Genomics

Analysis of Italian BRCA1/2 Pathogenic Variants Identifies a Private Spectrum in the Population from the Bergamo Province in Northern Italy

Figlioli G, De Nicolo A, Catucci I, Manoukian S, Peissel B, Azzollini J, Beltrami B, Bonanni B, Calvello M, Bondavalli D, Pasini B, Vignolo Lutati F, Ogliara P, Zuradelli M, Pensotti V, De Vecchi G, Volorio S, Verderio P, Pizzamiglio S, Matullo G, Aneli S, Birolo G, Zanardi F, Tondini C, Zambelli A, Livraghi L, Franchi M, Radice P, Peterlongo P.

 

Cancers (Basel). 2021 Jan 30;13(3):532. doi: 10.3390/cancers13030532.

Abstract

Germline pathogenic variants (PVs) in the BRCA1 or BRCA2 genes cause high breast cancer risk. Recurrent or founder PVs have been described worldwide including some in the Bergamo province in Northern Italy. The aim of this study was to compare the BRCA1/2 PV spectra of the Bergamo and of the general Italian populations. We retrospectively identified at five Italian centers 1019 BRCA1/2 PVs carrier individuals affected with breast cancer and representative of the heterogeneous national population. Each individual was assigned to the Bergamo or non-Bergamo cohort based on self-reported birthplace. Our data indicate that the Bergamo BRCA1/2 PV spectrum shows less heterogeneity with fewer different variants and an average higher frequency compared to that of the rest of Italy. Consistently, four PVs explained about 60% of all carriers. The majority of the Bergamo PVs originated locally with only two PVs clearly imported. The Bergamo BRCA1/2 PV spectrum appears to be private. Hence, the Bergamo population would be ideal to study the disease risk associated with local PVs in breast cancer and other disease-causing genes. Finally, our data suggest that the Bergamo population is a genetic isolate and further analyses are warranted to prove this notion.

 

Functional and clinical implications of genetic structure in 1686 Italian exomes

Giovanni Birolo*¶, Serena Aneli*¶, Cornelia Di Gaetano, Giovanni Cugliari, Alessia Russo, Alessandra Allione, Elisabetta Casalone, Elisa Giorgio, Elvezia Maria Paraboschi, Diego Ardissino,

 

Hum Mutat . 2021 Mar;42(3):272-289. doi: 10.1002/humu.24156. Epub 2021 Feb 2.

Abstract

To reconstruct the phenotypical and clinical implications of the Italian genetic structure, we thoroughly analyzed a whole-exome sequencing data set comprised of 1686 healthy Italian individuals. We found six previously unreported variants with remarkable frequency differences between Northern and Southern Italy in the HERC2, OR52R1, ADH1B, and THBS4 genes. We reported 36 clinically relevant variants (submitted as pathogenic, risk factors, or drug response in ClinVar) with significant frequency differences between Italy and Europe. We then explored putatively pathogenic variants in the Italian exome. On average, our Italian individuals carried 16.6 protein-truncating variants (PTVs), with 2.5% of the population having a PTV in one of the 59 American College of Medical Genetics (ACMG) actionable genes. Lastly, we looked for PTVs that are likely to cause Mendelian diseases. We found four heterozygous PTVs in haploinsufficient genes (KAT6A, PTCH1, and STXBP1) and three homozygous PTVs in genes causing recessive diseases (DPYD, FLG, and PYGM). Comparing frequencies from our data set to other public databases, like gnomAD, we showed the importance of population-specific databases for a more accurate assessment of variant pathogenicity. For this reason, we made aggregated frequencies from our data set publicly available as a tool for both clinicians and researchers (http://nigdb.cineca.it; NIG-ExIT).

 

DNA Methylation of FKBP5 as Predictor of Overall Survival in Malignant Pleural Mesothelioma

Giovanni Cugliari, Chiara Catalano, Simonetta Guarrera, Alessandra Allione, Elisabetta Casalone, Alessia Russo, Federica Grosso , Daniela Ferrante, Clara Viberti, Anna Aspesi, Marika Sculco, Chiara Pirazzini, Roberta Libener, Dario Mirabelli, Corrado Magnani, Irma Dianzani, Giuseppe Matullo

Cancers (Basel) . 2020 Nov 21;12(11):3470.  doi: 10.3390/cancers12113470.

