US20140310019A1 - Methods and Systems for Generation of Personalized Health Plans - Google Patents

Abstract

Personalized, health and performance programs are generated for individuals based on various biomarkers and performance and lifestyle assessments. In one embodiment, a diagnostic test of blood or other biological specimen(s) is used to determine key biological marker levels. Information and assessments of the user's physical performance, life style and health and wellness goals is also collected and provided to an expert system that matches the biomarker levels and assessments to a knowledgebase of scientific knowledge about biomarker levels and health and fitness outcomes. Personal recommendations and advice on nutrition and exercise is then generated, which may be used to help individuals reach their diet, fitness, and wellness goals and improve their physical and mental performance and well being in measurable ways.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS
• This application claims priority to and the benefit of U.S. provisional patent application with Ser. No. 61/368,002 filed on Jul. 27, 2010, entitled “Methods and Systems for Generation of Personalized Health Plans.”
FIELD OF INVENTION
• The present invention relates generally to computer systems and processes for collecting information from a proprietary diagnostic panel and questionnaire that may be used for the purposes of enhancing of personal wellness through the creation of science based individualized lifestyle, fitness, dietary and nutrient plans.
BACKGROUND OF THE INVENTION
• Current annual health care costs in the US are more than $3 trillion dollars and expected to increase over the next ten years to over $4 trillion. As of today, more than half of all Americans suffer from one or more chronic diseases and more than 75% of health care spending is to treat chronic conditions including cardiovascular disease and stroke, cancer, obesity, arthritis and diabetes. These serious diseases are often treatable but not always curable. Thus, an even greater burden befalls Americans from the disability and diminished quality of life resulting from chronic disease. These increased healthcare costs do not include costs due to loss of productivity resulting from chronic disease. The World Health Organization has estimated that eliminating certain major risk factors for chronic disease would result in an 80% reduction in the instances of heart disease, stroke, and type-2 diabetes, and a more than 40% reduction in cancer cases. These risk factors are linked to modifiable health behaviors that, if changed, can dramatically reduce the risk and prevalence of chronic disease.
• A Center for Disease Control report identified four modifiable health risk behaviors that are responsible for much of the illness, suffering, and early death related to chronic diseases: 1) lack of physical activity, 2) poor nutrition, 3) tobacco use, and 4) excessive alcohol consumption. For example, frequent physical activity has been shown to increase longevity, help control weight, reduce risks for cardiovascular disease,type 2 diabetes, metabolic syndrome, and some cancers, strengthen bones and muscles, improve mental health and mood, improve one's ability to perform daily activities, and to prevent falls among older adults. Balanced nutrition can help lower the risk for many chronic diseases, including heart disease, stroke, some cancers, diabetes, and osteoporosis. For example, it has been established that the increased consumption of fruits and vegetables helps reduce the risk for heart disease and certain cancers.
• The U.S. Department of Health and Human Services recognized that preventive medicine and evidence-based medicine will become an important part of the healthcare system, and established the U.S. Preventive Services Task Force agency to oversee, define and implement a variety of preventive medicine measures.
• The role of exercise and physical activity in the prevention and treatment of cardiovascular disease is well established and its significance as a preventive measure is widely recognized. In addition, the correlation between athletic performance, body composition, and nutrition status has also been firmly established. The effect of balanced nutrition and its effect on the health of the population has also been subject of many studies. Further, population studies have identified certain blood/plasma biomarkers that are related to balanced nutrition and metabolism, as well as the presence of vitamins and micronutrients such as vitamin D, Iron, selenium, copper and zinc. The present invention leverages existing population-based evidence and provides a new, personalized approach to nutrition and exercise to improve health and wellness and prevent chronic diseases.
SUMMARY OF THE INVENTION
• The present invention provides a computer-implemented and web-based personalized nutrition and exercise program and comprehensive wellness assessment. A unique panel of biomarkers from the user's blood or other biological specimen (such as: urine, buccal and nasal samples, exhalants, stool, tissues, organs, hair, nail clippings, or any other cells or fluids—whether collected for research purposes or as residual specimens from diagnostic, therapeutic, or surgical procedures) or devices capturing biological/biomedical activities, is assessed and provides information regarding the presence of vitamins, minerals, nutrients, cytokines and other messenger molecules. By biomarker we are inferring a biomarker, or biological marker, which is in general a substance used as an indicator of a biological state. It is a characteristic that is objectively measured and evaluated as an indicator of normal or impaired biological processes responses to an intervention of food, lifestyle or exercise. This biomarker information is paired and collected along with personal information about nutrition habits, wellness, physical fitness, and exercise regimens. Subsequently, an expert IT systems analyses process, derived from systems biology and artificial intelligence, is used to evaluate the collected data The expert analytic system includes a knowledgebase and an inference engine that provides optimal food, supplement, life style, and exercise recommendations for an individual according to their personal biology, physiology and personal habits.
