Background Large blood-based prospective studies can provide reliable assessment of the complex interplay of lifestyle, environmental and genetic factors as determinants of chronic disease. Methods The baseline survey of the China Kadoorie Biobank took place during 2004-08 in 10 geographically defined regions, with collection of questionnaire data, physical measurements and blood samples. Subsequently, a re-survey of 25,000 randomly selected participants was done (80% responded) using the same methods as in the baseline. All participants are being followed for cause-specific mortality and morbidity, and for any hospital admission through linkages with registries and health insurance (HI) databases. Results Overall, 512,891 adults aged 30-79 years were recruited, including 41% men, 56% from rural areas and mean age was 52 years. The prevalence of ever-regular smoking was 74% in men and 3% in women. The mean blood pressure was 132/79 mmHg in men and 130/77 mmHg in women. The mean body mass index (BMI) was 23.4 kg/m(2) in men and 23.8 kg/m(2) in women, with only 4% being obese (>30 kg/m(2)), and 3.2% being diabetic. Blood collection was successful in 99.98% and the mean delay from sample collection to processing was 10.6 h. For each of the main baseline variables, there is good reproducibility but large heterogeneity by age, sex and study area. By 1 January 2011, over 10,000 deaths had been recorded, with 91% of surviving participants already linked to HI databases. Conclusion This established large biobank will be a rich and powerful resource for investigating genetic and non-genetic causes of many common chronic diseases in the Chinese population.

https://ckbiobank.org/site/binaries/content/assets/resources/pdf/publications/ckb/2011ckbbaseline-ltfp_zmcije.pdf

China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up

The LifeLines Cohort Study is a large population-based cohort study and biobank that was established as a resource for research on complex interactions between environmental, phenotypic and genomic factors in the development of chronic diseases and healthy ageing. Between 2006 and 2013, inhabitants of the northern part of The Netherlands and their families were invited to participate, thereby contributing to a three-generation design. Participants visited one of the LifeLines research sites for a physical examination, including lung function, ECG and cognition tests, and completed extensive questionnaires. Baseline data were collected for 167 729 participants, aged from 6 months to 93 years. Follow-up visits are scheduled every 5 years, and in between participants receive follow-up questionnaires. Linkage is being established with medical registries and environmental data. LifeLines contains information on biochemistry, medical history, psychosocial characteristics, lifestyle and more. Genomic data are available including genome-wide genetic data of 15 638 participants. Fasting blood and 24-h urine samples are processed on the day of collection and stored at -80 °C in a fully automated storage facility. The aim of LifeLines is to be a resource for the national and international scientific community. Requests for data and biomaterials can be submitted to the LifeLines Research Office [LLscience@umcg.nl].

Cohort Profile: LifeLines, a three-generation cohort study and biobank

Objective To compare established associations between risk factors and mortality in UK Biobank, a study with an exceptionally low rate of response to its baseline survey, against those from representative studies that have conventional response rates. Design Prospective cohort study alongside individual participant meta-analysis of other cohort studies. Setting United Kingdom. Participants Analytical sample of 499 701 people (response rate 5.5%) in analyses in UK Biobank; pooled data from the Health Surveys for England (HSE) and the Scottish Health Surveys (SHS), including 18 studies and 89 895 people (mean response rate 68%). Both study populations were linked to the same nationwide mortality registries, and the baseline age range was aligned at 40-69 years. Main outcome measure Death from cardiovascular disease, selected malignancies, and suicide. To quantify the difference between hazard ratios in the two studies, a ratio of the hazard ratios was used with HSE-SHS as the referent. Results Risk factor levels and mortality rates were typically more favourable in UK Biobank participants relative to the HSE-SHS consortium. For the associations between risk factors and mortality endpoints, however, close agreement was seen between studies. Based on 14 288 deaths during an average of 7.0 years of follow-up in UK Biobank and 7861 deaths over 10 years of mortality surveillance in HSE-SHS, for cardiovascular disease mortality, for instance, the age and sex adjusted hazard ratio for ever having smoked cigarettes (versus never) was 2.04 (95% confidence interval 1.87 to 2.24) in UK Biobank and 1.99 (1.78 to 2.23) in HSE-SHS, yielding a ratio of hazard ratios close to unity (1.02, 0.88 to 1.19). The overall pattern of agreement between studies was essentially unchanged when results were compared separately by sex and when baseline years and censoring dates were aligned. Conclusion Despite a very low response rate, risk factor associations in the UK Biobank seem to be generalisable.

https://www.bmj.com/content/bmj/368/bmj.m131.full.pdf

Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis

https://www.ckbiobank.org/site/binaries/content/assets/resources/pdf/publications/ckb/2015smoking_mortality_trends_zclancet.pdf

Contrasting male and female trends in tobacco-attributed mortality in China: evidence from successive nationwide prospective cohort studies.

