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While numerous observational studies have indicated an association between lipids and Sudden Sensorineural Hearing Loss (SSNHL), it remains uncertain whether dyslipidemia serves as a causal risk factor for SSNHL. Our objective is to elucidate the potential causal relationship between lipid levels and SSNHL through Mendelian randomization analysis. The primary and secondary lipid data used in this study were sourced from the UK Biobank (UKBB) and the Global Lipid Genetics Consortium results (GLGC), respectively. These datasets were obtained from large, publicly available genome-wide association studies (GWAS). The outcome data for sudden sensorineural hearing loss (SSNHL) were acquired from the Finnegan Biobank, consisting of 1491 cases and 196,592 controls. Subsequently, both single-variable Mendelian randomization (SVMR) and multivariate Mendelian randomization (MVMR) methods were employed to evaluate the causal relationship between lipids and the occurrence of SSNHL. Among the primary lipid data, SVMR analysis showed a significant correlation between high density lipoprotein cholesterol (HDL-C) (OR: 0.822, 95 %CI: 0.694–0.974, p = 0.023) and SSNHL, and triglycerides (TG) (OR: 0.997, 95 %CI: 0.836–1.188, p = 0.975), low density lipoprotein cholesterol (LDL-C) (OR: 1.067, 95 %CI: 0.861–1.322, p = 0.552) did not correlate with SSNHL. In the secondary lipid data, SVMR analysis showed that HDL-C (OR: 0.987, 95 %CI: 0.805–1.210, p = 0.903), TG (OR: 0.991, 95 %CI: 0.787–1.246, p = 0.937) and LDL-C (OR: 1.092, 95 % CI: 0.926–1.287, p = 0.294) did not correlate with SSNHL. MVMR analysis of the primary lipid data showed that HDL-cholesterol (OR: 0.755, 95 % CI: 0.596–0.956, p = 0.019) was significantly associated with SSNHL, while TG (OR: 0.808, 95 %CI: 0.611–1.068, p = 0.134) and LDL-C (OR: 1.146, 95 %CI: 0.869–1.511, p = 0.333) did not correlate with SSNHL, consistent with the results of SVMR. Inverse MR results showed that SSNHL did not correlate with TG (OR: 0.999, 95 %CI: 0.997–1.001, p = 0.835), HDL-C (OR: 1.001, 95 %CI: 0.998–1.003), LDL-C (OR: 0.999, 95 %CI: 0.997–1.002, p = 0.863). Mendelian randomization (MR) results suggest that decreased serum HDL-C levels are an independent risk factor for SSNHL. Monitoring and focusing on lipid levels may be of value in the prevention and treatment of SSNHL. [ABSTRACT FROM AUTHOR]

Serum lipids are modifiable, routinely collected blood test features associated with cardiovascular health. We examined the association of commonly collected serum lipid measures (total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], and triglycerides) with intraocular pressure (IOP). Cross-sectional study in the UK Biobank and European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohorts. We included 94 323 participants from the UK Biobank (mean age, 57 years) and 6230 participants from the EPIC-Norfolk (mean age, 68 years) cohorts with data on TC, HDL-C, LDL-C, and triglycerides collected between 2006 and 2009. Multivariate linear regression adjusting for demographic, lifestyle, anthropometric, medical, and ophthalmic covariables was used to examine the associations of serum lipids with corneal-compensated IOP (IOPcc). Corneal-compensated IOP. Higher levels of TC, HDL-C, and LDL-C were associated independently with higher IOPcc in both cohorts after adjustment for key demographic, medical, and lifestyle factors. For each 1-standard deviation increase in TC, HDL-C, and LDL-C, IOPcc was higher by 0.09 mmHg (95% confidence interval [CI], 0.06–0.11 mmHg; P < 0.001), 0.11 mmHg (95% CI, 0.08–0.13 mmHg; P < 0.001), and 0.07 mmHg (95% CI, 0.05–0.09 mmHg; P < 0.001), respectively, in the UK Biobank cohort. In the EPIC-Norfolk cohort, each 1-standard deviation increase in TC, HDL-C, and LDL-C was associated with a higher IOPcc by 0.19 mmHg (95% CI, 0.07–0.31 mmHg; P = 0.001), 0.14 mmHg (95% CI, 0.03–0.25 mmHg; P = 0.016), and 0.17 mmHg (95% CI, 0.06–0.29 mmHg; P = 0.003). An inverse association between triglyceride levels and IOP in the UK Biobank (–0.05 mmHg; 95% CI, –0.08 to –0.03; P < 0.001) was not replicated in the EPIC-Norfolk cohort (P = 0.30). Our findings suggest that serum TC, HDL-C, and LDL-C are associated positively with IOP in 2 United Kingdom cohorts and that triglyceride levels may be associated negatively. Future research is required to assess whether these associations are causal in nature. [ABSTRACT FROM AUTHOR]

