Autoimmune Diseases, Cardiovascular Risk Factor, Dental Diseases, Gastrointestinal Dysfunction, Health, Subjective, Hormone Disturbance, Neurocognitive Dysfunction
Conditions
Keywords
wellness, personalized medicine, functional medicine, 4P medicine, employee wellness program, functional capacity, smart phone application
Brief summary
It has been suggested that the best medicine should include four principles (4P) - Medicine should be personalized, predictive, preventative and participatory. Technology has provided the tools to collect data in ways not previously possible. Individuals can now collect information on their genome (including their genetic predisposition to tolerate medications and to respond to healthy lifestyle programs) that will modify their lifestyle and therapeutic choices. Beyond spot checks of vital signs and weight, individuals can now collect information on body composition, continuous monitoring of heart rate, blood pressure, and even blood sugar. Data on food consumption at a caloric, macronutrient and even micronutrient level can be collected. Standard medical histories and detailed physical examination findings and laboratory biomarkers can be correlated with this data. Collections of individual patient data will need to be managed through computer programs and smart phone applications that provide direct feedback about the influence of lifestyle on health, wellness and biomarkers. To this end, Metagenics is designing and is launching a smart phone application, Personal Lifestyle Engine (PLX), for individual use by patients and their healthcare providers. The statistical analysis of these data is the primary objective of this study.
Detailed description
Technology has led to a significant revisioning and modification of the models of medicine in practice today. It has been suggested that the best medicine should include four principles - Medicine should be personalized, predictive, preventative and participatory. This 4P medicine will thus be patient centered with a focus on the person who has the disease and not the disease the person has. It will be predictive as it identifies the preclinical trend/decline towards illness sooner than onset of symptoms that herald the loss of function and health. It will be preventative as the information gathered should offer opportunities to modify these trajectories towards illness and finally it will be participatory as individuals will be intimately involved in the gathering of data to identify trends and in the application of lifestyle measures to improve the quality of their life. Technology has provided the tools to collect data in ways not previously possible. Individuals can now collect information on their genome (including their genetic predisposition to tolerate medications and to respond to healthy lifestyle programs) that will modify their lifestyle and therapeutic choices. Beyond spot checks of vital signs and weight, individuals can now collect information on body composition, continuous monitoring of heart rate, blood pressure, and even blood sugar. Data on food consumption at a caloric, macronutrient and even micronutrient level can be collected. Standard medical histories and detailed physical examination findings and laboratory biomarkers can be correlated with this data. As has been noted in the Nathan Price et al. article, A wellness study of 108 individuals using personal, dense, dynamic data clouds (PMID: 28714965), a significant challenge to the effective use of these complex sets of individual patient data is how to define the boundaries between disease, average health and optimal wellbeing. To meet this challenge, compiling and analyzing collections of de-identified, detailed patient histories, questionnaires regarding symptoms and general condition, and associated objective findings (genomic data, vital signs, and physical exam and laboratory biomarkers) will theoretically identify these boundaries and will facilitate the deliverance of 4P Medicine. Comprehensive data collections on each subject evaluated in aggregate provides a diversity of uniqueness markers that can be statistically probed to identify patterns that predict wellbeing and perhaps individual response to lifestyle interventions. An additional challenge for both the patient and their health care provider in 2018 and beyond is how to manage this data in an effective manner. Collections of individual patient data will need to be managed through computer programs and smart phone applications that provide direct feedback about the influence of lifestyle on health, wellness and biomarkers. To this end, Metagenics is designing and is launching a smart phone application, PLX, for individual use by patients and their healthcare providers. After and while a statistical analysis of this data set has been/is being completed, the data set will also be used in an initial beta test of the PLX operating system. The PLX application will not be used to conduct the statistical analysis which is the primary objective of this study.
Interventions
None listed
Sponsors
MetaProteomics LLC
Metagenics, Inc.
