Evidence map›Paper›PMID 42783430›Full record

ArticleMedical sciences (Basel, Switzerland)2026

Adiposity-Metabolic Discordance Identifies Cardiometabolically Heterogeneous Profiles in Normal-Weight Adults: A Cross-Sectional Study.

Diego González Carrasco, Pedro Juan Tárraga López, Mónica Silu Piña Dabreu, Lluis Rodas Cañellas, Ángel Arturo López-González, José Ignacio Ramírez-Manent

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Article in Medical sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Diego González CarrascoADEMA University School, University of the Balearic Islands, 07009 Palma, Spain.
Pedro Juan Tárraga LópezFaculty of Medicine, University of Castilla La Mancha (UCLM), 02008 Albacete, Spain.ORCID 0000-0002-5222-5784
Mónica Silu Piña DabreuADEMA University School, University of the Balearic Islands, 07009 Palma, Spain.ORCID 0000-0001-5450-8481
Lluis Rodas CañellasADEMA University School, University of the Balearic Islands, 07009 Palma, Spain.ORCID 0009-0006-3848-687X
Ángel Arturo López-GonzálezADEMA University School, University of the Balearic Islands, 07009 Palma, Spain.ORCID 0000-0002-7439-8117
José Ignacio Ramírez-ManentADEMA University School, University of the Balearic Islands, 07009 Palma, Spain.ORCID 0000-0001-6887-4562

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBody mass index (BMI) is widely used to classify obesity and cardiometabolic risk, although it may fail to detect hidden metabolic impairment in individuals with normal body weight. Increasing evidence suggests that adiposity and metabolic dysfunction may not always coexist, giving rise to biologically discordant phenotypes associated with different cardiometabolic profiles.

methodsThis cross-sectional study was a secondary analysis of a subset of normal-weight adults selected from a larger database of Spanish workers undergoing routine occupational health examinations between 2019 and 2024. The final analytical sample included 166,008 workers aged 18-69 years with BMI values between 18.5 and 24.9 kg/m

resultsThe concordant healthy phenotype was the most prevalent subgroup (78.9%), followed by adiposity-only (9.8%), metabolic-only (9.2%), and concordant high-risk phenotypes (2.0%). Despite normal BMI, approximately 21% of participants exhibited adiposity-metabolic discordance or combined high-risk status. The metabolic-only phenotype showed markedly adverse triglyceride, TG/HDL-c ratio, fasting glucose, and blood pressure values despite relatively preserved anthropometric parameters. Conversely, the adiposity-only phenotype exhibited increased central and visceral adiposity with comparatively preserved metabolic status. Sedentary lifestyle, unfavorable dietary adherence, smoking, older age, male sex, and higher BMI within the normal-weight range were independently associated with metabolically adverse phenotypes. The concordant high-risk phenotype demonstrated the greatest overall cardiometabolic burden.

conclusionsAdults with normal BMI are metabolically heterogeneous and may present clinically relevant hidden-risk phenotypes despite conventional normal-weight classification. Adiposity-metabolic discordance highlights the limitations of BMI-based screening alone and supports the incorporation of multidimensional anthropometric, metabolic, and lifestyle-related assessment strategies for earlier identification of cardiometabolic risk.

Indexed as

AdiposityCardiovascular DiseasesMetabolic SyndromeAdolescentAdultAgedBody Mass IndexCardiometabolic Risk FactorsCross-Sectional StudiesFemaleHumansMaleMiddle AgedObesityPhenotypeYoung Adultbody mass indexcardiometabolic risklife styleoccupational healthvisceral fat

Identifiers

PMID42783430
PMCPMC13609116

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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.