Evidence map›Paper›PMID 41062939›Full record

ArticleClinical proteomics2025

Assessing the consistency of mass spectrometry, a clinical-laboratory model, and immunohistochemistry in amyloid subtyping: a Brazilian experience.

Roberta Shcolnik Szor, Jussara Bianchi Castelli, Rodrigo Andrade Schuch, Valdemir Melechco Carvalho, Vanderson Rocha

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Article in Clinical proteomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Roberta Shcolnik SzorServiço de Hematologia, Hemoterapia e Terapia Celular, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. roberta.hemato@gmail.com.ORCID http://orcid.org/0000-0001-5254-3486
Jussara Bianchi CastelliGrupo Fleury, São Paulo, Brazil.
Rodrigo Andrade SchuchGrupo Fleury, São Paulo, Brazil.
Valdemir Melechco CarvalhoGrupo Fleury, São Paulo, Brazil.
Vanderson RochaServiço de Hematologia, Hemoterapia e Terapia Celular, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSystemic amyloidosis is a potentially fatal protein misfolding disorder usually underdiagnosed in low- and middle-income countries, where limited awareness and restricted access to diagnostic tools contribute to prolonged diagnostic journeys and delayed diagnoses. Accurate identification of the precursor protein is essential but remains a challenge, particularly in resource-limited settings. This study aimed to perform mass spectrometry (MS) for amyloid subtyping and to use it as the reference method to evaluate the consistency of a clinical-laboratory model (CLM) and immunohistochemistry (IHC) in determining the amyloid subtype.

methodsIn this retrospective, observational, single-center study, MS was performed on tissue biopsies from patients diagnosed with systemic amyloidosis between 2009 and 2018 at a public university hospital in Brazil. An IHC panel of four antibodies (anti-kappa, -lambda, -serum amyloid A, -transthyretin) was performed on samples with sufficient material. Review of medical records assessed the amyloid subtype determined by the clinical-laboratory model (CLM), which was based on clinical presentation, laboratory and imaging data, genetic testing, and pathological findings available at the time of the initial diagnosis.

resultsFrom 127 patients, 48 were excluded due to unavailable biopsies or insufficient material for MS analysis. The final cohort consisted of 79 patients, 61% male, with a median age of 61 years. Biopsies from 13 different tissues were analyzed by MS, revealing the following amyloid subtypes: AL (56%), ATTR (25%), AA (6%), AFib (3%), AH (1%). Seven cases (9%) remained inconclusive. IHC correctly subtyped amyloid in 28% of cases but failed in 66%. In 80% of patients the CLM correctly identified the amyloid subtype. However, it generated incorrect typing leading to inappropriate treatments.

conclusionThe consistency analysis between the CLM, IHC and MS demonstrated the superiority of MS in amyloid subtyping from tissue biopsies. While the CLM failed in 20% of cases and resulted in inappropriate treatments due to false-positive results, IHC showed very limited diagnostic performance, contrasting with results from reference centers, with less than one-third of cases correctly classified. These findings reinforce the role of MS as a more accurate and cost-competitive method for amyloid subtyping in middle-income countries.

Indexed as

AmyloidosisAmyloidosis diagnosisImmunohistochemistryMass spectrometry

Identifiers

PMID41062939
PMCPMC12505685

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.