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The Voloridge Health Biomarker Reference Guide
A guide for patients and clinicians to understand health through biomarkers
Introduction
Blood test results can be overwhelming. You are often presented with significant information, yet not enough to truly understand what is important. Voloridge Health (VH) created this guide as a resource to help individuals and their care providers interpret lab test data. The guide contains ranges of values for biomarkers and age-related diseases. These ranges aim to provide clarity when reviewing blood test results.
This guide explores seven common age-associated, disease-related outcomes related to cardiac, metabolic, lung, liver, kidney, cognitive, and all-cause mortality. The VH team chose these outcomes based on the prevalence and individual impacts on aging and mortality.
What is a biomarker?
The first step to understanding your bloodwork is recognizing what a blood test is collecting and measuring. A biomarker, or biological marker, is a measurable indicator of a biological process or condition in your body.[1] One of the most common ways to measure certain biomarkers is through blood tests, if the biomarker is present in your blood.
Biomarkers offer insights into your health, including early detection of diseases. A simple blood test can provide valuable information about your risk for heart (cardiac) disease, liver disease, mortality, and more.[2] Understanding what biomarkers reveal about your personal health is the key to an accurate interpretation of your blood test results. To assist with this challenge, we present information in this guide in the form of risk multipliers and Voloridge Modeled Range values or VMR values.
What is a risk multiplier?
A risk multiplier represents the association between biomarker values and future incidence of a disease. The biomarker range associated with the lowest incidence is assigned a risk multiplier of 1. After that, if for example a biomarker range has a 40% increase in incidence, it would have a value of 1.4. All risk multiplier values are separated by gender and represent relative incidence of the disease compared to others your age.
What is a VMR value?
Reference ranges typically provided with blood test results are population based. In contrast, the Voloridge Modeled Range or VMR is the range of a single biomarker’s values associated with the lowest future incidence of diseases. Because there are multiple disease groups, the VMRs are based on a weighted average incidence of the major disease groups addressed in this guide. Because the VMRs are calculated directly by Voloridge Health’s models based on a specific data set, and not designed to be medical recommendations, these values may differ from what you see as reference ranges with a blood test result.
What makes Voloridge Health different?
Many traditional models focus on an individual predictor and its relationship to a single outcome. An example of this is measuring cholesterol and its relationship to heart disease. Furthermore, ranges typical in medical literature are based on clinical recommendations and/or population averages rather than connecting biomarkers with longer-term health outcome probabilities. In contrast, Voloridge Health examines many health predictors across multiple potential disease outcomes together. Our VMR values are based on real data associations between biomarker ranges and 15-year disease outcomes. By evaluating each health predictor in an unbiased manner, we derive a fully data-driven, long-term view of health risks. This guide is designed to help you see patterns single-variable studies might miss, supporting a proactive and holistic approach to healthcare.
Predictive strength
Another way Voloridge Health evaluates how biomarkers can predict health is with a predictive strength value. This metric demonstrates how strongly each biomarker correlates with a specific disease. In the VMR Values by Disease section of this guide, the biomarkers are colored with a gradient as illustrated in the graphic below. White represents a lower strength, while green represents a higher strength.
Additionally, you can find all biomarkers and their predictive strength values in the Predictive Strength Values section. We scale predictive strength from zero (no predictive strength) to 10, the highest predictive strength. A value of 7 would have 70% of the predictive strength of a value of 10.
Disease definitions
Some diseases are defined by comparing specific biomarker levels to established thresholds. For example, type 2 diabetes is typically diagnosed if Hemoglobin A1C levels are at or above 6.5 percent.[3] In this case, a risk multiplier is no longer meaningful, given the likelihood someone is diagnosed with such a disease is nearly 100 percent if their biomarker value passes that threshold. However, for the purpose of this guide, values are provided for such biomarkers regardless of these definitions, to maintain consistency and show data trends.
Disclaimer
This guide presents blood biomarker and related information solely for informational and educational purposes. The information provided is derived from applying data science to a population dataset with limitations regarding the population demographics such as age, gender, race, ethnic background, potential bias in the population, and other factors that influence the data. This guide is not intended to serve as medical advice, diagnoses, or treatment for any disease or condition. The information provided here does not consider your individual health conditions, medical history, or specific circumstances, and should not be used as a substitute for consultation with a qualified healthcare professional. Individuals or entities using this information are doing so at their own risk as the providers do not make claims to its accuracy or the benefits of using such information. For more information on the data set used in preparing this information please visit UK Biobank website: www.ukbiobank.ac.uk.
[1] Califf, Robert M. “Biomarker definitions and their applications.” Experimental biology and medicine 243, no. 3 (2018): 213-221.
[2] Kristensen, Michael, Anne Kristine Servais Iversen, Thomas Alexander Gerds, Rebecca Østervig, Jakob Danker Linnet, Charlotte Barfod, Kai Henrik Wiborg Lange et al. “Routine blood tests are associated with short term mortality and can improve emergency department triage: a cohort study of> 12,000 patients.” Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 25 (2017): 1-8.
[3] Chatterjee, Sudesna, Kamlesh Khunti, and Melanie J. Davies. “Type 2 diabetes.”The lancet389, no. 10085 (2017): 2239-2251.