2020

Abstract

Malignant pleural mesothelioma (MPM) is an aggressive tumor with median survival of 12 months and limited effective treatments. The scope of this study was to study the relationship between blood DNA methylation (DNAm) and overall survival (OS) aiming at a noninvasive prognostic test. We investigated a cohort of 159 incident asbestos exposed MPM cases enrolled in an Italian area with high incidence of mesothelioma. Considering 12 months as a cut-off for OS, epigenome-wide association study (EWAS) revealed statistically significant (p value = 7.7 × 10-9) OS-related differential methylation of a single-CpG (cg03546163), located in the 5'UTR region of the FKBP5 gene. This is an independent marker of prognosis in MPM patients with a better performance than traditional inflammation-based scores such as lymphocyte-to-monocyte ratio (LMR). Cases with DNAm < 0.45 at the cg03546163 had significantly poor survival compared with those showing DNAm ≥ 0.45 (mean: 243 versus 534 days; p value< 0.001). Epigenetic changes at the FKBP5 gene were robustly associated with OS in MPM cases. Our results showed that blood DNA methylation levels could be promising and dynamic prognostic biomarkers in MPM.

 

Small Non-Coding RNA Profiling in Plasma Extracellular Vesicles of Bladder Cancer Patients by Next-Generation Sequencing: Expression Levels of miR-126-3p and piR-5936 Increase with Higher Histologic Grades

Sabo AA, Birolo G, Naccarati A, Dragomir MP, Aneli S, Allione A, Oderda M, Allasia M, Gontero P, Sacerdote C, Vineis P, Matullo G, Pardini B.

Cancers (Basel). 2020 Jun; 12(6): 1507. Published online 2020 Jun 9. doi: 10.3390/cancers12061507

2020

Abstract

Bladder cancer (BC) is the tenth most frequent cancer worldwide. Due to the need for recurrent cystoscopies and the lack of non-invasive biomarkers, BC is associated with a high management burden. In this respect, small non-coding RNAs (sncRNAs) have been investigated in urine as possible biomarkers for BC, but in plasma their potential has not yet been defined. The expression levels of sncRNAs contained in plasma extracellular vesicles (EVs) from 47 men with BC and 46 healthy controls were assessed by next-generation sequencing. The sncRNA profiles were compared with urinary profiles from the same subjects. miR-4508 resulted downregulated in plasma EVs of muscle-invasive BC patients, compared to controls (adj-p = 0.04). In World Health Organization (WHO) grade 3 (G3) BC, miR-126-3p was upregulated both in plasma EVs and urine, when compared to controls (for both, adj-p < 0.05). Interestingly, two sncRNAs were associated with the risk class: miR-4508 with a downward trend going from controls to high risk BC, and piR-hsa-5936 with an upward trend (adj-p = 0.04 and adj-p = 0.05, respectively). Additionally, BC cases with low expression of miR-185-5p and miR-106a-5p or high expression of miR-10b-5p showed shorter survival (adj-p = 0.0013, adj-p = 0.039 and adj-p = 0.047, respectively). SncRNAs from plasma EVs could be diagnostic biomarkers for BC, especially in advanced grade.

 