• The biomarker levels are collected through a blood test or test of other biological specimen(s) as defined above. Responses to a questionnaire provide information about the demographic, nutritional and physical habits of the individual. The web-based application provides the individual with her personalized optimal nutrition, supplement, life style, and physical training regime by matching her profile with the knowledgebase of facts about known relationships among biological specimen, demographic, and phenotypic data manually derived from scientific publications, databases and clinical trials.
• Therefore, in one aspect of the present invention, a system facilitates the derivation of a personalized health and fitness plan for an individual. The system includes a data storage device for storing an electronic knowledgebase of medical and health data from various sources as well as for storing a set of questions. The system also includes an inference engine for a subset of the set of questions to send to the individual based on the knowledgebase and biomarker data gathered for the individual. The inference engine further generates lifestyle recommendations for the individual based on the biomarker data and the answers to the subset of questions. The system further enables transmission of these recommendations to the individual.
• In some implementations, the system contains biomarker data for the same biomarkers and individual from different points in time. In further implementations, the computerized comparison includes comparison of these two different sets of biomarker data.
• In certain embodiments, the biomarker data is gathered from blood or some other biological specimen of the individual. In certain embodiments, the system includes creatine kinase or ferritin as biomarkers. In other embodiments, the questions presented to the individual include questions about demographic data or questions about athletic activity. In still other embodiments, the lifestyle recommendations include dietary recommendations or exercise recommendations.
• In another aspect of the present invention, a computer-implemented method facilitates the derivation of a personalized health and fitness plan for an individual. The method includes collecting biomarker data for the individual and determining questions to present to the individual based on a comparison between that biomarker data and an electronic knowledgebase of medical and health data from various sources. The method also includes presenting the questions to the individual and receiving the answers. The method further includes generating lifestyle recommendations for the individual based on the biomarker data and answers to questions. The method finally includes transmitting the recommendations to the user.
• The method may, in some cases, include collecting a second set of biomarker data from the individual for the same biomarkers and comparing these two sets of biomarker data to discern the difference. In certain embodiments, the biomarker data is gathered from blood or some other biological specimen of the individual. In certain embodiments, the system includes creatine kinase or ferritin as biomarkers. In other embodiments, the questions presented to the individual include questions about demographic data or questions about athletic activity. In still other embodiments, the lifestyle recommendations include dietary recommendations, supplements recommendations or exercise recommendations. In some embodiments the biomarkers include biometric measurements. Examples of biomarkers are selected from, but not limited to: Blood pressure, heart rate, exhaled volume, body temperature, perspiration rate, skin conductivity. In some embodiments of the invention biomarker characteristics and profiles collected may be indicative of a disease process and could be used to detect such processes. Examples of diseases are selected but not limited to metabolic diseases, cancer, anemia, cardiovascular diseases, diabetes. In some embodiments biomarkers will comprise SNP biomarkers (DNA based). The most useful biomarkers in this group are SNPs that are associated with metabolism and energy.
• It is to be understood that both the foregoing general description of the invention and the following detailed descriptions are exemplary, but are not restrictive, of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
• The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the inventions.
• FIG. 1 illustrates the invention's individualized nutrition and exercise approach.
• FIG. 2 illustrates an exemplary schematic diagram of the invention workflow.
• FIG. 3 illustrates is an exemplary approach to the invention's personalized algorithm workflow.
• FIG. 4 illustrates knowledgebase of facts in accordance with one embodiment of the invention.
• FIG. 5 illustrates an exemplary approach to the invention's personalized recommendations workflow.
• FIG. 6 illustrates an exemplary website workflow in accordance with one embodiment of the invention.
• FIG. 7 illustrates an exemplary athletic clinical trial dataset component.
• FIG. 8 illustrates an exemplary approach to microarray dataset analysis in accordance with one embodiment of the invention.
• FIG. 9 illustrates an exemplary website home page in accordance with one embodiment of the invention.
• FIG. 10 illustrates an exemplary “My progress” web page in accordance with one embodiment of the invention.
• FIG. 11 illustrates an exemplary “My food” web page in accordance with one embodiment of the invention.
• FIG. 12 illustrates an exemplary “My exercise” web page in accordance with one embodiment of the invention.
• Appendix A illustrates example of biomarkers.
