Method for estimating disease risk from microbiome data using structural equation modeling
The relationship between the human gut microbiota and disease is of increasing scientific interest. Previous investigations have focused on the differences in intestinal bacterial abundance between control and affected groups to identify disease biomarkers. However, different types of intestinal bac...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1035002/full |
| _version_ | 1797942781288644608 |
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| author | Hidetaka Tokuno Tatsuya Itoga Jumpei Kasuga Kana Okuma Kazumi Hasuko Hiroaki Masuyama Yoshimi Benno |
| author_facet | Hidetaka Tokuno Tatsuya Itoga Jumpei Kasuga Kana Okuma Kazumi Hasuko Hiroaki Masuyama Yoshimi Benno |
| author_sort | Hidetaka Tokuno |
| collection | DOAJ |
| description | The relationship between the human gut microbiota and disease is of increasing scientific interest. Previous investigations have focused on the differences in intestinal bacterial abundance between control and affected groups to identify disease biomarkers. However, different types of intestinal bacteria may have interacting effects and thus be considered biomarker complexes for disease. To investigate this, we aimed to identify a new kind of biomarker for atopic dermatitis using structural equation modeling (SEM). The biomarkers identified were latent variables, which are complex and derived from the abundance data for bacterial marker candidates. Groups of females and males classified as healthy participants [normal control (NC) (female: 321 participants, male: 99 participants)], and patients afflicted with atopic dermatitis only [AS (female: 45 participants, male: 13 participants)], with atopic dermatitis and other diseases [AM (female: 75 participants, male: 34 participants)], and with other diseases but without atopic dermatitis [OD (female: 1,669 participants, male: 866 participants)] were used in this investigation. The candidate bacterial markers were identified by comparing the intestinal microbial community compositions between the NC and AS groups. In females, two latent variables (lv) were identified; for lv1, the associated components (bacterial genera) were Alistipes, Butyricimonas, and Coprobacter, while for lv2, the associated components were Agathobacter, Fusicatenibacter, and Streptococcus. There was a significant difference in the lv2 scores between the groups with atopic dermatitis (AS, AM) and those without (NC, OD), and the genera identified for lv2 are associated with the suppression of inflammatory responses in the body. A logistic regression model to estimate the probability of atopic dermatitis morbidity with lv2 as an explanatory variable had an area under the curve (AUC) score of 0.66 when assessed using receiver operating characteristic (ROC) analysis, and this was higher than that using other logistic regression models. The results indicate that the latent variables, especially lv2, could represent the effects of atopic dermatitis on the intestinal microbiome in females. The latent variables in the SEM could thus be utilized as a new type of biomarker. The advantages identified for the SEM are as follows: (1) it enables the extraction of more sophisticated information when compared with models focused on individual bacteria and (2) it can improve the accuracy of the latent variables used as biomarkers, as the SEM can be expanded. |
| first_indexed | 2024-04-10T20:14:02Z |
| format | Article |
| id | doaj.art-f6d4962932ef4d1e8ccd1622a3b606d0 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-04-10T20:14:02Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-f6d4962932ef4d1e8ccd1622a3b606d02023-01-26T08:46:53ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2023-01-011410.3389/fmicb.2023.10350021035002Method for estimating disease risk from microbiome data using structural equation modelingHidetaka Tokuno0Tatsuya Itoga1Jumpei Kasuga2Kana Okuma3Kazumi Hasuko4Hiroaki Masuyama5Yoshimi Benno6Symbiosis Solutions Inc., Tokyo, JapanSymbiosis Solutions Inc., Tokyo, JapanSymbiosis Solutions Inc., Tokyo, JapanSymbiosis Solutions Inc., Tokyo, JapanSymbiosis Solutions Inc., Tokyo, JapanSymbiosis Solutions Inc., Tokyo, JapanBenno Institute for Gut Microflora, Saitama, JapanThe relationship between the human gut microbiota and disease is of increasing scientific interest. Previous investigations have focused on the differences in intestinal bacterial abundance between control and affected groups to identify disease biomarkers. However, different types of intestinal bacteria may have interacting effects and thus be considered biomarker complexes for disease. To investigate this, we aimed to identify a new kind of biomarker for atopic dermatitis using structural equation modeling (SEM). The biomarkers identified were latent variables, which are complex and derived from the abundance data for bacterial marker candidates. Groups of females and males classified as healthy participants [normal control (NC) (female: 321 participants, male: 99 participants)], and patients afflicted with atopic dermatitis only [AS (female: 45 participants, male: 13 participants)], with atopic dermatitis and other diseases [AM (female: 75 participants, male: 34 participants)], and with other diseases but without atopic dermatitis [OD (female: 1,669 participants, male: 866 participants)] were used in this investigation. The candidate bacterial markers were identified by comparing the intestinal microbial community compositions between the NC and AS groups. In females, two latent variables (lv) were identified; for lv1, the associated components (bacterial genera) were Alistipes, Butyricimonas, and Coprobacter, while for lv2, the associated components were Agathobacter, Fusicatenibacter, and Streptococcus. There was a significant difference in the lv2 scores between the groups with atopic dermatitis (AS, AM) and those without (NC, OD), and the genera identified for lv2 are associated with the suppression of inflammatory responses in the body. A logistic regression model to estimate the probability of atopic dermatitis morbidity with lv2 as an explanatory variable had an area under the curve (AUC) score of 0.66 when assessed using receiver operating characteristic (ROC) analysis, and this was higher than that using other logistic regression models. The results indicate that the latent variables, especially lv2, could represent the effects of atopic dermatitis on the intestinal microbiome in females. The latent variables in the SEM could thus be utilized as a new type of biomarker. The advantages identified for the SEM are as follows: (1) it enables the extraction of more sophisticated information when compared with models focused on individual bacteria and (2) it can improve the accuracy of the latent variables used as biomarkers, as the SEM can be expanded.https://www.frontiersin.org/articles/10.3389/fmicb.2023.1035002/fullmicrobiotahuman intestinal microbiomeatopic dermatitisamplicon sequence variantsstructural equation modelinglatent variables |
| spellingShingle | Hidetaka Tokuno Tatsuya Itoga Jumpei Kasuga Kana Okuma Kazumi Hasuko Hiroaki Masuyama Yoshimi Benno Method for estimating disease risk from microbiome data using structural equation modeling Frontiers in Microbiology microbiota human intestinal microbiome atopic dermatitis amplicon sequence variants structural equation modeling latent variables |
| title | Method for estimating disease risk from microbiome data using structural equation modeling |
| title_full | Method for estimating disease risk from microbiome data using structural equation modeling |
| title_fullStr | Method for estimating disease risk from microbiome data using structural equation modeling |
| title_full_unstemmed | Method for estimating disease risk from microbiome data using structural equation modeling |
| title_short | Method for estimating disease risk from microbiome data using structural equation modeling |
| title_sort | method for estimating disease risk from microbiome data using structural equation modeling |
| topic | microbiota human intestinal microbiome atopic dermatitis amplicon sequence variants structural equation modeling latent variables |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1035002/full |
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