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Objectives The white blood cell (WBC) count is a simple and convenient marker of inflammation for use in medical practice; however, its association with non-alcoholic fatty liver disease (NAFLD) has not been determined. We examined the relationship between WBC and NAFLD to provide a convenient and useful marker for the prediction of NAFLD. Setting A longitudinal cohort participating in a large health check-up programme for the Chinese population was selected and followed up from 2005 to 2011. Participants A total of 21 307 male and female participants without NAFLD who underwent health check-ups at least twice between 2005 and 2011 were included in this study. 15 201 participants (7286 men and 7915 women) were eligible for inclusion. Results The baseline distribution of age, WBC, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum total protein (TP), albumin (ALB) and globin (GLO) and the prevalence of males, hypertension, hyperglycaemia, smoking and regular exercise were significantly different between the incident NAFLD and non-NAFLD groups (p

Objectives 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. Setting 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. Participants 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 BN9s 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.