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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 APOE epsilon 4. 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 IGAPGWAS 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 (Delta-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 = 2.86x10(-16)). This association was stronger among persons with at least one APOE epsilon 4 allele (HRGRS = 1.24; 95% CI = [1.15-1.34]) than in others (HRGRS = 1.13; 95% CI = [1.08-1.18]; p(interaction) = 3.45x10(-2)). Risk prediction after seven years of follow-up showed a small improvement when adding the GRS to age, sex, APOE epsilon 4, and education (Delta-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 APOE epsilon 4 locus improved AD risk prediction and may facilitate risk stratification for prevention trials.

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.

BackgroundRisk stratification of patients with stable chronic heart failure (CHF) is critical to better identify those who may benefit the most from invasive strategies such as heart transplantation.MethodsTo improve cardiovascular (CV) death prediction in CHF, we performed a proteomic analysis using high throughput surface enhanced laser desorption ionization – time of fight – mass spectrometry (SELDI-TOF-MS). Plasma samples were pre-treated to access the deep proteome. The proteomic analysis was first performed in a case (CV death within 3 years) /control (survivors at 3 years) study including 198 patients with a left ventricular ejection fraction (LVEF)

BackgroundMultiple proteinchip arrays have been developed to selectively bind proteins with diverse physical and chemical properties before the profiling step with SELDI-TOF-MS. However, the additional value of each array is poorly described.MethodsA proteomic analysis has been performed in plasma of 198 patients with chronic heart failure with a left ventricular ejection fraction 20000 Da) m/z peaks were optimized separately. Correlation between peaks was analysed face to face by the test of Pearson.ResultsWe detected 203m/z peaks: 109 (52 LM and 42 HM) peaks with the CM10 array and 94 (69 LM and 40 HM) peaks with the H50 array. Among the peaks detected on the H50 array, 28 LM and 8 HM peaks were also present on the CM10 array. In the mass range 20000-30000 Da, peaks can be detected with both, LM and HM acquisition settings. We found 6 of the 13 HM peaks on the CM10 array and 7 of the 15 HM peaks on the H50 array also detected with LM settings. 093We then analysed the correlation among the 203m/z peaks detected with both types of arrays. We found that 56 (27.5%) peaks were highly correlated with at least one other peak with a correlation coefficient r>0.9. Altogether, 30 out of these 56 peaks were correlated with 1 other peak, 14 with 2 other peaks, 7 with 3, 3 with 4, 1 with 5 and 1 with 6 other peaks. These highly correlated peaks may correspond to a unique protein.ConclusionProfiling with multiple proteinchip arrays provides data with high redundancy and colinearity. This finding may be useful for chosen the SELDITOF- MS peaks to be purified and identified. In addition, a unique array (CM10) may be sufficient to obtain a relevant profiling of plasma proteins.