Health 2020 (The Melbourne Collaborative Cohort Study)

The MCCS was set up in the early 1990s to investigate prospectively the role of diet and other lifestyle factors in causing common chronic diseases - especially cancers of the prostate, breast and bowel - and to investigate possible interactions between these exposures and common genetic variants.  Cohort recruitment was funded by VicHealth and Cancer Council of Victoria.  Continuing data management and follow-up are funded by the Cancer Council of Victoria. 

The MCCS was designed to be the largest prospective cohort study conducted in Australia. Between 1990 and 1994, 41,500 people, (24,500 women and 17,000 men) aged 40-69 were recruited to the study. The unique features of this study are: a) 30% of participants are southern European migrants, giving the study its wide range of lifestyle exposures; b) collection of blood samples and physical measurements from all subjects; and c) face-to-face follow-up, enabling collection of further blood samples, repeated measures of exposures and assessment of a number of non-fatal, non-cancer endpoints.  The repeated measures of key lifestyle exposures and selected molecules in plasma reduce measurement error and further increase the statistical power and temporal relevance. 

Extensive information was collected at baseline in face-to-face interviews that included questionnaires (diet, physical activity etc.) and physical measurements, including lean and fat mass by bioelectric impedance, and blood pressure.  A food frequency questionnaire was developed specifically to measure dietary intake in the cohort, with the food list based on weighed food records in a group of around 800 men and women reflecting the main country of birth groups within the MCCS.  Blood samples were drawn and whole blood and plasma stored for analysis of DNA and other molecules of interest (e.g., sex hormones and growth factors, carotenoids and fatty acids involved in disease pathways).

Cases of cancer and deaths are identified by regular matching of the MCCS to cancer registries and death indices. 

Follow up 2 occurred between 2003 and 2007 with the aim of collecting similar type of data to baseline, but using up-dated survey instruments, especially for diet and physical activity.  Overall 62% of men and 68% of women attended.  It was also used as an opportunity to collect more of the non-fatal, non-cancer outcomes associated with ageing.  This included using the Kessler 10 questionnaire as a measure of psychological distress or anxiety, questions on ability to perform activities of daily living and instrumental activities of daily living, and details about a range of health conditions and the date of diagnosis

Overview

Following careful planning, instrument development, pilot studies, and extensive review by international experts, The Melbourne Collaborative Cohort Study was set up in the early 1990s to investigate prospectively the role of diet and other lifestyle factors in causing common chronic diseases – especially prostate cancer, breast cancer and bowel cancer – and to investigate possible interactions between these exposures and common genetic variants.

Cohort recruitment was funded by VicHealth ($1,430,000) and Cancer Council Victoria ($3,570,000). Continuing data management and follow-up has been funded by Cancer Council Victoria ($300,000 pa since 1995).

The Melbourne Collaborative Cohort Study was designed to be the largest prospective cohort study conducted in Australia. As we included 30% southern European migrants it's unusual in its wide range of lifestyle exposures. It's also unusual in the collection of blood samples and physical measurements from all subjects.

Of special significance is the face-to-face follow-up, enabling collection of further blood samples, repeated measures of exposures and assessment of a number of non-fatal, non-cancer endpoints. The other major international cohort studies can't and don't plan to achieve this. The repeated measures of key lifestyle exposures and selected molecules in plasma will reduce measurement error and further increase statistical power and temporal relevance.

Subjects

Between 1990 and 1994, 41,500 people, (24,500 women and 17,000 men) aged 40-69 were recruited. About 30% of the cohort are southern European migrants to Australia who were deliberately over-sampled to extend the range of lifestyle exposures and to increase genetic variation.

Measurements

Extensive information was collected at baseline in face-to-face interviews that included questionnaires (diet, physical activity etc.) and physical measurements, including lean and fat mass by bioelectric impedance, and blood pressure. A food frequency questionnaire was developed specifically to measure dietary intake in the cohort, with the food list based on weighed food records in a group of around 800 men and women reflecting the main country of birth groups within The Melbourne Collaborative Cohort Study. Blood samples were drawn and whole blood and plasma stored for analysis of DNA and other molecules of interest (e.g. sex hormones and growth factors, carotenoids and fatty acids involved in disease pathways).

Databooks

Volume 1: Clinical
Clinical data on physical measurements, glucose and cholesterol.

Volume 2: Health and lifestyle
Data on social life, family, health, smoking, exercise, women and feelings.

Volume 3: Diet and alcohol
Clinical data on diet, nutrient food groups and alcohol.

Appendix: Demographics
General demographic data on each Health 2020 participant.

Cases

Cases of cancer and mortality are identified by regular matching of The Melbourne Collaborative Cohort Study to cancer registries and death indices. Non-cancer, non-fatal health events were captured by following up the cohort with a mailed questionnaire and by telephone at 3 to 4 years after baseline. For incident cases of type 2 diabetes, 76% had their diagnosis confirmed by their doctor.

Statistical analysis

For cancer and CVD endpoints, analyses of exposures measured in the whole cohort (for example diet and physical measures) use Cox's proportional hazards models with age as the time axis to estimate the hazard ratios between various lifestyle factors and outcomes. For diabetes, logistic regression models have been used as length of follow-up was relatively constant, and dates of diagnosis were not well documented for all cases.

Analysis of baseline biospecimens

In 1999, with collaborators we were awarded four NHMRC project grants relating to prostate cancer, breast cancer, diabetes and cardiovascular disease. They included analysis of stored plasma and measurement of genetic polymorphisms and the cancer grants included funds for the retrieval and molecular analysis of archival tumour tissue. The plasma assays included biomarkers of dietary intake (carotenoids, vitamin E and phospholipid fatty acid profiles), steroid sex hormones, homocysteine, insulin, triglycerides, HDL cholesterol, insulin-like growth factor 1 (IGF-1) and insulin-like growth factor binding protein 3 (IGFBP3).

The study of each outcome was originally intended to be a nested case-control study, but because of the similarity of 'exposure' measures across outcomes, we explored the possibility of conducting a case-cohort study. By 2000, we had sufficient participants with colorectal cancer to include it in our program, which further tipped the balance in favour of a case-cohort study. We performed extensive simulations, based on our own data, and decided that a case-cohort study was indeed more efficient.

During the baseline sample collection, pooled plasma from several volunteers was stored in 1 mL aliquots alongside the participants' samples. We included eight pooled plasma aliquots (blinded) with each batch of participants' samples as an independent means of checking within and between batch variations in assay results.

Pilot study of reliability of plasma measurements: Before beginning the main study, we conducted reliability studies of the assays we wished to perform. This had 2 purposes: to identify assays that were too unreliable for us to use since we did not want to waste sample and money on poor measurements, and to enable us to correct for measurement error in our statistical analyses. About 200 participants had a second blood sample taken around 12 months after their baseline attendance. Each sample was thawed and divided into two parts.

All 4 aliquots from each person were refrozen and sent blind to the laboratories, which performed the assays in separate runs at least two weeks apart. Most of the reliability coefficients were sufficiently high for us to commence measurement in the main study but the initial analyses of insulin and E2 had poor reliability. We changed kits for measurement of E2 and a second reliability study gave results adequate for us to proceed. For insulin, we performed a second study at another laboratory, which achieved much better results, and we proceeded to measure insulin at this lab in the main study. Initial measurements of some carotenoids also had low reliability. We changed methods and conducted a second study, which measured laboratory error only (we no longer had any subjects with multiple samples). These results showed much less variability than the first set, so we proceeded with this method in the main study.