DETAILED DESCRIPTION OF THE INVENTION
• The detailed description set forth below, in connection with the associated drawings, is intended to provide a description of the presently-preferred embodiments of the invention, and is in no way intended to limit the forms in which the present invention may be construed or used. Accordingly, it is well-understood by those with ordinary skill in the art that the same or equivalent functions may be accomplished by different embodiments that are also intended to be encompassed within the spirit and scope of the present invention. Moreover, with respect to particular method steps, it is readily understood by those with skill in the art that the steps may be performed in any order, and are not limited to any particular order unless expressly stated or otherwise inherent within the steps. Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings.
• FIG. 1 illustrates the use of a personalized biofeedback approach to improve a user's health and implement a preventive regimen. Conventional approaches to recommending nutrition, exercise, and supplements are population-based statistics and do not take into account an individual's characteristics, such as the metabolism and fitness status. Embodiments of the invention go well beyond this traditional approach and generate a personalized nutrition and physical training program based on biomarkers in the blood or other biological specimens, demographic information, as well as the nutritional and physical fitness habits of the individual.
• FIG. 2 illustrates a process flow for implementing one embodiment of the present invention. In this embodiment, blood or other biological specimens of a user are tested for the diagnostic panel (table 1 or 2). Subsequently, the test results are run through an expert system that generates a tailored questionnaire to add information about the specific biology of the user. The tailored questionnaire can then be presented to the user in order to receive answers to the questionnaire. The answers to the questionnaire and the test results are run through the expert system again to generate a set of recommendations to optimize the blood or specimen results and to improve fitness and wellness. This improves over the existing approaches by generating these recommendations based on the additional knowledge provided by the specimen results and questionnaire answers. Existing approaches may use only basic information such as age, height, and weight and thus not provide the user specific recommendations possible with this invention.
• FIG. 3 illustrates an expert system in accordance with one embodiment of the invention that matches biomarkers with personal information of the user. Thedata store301 contains data for various categories of information about users, such as gender, age, ethnicity, and type of athletic activity. The expert system inFIG. 3 further has the ability to store relationships between various instances of the information in thedata store301 and store ananalyte range302 for those related instances of information. A set of related instances of information may be termed a “profile” for the purposes of the expert system. As illustrated inFIG. 3, the expert system further has the ability to process individualdiagnostic results303. These individual diagnostic results may result from a blood or other specimen diagnostic panel. As illustrated inFIG. 3, the expert system further has access to aknowledgebase304 which may contain information regarding previous user recommendations and monitored results. As illustrated inFIG. 3, the expert system has aninference engine305 that receivesanalyte range302, individualdiagnostic results303, and information fromknowledgebase304 in order to producepersonal recommendations306.
• In one embodiment of the invention, the expert system as illustrated inFIG. 3 uses answers provided by the user to a questionnaire in order to determine which profile fromdata store301 is most applicable to the user. In such an embodiment, the expert system may then retrieve ananalyte range302 specific to this applicable profile. Accordingly,inference engine305 may then compare theanalyte range302 with the individualdiagnostic results303 and information fromknowledgebase304 in order to determine analyte deficiencies of the specific user andpersonal recommendations306 that may consist of activities to correct those deficiencies. In another embodiment of the invention,inference engine305 may compareanalyte range302, individualdiagnostic results303, and information fromknowledgebase304, in order to producepersonal recommendations306 without specific analyte deficiencies targeted bypersonal recommendations306.
• In one embodiment of the invention,knowledgebase304 may contain information regarding relationships between biomarkers, food, supplements, and outcomes. Such outcomes may include changes in athletic performance, well-being, sleep, and mental stability. The information inknowledgebase304 may be collected from scientific research publications, guidelines issued by medical associations, and large healthcare-derived databases of biomarkers, nutrition, fitness and wellness data. In one embodiment of the invention,inference engine305 may use artificial intelligence algorithms in order to determine, based onanalyst range302, individualdiagnostic results303, andknowledgebase304, what interventions can optimize the fitness and wellness of the user. These artificial intelligence algorithms may use any number of machine learning and systems biology inference methods well known in the art in order to generate effective inferences based onanalyte range302, individualdiagnostic results303, andknowledgebase304. As illustrated inFIG. 3, the expert system may also store thepersonal recommendations306 inknowledgebase304 in order to provide comparison ofpersonal recommendations306 and individualdiagnostic results303 that may be received by the expert system at a later time for the same user. This follow-up monitoring has the advantage of enabling positive feedback for each individual to increase compliance with a healthy lifestyle.