Genomewide Association Study of Severe Covid-19 with Respiratory Failure

Ellinghaus D, Degenhardt F, Bujanda L, Buti M, Albillos A, Invernizzi P, Fernández J, Prati D, Baselli G, Asselta R, Grimsrud MM, Milani C, Aziz F, Kässens J, May S, Wendorff M, Wienbrandt L, Uellendahl-Werth F, Zheng T, Yi X, de Pablo R, Chercoles AG, Palom A, Garcia-Fernandez AE, Rodriguez-Frias F, Zanella A, Bandera A, Protti A, Aghemo A, Lleo A, Biondi A, Caballero-Garralda A, Gori A, Tanck A, Carreras Nolla A, Latiano A, Fracanzani AL, Peschuck A, Julià A, Pesenti A, Voza A, Jiménez D, Mateos B, Nafria Jimenez B, Quereda C, Paccapelo C, Gassner C, Angelini C, Cea C, Solier A, Pestaña D, Muñiz-Diaz E, Sandoval E, Paraboschi EM, Navas E, García Sánchez F, Ceriotti F, Martinelli-Boneschi F, Peyvandi F, Blasi F, Téllez L, Blanco-Grau A, Hemmrich-Stanisak G, Grasselli G, Costantino G, Cardamone G, Foti G, Aneli S, Kurihara H, ElAbd H, My I, Galván-Femenia I, Martín J, Erdmann J, Ferrusquía-Acosta J, Garcia-Etxebarria K, Izquierdo-Sanchez L, Bettini LR, Sumoy L, Terranova L, Moreira L, Santoro L, Scudeller L, Mesonero F, Roade L, Rühlemann MC, Schaefer M, Carrabba M, Riveiro-Barciela M, Figuera Basso ME, Valsecchi MG, Hernandez-Tejero M, Acosta-Herrera M, D'Angiò M, Baldini M, Cazzaniga M, Schulzky M, Cecconi M, Wittig M, Ciccarelli M, Rodríguez-Gandía M, Bocciolone M, Miozzo M, Montano N, Braun N, Sacchi N, Martínez N, Özer O, Palmieri O, Faverio P, Preatoni P, Bonfanti P, Omodei P, Tentorio P, Castro P, Rodrigues PM, Blandino Ortiz A, de Cid R, Ferrer R, Gualtierotti R, Nieto R, Goerg S, Badalamenti S, Marsal S, Matullo G, Pelusi S, Juzenas S, Aliberti S, Monzani V, Moreno V, Wesse T, Lenz TL, Pumarola T, Rimoldi V, Bosari S, Albrecht W, Peter W, Romero-Gómez M, D'Amato M, Duga S, Banales JM, Hov JR, Folseraas T, Valenti L, Franke A, Karlsen TH; Severe Covid-19 GWAS Group.

October 15, 2020 N Engl J Med 2020; 383:1522-1534 DOI: 10.1056/NEJMoa2020283

 

2020

Abstract

BACKGROUND

There is considerable variation in disease behavior among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19). Genomewide association analysis may allow for the identification of potential genetic factors involved in the development of Covid-19.

METHODS

We conducted a genomewide association study involving 1980 patients with Covid-19 and severe disease (defined as respiratory failure) at seven hospitals in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe. After quality control and the exclusion of population outliers, 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain were included in the final analysis. In total, we analyzed 8,582,968 single-nucleotide polymorphisms and conducted a meta-analysis of the two case–control panels.

RESULTS

We detected cross-replicating associations with rs11385942 at locus 3p21.31 and with rs657152 at locus 9q34.2, which were significant at the genomewide level (P<5×10−8) in the meta-analysis of the two case–control panels (odds ratio, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P=1.15×10−10; and odds ratio, 1.32; 95% CI, 1.20 to 1.47; P=4.95×10−8, respectively). At locus 3p21.31, the association signal spanned the genes SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6 and XCR1. The association signal at locus 9q34.2 coincided with the ABO blood group locus; in this cohort, a blood-group–specific analysis showed a higher risk in blood group A than in other blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P=1.48×10−4) and a protective effect in blood group O as compared with other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P=1.06×10−5).

CONCLUSIONS

We identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system. (Funded by Stein Erik Hagen and others.)

ACE2 gene variants may underlie interindividual variability and susceptibility

Elisa Benetti # 1, Rossella Tita # 2, Ottavia Spiga 3, Andrea Ciolfi 4, Giovanni Birolo 5, Alessandro Bruselles 6, Gabriella Doddato 7, Annarita Giliberti 7, Caterina Marconi 8, Francesco Musacchia 9, Tommaso Pippucci 10, Annalaura Torella 11, Alfonso Trezza 3, Floriana Valentino 7, Margherita Baldassarri 7, Alfredo Brusco 5 12, Rosanna Asselta 13 14, Mirella Bruttini 2 7, Simone Furini 1, Marco Seri 8 10, Vincenzo Nigro 9 11, Giuseppe Matullo 5 12, Marco Tartaglia 4, Francesca Mari 2 7, GEN-COVID Multicenter Study; Alessandra Renieri 15 16, Anna Maria Pinto 2

Affiliations

  • 1Department of Medical Biotechnologies, University of Siena, Siena, Italy.
  • 2Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy.
  • 3Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy.
  • 4Genetics and Rare Diseases Research Division, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy.
  • 5Department of Medical Sciences, University of Turin, Turin, Italy.
  • 6Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy.
  • 7Medical Genetics, University of Siena, Siena, Italy.
  • 8Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
  • 9Telethon Institute of Genetics and Medicine, Pozzuoli, Italy.
  • 10Sant'Orsola-Malpighi University Hospital, Bologna, Italy.
  • 11Dipartimento di Medicina di Precisione, Università della Campania "Luigi Vanvitelli", Napoli, Italy.
  • 12Genetica Medica, Città della Salute e della Scienza, Torino, Italy.
  • 13Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.
  • 14Humanitas Clinical and Research Center-IRCCS, Rozzano, Milan, Italy.
  • 15Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy. alessandra.renieri@unisi.it.
  • 16Medical Genetics, University of Siena, Siena, Italy. alessandra.renieri@unisi.it.
  • Contributed equally



  •  
 

Eur J Hum Genet. 2020 Nov;28(11):1602-1614. doi: 10.1038/s41431-020-0691-z. Epub 2020 Jul 17.

