scholarly journals Polygenic associations and causal inferences between serum immunoglobulins and amyotrophic lateral sclerosis

Author(s):  
Xu Chen ◽  
Xiaojun Shen ◽  
Xuzhuo Zhang ◽  
Yiqiang Zhan ◽  
Fang Fang
2019 ◽  
Vol 85 (4) ◽  
pp. 470-481 ◽  
Author(s):  
Sara Bandres‐Ciga ◽  
Alastair J. Noyce ◽  
Gibran Hemani ◽  
Aude Nicolas ◽  
Andrea Calvo ◽  
...  

2020 ◽  
Author(s):  
Xu Chen ◽  
Xiaojun Shen ◽  
Yiqiang Zhan ◽  
Fang Fang

AbstractChronic inflammation might contribute to the development of amyotrophic lateral sclerosis (ALS). The relationship between serum immunoglobulins and risk of ALS remains however greatly unknown. In order to overcome limitations like reverse causation and residual confounding commonly seen in observational studies, we applied polygenic risk score (PRS) and Mendelian randomization (MR) analyses on summary statistics from the large-scale genome-wide association studies (GWAS), to examine the polygenic and causal associations between three serum immunoglobulins (IgA, IgM, and IgG) and risk of ALS (first in a discovery phase and then in a replication phase). An inverse polygenic association was discovered between IgA and ALS as well as between IgM and ALS. Such associations were however not replicated using a larger ALS GWAS and no causal association was observed for either IgA-ALS or IgM-ALS. A positive polygenic association was both discovered [odds ratio (OR) = 1.18; 95% confidence interval (CI): 1.12-1.25, P=5.9×10−7] and replicated (OR=1.13, 95% CI: 1.06-1.20, P=0.001) between IgG and ALS. A causal association between IgG and ALS was also suggested in both the discovery (OR=1.06, 95%CI: 1.02-1.10, P=0.009) and replication (OR=1.07, 95%CI: 0.90-1.24, P=0.420) analyses, although the latter was not statistically significant. This study suggests a shared polygenic risk between serum IgG (as a biomarker for chronic inflammation) and ALS.


2020 ◽  
Vol 63 (1) ◽  
pp. 59-73 ◽  
Author(s):  
Panying Rong

Purpose The purpose of this article was to validate a novel acoustic analysis of oral diadochokinesis (DDK) in assessing bulbar motor involvement in amyotrophic lateral sclerosis (ALS). Method An automated acoustic DDK analysis was developed, which filtered out the voice features and extracted the envelope of the acoustic waveform reflecting the temporal pattern of syllable repetitions during an oral DDK task (i.e., repetitions of /tɑ/ at the maximum rate on 1 breath). Cycle-to-cycle temporal variability (cTV) of envelope fluctuations and syllable repetition rate (sylRate) were derived from the envelope and validated against 2 kinematic measures, which are tongue movement jitter (movJitter) and alternating tongue movement rate (AMR) during the DDK task, in 16 individuals with bulbar ALS and 18 healthy controls. After the validation, cTV, sylRate, movJitter, and AMR, along with an established clinical speech measure, that is, speaking rate (SR), were compared in their ability to (a) differentiate individuals with ALS from healthy controls and (b) detect early-stage bulbar declines in ALS. Results cTV and sylRate were significantly correlated with movJitter and AMR, respectively, across individuals with ALS and healthy controls, confirming the validity of the acoustic DDK analysis in extracting the temporal DDK pattern. Among all the acoustic and kinematic DDK measures, cTV showed the highest diagnostic accuracy (i.e., 0.87) with 80% sensitivity and 94% specificity in differentiating individuals with ALS from healthy controls, which outperformed the SR measure. Moreover, cTV showed a large increase during the early disease stage, which preceded the decline of SR. Conclusions This study provided preliminary validation of a novel automated acoustic DDK analysis in extracting a useful measure, namely, cTV, for early detection of bulbar ALS. This analysis overcame a major barrier in the existing acoustic DDK analysis, which is continuous voicing between syllables that interferes with syllable structures. This approach has potential clinical applications as a novel bulbar assessment.


2019 ◽  
Author(s):  
Naile Alankaya ◽  
Zeliha Tülek ◽  
Aylin Özakgül ◽  
Alper Kaya ◽  
Aynur Dik

2019 ◽  
Author(s):  
Naile Alankaya ◽  
Zeliha Tülek ◽  
Aylin Özakgül ◽  
Alper Kaya ◽  
Aynur Dik

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