Objectives: The UK Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS-OM) developed using 30-year (1977-2007) data from the UKPDS is widely used for health outcomes' projections and economic evaluations of therapies for patients with type 2 diabetes (T2D). Nevertheless, its reliability for contemporary UK T2D populations is unclear. We assessed the performance of version 2 of the model (UKPDS-OM2) using data from A Study of Cardiovascular Events in Diabetes (ASCEND), which followed participants with diabetes in the UK between 2005 and 2017.Methods: The UKPDS-OM2 was used to predict the occurrence of myocardial infarction (MI), other ischemic heart disease, stroke, cardiovascular (CV) death, and other death among the 14 569 participants with T2D in ASCEND, all without previous CV disease at study entry. Calibration (comparison of predicted and observed year-on-year cumulative incidence over 10 years) and discrimination (c-statistics) of the model were assessed for each endpoint. The percentage error in event rates at year 7 (mean duration of follow up) was used to quantify model bias.Results: The UKPDS-OM2 substantially overpredicted MI, stroke, CV death, and other death over the 10-year follow-up period (by 149%, 42%, 269%, and 52%, respectively, at year 7). Discrimination of the model for MI and other ischemic heart disease (c-statistics 0.58 and 0.60, respectively) was poorer than that for other outcomes (c-statistics ranging from 0.66 to 0.72).Conclusions: The UKPDS-OM2 substantially overpredicted risks of key CV outcomes and death in people with T2D in ASCEND. Appropriate adjustments or a new model may be required for assessments of long-term effects of treatments in contemporary T2D cohorts. [ABSTRACT FROM AUTHOR]

Background: Prospective studies have shown differences in some disease risks between vegetarians and nonvegetarians, but the potential biological pathways are not well understood.Objectives: We aimed to assess differences in concentrations of biomarkers related to disease pathways in people with varying degrees of animal foods exclusion.Methods: The UK Biobank recruited 500,000 participants aged 40-69 y (54.4% women) throughout the United Kingdom in 2006-2010. Blood and urine were collected at recruitment and assayed for more than 30 biomarkers related to cardiovascular diseases, bone and joint health, cancer, diabetes, renal disease, and liver health. In cross-sectional analyses, we estimated adjusted geometric means of these biomarkers by 6 diet groups (regular meat eaters, low meat eaters, poultry eaters, fish eaters, vegetarians, vegans) in 466,058 white British participants and 2 diet groups (meat eaters, vegetarians) in 5535 British Indian participants.Results: We observed differences in the concentrations of most biomarkers, with many biomarkers showing a gradient effect from meat eaters to vegetarians/vegans. Of the largest differences, compared with white British regular meat eaters, white British vegans had lower C-reactive protein [adjusted geometric mean (95% CI): 1.13 (1.03, 1.25) compared with 1.43 (1.42, 1.43) mg/L], lower low-density lipoprotein cholesterol [3.13 (3.07, 3.20) compared with 3.65 (3.65, 3.65) mmol/L], lower vitamin D [34.4 (33.1, 35.9) compared with 44.5 (44.4, 44.5) nmol/L], lower serum urea [4.21 (4.11, 4.30) compared with 5.36 (5.36, 5.37) mmol/L], lower urinary creatinine [5440 (5120, 5770) compared with 7280 (7260, 7300) μmol/L], and lower γ-glutamyltransferase [23.5 (22.2, 24.8) compared with 29.6 (29.6, 29.7) U/L]. Patterns were mostly similar in British Indians, and results were consistent between women and men.Conclusions: The observed differences in biomarker concentrations, including lower C-reactive protein, lower LDL cholesterol, lower vitamin D, lower creatinine, and lower γ-glutamyltransferase, in vegetarians and vegans may relate to differences in future disease risk. [ABSTRACT FROM AUTHOR]

