👤 Julia Brandts

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality in individuals with diabetes, partly driven by dyslipidemia. While low-density lipoprotein cholesterol (LDL-C) reduction is the primary target of lipid management, many patients with diabetes exhibit mixed dyslipidemia characterised by elevated triglycerides and increased concentrations of atherogenic remnant lipoproteins, which are more comprehensively captured by non-high-density lipoprotein cholesterol (non-HDL-C). Current guidelines from international societies, including the American Diabetes Association (ADA), the American Association of Clinical Endocrinology (AACE), and the European Society of Cardiology (ESC), recommend LDL-C and non-HDL-C targets based on individual cardiovascular risk profiles. Despite clear therapeutic algorithms, lipid target attainment remains suboptimal in routine clinical practice, necessitating more intensive and individualised treatment strategies. Lipid-lowering therapies, including statins, ezetimibe, bempedoic acid and PCSK9 inhibitors, effectively reduce LDL-C and non-HDL-C, significantly lowering cardiovascular risk. Triglyceride-lowering therapies, including omega-3 fatty acids and fibrates, have demonstrated substantial reductions in triglyceride levels, but their impact on cardiovascular outcomes remains uncertain. Given the heterogeneity of dyslipidemia in diabetes, non-HDL-C and apolipoprotein B (apoB) have emerged as superior markers for assessing atherogenic burden. While LDL-C reduction remains central, additional efforts are needed to optimise the management of residual atherogenic lipoprotein particles in diabetes. Future research should focus on refining risk stratification, improving lipid target attainment, and integrating novel lipid-modifying agents to enhance cardiovascular outcomes in this high-risk population. Show less

Elevated lipoprotein(a) [Lp(a)] is a common risk factor for cardiovascular disease (CVD) affecting ∼1.4 billion people globally, with novel treatments under development. Guidelines recommend one-lifetime measurement, yet <1% are tested. Population-wide screening faces cost and implementation challenges. We developed a machine learning (ML) model to help prioritise patients for Lp(a) testing. Ethnicity-calibrated ML models were developed to identify individuals with elevated Lp(a) in UK Biobank. Participants ≥37 years old (N=438,579) were split into feature importance/selection(20%), derivation(60%), and validation(20%) datasets. Performances across risk-enhancing Lp(a) thresholds recommended by clinical guidelines (90, 125, 430 nmol/L) or entry criteria for ongoing Lp(a)-lowering trials (150, 175, 200 nmol/L) were evaluated. External validation was conducted in NHANES III. Screening one million people using a universal approach would identify 222,717 cases above 90 nmol/L and 1950 above 430 nmol/L. In contrast, applying ML-targeted testing using the same number of tests would identify 280,899 (+26%; 95%CI:20-28%) and 6881 (+253%; 95%CI:192-310%) cases, respectively. At the thresholds of 125, 150, 175, and 200 nmol/L, yield increases were 38% (95%CI:35-40%), 51% (95%CI:47-54%), 59% (95%CI:55-63%), and 66% (95%CI:61-71%). Across thresholds 90-430 nmol/L, ML-targeted testing (Number Needed to Screen [NNS] 3.6-145, AUC 0.61-0.84) required 21%-72% fewer tests to identify one million cases. NHANES III validation demonstrated similar performance. Top 4 predictors included age, height (proxy for sex), total cholesterol and statin use. A ML-guided approach to prioritise testing for elevated Lp(a) would require fewer tests to identify those above risk-enhancing thresholds or potentially eligible for emerging therapies, offering a scalable interim compromise between the low current testing rates and universal screening aspirations. Show less

Nanoplastics are emerging aquatic contaminants capable of inducing subtle but physiologically relevant disruptions in fish. This study evaluated the effects of a chronic exposure to 44 nm polystyrene nanoplastics (PS-NPs) at 100 µg/L for 30 days on goldfish (Carassius auratus), integrating hepatic transcriptional responses land biochemical markers and intestinal metabolomics. Goldfish (n = 7 per group) showed no changes in weight, length or Fulton's condition factor, yet displayed distinct molecular and metabolic alterations. In the liver, PS-NPs significantly increased activities of ALT, AST, ALP, and EA (p < 0.05), indicating mild hepatocellular stress. Transcriptional analysis revealed upregulation of pparα, lpl, and cat, alongside downregulation of apoa1 and il1β, reflecting adjustments in lipid metabolism, antioxidant pathways and inflammatory tone. Systemic oxidative indicators (TAC, TOS, OSI) remained unchanged, suggesting the absence of whole-organism redox imbalance. Intestinal metabolomic profiling detected 255 metabolites, of which 53 were confidently identified. Significant changes occurred in six amino acids such as Asn, Arg, Pro decreased; Asp, Ser, Ala increased (p < 0.05) together with alterations in TCA-cycle intermediates such as malic acid. These shifts illustrate reorganization of nitrogen and carbon flow under PS-NP exposure, highlighting the intestine as the more metabolically responsive tissue compared with the liver. Overall, despite stable somatic growth, chronic PS-NPs elicited coordinated, tissue-specific physiological adjustments indicative of subclinical metabolic strain. Show less

To quantify international variations in lipid-lowering therapies (LLT) use among patients with coronary heart disease (CHD) and attainment of European guideline-recommended lipid goals. INTERASPIRE is an observational study (2020-23) covering 14 countries from all WHO regions. Patients (18-79 years) hospitalized in the preceding 6-36 months with CHD were invited for standardized interviews and examination, with central laboratory analyses for low-density lipoprotein cholesterol (LDL-C), non-HDL-C, and apolipoprotein B (apoB). Valid lipid data meeting quality control standards were available from 13 countries. Lipid goals followed the 2019 guidelines of the European Atherosclerosis Society and the European Society of Cardiology: LDL-C < 1.4 mmol/L, non-HDL-C < 2.2 mmol/L, and apoB <65 mg/dL.Among 4061 patients (78.8% male, mean age 60.3 years), between index event and interview, 66.3% had no change in treatment intensity. LLT use at interview was largely statin monotherapy: 49.6% high-intensity (inter-country range 5.3%-77.3%) and 24.1% low/moderate-intensity (inter-country range 5.1%-70.1%). Otherwise, 12.2% (inter-country range 0.2%-41.1%) were on combination therapy, and 12.7% on no LLT (inter-country range 3.5%-36.7%). Goal attainment for LDL-C was 17.5%. Corresponding non-HDL-C and apoB goals were achieved by 29.9% and 29.2%, respectively. Higher-income countries (defined by the World Bank's 2024-25 classification of income levels) did better in goal attainment than lower-middle-income countries. In this international study, contemporary lipid goals were not achieved in most CHD patients, with lower-middle-income countries having the worst goal attainment. Contributory factors include absence of any LLT use, low use of combinations and a failure to up-titrate LLT to achieve guideline targets. Show less

Familial hypercholesterolemia (FH), while highly prevalent, is a significantly underdiagnosed monogenic disorder. Improved detection could reduce the large number of cardiovascular events attributable to poor case finding. We aimed to assess whether machine learning algorithms outperform clinical diagnostic criteria (signs, history, and biomarkers) and the recommended screening criteria in the United Kingdom in identifying individuals with FH-causing variants, presenting a scalable screening criteria for general populations. Analysis included UK Biobank participants with whole exome sequencing, classifying them as having FH when (likely) pathogenic variants were detected in their Our machine learning-derived model provides a higher pretest probability of identifying individuals with a molecular diagnosis of FH compared with current approaches. This provides a promising, cost-effective scalable tool for implementation into electronic health records to prioritize potential FH cases for genetic confirmation. Show less