Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits.
PLoS Comput Biol
; 16(8): e1008044, 2020 08.
Article
en En
| MEDLINE
| ID: mdl-32797044
ABSTRACT
Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler R2visceral = 0.94, P < 2.2 × 10-16, R2subcutaneous = 0.91, P < 2.2 × 10-16), and faster run times (10,000 images 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10-69, ßsubq = 0.45; Pvisc = 2.5 × 10-55, ßvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10-7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Adipocitos
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Aprendizaje Automático
/
Obesidad
Tipo de estudio:
Prognostic_studies
/
Systematic_reviews
Límite:
Adult
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Female
/
Humans
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Male
/
Middle aged
Idioma:
En
Revista:
PLoS Comput Biol
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Reino Unido