Long-term sex differences in atherosclerotic cardiovascular disease in individuals with heterozygous familial hypercholesterolaemia in Spain: a study using data from SAFEHEART, a nationwide, multicentre, prospective cohort study.

BACKGROUND: Sex differences in atherosclerotic cardiovascular disease (ASCVD) in familial hypercholesterolaemia have been reported but are not fully established. We aimed to assess sex differences in the risk of ASCVD and life-time burden of ASCVD in patients with heterozygous familial hypercholesterolaemia. METHODS: SAFEHEART is a nationwide, multicentre, long-term prospective cohort study conducted in 25 tertiary care hospitals and one regional hospital in Spain. Participants in the SAFEHEART study aged 18 years or older with genetically confirmed familial hypercholesterolaemia were included in our analysis. Data were obtained between Jan 26, 2004, and Nov 30, 2022. ASCVD and age at onset were documented at enrolment and at follow-up. Our aim was to investigate the differences by sex in the risk and burden of ASCVD in patients with heterozygous familial hypercholesterolaemia, over the study follow-up and over the life course. The SAFEHEART study is registered with ClinicalTrials.gov, NCT02693548. FINDINGS: Of the 5262 participants in SAFEHEART at the time of analysis, 3506 (1898 [54·1%] female and 1608 [45·9%] male participants) met the inclusion criteria and were included in the current study. Mean age was 46·1 years (SD 15·5) and median follow-up was 10·3 years (IQR 6·4-13·0). Mean on-treatment LDL-cholesterol at follow-up was 3·1 mmol/L (SD 1·4) in females and 3·0 mmol/L (1·5) in males. LDL-cholesterol reductions over time were similar in both sexes (1·39 mmol/L [95% CI 1·30-1·47] absolute reduction in females vs 1·39 mmol/L [1·29-1·48] in males; p=0·98). At enrolment, 130 (6·8%) females and 304 (18·9%) males (p<0·0001) had cardiovascular disease. During follow-up, 134 (7·1%) females and 222 (13·8%) males (p<0·0001) had incident cardiovascular events. Median age at first ASCVD event (mostly due to coronary artery disease) was 61·6 years (IQR 50·0-71·4) in females and 50·6 years (42·0-58·6) in males (p<0·0001). The adjusted hazard ratio for ASCVD in males compared with females during follow-up was 1·90 (95% CI 1·49-2·42) and for cardiovascular death was 1·74 (1·11-2·73). Major adverse cardiovascular disease event (MACE)-free survival from birth was lower in males than females (hazard ratio 3·52 [95% CI 2·98-4·16]; p<0·0001). Median MACE-free survival time was 90·1 years (95% CI 86·5-not estimable) in females and 71·0 years (69·2-74·6) in males. The age at which 25% of female participants have had a MACE event was 74·9 years, this figure was 55·5 years in male participants. INTERPRETATION: Our findings suggest that the burden and risk of ASCVD are markedly lower in females than males with familial hypercholesterolaemia. The impact of sex needs to be considered to improve risk stratification and personalised management in patients with heterozygous familial hypercholesterolaemia. FUNDING: Fundación Hipercolesterolemia Familiar, the Instituto de Salud Carlos III, and Next Generation EU funds from the Recovery and Resilience Mechanism Program. TRANSLATION: For the Spanish translation of the abstract see Supplementary Materials section.

Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?

BACKGROUND: 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. METHODS AND RESULTS: Analysis included UK Biobank participants with whole exome sequencing, classifying them as having FH when (likely) pathogenic variants were detected in their LDLR, APOB, or PCSK9 genes. Data were stratified into 3 data sets for (1) feature importance analysis; (2) deriving state-of-the-art statistical and machine learning models; (3) evaluating models' predictive performance against clinical diagnostic and screening criteria: Dutch Lipid Clinic Network, Simon Broome, Make Early Diagnosis to Prevent Early Death, and Familial Case Ascertainment Tool. One thousand and three of 454 710 participants were classified as having FH. A Stacking Ensemble model yielded the best predictive performance (sensitivity, 74.93%; precision, 0.61%; accuracy, 72.80%, area under the receiver operating characteristic curve, 79.12%) and outperformed clinical diagnostic criteria and the recommended screening criteria in identifying FH variant carriers within the validation data set (figures for Familial Case Ascertainment Tool, the best baseline model, were 69.55%, 0.44%, 65.43%, and 71.12%, respectively). Our model decreased the number needed to screen compared with the Familial Case Ascertainment Tool (164 versus 227). CONCLUSIONS: 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.

Fungal microbiota in newborn infants with and without respiratory distress syndrome.

BACKGROUND: Pneumocytis jirovecii infection in preterm newborns has recently been associated with neonatal respiratory distress syndrome and bronchopulmonary dysplasia. Changes in the bacterial microbiota of the airways have also been described in infants with bronchopulmonary dysplasia. However, until now there has been no information on the airway mycobiota in newborns. The purpose of this study was to describe the airway mycobiota in term and preterm newborns and its possible association with respiratory distress syndrome. METHODS: Twenty-six matched preterm newborns with and without respiratory distress syndrome were studied, as well as 13 term babies. The identification of the fungal microbiota was carried out using molecular procedures in aspirated nasal samples at birth. RESULTS: The ascomycota phylum was identified in 89.7% of newborns, while the basidiomycota phylum was found in 33.3%. Cladosporium was the predominant genus in both term and preterm infants 38.4% vs. 73% without statistical differences. Candida sake and Pneumocystis jirovecii were only found in preterm infants, suggesting a potential relationship with the risk of prematurity. CONCLUSIONS: This is the first report to describe the fungal microbiota of the airways in term and preterm infants with and without respiratory distress syndrome. Although no differences have been observed, the number of cases analyzed could be small to obtain conclusive results, and more studies are needed to understand the role of the fungal microbiota of the airways in neonatal respiratory pathology.