{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:05:46Z","timestamp":1785409546989,"version":"3.56.0"},"reference-count":25,"publisher":"AOSIS","issue":"1","license":[{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Background: Monoclonal gammopathies, including multiple myeloma, present significant challenges in sub-Saharan Africa. Diagnosis is often missed because of limited screening tools. The gamma gap and albumin-globulin ratio (AGR) have been proposed as simple, cost-effective screening methods; however, their utility in settings with prevalent infectious and inflammatory diseases is unclear.\n\nObjective: This study evaluated the diagnostic accuracy of gamma gap and AGR in identifying patients who require further investigation for monoclonal gammopathies in South Africa.\n\nMethods: A retrospective analysis of 7946 patients who underwent investigations for monoclonal gammopathies at Groote Schuur Hospital, South Africa, between September 2015 and September 2022 was conducted. Patients were classified based on monoclonal protein detection, and the gamma gap, AGR, and multivariable models were evaluated for diagnostic performance.\n\nResults: Among the patients (median age: 61 years, 58% female [4632\/7946] and 42% [3314\/7946] male patients), 1231 had monoclonal proteins. A gamma gap cutoff of 46 g\/L identified 35% of monoclonal cases (sensitivity), with 91% specificity and an area under the curve (AUC) of 0.60. The AGR showed a slightly better AUC of 0.63, with 44% sensitivity and 80% specificity at a 0.85 cutoff. Multivariable models incorporating age, sex, and hypogammaglobulinaemia improved performance, with the gamma gap model achieving an AUC of 0.73, improving the sensitivity to 58%, with a specificity of 78%.\n\nConclusion: The gamma gap and AGR showed low sensitivity and moderate specificity in screening for monoclonal gammopathies, highlighting the need for integrated diagnostic approaches combining clinical, demographic, and laboratory data to improve early detection in resource-limited settings.\n\nWhat this study adds: Although cost-effective and widely available, gamma gap and AGR have limited accuracy for screening monoclonal gammopathies when used alone in settings with prevalent infectious and inflammatory diseases. Although the tests are good at ruling out monoclonality, they risk missing many true cases, delaying diagnosis and treatment.\n\nKeywords: gamma gap; globulins; paraproteins; paraproteinaemias; albumin\/globulin ratio; inflammation.<\/jats:p>","DOI":"10.4102\/ajlm.v14i1.2505","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:17:40Z","timestamp":1742984260000},"update-policy":"https:\/\/doi.org\/10.4102\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Gamma gap and albumin-globulin ratio show poor sensitivity for monoclonal gammopathy screening in South Africa"],"prefix":"10.4102","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6828-2495","authenticated-orcid":false,"given":"Njabulo","family":"Khumalo","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00znvbk37","id-type":"ROR","asserted-by":"publisher"}],"name":"National Health Laboratory Service"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2687-2419","authenticated-orcid":false,"given":"Cameron A.","family":"Francis","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00znvbk37","id-type":"ROR","asserted-by":"publisher"}],"name":"National Health Laboratory Service"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2044-5515","authenticated-orcid":false,"given":"Siphiwe M.","family":"Baloyi","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00znvbk37","id-type":"ROR","asserted-by":"publisher"}],"name":"National Health Laboratory Service"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0874-3082","authenticated-orcid":false,"given":"Jody A.","family":"Rusch","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00znvbk37","id-type":"ROR","asserted-by":"publisher"}],"name":"National Health Laboratory Service"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2580","published-online":{"date-parts":[[2025,3,26]]},"reference":[{"key":"ref0","doi-asserted-by":"crossref","unstructured":"Rajkumar SV, Dimopoulos MA, Palumbo A, et al. 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