Alessandra Macciotta ,1,2 Carlotta Sacerdote,3 Claudia Giachino,1 Chiara Di Girolamo,1 Matteo Franco,1 Yvonne T van der Schouw,4 Raul Zamora-Ros,5 Elisabete Weiderpass,6 Cloé Domenighetti,7 Alexis Elbaz ,7 Thérèse Truong,7 Claudia Agnoli,8 Benedetta Bendinelli,9 Salvatore Panico,10 Paolo Vineis ,11 Sofia Christakoudi,11,12 Matthias B Schulze,13,14,15 Verena Katzke,16 Rashmita Bajracharya,16 Christina C Dahm,17 Susanne Oksbjerg Dalton,18,19 Sandra M Colorado-Yohar,20,21,22 Conchi Moreno-Iribas,23 Pilar Amiano Etxezarreta,21,24,25 María José Sanchez,21,26,27 Nita G Forouhi,28 Nicholas Wareham,28 Fulvio Ricceri 1.
Additional supplemental material is published online only. To view, please visit the journal online (https://doi.org/ 10.1136/jech-2024-222734). For numbered affiliations see end of article.
Correspondence to
Professor Carlotta Sacerdote; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 NW and FR contributed equally. Received 10 July 2024 Accepted 19 November 2024© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group
To cite: Macciotta A, Sacerdote C, Giachino C, et al. J Epidemiol Community Health Epub ahead of print:[please include Day MonthYear]. doi:10.1136/jech- 2024-222734
ABSTRACT
Introduction Observational studies have shown that more educated people are at lower risk of developing type 2 diabetes (T2D). However, robust study designs are needed to investigate the likelihood that such a relationship is causal. This study used genetic instruments for education to estimate the effect of education on T2D using the Mendelian randomisation (MR) approach.
Methods Analyses have been conducted in the European Prospective Investigation into Cancer and Nutrition (EPIC)- InterAct study (more than 20000 individuals), a case-cohort study of T2D nested in the EPIC cohort. Education was measured as Years of Education and Relative Index of Inequality. Prentice-weighted Cox models were performed to estimate the association between education and T2D. One-sample MR analyses investigated whether genetic predisposition towards longer education was associated with risk of T2D and investigated potential mediators of the
Results MR estimates indicated a risk reduction of about 15% for each year of longer education on the risk of developing T2D, confirming the protective role estimated by observational models (HR 0.96, 95% CI 0.95 to 0.96). MR analyses on putative mediators showed a significant role of education on body mass index, alcohol consumption, adherence to the Mediterranean diet and smoking habits.
Conclusion The results supported the hypothesis that higher education is a protective factor for the risk of developing T2D. Based on its position in the causal chain, education may be antecedent of other known risk factors for T2D including unhealthy behaviours. These findings reinforce evidence obtained through observational study designs and bridge the gap between correlation and causation.
Stephanie J. Hanna1 · Rachel H. Bonami2,3,4,5 · Brian Corrie6,7 · Monica Westley8 · Amanda L. Posgai9 · Eline T. Luning Prak10 · Felix Breden6,7 · Aaron W. Michels11 · Todd M. Brusko9,12,13 · Type 1 Diabetes AIRR Consortium
Received: 26 May 2024 / Accepted: 19 August 2024 / Published online: 29 October 2024
© The Author(s) 2024
Extended author information available on the last page of the article
Abstract
Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissuespecific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR/ BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.
Keywords AIRR · AIRR Data Commons · Autoantibodies · B cell receptors · FAIR data · Next-generation sequencing · Single-cell RNA-seq · T cell receptors · Type 1 diabetes
Abbreviations
AAb Autoantibody/autoantibodies
ADC AIRR Data Commons
AIM Activation-induced marker
AIRR Adaptive Immune Receptor Repertoire
AIRR-seq AIRR sequencing
BCR B cell receptor
CDR3 Complementarity determining region 3
FAIR Findable, Accessible, Interoperable, Reusable
GEO Gene Expression Omnibus
HPAP Human Pancreas Analysis Program
IEDB Immune Epitope Database
MiAIRR Minimal information about AIRR
ML Machine learning
pLN Pancreatic lymph node(s)
SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2
scRNA-seq Single-cell RNA-seq
SRA Sequence Read Archive
T1D TCR/BCR Repository The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository
TCR T cell receptor
TCRβ T cell receptor β chain
Tfh T follicular helper
Treg Regulatory T cell(s)
VDJ Variable, diversity and joining gene segments
Stephanie J. Hanna and Rachel H. Bonami contributed equally to this work. Aaron W. Michels and Todd M. Brusko contributed equally as joint senior authors.
Members of the Type 1 Diabetes AIRR Consortium are listed in the Acknowledgements.
创伤是指由于各种致伤因素导致的机体软组织、骨骼甚至内脏器官等等各个系统的损伤,创伤可以根据发生地点、受伤部位、受伤组织、致伤因素及皮肤完整程度进行分类。 按发生地点分为战争伤、工业伤、农业伤、交通伤、体育伤、生活伤等;按受伤部位分为颅脑创伤、胸部创伤、腹部创伤、各部位的骨折和关节脱位、手部伤等;按受伤类型分为骨折、脱位、脑震荡、器官破裂等;相邻部位同时受伤者称为联合伤(如胸腹联合伤);按受伤的组织或器官分类时,又可按受伤组织的深浅分为软组织创伤、骨关节创伤和内脏创伤。软组织创伤指皮肤、皮下组织和肌肉的损伤,也包括行于其中的血管和神经。单纯的软组织创伤一般较轻,但广泛的挤压伤可致挤压综合征。血管破裂大出血亦可致命。骨关节创伤包括骨折和脱位,并按受伤的骨或关节进一步分类并命名。如股骨骨折、肩关节脱位等。内脏创伤又可按受伤的具体内脏进行分类和命名。如脑挫裂伤、肺挫伤、肝破裂等。同一致伤原因引起两个以上部位或器官的创伤,称为多处伤或多发伤。按致伤因素,分为火器伤、切伤、刺伤、撕裂伤、挤压伤、扭伤、挫伤等。按皮肤完整程度,分为闭合性创伤、开放性创伤等。
伤口世界平台生态圈,以“关爱人间所有伤口患者”为愿景,连接、整合和拓展线上和线下的管理慢性伤口的资源,倡导远程、就近和居家管理慢性伤口,解决伤口专家的碎片化时间的价值创造、诊疗经验的裂变复制、和患者的就近、居家和低成本管理慢性伤口的问题。
2019广东省医疗行业协会伤口管理分会年会
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