Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learning
Abstract This cohort analysis investigated the prevalence of nonalcoholic fatty liver disease (NAFLD) and NAFLD with fibrosis at different stages, associated clinical characteristics, and comorbidities in the general United States population and a subpopulation with type 2 diabetes mellitus (T2DM),...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Health/LWW
2022-07-01
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| Series: | Hepatology Communications |
| Online Access: | https://doi.org/10.1002/hep4.1935 |
| _version_ | 1811174097963450368 |
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| author | Mazen Noureddin Fady Ntanios Deepa Malhotra Katherine Hoover Birol Emir Euan McLeod Naim Alkhouri |
| author_facet | Mazen Noureddin Fady Ntanios Deepa Malhotra Katherine Hoover Birol Emir Euan McLeod Naim Alkhouri |
| author_sort | Mazen Noureddin |
| collection | DOAJ |
| description | Abstract This cohort analysis investigated the prevalence of nonalcoholic fatty liver disease (NAFLD) and NAFLD with fibrosis at different stages, associated clinical characteristics, and comorbidities in the general United States population and a subpopulation with type 2 diabetes mellitus (T2DM), using the National Health and Nutrition Examination Survey (NHANES) database (2017–2018). Machine learning was explored to predict NAFLD identified by transient elastography (FibroScan®). Adults ≥20 years of age with valid transient elastography measurements were included; those with high alcohol consumption, viral hepatitis, or human immunodeficiency virus were excluded. Controlled attenuation parameter ≥302 dB/m using Youden’s index defined NAFLD; vibration‐controlled transient elastography liver stiffness cutoffs were ≤8.2, ≤9.7, ≤13.6, and >13.6 kPa for F0–F1, F2, F3, and F4, respectively. Predictive modeling, using six different machine‐learning approaches with demographic and clinical data from NHANES, was applied. Age‐adjusted prevalence of NAFLD and of NAFLD with F0–F1 and F2–F4 fibrosis was 25.3%, 18.9%, and 4.4%, respectively, in the overall population and 54.6%, 32.6%, and 18.3% in those with T2DM. The highest prevalence was among Mexican American participants. Test performance for all six machine‐learning models was similar (area under the receiver operating characteristic curve, 0.79–0.84). Machine learning using logistic regression identified male sex, hemoglobin A1c, age, and body mass index among significant predictors of NAFLD (P ≤ 0.01). Conclusion: Data show a high prevalence of NAFLD with significant fibrosis (≥F2) in the general United States population, with greater prevalence in participants with T2DM. Using readily available, standard demographic and clinical data, machine‐learning models could identify subjects with NAFLD across large data sets. |
| first_indexed | 2024-04-10T17:57:40Z |
| format | Article |
| id | doaj.art-c1e8a0dcbb174e93a60b11a0c967f712 |
| institution | Directory Open Access Journal |
| issn | 2471-254X |
| language | English |
| last_indexed | 2024-04-10T17:57:40Z |
| publishDate | 2022-07-01 |
| publisher | Wolters Kluwer Health/LWW |
| record_format | Article |
| series | Hepatology Communications |
| spelling | doaj.art-c1e8a0dcbb174e93a60b11a0c967f7122023-02-02T17:27:39ZengWolters Kluwer Health/LWWHepatology Communications2471-254X2022-07-01671537154810.1002/hep4.1935Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learningMazen Noureddin0Fady Ntanios1Deepa Malhotra2Katherine Hoover3Birol Emir4Euan McLeod5Naim Alkhouri6Karsh Division of Gastroenterology and Hepatology Comprehensive Transplant Center Cedars‐Sinai Medical Center Los Angeles California USAPfizer Inc New York New York USAPfizer Inc New York New York USAPfizer Inc New York New York USAPfizer Inc New York New York USAPfizer Ltd Tadworth UKArizona Liver Health Chandler Arizona USAAbstract This cohort analysis investigated the prevalence of nonalcoholic fatty liver disease (NAFLD) and NAFLD with fibrosis at different stages, associated clinical characteristics, and comorbidities in the general United States population and a subpopulation with type 2 diabetes mellitus (T2DM), using the National Health and Nutrition Examination Survey (NHANES) database (2017–2018). Machine learning was explored to predict NAFLD identified by transient elastography (FibroScan®). Adults ≥20 years of age with valid transient elastography measurements were included; those with high alcohol consumption, viral hepatitis, or human immunodeficiency virus were excluded. Controlled attenuation parameter ≥302 dB/m using Youden’s index defined NAFLD; vibration‐controlled transient elastography liver stiffness cutoffs were ≤8.2, ≤9.7, ≤13.6, and >13.6 kPa for F0–F1, F2, F3, and F4, respectively. Predictive modeling, using six different machine‐learning approaches with demographic and clinical data from NHANES, was applied. Age‐adjusted prevalence of NAFLD and of NAFLD with F0–F1 and F2–F4 fibrosis was 25.3%, 18.9%, and 4.4%, respectively, in the overall population and 54.6%, 32.6%, and 18.3% in those with T2DM. The highest prevalence was among Mexican American participants. Test performance for all six machine‐learning models was similar (area under the receiver operating characteristic curve, 0.79–0.84). Machine learning using logistic regression identified male sex, hemoglobin A1c, age, and body mass index among significant predictors of NAFLD (P ≤ 0.01). Conclusion: Data show a high prevalence of NAFLD with significant fibrosis (≥F2) in the general United States population, with greater prevalence in participants with T2DM. Using readily available, standard demographic and clinical data, machine‐learning models could identify subjects with NAFLD across large data sets.https://doi.org/10.1002/hep4.1935 |
| spellingShingle | Mazen Noureddin Fady Ntanios Deepa Malhotra Katherine Hoover Birol Emir Euan McLeod Naim Alkhouri Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learning Hepatology Communications |
| title | Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learning |
| title_full | Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learning |
| title_fullStr | Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learning |
| title_full_unstemmed | Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learning |
| title_short | Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017–2018 transient elastography data and application of machine learning |
| title_sort | predicting nafld prevalence in the united states using national health and nutrition examination survey 2017 2018 transient elastography data and application of machine learning |
| url | https://doi.org/10.1002/hep4.1935 |
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