Rebecca Seguin-Fowler

Ph.D.,R.D., L.D., C.S.C.S.

Texas A&M AgriLife Research

Professor

PLCO (Learn more about this study)

PLCO-1379

Nov 13, 2023

Operationalizing recommendations for chronic disease and cancer prevention into a composite score

We do not have a consistent way to measure the multicomponent impact of interventions on health behaviors and metabolic status and thus their overall potential for cancer and chronic disease prevention. To operationalize health behaviors and metabolic status into a single composite index would provide a holistic and comprehensive assessment of risk for chronic disease, including cancer, with many benefits. Conceptually, many of the biological pathways (e.g., inflammation) that are related to chronic disease development overlap across behaviors; thus, individual measurement of behaviors may mask important cumulative and interactive effects. Pragmatically, a composite measure would allow easy and meaningful interpretation across studies and time and quantify individual change in the context of intervention program effects. This project will utilize the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial data to inform the development of an index predictive of chronic disease.
The variables of interest are:
1. Healthy weight:
• Measured: Measured height, weight, waist and hip circumference
• Self-report: Height, weight, waist and hip circumference
2. Blood glucose:
• Measured: Fasting blood glucose and HbA1c
• Self-report: Diagnosed with diabetes or prediabetes, Diabetes drug use
3. Blood lipids:
• Measured: Serum cholesterol
• Self-report: Elevated cholesterol, Cholesterol lowering drugs
4. Blood pressure:
• Measured: Systolic and Diastolic blood pressure
• Self-report: High blood pressure, Anti-hypertensive drug
5. Healthy diet:
• Measured: Biomarkers related to diet that may have been collected and analyzed in the ancillary studies
• Self-report: FFQ, Dietary recall, surveys
6. Be physically active:
• Measured: Accelerometry data, Resting heart rate
• Self-report: Leisure time PA, Self-reported pulse rate
7. Limit alcohol consumption:
• Measured: ALT or AST
• Self-report: FFQ, Dietary recall, surveys
8. Sleep
• Measured: Accelerometry data
• Self-report: Sleep patterns
9. Smoking
• Self-report: Smoking related variables
We will also use the following values to test how predictive our composite measure is for the following outcomes:
• C-Reactive Protein values
• Diabetes, cardiovascular diseases, cancer, and all chronic diseases, and deaths.
We would also like to include risk factor variables such as:
• Gender, age, income, education, relationship status, family history of diabetes, myocardial infarction, or cancer, and, for women, menopausal status and hormone use.
We are particularly interested in measures that span the spectrum of data collection methods (e.g., self-report, biomarkers, objective measurement). We have done our best to comb through the available data dictionaries and existing publications to identify measures within PLCO. We would value input, guidance, and recommendations if anyone has the capacity to do so. Appreciate the opportunity to work with this data. Thank you!

The objective of this study is to integrate existing cohort repositories to operationalize a new comprehensive measure – the Chronic Disease Prevention Index (CDP Index) and corresponding measurement tool – that is demonstrably sensitive to intervention effects, reliable across time, and easy to use. To accomplish this objective, we will execute these specific aims:

Aim 1: Operationalize a self-reported CDP Index that includes modifiable metabolic and behavioral components that contribute to cancer risk and other chronic diseases utilizing a combination of diverse cohort datasets.
Aim 1a. Integrate multiple, diverse cohort and intervention data sources to inform the development of the CPD Index.
Aim 1b. Triangulate measures across methods (e.g., self-report, objective, biomarkers) and identify the best self-report measures for each health component of the CDP Index.

Aim 2: Validate the CDP Index to chronic disease outcomes and intervention effects.
Aim 2a. Validate and refine the CDP Index by modeling relative risk of chronic disease.
Aim 2b. Test sensitivity to intervention impact of the CDP Index.

Aim 3: Create a web-based assessment tool for individuals and research participants to capture their CDP Index using a participant-informed design process.