Familial hypercholesterolaemia in children and adolescents from 48 countries: a cross-sectional study

Background: Approximately 450 000 children are born with familial hypercholesterolaemia worldwide every year, yet only 2·1% of adults with familial hypercholesterolaemia were diagnosed before age 18 years via current diagnostic approaches, which are derived from observations in adults. We aimed to characterise children and adolescents with heterozygous familial hypercholesterolaemia (HeFH) and understand current approaches to the identification and management of familial hypercholesterolaemia to inform future public health strategies. Methods: For this cross-sectional study, we assessed children and adolescents younger than 18 years with a clinical or genetic diagnosis of HeFH at the time of entry into the Familial Hypercholesterolaemia Studies Collaboration (FHSC) registry between Oct 1, 2015, and Jan 31, 2021. Data in the registry were collected from 55 regional or national registries in 48 countries. Diagnoses relying on self-reported history of familial hypercholesterolaemia and suspected secondary hypercholesterolaemia were excluded from the registry; people with untreated LDL cholesterol (LDL-C) of at least 13·0 mmol/L were excluded from this study. Data were assessed overall and by WHO region, World Bank country income status, age, diagnostic criteria, and index-case status. The main outcome of this study was to assess current identification and management of children and adolescents with familial hypercholesterolaemia. Findings: Of 63 093 individuals in the FHSC registry, 11 848 (18·8%) were children or adolescents younger than 18 years with HeFH and were included in this study; 5756 (50·2%) of 11 476 included individuals were female and 5720 (49·8%) were male. Sex data were missing for 372 (3·1%) of 11 848 individuals. Median age at registry entry was 9·6 years (IQR 5·8-13·2). 10 099 (89·9%) of 11 235 included individuals had a final genetically confirmed diagnosis of familial hypercholesterolaemia and 1136 (10·1%) had a clinical diagnosis. Genetically confirmed diagnosis data or clinical diagnosis data were missing for 613 (5·2%) of 11 848 individuals. Genetic diagnosis was more common in children and adolescents from high-income countries (9427 [92·4%] of 10 202) than in children and adolescents from non-high-income countries (199 [48·0%] of 415). 3414 (31·6%) of 10 804 children or adolescents were index cases. Familial-hypercholesterolaemia-related physical signs, cardiovascular risk factors, and cardiovascular disease were uncommon, but were more common in non-high-income countries. 7557 (72·4%) of 10 428 included children or adolescents were not taking lipid-lowering medication (LLM) and had a median LDL-C of 5·00 mmol/L (IQR 4·05-6·08). Compared with genetic diagnosis, the use of unadapted clinical criteria intended for use in adults and reliant on more extreme phenotypes could result in 50-75% of children and adolescents with familial hypercholesterolaemia not being identified. Interpretation: Clinical characteristics observed in adults with familial hypercholesterolaemia are uncommon in children and adolescents with familial hypercholesterolaemia, hence detection in this age group relies on measurement of LDL-C and genetic confirmation. Where genetic testing is unavailable, increased availability and use of LDL-C measurements in the first few years of life could help reduce the current gap between prevalence and detection, enabling increased use of combination LLM to reach recommended LDL-C targets early in life

Flour moisture detection with an europium-based luminescent sensor

In this work, we describe the application of the self-assembled europium complex 1:Eu(III) (where 1: diethyl 8-methoxy-2-oxo-1,2,4,5-tetrahydro-cyclopenta[de]quinolin-3-yl)phosphonate) for the analysis of water content of wheat flour. The methodology herein described represents a robust and accurate way for the detection of small amounts of water in food, providing comparing results with well-established methods such as thermogravimetry and Karl-Fischer titration. Interestingly, as an advantage over other methods, our luminescence-based approach can be implemented in lab-on-a-chip microfluidic devices for real-time and on-line detection of water content of food samples. Additionally, the remarkably long luminescence lifetime of Eu(III) allows the use of state-of-the-art imaging strategies based on PLIM microscopy for the direct visualization of unique water content maps of wheat flour particles

Correction: Influence of Obesity and Metabolic Disease on Carotid Atherosclerosis in Patients with Coronary Artery Disease (CordioPrev Study).

[This corrects the article DOI: 10.1371/journal.pone.0153096.]

Inclusion and exclusion criteria for CORDIOPREV study.

<p>Inclusion and exclusion criteria for CORDIOPREV study.</p

Influence of metabolic phenotypes on IMT-CC of coronary patients in the CordioPrev study.

<p>All data are Mean +/- SEM (mm). Columns which do not share at least one letter are different at p<0.05. Sample size for the different groups is as follows: Metabolically healthy normal-weight (n = 36); metabolically sick normal-weight (n = 22); metabolically healthy overweight (n = 136); metabolically sick overweight (n = 221); metabolically healthy obese subjects (n = 122); metabolically sick obese subjects (n = 402). Specific p values for the different significant comparisons are as follows: Metabolically healthy normal-weight subjects are different to metabolically sick normal-weight (p = 0.0036), metabolically sick overweight (p = 0.0134) and metabolically sick obese subjects (p = 0.0048); Metabolically healthy overweight subjects are different to metabolically sick normal-weight (p = 0.0014), metabolically sick overweight (p = 0.0004) and metabolically sick obese subjects (p = 0.0001).</p

Influence of weight and metabolic disease on IMT-CC of coronary patients in the CordioPrev study.

<p>All data are Mean +/- SEM (mm). Panel A: IMT-CC of patients with and without metabolic disease (adjusted by age and gender). *p 1.7+10⁻⁶. Panel B: Influence of weight in three categories (normal, overweight and obese). Panel C: Influence of weight in two categories (not obese versus obese).** p 0.016.</p

Baseline.

<p>Baseline.</p