| [Background] China possesses the highest incidence of liver cancer globally and accounts for almost half of the liver cancer burden in the world.The prognosis of liver cancer patients is generally poor,with a 5-year survival rate of less than 5%.However,prognosis varies strongly by disease stage at diagnosis.Early-stage liver cancer is amenable to curative therapy,enabling a 5-year survival rate of 40%-70%.Taken together,these facts highlight the importance of prevention,screening and early diagnosis of liver cancer.In China,hepatitis B virus(HBV)infection has been implicated in the etiology of60%-80% of liver cancer.Hence the current recommended screening approach is limited in HBV carriers.Due to the implementation of maternal-to-infant blocks and high vaccination coverage among children,China has made rapid progress in reducing the incidence of HBV infection in the past three decades.The prevalence rate of hepatitis B surface antigen(HBs Ag)declined from 9.8% in 1992 to 6.1% as an estimated prevalence in 2016.Meanwhile,rapidly increasing in income and education levels,a shift in lifestyle,and aging population have led to dramatic changes in the liver cancer risk factors pattern in China during the past decades.In addition,the prevalence of metabolic diseases centered on insulin resistance is increasing,and a growing number of cases of metabolic diseases have been observed transformed to liver cancer.However,previous liver cancer prediction models were limited by focusing on specific disease populations(e.g.,HBV infection,diabetes)or putting excessive weight on clinical indicators(e.g.,alanine aminotransferase(ALT),alpha-fetoprotein(AFP)),especially for a country with a large population base like China.A large body of evidence has been established that unhealthy lifestyle factors,such as smoking,alcohol consumption,diet,and physical inactivity,have been consistently linked to an elevated liver cancer risk.Notwithstanding,since many of these lifestyle behaviors often coexist,investigating the combined impact of these lifestyle factors on liver cancer risk is highly relevant.Recently,one nested case-control study has demonstrated that adherence to a healthy lifestyle defined by a combination of above modifiable factors was related to a reduction in liver cancer incidence in European populations.To date,solid evidence of the protective effects from the prospective cohort study still lacks.Besides,the existing evidence on the protective effect of lifestyle factors on liver cancer were mostly conducted in developed countries.However,little prospective evidence exists about whether a combination of healthy lifestyle factors is related to a considerable reduction of liver cancer risk in Chinese.Thus,a new personalized risk prediction model for liver cancer based on routinely available predictors such as HBV infection status,medical history,metabolic risk factors and lifestyle factors is urgently needed to encourage high-risk people to be screened early.And we also aimed to examine the joint association of several modifiable lifestyle factors with liver cancer risk.[Method] The China Kadoorie Biobank(CKB)is a prospective cohort study of512 714 adults aged between 30 and 79 years at the time of recruitment.The cohort was conducted between 2004 and 2008 in ten study areas geographically(5 urban and5 rural)spread across China.In the baseline questionnaire,participants completed an interviewer-administered,laptop-based questionnaire,including sociodemographic characteristics,personal medical history,family medical history,and modifiable lifestyle factors.Physical measurement and blood test results were measured according to a standard protocol.Incident outcome cases since the participants' enrollment into the cohort were identified by means of linkage with local disease and death registries,the national health insurance system,and by active follow-up.Nearly all of participants were covered by the health insurance system,which recorded details of all episodes of hospitalization and coded examination and treatment procedures.The 10 th revision of the International Classification of Diseases(ICD-10)was used to code the incident events by trained staff “blinded” to baseline information.In the present analysis,we focused on established predictor risk factors for liver cancer that are routinely available.We excluded participants who reported a medical history of liver cancer at baseline(n=37).Participants with missing covariate data were also excluded,e.g.,parental or sibling history of cancer(n=14 831),body mass index(n=2),HBV surface antigen(n=11 731)and random plasma glucose level(n=8 340),leaving 486 285 participants for the subsequent analysis.And participants were followed up from the date of baseline enrollment until the date of diagnosis of liver cancer(ICD-10 code: C22),date of death,or January 1,2017,whichever occurred first.We used Poisson regression models to estimate age-,sex-,and residential areaadjusted incidence rates of liver cancer events per 100,000 person-years.Using the randomized grouping method,a development/validation split where the model was fitted to 80%(n=389 028)of the data and evaluated on the remaining 20%(n=97 257).For model development,we separately modeled the hazard of liver cancer and the hazard of death from all causes to account for death as competing events.Flexible parametric survival models were used on the cumulative hazard scale to estimate baseline hazards and hazard ratios.We used restricted cubic splines to model potential nonlinear relationships with continuous variables.Separate models for liver cancer risk were fitted for HBs Ag seronegative and HBs Ag seropositive participants due to the significant variance in liver cancer incidences.We estimated the 10-year absolute risk of liver cancer for each participant using the cumulative incidence function.We performed an internal validation of the development model.The performance of the model was assessed by calibration,which measures the agreement between observed and predicted liver cancer risk,and by discrimination ability to differentiate between participants who experienced liver cancer and those who did not.Decision curve analysis(DCA)was used to evaluate the clinical utility of the prediction model.After dichotomizing,points for the 4 modifiable lifestyle factors were summed to obtain a healthy lifestyle score,which ranged from 0(least healthy)to 4(most healthy),and were subsequently categorized as favorable(3 or 4 healthy lifestyle factors),intermediate(2 healthy lifestyle factors),and unfavorable(0 or 1 healthy lifestyle factors)lifestyles.Cox proportional hazard regression models were used to examine the association of lifestyle categories with time to incident liver cancer and to estimate hazard ratio(HR)and 95% confidence interval(95% CI).Absolute risk was calculated as the percentage of incident liver cancer cases occurring in a given group.We calculated the numbers needed to adhere to a favorable lifestyle to prevent one liver cancer by extrapolating the differences of 10-year event rates for given groups.