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
* Male or Female * Ages 18-80, inclusive * Willing to give written informed consent to participate in the study
Exclusion criteria
* A serious, unstable illness including cardiac, hepatic, renal, gastrointestinal, respiratory, endocrinologic, neurologic, immunologic, or hematologic disease. * Known infection with human immunodeficiency virus (HIV), tuberculosis (TB), or hepatitis B or C. * Inability to comply with study and/or follow-up visits. * Any concurrent condition (including clinically significant abnormalities in medical history, physical examination or laboratory evaluations) which, in the opinion of the Principle Investigator (PI), would preclude safe participation in this study or interfere with compliance. * Any sound medical, psychiatric and/or social reason which, in the opinion of the PI, would preclude safe participation in this study or interfere with compliance.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Anti-Nuclear Antibodies (ANA) | Baseline | ANA is measured as a titer by serum dilution detects autoimmune disease. |
| 25-hydroxy (OH) Vitamin D3 | Baseline | 25-OH vitamin D3 is measured in blood in nanogram/milliLiter (ng/mL) and detects deficiencies. |
| Height | Baseline | measured in meter (m) |
| Weight | Baseline | measured in kilogram (kg) |
| Body Mass index (BMI) | Baseline | BMI is measured in (weight in kilogram (kg)/ height in meter (m)\^2) outcome in double digits. |
| Waist Circumference (WC) | Baseline | measured in centimeters (cm) |
| Hip Circumference (HC) | Baseline | measured in cm |
| Waist-to-Hip Ratio (WHR) | Baseline | WHR is numerical (0.00) and is and indicator for major health risk. |
| Glucose | Baseline | Fasting glucose levels measured in blood in milligram/deciLiter (mg/dL) |
| Total Cholesterol | Baseline | Fasting total cholesterol level is measured in serum in mg/dL |
| High sensitivity C-Reactive Protein (Hs-CRP) | Baseline | Hs-CRP is measured in blood in mg/L detects inflammation. |
| Homocysteine | Baseline | Homocysteine is measured in serum in micromol/Liter (µmol/L) |
| Omega-3 Fatty Acids | Baseline | Omega-3 fatty acids: Eicosapentaenoic (EPA), Docosahexaenoic (DHA), and Docosapentaenoic (DPA) levels are measured in % weight. |
| Beck Depression Inventory (BDI) | Baseline | BDI is a 21-question multiple-choice self-report inventory, one of the most widely used psychometric tests for measuring the severity of depression. Rating system: 1-10: These ups and downs are considered normal; 11-16: Mild mood disturbance; 17-20: Borderline clinical depression; 21-30: Severe depression; over 40: Extreme depression |
| Beck Anxiety Inventory (BAI) | Baseline | BAI is a multiple-choice self-report inventory that is used for measuring the severity of anxiety in children and adults.The BAI contains 21 questions, each answer being scored on a scale value of 0 (not at all) to 3 (severely). Higher total scores indicate more severe anxiety symptoms. The standardized cutoffs\[4\] are: 0-7: minimal anxiety 8-15: mild anxiety 16-25: moderate anxiety 26-63: severe anxiety |
| Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance Test | Baseline | PROMIS Sleep Disturbance Test is a self-scored test to identify sleep disturbance. Each item on the measure is rated on a 5-point scale (1=never; 2=rarely; 3=sometimes; 4=often; and 5=always) with a range in score from 8 to 40 with higher scores indicating greater severity of sleep disturbance. The T-scores are interpreted as follows: Less than 55 = None to slight 55.0-59.9 = Mild 60.0-69.9 = Moderate 70 and over = Severe |
| Patient-Reported Outcomes Measurement Information System (PROMIS) Anxiety Test | Baseline | PROMIS Anxiety test is a 7-item questionnaire that assesses the pure domain of anxiety in individuals age 18 and older. Each item on the measure is rated on a 5-point scale (1=never; 2=rarely; 3=sometimes; 4=often; and 5=always) with a range in score from 7 to 35 with higher scores indicating greater severity of anxiety. The T-scores are interpreted as follows: Less than 55 = None to slight 55.0-59.9 = Mild 60.0-69.9 = Moderate 70 and over = Severe |
| Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Short Form (SF) | Baseline | PROMIS Depression SF is an 8-item questionnaire that assesses the pure domain of depression in individuals age 18 and older. Each item on the measure is rated on a 5-point scale (1=never; 2=rarely; 3=sometimes; 4=often; and 5=always) with a range in score from 8 to 40 with higher scores indicating greater severity of depression.The T-scores are interpreted as follows: Less than 55 = None to slight 55.0-59.9 = Mild 60.0-69.9 = Moderate 70 and over = Severe |
| Genomics | Baseline | DNA polymorphism measured using 23&Me |
| Stool | Baseline | Stool analysis measured by a stool analysis kit from American Gut |
Countries
United States
Outcome results
None listed