Vegan and vegetarian dietary patterns are known to beneficially modulate risk factors for cardiovascular disease; however, the current literature does not differentiate between various plant-based diets. This study aimed to examine the association between various plant-based diets and plasma lipids and glycaemic indices compared to a regular meat-eating diet.

Methods: A cross-sectional study of Australian adults (n = 230) aged 30-75yrs habitually consuming the following were recruited: vegan, lacto-vegetarian, pesco-vegetarian, semi-vegetarian, or regular meat-eater. Multivariable regression analysis was used to adjust for covariates.

Results: Compared to regular meat-eaters, vegans had significantly lower total cholesterol (-0.77mmol/L,95% CI -1.15, -0.39, P < 0.001), low-density lipoprotein cholesterol (LDL-C, -0.71mmol/L, 95% CI -1.05, -0.38, P < 0.001), non-high-density lipoprotein cholesterol (non-HDL-C, -0.75mmol/L, 95% CI -1.11, -0.39, P < 0.001), total cholesterol/HDL-C-ratio (-0.49mmol/L, 95% CI -0.87, -0.11, P = 0.012), fasting blood glucose (FBG, -0.29mmol/L, 95% CI -0.53, -0.06, P = 0.014), haemoglobin A1C (-1.85mmol/mol, 95% CI -3.00, -0.71, P = 0.002) and insulin (-1.76mU/L, 95% CI -3.26, -0.26, P = 0.021) concentrations. Semi-vegetarians had significantly lower LDL-C (-0.41mmol/L, 95% CI -0.74, -0.08, P = 0.041) and non-HDL-C (-0.40mmol/L, 95% CI -0.76, -0.05, P = 0.026) and lacto-ovo vegetarians had significantly lower FBG (-0.34mmol/L, 95% CI -0.56, -0.11, P = 0.003) compared to regular meat-eaters. There were no differences in HDL-C and triglycerides between plant-based and regular-meat diets.

Conclusions: Plasma lipaemic and glycaemic measures as a collective were more favourable among vegans, whereas among lacto-ovo vegetarians and semi-vegetarians, only some measures were favourable.

Trial registration: ACTRN12621000743864. Date 6/11/2021.

Background: Gestational diabetes mellitus (GDM) is a common complication of mid-to-late pregnancy. Here, we constructed a predictive model for GDM based on a combination of clinical characteristics and relevant serum markers.

Methods: Data from full-term singleton vaginal deliveries from January 2022 to January 2023 were retrospectively collected from the obstetrics department. The data collected were segregated and assigned to training, validation, and external test sets. Maternal demographic characteristics, living and working habits, and haematological indicators, such as liver function and lipids were collected using a questionnaire designed for the study. The "rms" package in R was used to explore GDM-associated factors through stepwise regression at P < 0.05. A predictive model was developed based on the results of multifactorial logistic regression analysis. We then evaluated the differentiation of the column-line graphical model and performed internal and external validation. To assess the accuracy of the bar graphical model, we plotted calibration and decision curves.

Results: Data from 265 pregnant women were included in the training and internal validation sets, and data from 113 pregnant women were included in the external validation set. The logistic regression algorithm screened 8 indicators as predictors. A prediction model was constructed with ALT, TBA, TC, and TG levels while considering whether GDM affects appetite, the husband- wife relationship, family history, and parental relationships as predictors. The Hosmer-Lemeshow goodness-of-fit test revealed that the chi-square values for the modelling, internal validation, and external validation groups (χ² = 5.964, 3.249, and 12.182, respectively) were all P > 0.05. The ROC curve AUCs for the three groups were 0.93 (95% CI: 0.89-0.97), 0.72 (95% CI: 0.62-0.81), and 0.68 (95% CI: 0.53-0.83), respectively.

Conclusion: In this study, a GDM prediction model was constructed to achieve high performance in GDM risk prediction based on routine obstetric tests and information.

Background: The Platelet to High-Density Lipoprotein cholesterol Ratio (PHR) is a novel indicator of inflammatory response and metabolic disorders, linked to various chronic diseases. This study aims to investigate the relationship between PHR and hypertension.

Methods: Data from the National Health and Nutrition Examination Survey (NHANES), collected across seven consecutive cycles from 2005 to 2018, were analyzed. The dataset included participants' hypertension status as reported by a doctor, their use of antihypertensive medications, and the average of three blood pressure measurements to identify hypertensive adults, along with complete information for PHR calculation. PHR was calculated based on Platelet (PLT) count and High-Density Lipoprotein cholesterol (HDL-C) using the following formula: PHR = [PLT (1000 cells/µL) / HDL-C (mmol/L)]. A multivariable logistic regression model was employed to assess the association between PHR and hypertension, and subgroup analyses were conducted to explore potential influencing factors. Additionally, Restricted Cubic Spline (RCS) curves were applied for threshold effect analysis to describe nonlinear relationships.

Results: Higher PHR was associated with an increased prevalence of hypertension. After adjusting for various covariates, including race, education level, Family Poverty Income Ratio (PIR), smoking, alcohol consumption, sleep disturbances, waist circumference, diabetes, coronary heart disease, angina, heart attack, and stroke, the results remained significant (OR = 1.36; 95% CI, 1.32, 1.41, P < 0.001). Participants with the highest PHR levels had a 104% higher risk of hypertension compared to those with the lowest PHR levels (OR = 2.04; 95% CI, 1.89, 2.21, P < 0.001).

Conclusion: Elevated PHR levels are strongly associated with an increased risk of hypertension. Specifically, when PHR is below 280, the risk of hypertension increases in proportion to PHR. This suggests that regular monitoring of PHR may help identify patients at risk of hypertension early, allowing for timely interventions to slow disease progression. Larger cohort studies are necessary to confirm these findings.