Sleep/wake cycle alterations as a cause of neurodegenerative diseases : A Mendelian randomization study

Sleep and/or wake cycle alterations are common in neurodegenerative diseases (ND). Our aim was to determine whether there is a causal relationship between sleep and/or wake cycle patterns and ND (Parkinson's disease (PD) age at onset (AAO), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS)) using two-sample Mendelian Randomization (MR). We selected 12 sleep traits with available Genome-Wide Association Study (GWAS) to evaluate their causal relationship with the ND risk through Inverse-Variance Weighted regression as main analysis. We used as outcome the latest ND GWAS with available summary-statistics: PD-AAO (N = 17,996), AD (N = 21,235) and ALS (N = 40,136). MR results pointed to a causal effect of subjective and objective-measured morning chronotype on later PD-AAO (95%CI:0.33-1.81, p = 8.47×10 and 95%CI:-7.28 to -4.44, p = 5.87×10, respectively). Sleep efficiency was causally associated with a decreased AD risk (95%CI:-20.408 to -0.66, p = 0.04) and daytime sleepiness with an increased ALS risk (95%CI:0.15 to 1.61, p = 0.01). Our study suggests that sleep and/or wake patterns have causal relationship with ND. Given that sleep and/or wake patterns are modifiable risk factors, sleep interventions should be investigated as a potential treatment in PD-AAO, AD and ALS

Sleep/wake cycle alterations as a cause of neurodegenerative diseases: a Mendelian randomization study

Sleep and/or wake cycle alterations are common in neurodegenerative diseases (ND). Our aim was to determine whether there is a causal relationship between sleep and/or wake cycle patterns and ND (Parkinson's disease (PD) age at onset (AAO), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS)) using two-sample Mendelian Randomization (MR). We selected 12 sleep traits with available Genome-Wide Association Study (GWAS) to evaluate their causal relationship with the ND risk through Inverse-Variance Weighted regression as main analysis. We used as outcome the latest ND GWAS with available summary-statistics: PD-AAO (N = 17,996), AD (N = 21,235) and ALS (N = 40,136). MR results pointed to a causal effect of subjective and objective-measured morning chronotype on later PD-AAO (95%CI:0.33-1.81, p = 8.47×10−09 and 95%CI:-7.28 to -4.44, p = 5.87×10−16, respectively). Sleep efficiency was causally associated with a decreased AD risk (95%CI:-20.408 to -0.66, p = 0.04) and daytime sleepiness with an increased ALS risk (95%CI:0.15 to 1.61, p = 0.01). Our study suggests that sleep and/or wake patterns have causal relationship with ND. Given that sleep and/or wake patterns are modifiable risk factors, sleep interventions should be investigated as a potential treatment in PD-AAO, AD and ALS

Publisher Correction: Stroke genetics informs drug discovery and risk prediction across ancestries (Nature, (2022), 611, 7934, (115-123), 10.1038/s41586-022-05165-3)

In the version of this article initially published, the name of the PRECISE4Q Consortium was misspelled as “PRECISEQ” and has now been amended in the HTML and PDF versions of the article. Further, data in the first column of Supplementary Table 55 were mistakenly shifted and have been corrected in the file accompanying the HTML version of the article

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

A parsimonious score with a free web tool for predicting disability after an ischemic stroke:the Parsifal Score

BACKGROUND: Most of the models to predict prognosis after an ischemic stroke include complex mathematical equations or too many variables, making them difficult to use in the daily clinic. We want to predict disability 3 months after an ischemic stroke in an independent patient not receiving recanalization treatment within the first 24 h, using a minimum set of variables and an easy tool to facilitate its implementation. As a secondary aim, we calculated the capacity of the score to predict an excellent/devastating outcome and mortality.METHODS: Eight hundred and forty-four patients were evaluated. A multivariable ordinal logistic regression was used to obtain the score. The Modified Rankin Scale (mRS) was used to estimate disability at the third month. The results were replicated in another independent cohort (378 patients). The "polr" function of R was used to perform the regression, stratifying the sample into seven groups with different cutoffs (from mRS 0 to 6).RESULTS: The Parsifal score was generated with: age, previous mRS, initial NIHSS, glycemia on admission, and dyslipidemia. This score predicts disability with an accuracy of 80-76% (discovery-replication cohorts). It has an AUC of 0.86 in the discovery and replication cohort. The specificity was 90-80% (discovery-replication cohorts); while, the sensitivity was 64-74% (discovery-replication cohorts). The prediction of an excellent or devastating outcome, as well as mortality, obtained good discrimination with AUC &gt; 0.80.CONCLUSIONS: The Parsifal Score is a model that predicts disability at the third month, with only five variables, with good discrimination and calibration, and being replicated in an independent cohort.</p

A parsimonious score with a free web tool for predicting disability after an ischemic stroke:the Parsifal Score

BACKGROUND: Most of the models to predict prognosis after an ischemic stroke include complex mathematical equations or too many variables, making them difficult to use in the daily clinic. We want to predict disability 3 months after an ischemic stroke in an independent patient not receiving recanalization treatment within the first 24 h, using a minimum set of variables and an easy tool to facilitate its implementation. As a secondary aim, we calculated the capacity of the score to predict an excellent/devastating outcome and mortality.METHODS: Eight hundred and forty-four patients were evaluated. A multivariable ordinal logistic regression was used to obtain the score. The Modified Rankin Scale (mRS) was used to estimate disability at the third month. The results were replicated in another independent cohort (378 patients). The "polr" function of R was used to perform the regression, stratifying the sample into seven groups with different cutoffs (from mRS 0 to 6).RESULTS: The Parsifal score was generated with: age, previous mRS, initial NIHSS, glycemia on admission, and dyslipidemia. This score predicts disability with an accuracy of 80-76% (discovery-replication cohorts). It has an AUC of 0.86 in the discovery and replication cohort. The specificity was 90-80% (discovery-replication cohorts); while, the sensitivity was 64-74% (discovery-replication cohorts). The prediction of an excellent or devastating outcome, as well as mortality, obtained good discrimination with AUC &gt; 0.80.CONCLUSIONS: The Parsifal Score is a model that predicts disability at the third month, with only five variables, with good discrimination and calibration, and being replicated in an independent cohort.</p