• In one embodiment of the invention,knowledgebase304 may be constructed using a relational database system containing data organized in tables relating to individual concepts, their relationships to each other, additional qualifiers to these relationships, and references to the peer-reviewed article, clinical study or database from which they were curated. These concepts may include such topics as food, supplements, biomarkers, and outcomes, their relationships to each other. These relationships between concepts may include such items as contains, increases, optimizes at certain level, and releases. In such an embodiment of the invention,knowledgebase304 may be constructed using several techniques, including: (i) manually entering findings into the system from scientific papers and clinical trials about concepts and their interaction (simplified example: concept1=‘low serum iron’ relationship=‘causes’ concept2=decrease in endurance performance’), (ii) by converting information from other databases (e.g. content of food), and (iii) automatically from accumulation of customer data including previous data processed by the expert system (e.g. information about success rates of certain recommendations). If a relational database management system is used for the construction ofknowledgebase304 ordata store301, any off-the-shelf product such as MySQL Database, Microsoft SQL Server, or IBM DB2 database may be used. It is foreseen that a relational database management system may be chosen for particular technical benefits provided as to size of storage on disk or performance of data retrieval. Non-relational database management system solutions are also foreseen that make use of alternative storage and data access techniques.
• In one embodiment of the invention, the validity of the facts collected and the markers used may be strengthened by confirming data inknowledgebase304 using gene expression microarray experiments. These markers may be either directly implicated, or they are inferred as in the same pathway as the markers captured in the knowledgebase (example 2).
• When the input data changes, either through addition of new biomarker measurements or new personal information (wellness and fitness assessments and goals),inference engine305 may use both forward and backward chaining to derive new or updated recommendations. For example, to infer the right food to battle a certain marker imbalance,inference engine305 may use backward chaining. In order to detect whether this marker may be influenced through different kinds of exercises or whether it is affected by ethnic background,inference engine305 may use forward chaining.
• When making these inferences, the expert system may use a mechanism that can make complete deductions (any logically valid implication) instead of sound deductions (only deriving correct results). To decide between the various available deductions,inference engine305 may apply a weighting system based on the success or failure of pastpersonal recommendations306, thereby creating a self-learning system. In order determine the success or failure ofpersonal recommendations306, it is foreseen that the system may store the relationship between individualdiagnostic results303 andpersonal recommendations306 as well as an identifier for the user so that subsequent individualdiagnostic results303 for the same user can be compared to the previous individualdiagnostic results303 and previouspersonal recommendations306 in order to determine the changes in the user's biomarkers after having receivedpersonal recommendations306. Such knowledge may allow the system to modify thepersonal recommendations306 given to individual users in subsequent processing of their individualdiagnostic results303 in order to improve the health outcomes of those users.
• FIG. 4 illustrates exemplary facts in a simplified example ofknowledgebase304. In this particular embodiment,knowledgebase304 contains data about food and supplements401,biomarkers402,outcomes403, andfurther biomarkers404. As illustrated inFIG. 4,knowledgebase304 of this example represents relationships between these various entities, namely, how food and supplements401influence biomarkers402, howbiomarkers402 have an effect onoutcomes403, and how these activities as well as demographic information have an influence onbiomarkers404.
• FIG. 5 andFIG. 6 illustrate a representative user experience when utilizing one embodiment of the present invention. In such an embodiment, the user may be able to access a website hosted on a web server providing a graphical user interface allowing the user to dynamically interact with the system or at least an interface to the system. It is foreseen that the user may receive a user name and password to uniquely identify the user and securely connect the user to the system. The user connection may be further secured via a secure socket connection to the web server or through some other common network security protocol. The user may connect to this system via any number of computing devices, including a desktop computer, laptop computer, or smartphone.
• In the embodiment of the invention illustrated inFIG. 5, the user may provide her activity level, set goals, initiate blood tests, and view recommendations. The input data derived from the goals and questionnaires may be stored aspersonal profile501. Usingpersonal profile501 together withknowledgebase502, expert system usesartificial intelligence algorithm503 to createpersonalized recommendations504 which may lead toimproved health outcomes505.
