title: Identification of reciprocal causality between non-alcoholic fatty liver disease and metabolic syndrome by a simplified Bayesian network in a Chinese population.
abstract: OBJECTIVE
  It remains unclear whether non-alcoholic fatty liver disease (NAFLD) is a cause or a consequence of metabolic syndrome (MetS). We proposed a simplified Bayesian network (BN) and attempted to confirm their reciprocal causality.
  
  
  METHODS
  Bidirectional longitudinal cohorts (subcohorts A and B) were designed and followed up from 2005 to 2011 based on a large-scale health check-up in a Chinese population.
  
  
  METHODS
  Subcohort A (from NAFLD to MetS, n=8426) included the participants with or without NAFLD at baseline to follow-up the incidence of MetS, while subcohort B (from MetS to NAFLD, n=16,110) included the participants with or without MetS at baseline to follow-up the incidence of NAFLD.
  
  
  RESULTS
  Incidence densities were 2.47 and 17.39 per 100 person-years in subcohorts A and B, respectively. Generalised estimating equation analyses demonstrated that NAFLD was a potential causal factor for MetS (relative risk, RR, 95% CI 5.23, 3.50 to 7.81), while MetS was also a factor for NAFLD (2.55, 2.23 to 2.92). A BN with 5 simplification strategies was used for the reciprocal causal inference. The BN's causal inference illustrated that the total effect of NAFLD on MetS (attributable risks, AR%) was 2.49%, while it was 19.92% for MetS on NAFLD. The total effect of NAFLD on MetS components was different, with dyslipidemia having the greatest (AR%, 10.15%), followed by obesity (7.63%), diabetes (3.90%) and hypertension (3.51%). Similar patterns were inferred for MetS components on NAFLD, with obesity having the greatest (16.37%) effect, followed by diabetes (10.85%), dyslipidemia (10.74%) and hypertension (7.36%). Furthermore, the most important causal pathway from NAFLD to MetS was that NAFLD led to elevated GGT, then to MetS components, while the dominant causal pathway from MetS to NAFLD began with dyslipidaemia.
  
  
  CONCLUSIONS
  The findings suggest a reciprocal causality between NAFLD and MetS, and the effect of MetS on NAFLD is significantly greater than that of NAFLD on MetS.
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan, Shandong, China Medical Department, Qilu Hospital of Shandong University, Jinan, Shandong, China.
authors.forename: Yongyuan
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan, Shandong, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Health Management Center, Shandong Provincial QianFoShan Hospital, Jinan, Shandong, China.
authors.forename: Chengqi
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Health Management Center, Shandong Provincial QianFoShan Hospital, Jinan, Shandong, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Tang
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan, Shandong, China.
authors.forename: Nvjuan
authors.initials: N
authors.lastname: Zhong
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan, Shandong, China.
authors.forename: Hongkai
authors.initials: H
authors.lastname: Li
authors.affiliation: Health Management Center, Shandong Provincial QianFoShan Hospital, Jinan, Shandong, China.
authors.forename: Xinhong
authors.initials: X
authors.lastname: Song
authors.affiliation: Health Management Center, Shandong Provincial QianFoShan Hospital, Jinan, Shandong, China.
authors.forename: Haiyan
authors.initials: H
authors.lastname: Lin
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan, Shandong, China.
authors.forename: Yanxun
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan, Shandong, China.
authors.forename: Fuzhong
authors.initials: F
authors.lastname: Xue
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001786:Blood Glucose / Q000378:metabolism; D015992:Body Mass Index; D002681:China / Q000453:epidemiology; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D005544:Forecasting; D006801:Humans; D015994:Incidence; D008297:Male; D024821:Metabolic Syndrome / Q000150:complications* / Q000175:diagnosis / Q000453:epidemiology; D008875:Middle Aged; D065626:Non-alcoholic Fatty Liver Disease / Q000000981:diagnostic imaging / Q000453:epidemiology / Q000209:etiology*; D015995:Prevalence; D011379:Prognosis; D012189:Retrospective Studies; D012307:Risk Factors; D014463:Ultrasonography
chemical_list: D001786:Blood Glucose
keywords: Bayesian Network; Bi-directional Longitudinal Cohort; Generalized Estimating Equation; Metabolic Syndrome; Nonalcolic Fatty Liver Disease; Reciprocal Causality