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.

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

Loϊc Binan,1,2 Aiping Jiang,3,4,5 Serwah A. Danquah,1,6,12 Vera Valakh,1,6,13 Brooke Simonton,2,14 Jon Bezney,2,15 Robert T. Manguso,3,4,5 Kathleen B. Yates,3,4,5 Ralda Nehme,6 Brian Cleary,7,8,9,10,11,* and Samouil L. Farhi1,16,*

1 Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

2 Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

3 Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA

4 Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02144, USA

5 Department of Medicine, Harvard Medical School, Boston, MA 02115, USA

6 Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

7 Faculty of Computing and Data Sciences, Boston University, Boston, MA 02215, USA

8 Department of Biology, Boston University, Boston, MA 02215, USA

9 Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA

10 Program in Bioinformatics, Boston University, Boston, MA 02215, USA

11 Biological Design Center, Boston University, Boston, MA 02215, USA

12 Present address: Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA

13 Present address: Stoke Therapeutics, Bedford, MA, USA

14 Present address: The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

15 Present address: Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

16 Lead contact

*Correspondence: 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。 (B.C.), 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。 (S.L.F.)

https://doi.org/10.1016/j.cell.2025.02.012

SUMMARY

Pooled optical screens have enabled the study of cellular interactions, morphology, or dynamics at massive scale, but they have not yet leveraged the power of highly plexed single-cell resolved transcriptomic readouts to inform molecular pathways. Here, we present a combination of imaging spatial transcriptomics with parallel optical detection of in situ amplified guide RNAs (Perturb-FISH). Perturb-FISH recovers intracellular effects that are consistent with single-cell RNA-sequencing-based readouts of perturbation effects (Perturb-seq) in a screen of lipopolysaccharide response in cultured monocytes, and it uncovers intercellular and density-dependent regulation of the innate immune response. Similarly, in three-dimensional xenograft models, Perturb-FISH identifies tumor-immune interactions altered by genetic knockout. When paired with a functional readout in a separate screen of autism spectrum disorder risk genes in human-induced pluripotent stem cell (hIPSC) astrocytes, Perturb-FISH shows common calcium activity phenotypes and their associated genetic interactions and dysregulated molecular pathways. Perturb-FISH is thus a general method for studying the genetic and molecular associations of spatial and functional biology at single-cell resolution.