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    Custom Mod Mega1

    主任医师、教授、博导,南方医科大学第三附属医院(广东省骨科医院)院长

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    • Examining causal relationships between educational attainment and type 2 diabetes using genetic analysis: findings from the EPIC-InterAct study through Mendelian randomisation 2025-07-23 00:00

            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; carlotta.sacerdote@uniupo.it 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.

    • The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium 2025-07-18 00:00

      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.

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Author :   伤口世界
查丁胜

擅长:各种创伤、骨质疏松症及骨质疏松性骨折、颈肩腰腿痛的诊断和治疗(腰椎间盘突出症的微创治疗);致力于骨质疏松性骨折防治的基础与临床研究,主持国家自然科学基金、省级课题及校级课题各1项。广州华侨医院骨科主治医师、骨科学博士、科学型硕士生导师。

Latest from  伤口世界

  • Examining causal relationships between educational attainment and type 2 diabetes using genetic analysis: findings from the EPIC-InterAct study through Mendelian randomisation
  • The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium
  • Unmet needs of Italian centers for pediatric diabetes care: analysis of a survey among pediatric diabetologists facing the national screening program for Type 1 Diabetes
  • Simultaneous CRISPR screening and spatial transcriptomics reveal intracellular, intercellular, and functional transcriptional circuits
  • Resurrection of endogenous retroviruses during aging reinforces senescence

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  • K2 Content
  • Examining causal relationships between educational attainment and type 2 diabetes using genetic analysis: findings from the EPIC-InterAct study through Mendelian randomisation 2025-07-23 00:00

          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; carlotta.sacerdote@uniupo.it 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.

  • The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium 2025-07-18 00:00

    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.

  • Unmet needs of Italian centers for pediatric diabetes care: analysis of a survey among pediatric diabetologists facing the national screening program for Type 1 Diabetes 2025-07-16 00:00

    Marco Marigliano1,11*† , Roberto Franceschi2†, Enza Mozzillo3†, Valentina Tiberi4†, Monica Marino4 , Giada Boccolini4 , Malgorzata Wasniewska5 , Maria Elizabeth Street6 , Maria Rosaria Licenziati7 , Riccardo Bonfanti8 , Felice Citriniti9 , Giuseppe D’Annunzio10, Maria Carolina Salerno3 , Valentino Cherubini4 and the Diabetes Study Group of the Italian Society of Pediatric Endocrinology and Diabetology (ISPED)

    † Marco Marigliano, Roberto Franceschi, Enza Mozzillo and Valentina Tiberi contributed equally to this work.

    Abstract

    Backgrounds The incidence of Type 1 Diabetes (T1D) in children and adolescents is increasing by 3–4% per year. Children and adolescents with T1D (CwD) should receive person-centered, specialized treatment from a multidisciplinary team to ensure appropriate care. Italy is the first to implement a countrywide T1D screening program, which will raise the need for funding for specialized pediatric care. The study aims to update the organization of the Italian Centers for pediatric diabetes care.

    Methods In 2022, members of the 59 Italian Centers following CwD were invited to complete an email survey regarding the Centers’ organization, characteristics, and activities. The questionnaire included information on responders, department organization, team composition, activities, and the organizational structures: department, ambulatory care services (AC), simple operational units (UOS), simple departmental operational units (UOSd), and complex operational units (UOC).

    Results The data collected referred to the year 2022. According to the results, 21,318 people with diabetes were treated. Of these, 19,643 subjects (92.1%) have T1D (16,672 were CwD), 387 (1,8%) have Type 2 Diabetes, and 1,288 (6,1%) have other forms of diabetes. Compared to the 2012 survey, a 13% decrease (from 68 to 59 Centers) in the number of pediatric Centers caring for CwD was observed with a parallel increase of total (+6.6%) and average (+22%) number of CwD per Center. The estimated prevalence of T1D has increased (1.4 vs. 1.7 per 1,000 CwD—2012 vs. 2022). A reduction in numbers for AC (-22%) and UOS (-35%) was observed, whereas UOSd/UOC increased by 50%. Almost 35% of the dietitians and 40% of the psychologists were not permanent members of the multidisciplinary diabetes team.

    Conclusions The observed decrease in the overall number of pediatric diabetes Centers, the reduction in specialized and dedicated HCPs, and the concurrent increase in the number of treated CwD in the last ten years indicate an alarming situation for pediatric diabetes treatment in Italy. Furthermore, the projected rise in CwD due to the National T1D screening program emphasizes the need for increased resources for specialized pediatric care of CwD at all stages.

    Keywords Type 1 diabetes, Care, Children, Adolescents, Benchmarking, Team, Screening, Technology

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