• As illustrated inFIG. 6, some website content may be available to all users including guests, while particular content such as a personal profile and personal recommendations may only be available to registered users. It may be desirable to encrypt transmissions between the website and the user when the user is accessing a webpage only accessible to registered users, whereas communication with other webpages may use an unencrypted transmission. It is desirable that site activity be highly interactive. This may include allowing users to drill down into any kind of information: each biomarker, biomarker values, biomarker and biomarker value explanations, and reference to papers or other sources corroborating personal recommendations. The system may be subject to a comprehensive privacy and security policy, comprised of a processes, operating procedures, and technical security safeguards to ensure that the data is safe and only accessible by the intended users.
• Corresponding to various parts of the detailed description to this point, a preferred embodiment of the present invention contains the following process workflow:
• 1. Measure the level of 2 to 100 biomarkers from the user.
• 2. Use the expert system to generate further qualifying questions about age, ethnicity, exercise, diet etc.
• 3. User completes the questionnaire.
• 4. Use the expert system to generate recommendations.
• 5. Deliver recommendations online.
• 6. Repeat and refine.
• FIG. 7 illustrates an example of biomarker analysis based on a previously published athletic clinical trial and the results of the trial. A putative supplement recommendation is shown below the table as a representation of a fact that may populate a knowledgebase in accordance various embodiments of the present invention. Such a fact may then be used to generate a personalized recommendation as shown boxed in red inFIG. 7. This exemplary analysis illustrates the use of a biochemical blood marker level as input into an inference engine and the construction of personalized dietary intake, supplement, and exercise recommendations as foreseen in various embodiments of the present invention.
• As illustrated inFIG. 8, one exemplary embodiment of the present invention ten muscle tissues from sedentary and trained subjects are taken in order to be analyzed using gene expression microarrays. Sedentary subjects are typically defined as subjects who exercise less than 30 min/day twice a week. Trained subjects are typically defined as subjects performing≧one hour of cycling or running six days a week over the past four years. The upper left panel of the heat map shown inFIG. 8 shows an example of genes that are significantly different between the sedentary and the trained groups. Most of the genes in this example are up-regulated (red) in the trained group and down regulated (green) in the sedentary group. Interestingly, two subjects from the trained group (subjects 3 and 5) have an expression pattern that looks more like the sedentary than the trained. In addition, the expression pattern ofsubject number 7 of the trained group may indicate over-exercise based on the strong expression changes. Therefore, this panel shows that: 1) exercise induces gene expression change and 2) this response is specific to an individual.
• The upper right panel shows the model constructed based on the gene expression results. One of the key processes identified to be up-regulated by exercise by this system biology analysis is creatine kinase (CK). The CK gene, CKMT2, is shown to be up-regulated in most of the trained subjects. The lower right panel shows the current knowledge about CK as a muscle injury marker. The lower left panel presents possible interventions to relieve muscle injury identified by a high level of CK. In summary, this example emphasizes the capabilities of gene expression analysis with a system biology approach to identify relevant athletic markers and to connect those to dietary and exercise interventions in such a way that can be used by various embodiments of the present invention.
• In another exemplary embodiment of the present invention, National Health and Nutrition Examination Survey (NHANES) data may be used as input. NHANES data are unique in that they combine blood biomarker levels with information from interviews about life style habits and physical exams. Relevant information about blood biomarkers is extracted from these data and organized by age, gender and ethnicity.FIG. 3 illustrates one entry from the NHANES. The entry as illustrated inFIG. 3 is that of a 20- to 30-year-old Asian male with a prescribed exercise regime and both typical and actual blood levels of ferritin, and Vitamin B12. Such age-, gender- and ethnicity-based criteria may provide additional refinements to the recommendations in various embodiments of the present invention.
• The system may also be provided as an article of manufacture having a computer-readable medium with computer-readable instructions embodied thereon for performing the methods and services described in the preceding paragraphs. In some embodiments, the functions may be executed on one or more computers, tablets, smart phones or other computing devices having the processor(s) and memory necessary to implement the system and methods described herein. In some instances, the functionality of methods of the present invention may be embedded on a computer-readable medium, such as, but not limited to, a floppy disk, a hard disk, an optical disk, a magnetic tape, a PROM, an EPROM, CD-ROM, or DVD-ROM or downloaded from a server. The functionality of the techniques may be embedded on the computer-readable medium in any number of computer-readable instructions, or languages such as, for example, FORTRAN, PASCAL, C, C++, PHP, Ruby on Rails, Java, JavaScript, Flash Script, C#, Tcl, BASIC and assembly language and executed by one or more processors. Further, the computer-readable instructions may, for example, be written in a script, macro, or functionally embedded in commercially available software (such as, e.g., EXCEL or VISUAL BASIC).
• FIG. 9,FIG. 10,FIG. 11, andFIG. 12 illustrate screen shots of an exemplary website for providing functionality described herein for various embodiments of the present invention.
• Appendix A illustrates exemplary biomarkers that could be used in the performance of current invention.