Importance In China, diabetes prevalence has increased substantially in recent decades, but there are no reliable estimates of the excess mortality currently associated with diabetes. Objectives To assess the proportional excess mortality associated with diabetes and estimate the diabetes-related absolute excess mortality in rural and urban areas of China. Design, Setting, and Participants A 7-year nationwide prospective study of 512 869 adults aged 30 to 79 years from 10 (5 rural and 5 urban) regions in China, who were recruited between June 2004 and July 2008 and were followed up until January 2014. Exposures Diabetes (previously diagnosed or detected by screening) recorded at baseline. Main Outcomes and Measures All-cause and cause-specific mortality, collected through established death registries. Cox regression was used to estimate adjusted mortality rate ratio (RR) comparing individuals with diabetes vs those without diabetes at baseline. Results Among the 512 869 participants, the mean (SD) age was 51.5 (10.7) years, 59% (n = 302 618) were women, and 5.9% (n = 30 280) had diabetes (4.1% in rural areas, 8.1% in urban areas, 5.8% of men, 6.1% of women, 3.1% had been previously diagnosed, and 2.8% were detected by screening). During 3.64 million person-years of follow-up, there were 24 909 deaths, including 3384 among individuals with diabetes. Compared with adults without diabetes, individuals with diabetes had a significantly increased risk of all-cause mortality (1373 vs 646 deaths per 100 000; adjusted RR, 2.00 [95% CI, 1.93-2.08]), which was higher in rural areas than in urban areas (rural RR, 2.17 [95% CI, 2.07-2.29]; urban RR, 1.83 [95% CI, 1.73-1.94]). Presence of diabetes was associated with increased mortality from ischemic heart disease (3287 deaths; RR, 2.40 [95% CI, 2.19-2.63]), stroke (4444 deaths; RR, 1.98 [95% CI, 1.81-2.17]), chronic liver disease (481 deaths; RR, 2.32 [95% CI, 1.76-3.06]), infections (425 deaths; RR, 2.29 [95% CI, 1.76-2.99]), and cancer of the liver (1325 deaths; RR, 1.54 [95% CI, 1.28-1.86]), pancreas (357 deaths; RR, 1.84 [95% CI, 1.35-2.51]), female breast (217 deaths; RR, 1.84 [95% CI, 1.24-2.74]), and female reproductive system (210 deaths; RR, 1.81 [95% CI, 1.20-2.74]). For chronic kidney disease (365 deaths), the RR was higher in rural areas (18.69 [95% CI, 14.22-24.57]) than in urban areas (6.83 [95% CI, 4.73-9.88]). Among those with diabetes, 10% of all deaths (16% rural; 4% urban) were due to definite or probable diabetic ketoacidosis or coma (408 deaths). Conclusions and Relevance Among adults in China, diabetes was associated with increased mortality from a range of cardiovascular and noncardiovascular diseases. Although diabetes was more common in urban areas, it was associated with greater excess mortality in rural areas.

/pdf/association-between-diabetes-and-cause-specific-mortality-in-4f1tovh3tt.pdf

Association Between Diabetes and Cause-Specific Mortality in Rural and Urban Areas of China.

During recent decades China has undergone a rapid transition in the main disease patterns of its population. There has been a substantial decrease in maternal and child mortality and in adult mortality from themain infectious/parasitic diseases, whereas for ischaemic heart disease and some other non-communicable chronic diseases, there has been a moderate increase in mortality rates, at least in some parts of China. Consequently, most of the premature mortality that still remains in China now involves the chronic diseases of middle age, such as cancer, stroke, heart disease, or chronic lung disease. 1–4 In the mid-1970s a retrospective survey of the causes of 20 million deaths in China during 1973–75 showed that for each major disease there was large unexplained variation in the age-specific rates between different parts of China, indicating that there must be some large avoidable causes. 5 This finding was confirmed and extended in the late 1980s by a more careful retrospective survey of the causes of one million deaths during 1986–88 in 69 rural counties and 24 cities (Figure 1). 3,4 These big differences in disease rates between one area and another probably reflect differences in the ways people live rather than genetic differences. Moreover, even within one area there are likely to be substantial differences between individuals in genetic factors, as well as in patterns of chronic infection, personal biochemistry, physical characteristics, lifestyle, etc. that persist for many years and eventually influence the likelihood of particular individuals developing particular diseases. 3,4

http://www.ckbiobank.org/site/binaries/content/assets/resources/pdf/publications/ckb/2005cohortprofile-kscdc_zmcije.pdf

Liming Lee

Papers

Cohort Profile: The Kadoorie Study of Chronic Disease in China (KSCDC)