 

2020

Abstract

In December 2019, an initial cluster of interstitial bilateral pneumonia emerged in Wuhan, China. A human-to-human transmission was assumed and a previously unrecognized entity, termed coronavirus disease-19 (COVID-19) due to a novel coronavirus (SARS-CoV-2) was described. The infection has rapidly spread out all over the world and Italy has been the first European country experiencing the endemic wave with unexpected clinical severity in comparison with Asian countries. It has been shown that SARS-CoV-2 utilizes angiotensin converting enzyme 2 (ACE2) as host receptor and host proteases for cell surface binding and internalization. Thus, a predisposing genetic background can give reason for interindividual disease susceptibility and/or severity. Taking advantage of the Network of Italian Genomes (NIG), here we mined whole-exome sequencing data of 6930 Italian control individuals from five different centers looking for ACE2 variants. A number of variants with a potential impact on protein stability were identified. Among these, three more common missense changes, p.(Asn720Asp), p.(Lys26Arg), and p.(Gly211Arg) were predicted to interfere with protein structure and stabilization. Rare variants likely interfering with the internalization process, namely p.(Leu351Val) and p.(Pro389His), predicted to interfere with SARS-CoV-2 spike protein binding, were also observed. Comparison of ACE2 WES data between a cohort of 131 patients and 258 controls allowed identifying a statistically significant (P value < 0.029) higher allelic variability in controls compared with patients. These findings suggest that a predisposing genetic background may contribute to the observed interindividual clinical variability associated with COVID-19, allowing an evidence-based risk assessment leading to personalized preventive measures and therapeutic options.

 

 

Design of a multiplex ligation-dependent probe amplification assay for SLC20A2: identification of two novel deletions in primary familial brain calcification

Giorgio E, Garelli E, Carando A, Bellora S, Rubino E, Quarello P, Sirchia F, Marrama F, Gallone S, Grosso E, Pasini B, Massa R, Brussino A, Brusco A.
 
Journal of Human Genetics volume 64, pages1083–1090(2019)
    2019

    Abstract

    AbstractPrimary familial brain calcification (PFBC) is a rare disease characterized by brain calcifications that mainly affect the basal ganglia, thalamus, and cerebellum. Among the four autosomal-dominant genes known to be associated with the disease, SLC20A2 pathogenic variants are the most common, accounting for up to 40% of PFBC dominant cases; variants include both point mutations, small insertions/deletions and intragenic deletions. Over the last 7 years, we have collected a group of 50 clinically diagnosed PFBC patients, who were screened for single nucleotide changes and small insertions/deletions in SLC20A2 by Sanger sequencing. We found seven pathogenic/likely pathogenic variants: four were previously described by our group, and three are reported here (c.303delG, c.21delG, and c.1795-1G>A). We developed and validated a synthetic Multiplex Ligation-dependent Probe Amplification (MLPA) assay for SLC20A2 deletions, covering all ten coding exons and the 5′ UTR (SLC20A2-MLPA). Using this method, we screened a group of 43 PFBC-patients negative for point mutations and small insertions/deletions, and identified two novel intragenic deletions encompassing exon 6 NC_000008.10:g.(42297172_42302163)_(423022281_42317413)del, and exons 7–11 including the 3′UTR NC_000008.10:g.(?_42275320)_(42297172_42302163)del. Overall, SLC20A2 deletions may be highly underestimated PFBC cases, and we suggest MLPA should be included in the routine molecular test for PFBC diagnosis.