• APPENDIX A
  Examples of biomarkers
  Marker

  	Adiponectin
  	Adrenocorticotropic
  	Hormone
  	Agouti-Related Protein
  	α-1-Antichymotrypsin
  	α-1-Antitrypsin
  	α-1-Microglobulin
  	α-2-Macroglobulin
  	α-Fetoprotein
  	Amphiregulin
  	Angiopoietin-2
  	Angiotensin-
  	Converting Enzyme
  	Angiotensinogen
  	Apolipoprotein A-I
  	Apolipoprotein A-II
  	Apolipoprotein A-IV
  	Apolipoprotein B
  	Apolipoprotein C-I
  	Apolipoprotein C-III
  	Apolipoprotein D
  	Apolipoprotein E
  	Apolipoprotein H
  	Apolipoprotein(a)
  	AXL Receptor
  	Tyrosine Kinase
  	B Lymphocyte
  	Chemoattractant
  	β-2-Microglobulin
  	β-cellulin
  	Bone Morphogenetic
  	Protein 6
  	Brain Natriuretic
  	Peptide
  	Brain-Derived
  	Neurotrophic Factor
  	Calbindin
  	Calcitonin
  	CD 40 antigen
  	CD40 Ligand
  	CD5
  	Chemokine CC-4
  	Chromogranin-A
  	Ciliary Neurotrophic
  	Factor
  	Clusterin
  	Complement C3
  	Complement Factor H
  	Connective Tissue
  	Growth Factor
  	Cortisol
  	C-Peptide
  	C-Reactive Protein
  	Creatine Kinase-MB
  	Cystatin-C
  	Endothelin-1
  	EN-RAGE
  	Eotaxin-1
  	Eotaxin-3
  	Epidermal Growth
  	Factor
  	Epidermal Growth
  	Factor Receptor
  	Epiregulin
  	Epithelial-Derived
  	Neutrophil-Activating
  	Protein 78
  	Erythropoietin
  	E-Selectin
  	Factor VII
  	Fas Ligand
  	FASLG Receptor
  	Fatty Acid-Binding
  	Protein, heart
  	Ferritin
  	Fetuin-A
  	Fibrinogen
  	Fibroblast Growth
  	Factor 4
  	Fibroblast Growth
  	Factor basic
  	Follicle-Stimulating
  	Hormone
  	Glucagon
  	Glucagon-like
  	Peptide 1, total
  	Glutathione S-
  	Transferase α
  	Granulocyte Colony-
  	Stimulating Factor
  	Granulocyte-
  	Macrophage
  	Colony-Stimulating
  	Factor
  	Growth Hormone
  	Growth-Regulated α
  	protein
  	Haptoglobin
  	Heat Shock Protein
  	60
  	Heparin-Binding
  	EGF-Like Growth
  	Factor
  	Hepatocyte Growth
  	Factor
  	Immunoglobulin A
  	Immunoglobulin E
  	Immunoglobulin M
  	Insulin
  	Insulin-like Growth
  	Factor I
  	Insulin-like Growth
  	Factor-Binding
  	Protein 2
  	Intercellular
  	Adhesion Molecule 1
  	Interferon □
  	Interferon □ Induced
  	Protein 10
  	IL-1 α
  	IL-1 β
  	IL-1 receptor
  	antagonist
  	IL-10
  	IL-11
  	IL-12 Subunit p40
  	IL-12 Subunit p70
  	IL-13
  	IL-15
  	IL-16
  	IL-2
  	IL-25
  	IL-3
  	IL-4
  	IL-5
  	IL-6
  	IL-6 receptor
  	IL-7
  	IL-8
  	Kidney Injury
  	Molecule-1
  	Lectin-Like
  	Oxidized LDL
  	Receptor 1
  	Leptin
  	Luteinizing
