PMID- 35365931
OWN - NLM
STAT- MEDLINE
DCOM- 20220628
LR  - 20230310
IS  - 2471-254X (Electronic)
IS  - 2471-254X (Linking)
VI  - 6
IP  - 7
DP  - 2022 Jul
TI  - Predicting NAFLD prevalence in the United States using National Health and 
      Nutrition Examination Survey 2017-2018 transient elastography data and 
      application of machine learning.
PG  - 1537-1548
LID - 10.1002/hep4.1935 [doi]
AB  - 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((R)) ). 
      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.
CI  - (c) 2022 The Authors. Hepatology Communications published by Wiley Periodicals LLc 
      on behalf of the American Association for the Study of Liver Diseases.
FAU - Noureddin, Mazen
AU  - Noureddin M
AD  - Karsh Division of Gastroenterology and HepatologyComprehensive Transplant 
      CenterCedars-Sinai Medical CenterLos AngelesCaliforniaUSA.
FAU - Ntanios, Fady
AU  - Ntanios F
AD  - Pfizer IncNew YorkNew YorkUSA.
FAU - Malhotra, Deepa
AU  - Malhotra D
AD  - Pfizer IncNew YorkNew YorkUSA.
FAU - Hoover, Katherine
AU  - Hoover K
AD  - Pfizer IncNew YorkNew YorkUSA.
FAU - Emir, Birol
AU  - Emir B
AD  - Pfizer IncNew YorkNew YorkUSA.
FAU - McLeod, Euan
AU  - McLeod E
AD  - Pfizer LtdTadworthUK.
FAU - Alkhouri, Naim
AU  - Alkhouri N
AD  - Arizona Liver HealthChandlerArizonaUSA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220401
PL  - United States
TA  - Hepatol Commun
JT  - Hepatology communications
JID - 101695860
SB  - IM
MH  - Adult
MH  - *Diabetes Mellitus, Type 2/diagnostic imaging
MH  - *Elasticity Imaging Techniques
MH  - Humans
MH  - Liver Cirrhosis/diagnostic imaging
MH  - Machine Learning
MH  - Male
MH  - *Non-alcoholic Fatty Liver Disease/diagnostic imaging
MH  - Nutrition Surveys
MH  - Prevalence
MH  - United States/epidemiology
PMC - PMC9234676
COIS- Dr. Noureddin has advised 89BIO, Abbott, Allergan, Blade, EchoSens, Fractyl, 
      Gilead, Intercept, Novartis, Novo Nordisk, OWL, Roche Diagnostics, Siemens, and 
      Terns; he received research support from Allergan, Bristol-Myers Squibb, Conatus, 
      Enanta, Galectin, Galmed, Genfit, Gilead, Madrigal, Novartis, Shire, Viking, and 
      Zydus; he is a shareholder of or has stock in Anaetos and Viking. Dr. Ntanios, 
      Ms. Malhotra, Dr. Hoover, Dr. Emir, and Mr. McLeod are stockholders and employees 
      of Pfizer Inc. Dr. Alkhouri participated in a speakers' bureau for and received 
      grants/research funding from Gilead and Intercept; he received grants/research 
      funding from Akero, Allergan, Bristol-Myers Squibb, Corcept, Galectin, Genfit, 
      Madrigal, NGM, Pfizer Inc, Poxel, and Zydus.
EDAT- 2022/04/03 06:00
MHDA- 2022/06/29 06:00
PMCR- 2022/04/01
CRDT- 2022/04/02 05:28
PHST- 2022/02/10 00:00 [revised]
PHST- 2021/10/28 00:00 [received]
PHST- 2022/02/19 00:00 [accepted]
PHST- 2022/04/03 06:00 [pubmed]
PHST- 2022/06/29 06:00 [medline]
PHST- 2022/04/02 05:28 [entrez]
PHST- 2022/04/01 00:00 [pmc-release]
AID - 02009842-202207000-00005 [pii]
AID - HEP41935 [pii]
AID - 10.1002/hep4.1935 [doi]
PST - ppublish
SO  - Hepatol Commun. 2022 Jul;6(7):1537-1548. doi: 10.1002/hep4.1935. Epub 2022 Apr 1.