     

    Therapeutic application of allele-specific silencing by siRNA for gene duplication disorders: a proof-of-principle in Autosomal Dominant LeukoDystrophy (ADLD)

     2019 Jul 1;142(7):1905-1920. doi: 10.1093/brain/awz139.
     
    1
    University of Torino, Department of Medical Sciences, Torino, Italy.
    2
    University of Torino, Department of Neuroscience Rita Levi Montalcini and Neuroscience Institute Cavalieri Ottolenghi (NICO), Orbassano, Torino, Italy.
    3
    University of Milan, Department of Biosciences, Laboratory of Stem Cell Biology and Pharmacology of Neurodegenerative Diseases, Milan, Italy.
    4
    IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, Bologna, Italy.
    5
    Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Universiteitsweg 99, CG, Utrecht, The Netherlands.
    6
    Department of Molecular Medicine and Medical Biotechnology, University of Naples 'Federico II', Naples, Italy.
    7
    University of Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy.
    8
    University of Trento, Centre for Integrative Biology (CIBIO), Laboratory of Computational Oncology, Trento, Italy.
    9
    National Institute of Molecular Genetics (INGM) Romeo and Enrica Invernizzi, Milano, Italy.
    10
    Città della Salute e della Scienza University Hospital, Medical Genetics Unit, Torino, Italy.

     

    2019

    Abstract

    Allele-specific silencing by RNA interference (ASP-siRNA) holds promise as a therapeutic strategy for downregulating a single mutant allele with minimal suppression of the corresponding wild-type allele. This approach has been effectively used to target autosomal dominant mutations and single nucleotide polymorphisms linked with aberrantly expanded trinucleotide repeats. Here, we propose ASP-siRNA as a preferable choice to target duplicated disease genes, avoiding potentially harmful excessive downregulation. As a proof-of-concept, we studied autosomal dominant adult-onset demyelinating leukodystrophy (ADLD) due to lamin B1 (LMNB1) duplication, a hereditary, progressive and fatal disorder affecting myelin in the CNS. Using a reporter system, we screened the most efficient ASP-siRNAs preferentially targeting one of the alleles at rs1051644 (average minor allele frequency: 0.45) located in the 3' untranslated region of the gene. We identified four siRNAs with a high efficacy and allele-specificity, which were tested in ADLD patient-derived fibroblasts. Three of the small interfering RNAs were highly selective for the target allele and restored both LMNB1 mRNA and protein levels close to control levels. Furthermore, small interfering RNA treatment abrogates the ADLD-specific phenotypes in fibroblasts and in two disease-relevant cellular models: murine oligodendrocytes overexpressing human LMNB1, and neurons directly reprogrammed from patients' fibroblasts. In conclusion, we demonstrated that ASP-silencing by RNA interference is a suitable and promising therapeutic option for ADLD. Moreover, our results have a broad translational value extending to several pathological conditions linked to gene-gain in copy number variations.

     

    Consumption of Meat, Fish, Dairy Products, and Eggs and Risk of Ischemic Heart Disease

    Key TJ1Appleby PN1Bradbury KE1,2Sweeting M3Wood A3Johansson I4Kühn T5Steur M6Weiderpass E7,8,9,10Wennberg M11Lund Würtz AM12Agudo A13Andersson J14Arriola L15,16Boeing H17Boer JMA18Bonnet F19,20,21,22Boutron-Ruault MC19,20Cross AJ23Ericson U24Fagherazzi G19,20Ferrari P25Gunter M25Huerta JM26Katzke V5Khaw KT27Krogh V28La Vecchia C29,30Matullo G31,32Moreno-Iribas C33Naska A34Nilsson LM35Olsen A36Overvad K12Palli D37Panico S38Molina-Portillo E39Quirós JR40Skeie G7Sluijs I41Sonestedt E24Stepien M25Tjønneland A36Trichopoulou A29,34Tumino R42Tzoulaki I43,44,45van der Schouw YT41Verschuren WMM41di Angelantonio E3Langenberg C6Forouhi N6Wareham N6Butterworth A3Riboli E23Danesh J3.