  	Hormone
  	Lymphotactin
  	Macrophage
  	Colony-Stimulating
  	Factor 1
  	Macrophage
  	Inflammatory
  	Protein-1 α
  	Macrophage
  	Inflammatory
  	Protein-1 α
  	Macrophage
  	Inflammatory
  	Protein-3 α
  	Macrophage
  	Migration Inhibitory
  	Factor
  	Macrophage-
  	Derived Chemokine
  	Malondialdehyde-
  	Modified Low-
  	Density Lipoprotein
  	Matrix
  	Metalloproteinase-1
  	Matrix
  	Metalloproteinase-
  	10
  	Matrix
  	Metalloproteinase-2
  	Matrix
  	Metalloproteinase-3
  	Matrix
  	Metalloproteinase-7
  	Matrix
  	Metalloproteinase-9
  	Matrix
  	Metalloproteinase-
  	9, total
  	Monocyte
  	Chemotactic
  	Protein 1
  	Monocyte
  	Chemotactic
  	Protein 2
  	Monocyte
  	Chemotactic
  	Protein 3
  	Monocyte
  	Chemotactic
  	Protein 4
  	Monokine Induced
  	by Γ Interferon
  	Myeloid Progenitor
  	Inhibitory Factor 1
  	Myeloperoxidase
  	Myoglobin
  	Nerve Growth
  	Factor β
  	Neuronal Cell
  	Adhesion Molecule
  	Neutrophil
  	Gelatinase-
  	Associated
  	Lipocalin
  	Osteopontin
  	Pancreatic
  	Polypeptide
  	Peptide YY
  	Placenta Growth Factor
  	Plasminogen Activator
  	Inhibitor 1
  	Platelet-Derived Growth
  	Factor BB
  	Pregnancy-Associated
  	Plasma Protein A
  	Progesterone
  	Proinsulin
  	Prolactin
  	Prostate-Specific
  	Antigen, Free
  	Prostatic Acid
  	Phosphatase
  	Pulmonary and
  	Activation-Regulated
  	Chemokine
  	RANTES
  	Receptor for advanced
  	glycosylation end
  	products
  	Resistin
  	S100 calcium-binding
  	protein B
  	Secretin
  	Serotransferrin
  	Serum Amyloid P-
  	Component
  	Serum Glutamic
  	Oxaloacetic
  	Transaminase
  	Sex Hormone-Binding
  	Globulin
  	Sortilin
  	Stem Cell Factor
  	Superoxide Dismutase
  	1, soluble
  	T Lymphocyte-Secreted
  	Protein I-309
  	Tamm-Horsfall Urinary
  	Glycoprotein
  	Tenascin-C
  	Testosterone, Total
  	Thrombomodulin
  	Thrombopoietin
  	Thrombospondin-1
  	Thymus-Expressed
  	Chemokine
  	Thyroid-Stimulating
  	Hormone
  	Thyroxine-Binding
  	Globulin
  	Tissue Factor
  	Tissue Inhibitor of
  	Metalloproteinases 1
  	TNF-Related Apoptosis-
  	Inducing Ligand
  	Receptor 3
  	Transforming Growth
  	Factor α
  	Transforming Growth
  	Factor β-3
  	Transthyretin
  	Trefoil Factor 3
  	Tumor Necrosis Factor α
  	Tumor Necrosis Factor β
  	Tumor Necrosis Factor
  	Receptor-Like 2
  	Vascular Cell Adhesion
  	Molecule-1
  	Vascular Endothelial
  	Growth Factor
  	Vitamin K-Dependent
  	Protein S
  	Vitronectin

