Circulating long noncoding RNA, LIPCAR, predicts survival in patients with heart failure.

RATIONALE: Long noncoding RNAs represent a novel class of molecules regulating gene expression. Long noncoding RNAs are present in body fluids, but their potential as biomarkers was never investigated in cardiovascular disease. OBJECTIVE: To study the role of long noncoding RNAs as potential biomarkers in heart disease. METHODS AND RESULTS: Global transcriptomic analyses were done in plasma RNA from patients with or without left ventricular remodeling after myocardial infarction. Regulated candidates were validated in 3 independent patient cohorts developing cardiac remodeling and heart failure (788 patients). The mitochondrial long noncoding RNA uc022bqs.1 (LIPCAR) was downregulated early after myocardial infarction but upregulated during later stages. LIPCAR levels identified patients developing cardiac remodeling and were independently to other risk markers associated with future cardiovascular deaths. CONCLUSIONS: LIPCAR is a novel biomarker of cardiac remodeling and predicts future death in patients with heart failure.

Stroke but no hospital admission: Lost opportunity for whom?

To counter the spread of COVID-19, the French government imposed several stringent social and political measures across its entire population. We hereto assess the impact of these political decisions on healthcare access in 2020, focusing on patients who suffered from an ischemic stroke. We divide our analysis into four distinct periods: the pre-COVID-19 pandemic period, the lockdown period, the "in-between" or transitional period, and the shutdown period. Our methodology involves utilizing a retrospective dataset spanning 2019-2020, an exhaustive French national hospital discharge diagnosis database for stroke inpatients, integrated with income information from the reference year of 2019. The results reveal that the most affluent were more likely to forgo medical care, particularly in heavily affected areas. Moreover, the most disadvantaged exhibited even greater reluctance to seek care, especially in the most severely impacted regions. The data suggest a loss of opportunity for less severely affected patients to benefit from healthcares during this lockdown period, regardless of demographic, location, and socioeconomic determinants. Furthermore, our analysis reveals a notable discrepancy in healthcare-seeking behavior, with less affluent patients and seniors (over 75 years old) experiencing slower rates of return to healthcare access compared to pre-pandemic levels. This highlights a persistent gap in healthcare accessibility, particularly among socioeconomically disadvantaged groups, despite the easing of COVID-19 restrictions.

Evaluation of a Genetic Risk Score to Improve Risk Prediction for Alzheimer's Disease.

Effective prevention of Alzheimer's disease (AD) requires the development of risk prediction tools permitting preclinical intervention. We constructed a genetic risk score (GRS) comprising common genetic variants associated with AD, evaluated its association with incident AD and assessed its capacity to improve risk prediction over traditional models based on age, sex, education, and APOEɛ4. In eight prospective cohorts included in the International Genomics of Alzheimer's Project (IGAP), we derived weighted sum of risk alleles from the 19 top SNPs reported by the IGAP GWAS in participants aged 65 and older without prevalent dementia. Hazard ratios (HR) of incident AD were estimated in Cox models. Improvement in risk prediction was measured by the difference in C-index (Δ-C), the integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI>0). Overall, 19,687 participants at risk were included, of whom 2,782 developed AD. The GRS was associated with a 17% increase in AD risk (pooled HR = 1.17; 95% CI =   [1.13-1.21] per standard deviation increase in GRS; p-value =  2.86×10-16). This association was stronger among persons with at least one APOEɛ4 allele (HRGRS = 1.24; 95% CI =   [1.15-1.34]) than in others (HRGRS = 1.13; 95% CI =   [1.08-1.18]; pinteraction = 3.45×10-2). Risk prediction after seven years of follow-up showed a small improvement when adding the GRS to age, sex, APOEɛ4, and education (Δ-Cindex =  0.0043 [0.0019-0.0067]). Similar patterns were observed for IDI and NRI>0. In conclusion, a risk score incorporating common genetic variation outside the APOEɛ4 locus improved AD risk prediction and may facilitate risk stratification for prevention trials.

Multimarker proteomic profiling for the prediction of cardiovascular mortality in patients with chronic heart failure.

Risk stratification of patients with systolic chronic heart failure (HF) is critical to better identify those who may benefit from invasive therapeutic strategies such as cardiac transplantation. Proteomics has been used to provide prognostic information in various diseases. Our aim was to investigate the potential value of plasma proteomic profiling for risk stratification in HF. A proteomic profiling using surface enhanced laser desorption ionization - time of flight - mass spectrometry was performed in a case/control discovery population of 198 patients with systolic HF (left ventricular ejection fraction <45%): 99 patients who died from cardiovascular cause within 3 years and 99 patients alive at 3 years. Proteomic scores predicting cardiovascular death were developed using 3 regression methods: support vector machine, sparse partial least square discriminant analysis, and lasso logistic regression. Forty two ion m/z peaks were differentially intense between cases and controls in the discovery population and were used to develop proteomic scores. In the validation population, score levels were higher in patients who subsequently died within 3 years. Similar areas under the curves (0.66 - 0.68) were observed for the 3 methods. After adjustment on confounders, proteomic scores remained significantly associated with cardiovascular mortality. Use of the proteomic scores allowed a significant improvement in discrimination of HF patients as determined by integrated discrimination improvement and net reclassification improvement indexes. In conclusion, proteomic analysis of plasma proteins may help to improve risk prediction in HF patients.