     2019 Jun 18;139(25):2835-2845. doi: 10.1161/CIRCULATIONAHA.118.038813. Epub 2019 Apr 22. PMID: 31006335

     

    2019

    Author information

    1
    Nuffield Department of Population Health, University of Oxford, United Kingdom (T.J.K., P.N.A., K.E.B.).
    2
    National Institute for Health Innovation, School of Population Health, University of Auckland, New Zealand (K.E.B.).
    3
    Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, United Kingdom (M. Sweeting, A.W., E.d.A., A.B., J.D.).
    4
    Department of Odontology, Umeå University, Sweden (I.J.).
    5
    German Cancer Research Center, Division of Cancer Epidemiology, Heidelberg (T.K., V. Katzke).
    6
    Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, United Kingdom (M. Steur, C.L., N.F., N.W.).
    7
    Department of Community Medicine, Faculty of Health Sciences, Universitetet i Tromsø, Arctic University of Norway, Tromsø (E.W., G.S.).
    8
    Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo (E.W.).
    9
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (E.W.).
    10
    Genetic Epidemiology Group, Folkhälsan Research Center, and Faculty of Medicine, University of Helsinki, Finland (E.W.).
    11
    Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Sweden (M.W.).
    12
    Section for Epidemiology, Department of Public Health, Aarhus University, Denmark (A.M.L.W., K.O.).
    13
    Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology-Institut d'Investigació Biomédica de Bellvitge, Barcelona, Spain (A.A.).
    14
    Department of Public Health and Clinical Medicine, Research Unit Skellefteå, Umeå University, Sweden (J.A.).
    15
    Public Health Division of Gipuzkoa, Instituto BIO-Donostia, Basque Government, San Sebastian, Spain (L.A.).
    16
    CIBER (Biomedical Research Networking Centres) de Epidemiología y Salud Pública, Madrid, Spain (L.A., J.M.H.).
    17
    Department of Epidemiology, German Institute of Human Nutrition (DIfE), Potsdam-Rehbrücke (H.B.).
    18
    Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands (J.M.A.B.).
    19
    CESP, INSERM (Centre de recherche en Epidémiologie et Santé des Populations, Institut national de la santé et de la recherche médicale) U1018, Université Paris-Sud, UVSQ, Université Paris-Saclay, Villejuif Cedex, France (F.B., M.-C.B.-R., G.F.).
    20
    Gustave Roussy, Villejuif Cedex, Paris, France (F.B., M.-C.B.-R., G.F.).
    21
    Department of Endocrinology, Rennes University Hospital (CHU), France (F.B.).
    22
    Rennes 1 University, France (F.B.).
    23
    School of Public Health, Imperial College, London, United Kingdom (A.J.C., E.R.).
    24
    Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden (U.E., E.S.).
    25
    International Agency for Research on Cancer, World Health Organization, Lyon, France (P.F., M.G., M. Stepien).
    26
    Department of Epidemiology, Murcia Regional Health Council, IMIB (Instituto Murciano de Investigación Biosanitaria)-Arrixaca, Spain (J.M.H.).
    27
    Clinical Gerontology, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, United Kingdom (K.-T.K.).
    28
    Epidemiology and Prevention Unit, Fondazione IRCCS (Institute for Research, Hospitalization and Health Care) Istituto Nazionale dei Tumori, Milan, Italy (V. Krogh).
    29
    Hellenic Health Foundation, Athens, Greece (C.L.V., A. Trichopoulou).
    30
    Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy (C.L.V.).
    31
    Italian Institute for Genomic Medicine, Turin (G.M.).
    32
    Department of Medical Sciences, University of Turin, Italy (G.M.).
    33
    Instituto de Salud Pública de Navarra, IdiSNA-Navarre Institute for Health Research, Pamplona, Spain (C.M.-I.).
    34
    World Health Organization Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Greece (A.N., A. Trichopoulou).
    35
    Arctic Research Center at Umeå University, Sweden (L.M.N.).
    36
    Danish Cancer Society Research Center, Copenhagen, Denmark (A.O., A.Tjønneland).
    37
    Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network-ISPRO, Florence, Italy (D.P.).
    38
    Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy (S.P.).
    39
    Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria, Universidad de Granada, Spain (E.M.-P.).
    40
    Public Health Directorate of Asturias, Oviedo, Spain (J.R.Q.).
    41
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands (I.S., Y.T.v.d.S., W.M.M.V.).
    42
    Cancer Registry and Histopathology Unit, "Civic-M.p.Arezzo" Hospital, ASP (Azienda Sanitaria Provinciale) Ragusa, Italy (R.T.).
    43
    Department of Epidemiology and Biostatistics (I.T.), School of Public Health, Imperial College London, United Kingdom.
    44
    Medical Research Council-Public Health England Centre for Environment (I.T.), School of Public Health, Imperial College London, United Kingdom.
    45
    Department of Hygiene and Epidemiology, University of Ioannina Medical School, Greece (I.T.).

