Risk Factors for the Development of Hepatocellular Carcinoma (HCC) in Chronic Hepatitis B Virus (HBV) Infection

A Systematic Review and Meta-analysis

Cori Campbell; Tingyan Wang; Anna L. McNaughton; Eleanor Barnes; Philippa C. Matthews


J Viral Hepat. 2021;28(3):493-507. 

In This Article


Search Strategy and Selection Criteria

In June 2020, we systematically searched three databases (Web of Science, EMBASE and MEDLINE) in accordance with PRISMA guidelines;[27] search terms are listed in Table S1. We searched all databases from 1 January 2000 until 24 June 2020, without application of any restrictions for study design applied to search terms or results, but including only full-text human studies published in English.

We combined and deduplicated search results from the three databases, prior to screening for eligibility. We excluded articles not investigating associations of comorbidities with risk of HCC and/or not restricted to CHB-infected participants. We also searched reference lists of relevant systematic reviews/meta-analyses and studies identified for inclusion to identify additional studies for inclusion. Search terms were constructed and agreed on by three authors (PM, TW and CC) and articles were screened and selected by one author (CC).

Data Extraction and Statistical Analysis

One author (CC) extracted the following summary characteristics from included studies: country, publication year, study design, follow-up period, comorbidities investigated, number of participants, number of HCC cases, sex, age at baseline, risk ratio and covariates adjusted for.

We carried out meta-analysis in R (version 3.5.1) using the 'meta' package (version 4.12–0),[28] including only hazard ratios (HRs) minimally adjusting for age and sex reported in cohort or nested case-control studies. We calculated pooled summary effect estimates using the inverse-variance weighting of HRs on the natural logarithmic scale, and quantified between-study heterogeneity using the I2 statistic; significance of heterogeneity was investigated using Cochran's Q test (p threshold = 0.05). Where I 2 was >0 and heterogeneity was significant, we present both fixed- and random-effects summary estimates. We undertook multiple sensitivity analyses whereby analyses were restricted to studies adjusting for various additional confounders and for DM treatment, and stratified by DM type, in order to investigate robustness of observed associations.


For DM, we considered diagnoses of type 1 and type 2 DM, as well as unspecified DM, for pooling the effect, followed by further stratification by subtypes of diabetes if enough studies were eligible. Hypertension (HT) was defined by either a diagnosis of HT recorded as part of the medical history or current health assessment, or a measurement with mean arterial pressure (MAP) above a specified threshold. Obesity was based on BMI values, by referring to the cut-off in the included studies, where 25, 27 and 30 kg/m2 were the common threshold values used. Cardiovascular disease (CVD) was defined broadly as an umbrella term including any of the following disease subtypes: ischaemic heart disease (IHD)/coronary heart disease (CHD) and cerebrovascular disease. Dyslipidaemia was defined according to serum lipid concentrations above a certain threshold defined in the primary studies (thresholds may vary depending on healthcare setting).

Quality Appraisal

We used the Newcastle-Ottawa Scale (NOS) to assess the quality of nonrandomized studies, including cohort and case-control studies,[29] judging studies based on points awarded for selection of study groups, comparability of groups and exposure/outcome ascertainment. Studies with scores of <5, 5–7 and >7 points were considered to be of low, sufficient and high quality, respectively.