Claims (20)

1. A system for deriving a personalized health and fitness plan for an individual, said system comprising:

a data storage device for storing (i) an electronic knowledgebase of medical and health data collected from a plurality of sources and (ii) a plurality of questions;

an inference engine for (i) determining, based on a computerized comparison between said knowledgebase and biomarker data for a plurality of biomarkers for said individual, a subset of said plurality of questions to be presented to said individual and (ii) generating, based on said biomarker data and answers received from said individual to said subset of said plurality of questions, a series of lifestyle recommendations specific to said individual; and

a communication server for transmitting said series of lifestyle recommendations to said individual over a secure network connection.

2. The system ofclaim 1, wherein said electronic knowledgebase includes second biomarker data for said plurality of biomarkers having been collected for said individual prior to said biomarker data.

3. The system ofclaim 2, wherein said computerized comparison between said knowledgebase and said biomarker data includes comparison between said biomarker data and said second biomarker data.

4. The system ofclaim 1, wherein said biomarker data is collected using a sample of blood from said individual.

5. The system ofclaim 1, wherein said biomarker data is collected using a biological specimen from said individual where said biological specimen does not comprise blood.

6. The system ofclaim 1, wherein said plurality of biomarkers comprises creatine kinase.

7. The system ofclaim 1, wherein said plurality of biomarkers comprises ferritin.

8. The system ofclaim 1, wherein said subset of said plurality of questions comprises questions about said individual's demographic data.

9. The system ofclaim 1, wherein said subset of said plurality of questions comprises questions about said individual's athletic activity.

10. The system ofclaim 1, wherein said lifestyle recommendations comprise dietary recommendations.

11. The system ofclaim 1, wherein said lifestyle recommendations comprise exercise recommendations.

12. The system ofclaim 1, wherein said biomarkers comprise: Creatine Kinase, Magnesium, Calcium, Ferritin, Folate, Vitamin B 12, Vitamin D, Hemoglobin, Lactic acid, White Blood Cells, IL6, Thiamin, Sodium, Zinc, Glycerol, Glutamine, IL-Ib, sTNF-R I, CRP, b-2 micro globulin, Neopterin, TNF-a

13. The system ofclaim 1, wherein said biomarkers comprise: Beta-2-microglobulin, IL6, Calcium, Calcitonin, Ferritin, Sodium, GCSF, CRP, Folate, IL2, Creatin Kinase, Vitamin BI2, IL1b, Magnesium, Vitamin D

14. A computer-implemented method for deriving a personalized health and fitness plan for an individual, said method comprising the steps of:

collecting biomarker data for a plurality of biomarkers for said individual;

determining, based on a computerized comparison between said biomarker data and an electronic knowledgebase of medical and health data collected from a plurality of sources, a series of questions to be presented to said individual;

presenting said series of questions to said individual;

receiving answers to said series of questions from said individual;

generating, based on said biomarker data and said answers, a series of lifestyle recommendations specific to said individual; and

transmitting said series of lifestyle recommendations to said individual over a secure network connection.

15. The method ofclaim 14, further comprising the steps of:

collecting second biomarker data for said plurality of biomarkers for said individual at a time subsequent to said step of transmitting; and

comparing said biomarker data to said second biomarker data in order to determine the difference between said biomarker data and said second biomarker data.

16. The method ofclaim 14, wherein said biomarker data is collected using a sample of blood from said individual.

17. The method ofclaim 14, wherein said biomarker data is collected using a biological specimen from said individual where said biological specimen does not comprise blood.

18. The method ofclaim 14, wherein said plurality of biomarkers comprises creatine kinase.

19. The method ofclaim 14, wherein said plurality of biomarkers comprises ferritin.

20-25. (canceled)

Images