[Results] Among the 486 285 study participants,there were 2 706 incident liver cancer diagnoses over 4 814 320 person-years of follow-up(median [interquartile range] length of follow-up,10.12 [9.19-11.09] years).As expected,fifteen risk factors for liver cancer involving sociodemographic characteristics,HBV infection status,medical history,metabolic risk factors and lifestyle-related factors showed high predictive power.Excellent calibration and discrimination of the CKB-PLR(Prediction for Liver cancer Risk based on the China Kadoorie Biobank study)model was observed in both development and validation datasets.In the development dataset,when applied to all participants regardless of HBs Ag status,the CKB-PLR model was able to discriminate well between participants who were diagnosed with liver cancer and those who did not within 10 years(c-statistic [95% CI] = 0.80 [0.79-0.81]).The discriminative ability performed equally well when applied only to HBs Ag seronegative(c-statistic [95% CI] = 0.76 [0.75-0.77])or seropositive(c-statistic [95%CI] = 0.76 [0.74-0.77])participants.And the calibration plots suggest excellent calibration agreement of observed probabilities with predicted probabilities of developing liver cancer in 10 years.When the model was applied to the validation dataset,a higher distribution of the10-year predicted probability was observed in participants who were diagnosed with liver cancer compared to those who were not within the first 10-years.Subsequently,the calibration and discriminative ability of the model were further validated in the validation dataset.Similar to the development model,the observed probabilities coincided with the predicted probabilities of developing liver cancer in 10 years in the validation dataset.The c-statistic of the model applied to the validation dataset was0.76(95% CI = 0.73-0.78)for HBs Ag seronegative participants,0.76(95% CI = 0.73-0.80)for HBs Ag seropositive participants,and 0.80(95% CI = 0.78-0.82)for all participants.Overall,internal independent validation suggested good performance of the CKB-PLR model when applied to new observations or data sets,due to the high capability of calibration and discrimination.To assess the possible implications of adopting a model-based inclusion criterion for liver cancer screening,we compared the sensitivity and specificity of the CKB-PLR model-based strategy with that of the HBs Ag seropositive criteria in the validation dataset.All 3 069 HBs Ag seropositive participants were eligible for screening in the validation dataset when adopting the HBs Ag seropositive criteria.For an equal number of participants determined by the CKB-PLR model,participants with a 10-year predicted risk higher than 1.926% were eligible.A vertical line is drawn at 1.926%,68% of HBs Ag seropositive participants have predicted risks exceeding this threshold,and the CKB-PLR model criteria reduced the likelihood of false positive and false negative.Decision curve analysis revealed that use of the model in selecting participants for screening improved benefit at a threshold of 2% 10-year risk,compared with current guideline of screening all HBs Ag carriers.Our model was more sensitive than current guideline for cancer screening(28.17% vs.25.96%).And the CKB-PLR model had greater net benefit than HBs Ag seropositive strategy at a risk threshold of 2%(0.000912 [0.000805-0.001022] vs.0.000734 [0.000623-0.000826]).When the 4 modifiable lifestyle factors were dichotomized and summed to obtain a healthy lifestyle score.There was a significant decrease in association strength of liver cancer risk with the increasing of the lifestyle index scores(P<0.001).Compared with participants with a lifestyle index score of 0,the lowest risk of incident liver cancer was observed in participants with a lifestyle index score of 4: HR 0.45,95% CI 0.27 to0.75.Participants were subsequently divided into three categories: favorable(3 or 4healthy lifestyle factors),intermediate(2 healthy lifestyle factors),or unfavorable(0 or1 healthy lifestyle factor),corresponding to a proportion of 23.09%,48.01% or 28.90%,respectively.Participants with favorable lifestyle or intermediate lifestyle had 38% or24% reduced liver cancer risk compared with those with an unfavorable lifestyle.Similarly,a significant gradient for liver cancer risk was observed across lifestyle categories regardless of liver cancer risk status.Overall,compared with normal participants,fewer number of liver cancer high-risk participants needed to adhere to a favorable lifestyle to prevent one incident liver cancer case in 10 years(40 vs.796).[Conclusion] We developed and internally validated a liver cancer risk prediction model using data from a large prospective cohort of the Chinese population.The model was capable of discriminating well between those at high and low risk of liver cancer,and the model-predicted probabilities were well calibrated.The clinical utility assessment of the CKB-PLR model showed potential for improved risk counseling.Our model could thus be easily implemented as an aid to physicians and individuals who may be considering undergoing screening for liver cancer.And we confirmed that a substantial reduction in the burden of liver cancer could be achieved by adherence to a healthy lifestyle pattern.In light of the heavy burden of liver cancer and constrained medical resources in China,population-wide lifestyle interventions could be a costeffective way to respond to the challenges posed by liver cancer. |
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