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

Characterization of data-driven geriatric syndrome clusters in older people with HIV: a Mexican multicenter cross-sectional studyResearch in context

Summary: Background: As living with HIV has been proposed as a condition that may accelerate aging, the main objective of this work was to estimate the prevalence of geriatric syndromes (GS) among older Mexicans with HIV dwelling in the community. Secondly, to evaluate whether the accumulation of GS could be associated with an adverse HIV-related clinical profile, independent of chronological age. Methods: Multicenter, cross-sectional study including 501 community-dwelling people aged ≥50 years with HIV. The overall prevalence of nine selected GS and their cumulative number were estimated. An Age-Independent Cumulative Geriatric Syndromes scale (AICGSs) was constructed, and correlations between the AICGSs and HIV-related parameters assessed. Finally, k-mean clustering analyses were performed to test the secondary objective. Findings: Median age 56 (IQR: 53–61) years, 81.6% of men. Polypharmacy (74.8%), sensorial deficit (71.2%), cognitive impairment (53.6%), physical disability (41.9%), pre-frailty (27.9%), and falls (29.7%), were the more prevalent GS. A significant negative correlation was found between the AICGSs and normalized values of CD4+ nadir cell counts (r = −0.126; 95%: CI: −0.223 to −0.026, p < 0.05). Similarly, a significant inverse adjusted association between the CD4+ nadir cells and the AICGSs was observed on linear regression analysis (β −0.058; 95%: CI: −0.109 to −0.007, p = 0.03). Cluster analysis identified three differentiated groups varying by age, metabolic comorbidities, AICGSs, and HIV-related parameters. Interpretation: An elevated prevalence of GS was observed in the studied population. Moreover, the accumulation of GS was associated with adverse HIV-related profiles, independent of age. Thus, early detection and management of GS are crucial to promote healthier aging trajectories in people with HIV. Funding: This work was funded in part by the National Center for the Prevention and Control of HIV/AIDS in Mexico (CENSIDA)—National Ministry of Health

Validation and repurposing of the MSL-COVID-19 score for prediction of severe COVID-19 using simple clinical predictors in a triage setting: The Nutri-CoV score.

BackgroundDuring the COVID-19 pandemic, risk stratification has been used to decide patient eligibility for inpatient, critical and domiciliary care. Here, we sought to validate the MSL-COVID-19 score, originally developed to predict COVID-19 mortality in Mexicans. Also, an adaptation of the formula is proposed for the prediction of COVID-19 severity in a triage setting (Nutri-CoV).MethodsWe included patients evaluated from March 16th to August 17th, 2020 at the Instituto Nacional de Ciencias Médicas y Nutrición, defining severe COVID-19 as a composite of death, ICU admission or requirement for intubation (n = 3,007). We validated MSL-COVID-19 for prediction of mortality and severe disease. Using Elastic Net Cox regression, we trained (n = 1,831) and validated (n = 1,176) a model for prediction of severe COVID-19 using MSL-COVID-19 along with clinical assessments obtained at a triage setting.ResultsThe variables included in MSL-COVID-19 are: pneumonia, early onset type 2 diabetes, age > 65 years, chronic kidney disease, any form of immunosuppression, COPD, obesity, diabetes, and age ConclusionsMSL-COVID-19 predicts inpatient COVID-19 lethality. The Nutri-CoV score is an adaptation of MSL-COVID-19 to be used in a triage environment. Both scores have been deployed as web-based tools for clinical use in a triage setting

Evaluation of arterial stiffness in primary atherogenic dyslipidemias

Background: Arterial stiffness [assessed with pulse wave velocity (PWV)] can predict atherosclerotic cardiovascular disease. Objective: To evaluate arterial stiffness in patients with familial hypercholesterolemia (FH) and familial combined hyperlipidemia (FCHL). Methods: Patients were paired with a healthy control group and arterial stiffness was measured using carotid femoral PWV. Linear regression models were used to assess the relationship between PWV and clinical status. Results: The participants included 99 (50.0%) controls, 73 (36.8%) FH, and 26 (13.2%) FCHL. There was no significant difference in PWV among the three groups. The FCHL group had a numerically higher augmentation index (AIX75%) compared to the FH group. The FH group had an increased likelihood (OR: 2.450, 95% CI: 1.151-5.338; p = 0.021) of a decreased Buckberg subendocardial viability ratio (SERV) index, suggesting low coronary blood flow. Conclusions: This pilot study provides initial insights into subclinical atherosclerosis in primary dyslipidemias. Patients with FCHL had greater arterial stiffness compared to the other groups. Studies with a larger number of participants, ideally statin-free, are necessary to confirm findings

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Introduction Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methods We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.Results SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).Conclusions Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications

Curr Diabetes Rev

BACKGROUND: Type 2 diabetes represents an increasing health burden world-wide and its prevalence in particularly higher in elderly population. Consistent epidemiological evidence suggests an increased risk of dementia associated to type 2 diabetes; the mechanisms underlying these associations, however, remain unclear. OBJECTIVE: The study aims to review epidemiological, clinical and pre-clinical data that weigh on pathophysiological links, mechanisms of disease and associations between type 2 diabetes and dementia to identify areas of opportunity for future research. METHODS: We searched the following electronic bibliographic databases: PUBMED, EMBASE, SCIELO, MEDLINE and OVID for clinical, translational and epidemiological research literature that summarize diabetes-related risk factors for dementia, metabolic and neurological changes associated to T2D, evidence of therapeutic approaches in type 2 diabetes and its pathophysiological implications for dementia. RESULTS: Type 2 diabetes mellitus increases risk for all-cause dementia, vascular dementia and Alzheimer's disease. The most evaluated mechanisms linking both disorders in pre-clinical studies include an increase in neuronal insulin resistance, impaired insulin signaling, pro-inflammatory state, mitochondrial dysfunction and vascular damage which increase deposition of beta-amyloid, tau proteins and GSK3beta, leading to an earlier onset of dementia in individuals with impairment in the glucose metabolism. Neuroimaging and neuropathology evidence linking cerebrovascular lesions, neurodegeneration and particularly small-vessel disease in the onset of dementia is consistent with the increased risk of incident dementia in type 2 diabetes, but consistent evidence of AD-related pathology is scarce. Epidemiological data shows increased risk of dementia related to hypoglycemic episodes, glycemic control, metabolic syndrome, insulin resistance and genetic predisposition, but the evidence is not consistent and statistical analysis might be affected by inconsistent covariate controlling. Therapeutic approaches for T2D have shown inconsistent result in relation to dementia prevention and delay of cognitive decline; lifestyle intervention, particularly physical activity, is a promising alternative to ameliorate the impact of disability and frailty on T2D-related dementia. CONCLUSION: Vascular disease, inflammation and impaired brain insulin signaling might occur in T2D and contribute to dementia risk. Evidence from epidemiological studies has not consistently reported associations that could integrate a unified mechanism of disease in humans. Evaluation of the effect of antidiabetic medications and non-pharmacological interventions in dementia prevention in type 2 diabetes is promising but has thus far offered inconsistent results