亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Alzheimer's disease and its family history reduce the risk of non‐Hodgkin's lymphoma: A Mendelian randomization study

孟德尔随机化 家族史 疾病 霍奇金淋巴瘤 医学 淋巴瘤 随机化 孟德尔遗传 肿瘤科 儿科 内科学 临床试验 遗传学 生物 基因型 基因 遗传变异
作者
Zilong Wang,Binyang Song,Jinhua Wang,Xiaobo Wang,Bo Tang
出处
期刊:Alzheimers & Dementia [Wiley]
标识
DOI:10.1002/alz.14205
摘要

We recently read Association between Alzheimer's disease and risk of cancer: A retrospective cohort study in Shanghai, China, and the results suggested that Alzheimer's disease (AD) significantly increases the risk of lymphoma.1 However, several previous studies have consistently shown an inverse correlation between AD and cancer.2-4 Currently, the association between AD and hematological diseases is predominantly studied through cohort investigations, with limited attention given to lymphoma specifically.4, 5 Hence, due to less research and conflicting results from these cohort studies, we opted for Mendelian randomization (MR), a method that offers a higher level of evidence, to explore the causal relationship between AD and lymphoma.6 We were surprised to discover that AD significantly decreased the risk of non-Hodgkin's lymphoma (NHL; about 90% of all lymphomas), but not Hodgkin's lymphoma (HL; about 10% of all lymphomas; see Table S1 and S2, detailed analyses of HL are shown in Table S3 and Figures S1–S3 in supporting information).7 We used genome-wide association studies (GWAS) data from two major publicly available databases, namely, IEU Open GWAS (https://gwas.mrcieu.ac.uk) and FinnGen (R10, https://www.finngen.fi/fi), to assemble multiple cohorts of relevant GWAS data pertaining to AD, family history of AD, NHL and HL (Table S4 and Figure S4 in supporting information). The analysis process involves five methods named MR-Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, and we selected the IVW method as the primary reference index. Analysis of three cohorts of AD revealed a significant reduction in the risk of developing NHL (I: ORIVW = 0.823, PIVW = 0.046; II: ORIVW = 1.45E-15, PIVW = 0.023; III: ORIVW = 0.905, and PIVW = 0.016), indicating that AD served as a protective factor (Figure 1, Figure S5 in supporting information, and Table S1). Additionally, we examined the effects of a family history of AD and it also significantly decreased the risk of developing NHL (ORIVW = 0.777, PIVW = 0.004). In addition to the IVW method, we also observed varying degrees of statistical significance with the other four methods (Figure 1). Meanwhile, we conducted heterogeneity, pleiotropy, and sensitivity analyses of the aforementioned results using MR-Egger, MR-PRESSO (pleiotropy residual sum and outlier), and "leave-one-out" methods. The findings demonstrated the robustness and reliability of our conclusions (Table S5, Figure S6 and S7 in supporting information). Detailed methods of analysis and instrumental variable (single nucleotide polymorphisms) information used are available in the supplementary materials (Supplementary Methods and Table S6 in supporting information). In fact, we are not the first to elucidate the relationship between AD and NHL. Previous cohort studies, systematic reviews, and meta-analyses on AD and NHL have indicated that AD may serve as a protective factor, reducing the incidence of NHL.3, 4, 8 Only one MR study investigated AD in relation to follicular lymphoma (FL), a subtype of NHL, but the IVW method did not reveal a significant difference.2 It is noteworthy that FL represents only 20% to 25% of new NHL cases and cannot fully represent all NHL subtypes.9 In our study, we utilized three different cohorts to investigate the effect of AD on NHL, all of which consistently showed AD as a protective factor. Nevertheless, it is important to acknowledge that we utilized GWAS studies from European populations, whereas Ren et al. utilized data from Chinese populations. Moreover, NHL does not represent the entirety of lymphoma. The above two points may be important reasons for the difference between our results. Furthermore, we are the first to report that a family history of AD reduces the risk of developing NHL. Considering the significant role of genetic factors in AD pathogenesis, this finding extends the study's scope to a broader population and a longer timeline for understanding the relationship between the two. For instance, Valentine et al., utilizing the Utah Population Database (UPDB), identified individuals with a family history of AD as high-risk groups for AD to investigate various common diseases and cancers.10 Surprisingly, their findings indicated that high-risk groups for AD significantly elevated the risk of NHL, contradicting the conclusions drawn from other studies.10 Certainly, there is a broad spectrum of opinions regarding the mechanisms underlying the interactions between AD and NHL. While researchers have proposed a range of related mechanisms such as inflammation, immune response, infectious agents, and oxidative stress, these can only partially account for this inverse correlation.8 Clarifying the relationship between the two and identifying common pathophysiological pathways will aid clinics in developing new, optimal programs for the prevention and treatment of both AD and NHL. In conclusion, from the perspective of evidence-based medicine, the MR methodology used in our study can offer a higher level of evidence regarding the relationship between the two. This can further stimulate research on their association, aiming to elucidate the intricate mechanisms underlying the interactions between AD and NHL. All GWAS data used in our study came from the IEU OpenGWAS project and FinnGen study, and we thank both databases and all participants and investigators who participated in relevant GWAS studies. There are no relevant conflicts of interest for this publication. Author disclosures are available in the supporting information. This research was supported by the Undergraduate Teaching Reform Fund of Dalian Medical University (112007010205), the College Students' Innovation Project of Dalian Medical University (S202310161043), and the National Natural Science Foundation of China (81800203). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