     

    Abstract

    Background: There is uncertainty about the relevance of animal foods to the pathogenesis of ischemic heart disease (IHD). We examined meatfishdairy products, and eggs and risk for IHD in the pan-European EPIC cohort (European Prospective Investigation Into Cancer and Nutrition).

    Methods: In this prospective study of 409 885 men and women in 9 European countries, diet was assessed with validated questionnaires and calibrated with 24-hour recalls. Lipids and blood pressure were measured in a subsample. During a mean of 12.6 years of follow-up, 7198 participants had a myocardial infarction or died of IHD. The relationships of animal foods with risk were examined with Cox regression with adjustment for other animal foods and relevant covariates.

    Results: The hazard ratio (HR) for IHD was 1.19 (95% CI, 1.06-1.33) for a 100-g/d increment in intake of red and processed meat, and this remained significant after exclusion of the first 4 years of follow-up (HR, 1.25 [95% CI, 1.09-1.42]). Risk was inversely associated with intakes of yogurt (HR, 0.93 [95% CI, 0.89-0.98] per 100-g/d increment), cheese (HR, 0.92 [95% CI, 0.86-0.98] per 30-g/d increment), and eggs (HR, 0.93 [95% CI, 0.88-0.99] per 20-g/d increment); the associations with yogurt and eggs were attenuated and nonsignificant after exclusion of the first 4 years of follow-up. Risk was not significantly associated with intakes of poultry, fish, or milk. In analyses modeling dietary substitutions, replacement of 100 kcal/d from red and processed meat with 100 kcal/d from fatty fish, yogurt, cheese, or eggs was associated with ≈20% lower risk of IHD. Consumption of red and processed meat was positively associated with serum non-high-density lipoprotein cholesterol concentration and systolic blood pressure, and consumption of cheese was inversely associated with serum non-high-density lipoprotein cholesterol.

    Conclusions: Risk for IHD was positively associated with consumption of red and processed meat and inversely associated with consumption of yogurt, cheese, and eggs, although the associations with yogurt and eggs may be influenced by reverse causation bias. It is not clear whether the associations with red and processed meat and cheese reflect causality, but they were consistent with the associations of these foods with plasma non-high-density lipoprotein cholesterol and for red and processed meat with systolic blood pressure, which could mediate such effects.

     

    Genetic predisposition for malignant mesothelioma: A concise review

    Betti M1Aspesi A2Sculco M1Matullo G3Magnani C4Dianzani I1

     2019 Jul - Sep;781:1-10. doi: 10.1016/j.mrrev.2019.03.001. Epub 2019 Mar 6. PMID: 31416570

    2019

    Author information

    1. Department of Health Sciences, Università del Piemonte Orientale, via Solaroli 17, 28100 Novara, Italy.
    2. Department of Health Sciences, Università del Piemonte Orientale, via Solaroli 17, 28100 Novara, Italy. Electronic address: anna.aspesi@med.uniupo.it.
    3. Department of Medical Sciences, Università degli Studi di Torino e Unità di Genetica Medica, AOU Città della Salute e della Scienza, 10126 Turin, Italy.
    4. Department of Translational Medicine, Università del Piemonte Orientale, SSD Epidemiologia dei Tumori, AOU Maggiore della Carità e CPO-Piemonte, via Solaroli 17, 28100 Novara, Italy.
     

     

    Abstract

    Malignant mesothelioma (MM) is an aggressive cancer associated with asbestos exposure. Studies of familial malignant pleural mesothelioma (MPM) have suggested the existence of a genetic predisposition. Information on the role of genetic risk factors in the development of MM has been growing in the last years, and both low- and high-risk genetic factors have been identified, but genetic factors alone (without any exposure to asbestos or other mineral fibers) have never been shown to induce MM. Low-risk genetic factors have been identified in studies that systematically analyzed the whole genome. When considered alone these low-risk genetic factors carry a relative risk of MPM that is 10- to 15-fold lower than that carried by asbestos exposure; however, a large number of these factors in combination may increase the impact of asbestos exposure. High-risk genetic factors include truncating variants in the tumor suppressor BAP1 and in other tumor suppressor genes belonging to DNA repair pathways. Heterozygous germline variants in these genes may favor carcinogenesis if a second somatic variant occurs that impairs the wild-type allele. This impairment can cause genetic instability due to the suppression of a specific DNA repair pathway, and transformation. This genetic predisposition may have translational consequences, as it may predict patient response to drugs that induce tumor-specific synthetic lethality.

     

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