The lipid profile is consistently associated with coronary artery disease (CAD) and ischemic stroke (IS). However, the lipoprotein subfractions have not been deeply explored in stroke subtypes, especially in IS outcome. We performed two-sample Mendelian randomization (MR) analysis using 92 lipid traits measured by nuclear magnetic resonance in 115,000 subjects from the UK Biobank. Data for genetic associations with IS, its subtypes, and long-term outcome (LTO) were obtained from three cohorts of European ancestry: GIGASTROKE (73,652 cases, 1,234,808 controls), GODS (n = 1791) and GISCOME (n = 6165). Results obtained using CARDIoGRAMPlusC4D were used to identify differences with CAD. Genetically determined low concentration of medium high-density lipoprotein (HDL) particles (odds ratio (OR) = 0.92, 95% CI 0.88–0.96; p = 3.6 × 10−4) and its cholesterol content (OR = 0.92, 95% CI 0.88–0.96; p = 1.9 × 10−4) showed causal associations with an increased risk of stroke. Genetic predisposition to high apolipoprotein (apo)B to apoA-I ratio was causally associated with an increased risk of IS (OR = 1.12, 95% CI 1.06–1.18, p = 1.1 × 10−4), and a highly suggestive association was found between non-esterified cholesterol in low-density lipoprotein (LDL) and increased risk of atherothrombotic stroke (LAS) (OR = 1.35, 95% CI 1.10–1.66; p = 4.0 × 10−3). Low cholesterol in small and medium LDL was suggestively associated with poor LTO. Our results support that low medium HDL concentration was causally associated with an increased stroke risk, while high levels of non-esterified cholesterol in LDL were suggestively associated with an increased risk of LAS and with a better LTO. [Display omitted] • Causal association of low HDL-C with stroke risk is due to the medium subfraction. • Free cholesterol content is implicated in the association of high LDL with stroke risk. • The association of triglyceride-rich lipoproteins with stroke remains inconclusive. • ApoB-lipoproteins play a more important role in CAD risk than in stroke risk. • The association of low LDL-C with a poor long-term outcome needs further studies. [ABSTRACT FROM AUTHOR]

Traditional classification systems of metabolic-associated fatty liver disease (MAFLD) do not account for the high rate of extrahepatic complications. To create a new classification of MAFLD using metabolic parameters to identify risks of complications more accurately. The retrospective study included MAFLD patients from the First Affiliated Hospital of Sun Yat-sen University for model development, and the model was validated respectively using Chinese cohort and UK Biobank database. Cluster analysis with k-means cluster was built using age, body mass index (BMI), glycosylated hemoglobin (HbA1c), total cholesterol/high density lipoprotein cholesterol (HDL-C) ratio, triglyceride, and lipoprotein(a) [Lp(a)] levels. Cox regression models were used to compare the risk of type 2 diabetes (T2DM), chronic heart disease (CHD), stroke and mortality between the clusters. 1038 MAFLD patients from cross-sectional population were recruited for the model derivation, with 10,451 cases (33.4 % of MAFLD) from Chinese cohort and 304,141 cases (34.9 % of MAFLD, 1010 cases with magnetic resonance imaging proton density fat fraction measurement [MRI-PDFF]) from the international cohort validated. Five replicable clusters of MAFLD patients were identified: Cluster 1(mild obesity and dyslipidemia-related), Cluster 2 (age related), Cluster 3 (severe insulin resistance-related), Cluster 4[high Lp(a)-related], and Cluster 5 (severe mixed hyperlipidemia-related). Patients in different clusters exhibited differences in the development of T2DM, CHD, stroke and all-causes mortality. Patients in Cluster 3 had significantly worst survival outcomes and higher risks of T2DM and CVD than those in other clusters. The novel classification offers improved discrimination of new-onset MAFLD patients with different metabolic complications. [Display omitted] • A data driven cluster analysis with simple variables at diagnosis reclassify MAFLD subtypes. • Baseline clusters facilitate predicting the risk of long-term extrahepatic complication. • Novel MAFLD subtypes could be replicated in the international cohorts. [ABSTRACT FROM AUTHOR]