科研通智能强力驱动
Strongly Powered by AbleSci AI

祝大家在新的一年里科研腾飞
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
29秒前
nikg发布了新的文献求助10
34秒前
诗梦完成签到,获得积分10
46秒前
YifanWang应助科研通管家采纳,获得30
57秒前
青葱鱼块完成签到 ,获得积分10
1分钟前
1分钟前
以七完成签到 ,获得积分10
1分钟前
sdkabdrxt完成签到,获得积分10
1分钟前
1分钟前
krajicek发布了新的文献求助10
2分钟前
2分钟前
闪闪沂完成签到 ,获得积分10
3分钟前
科研通AI6.2应助刻苦不弱采纳,获得10
3分钟前
3分钟前
小神仙完成签到 ,获得积分10
3分钟前
3分钟前
Isaac完成签到 ,获得积分10
3分钟前
刻苦不弱发布了新的文献求助10
3分钟前
4分钟前
毛耳朵发布了新的文献求助10
4分钟前
yzy完成签到 ,获得积分10
4分钟前
互助应助毛耳朵采纳,获得10
4分钟前
乐乐应助毛耳朵采纳,获得10
4分钟前
NattyPoe发布了新的文献求助10
4分钟前
忧心的士萧完成签到,获得积分10
4分钟前
今后应助科研通管家采纳,获得10
4分钟前
5分钟前
5分钟前
夏天无完成签到 ,获得积分10
5分钟前
Cloud发布了新的文献求助10
5分钟前
5分钟前
gkhsdvkb发布了新的文献求助10
5分钟前
yin景景完成签到,获得积分10
5分钟前
科研通AI6.2应助开霁采纳,获得10
6分钟前
李健的小迷弟应助颖颖采纳,获得10
6分钟前
6分钟前
颖颖发布了新的文献求助10
6分钟前
颖颖完成签到,获得积分10
6分钟前
酷波er应助科研通管家采纳,获得10
6分钟前
单薄咖啡豆完成签到 ,获得积分10
7分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Les Mantodea de guyane 2500
Signals, Systems, and Signal Processing 510
Discrete-Time Signals and Systems 510
生活在欺瞒的年代:傅树介政治斗争回忆录 260
Mastering Prompt Engineering: A Complete Guide 200
Elastography for characterization of focal liver lesions: current evidence and future perspectives 200
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5870851
求助须知:如何正确求助?哪些是违规求助? 6468547
关于积分的说明 15665078
捐赠科研通 4987083
什么是DOI,文献DOI怎么找? 2689159
邀请新用户注册赠送积分活动 1631508
关于科研通互助平台的介绍 1589536