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Disease Body Whole Wellness 5) and Prevention

Pim3333
30.05.2018

Content:

  • Disease Body Whole Wellness 5) and Prevention
  • What You Can Do to Maintain Your Health
  • 1. Maintain a Healthy Weight
  • Exercise can help prevent heart disease, stroke, diabetes, and colon cancer. Try to be active for 30 to 60 minutes about 5 times a week. In addition to the factors listed above, you should make time for whole body health. Categories: Family Health, Healthy Living, Prevention and Wellness, Staying. Whole Body Wellness [wds Walter Sorochan PhD] on lakemedelbestalla.top *FREE* shipping on You can prevent most disorders and disease. Researchers tell us that. Tuesday, AM to PM Wellness and Whole Body Care in Golden, CO care, promoting health as a positive vitality, beyond just the absence of disease. creating a focus on prevention through nutrition, diet, and movement, and the.

    Disease Body Whole Wellness 5) and Prevention

    She appears to be someone for whom genomic medicine alongside longitudinal clinical profiling could have important implications for health maintenance. Individual CHD-4 is another very fit woman in her 40s, whose primary health concern is allergies, which run in three generations of her family. Like many in the study, she takes supplements for heart and bone health, which may offer some protection against her increased risk of CAD and Paget's disease from common variants.

    She has a family history of cancer, and increased breast cancer risk was indicated genetically, so careful surveillance may be advisable. Individual CHD-5 is a woman in her late 50s, whose profiles are highlighted in Figure 5. We observed strong concordance of cardiovascular genetic and clinical risk, as well as a history of intestinal issues that would be consistent with a genotypic liability to ulcerative colitis.

    However, her genetically increased T2D risk not indicated by her excellent fitness, low BMI, and normal clinical indicators of diabetes. Two rare variants not shown suggest visual impairment and color vision deficiency, but there is no indication that these are issues for this woman. Individual CHD-6 is a relatively younger man in excellent health except for triglyceride levels at the high extreme for the entire cohort, which is consistent with the very strong genetic prediction of hypertriglyceridemia from his common variants.

    Deep analysis of his genome may be revealing with respect to the mechanisms responsible for his low level of inflammation. Individual CHD-8 is a man in his 40s who was discordant for multiple indicators of heart disease, including high diastolic blood pressure, arterial stiffness, and serum lipids, combined with a high FRS for CAD. These were only mildly indicated by common variant evaluation, but rare homozygous variants have been linked to cardiomyopathy and to carotid stenosis or thromboembolism.

    Alcoholism was reported in his family, so this person certainly is a good candidate for careful clinical and possibly genetic consideration in development of his health behaviors. It is inevitable that genome sequence information will be incorporated into individualized medical care over the next couple of decades, but just how it will be utilized remains to be seen.

    A spectrum of applications ranging from explanation of rare conditions at or before birth to enhancement of medical interventions is likely, and to some extent, genome-wide data may be used to predict and potentially help prevent early onset of chronic disease.

    Clinicians already utilize family history and clinical information for disease prognosis and diagnosis in a similar manner, while recognizing that these are also not individually definitive indicators of the likelihood of disease progression.

    Family history and polygenic genotype scores from SNPs identified by GWAS have similar predictive ability for common diseases, but genotypes already outperform family history for many rare conditions [ 34 ]. The holistic 'total evidence' approach to integration of clinical and genetic factors in medical evaluation will surely see dramatic improvements in the near future, and will be advanced by developments in several aspects of genomic risk assessment.

    First, there is ample room for improvement of baseline risk assessment. We emphasize that more research needs to be carried out before this strategy can be considered to be robust, and that medical utility remains to be demonstrated.

    Note that the OR approach to computation of genetic risk is just one of several methods that could be used. In addition, there are important theoretical issues surrounding the computation of genetic risk [ 35 , 36 ], particularly in populations of mixed ancestry.

    This amount of explained variance does not translate into significant risk prediction, for example by receiver operating characteristicanalysis [ 37 ], although there are reasons to believe that it does classify individuals who are toward the tails of the distribution. Even in the presence of complete knowledge of the genetic contributions, risk prediction is limited to the square root of the heritability, but we emphasize that none of the scores available to date approach this limit.

    As the sample sizes of GWAS continue to increase to hundreds of thousands of cases for more common diseases, expanding discovery from dozens to hundreds of loci, genotypic risk assessment will certainly improve [ 38 ]. This study was conceived as a pilot investigation of how WGS may be utilized in the context of health maintenance.

    Participants in the CHDWB interact regularly with a health partner who helps them to interpret their clinical profiles in the context of their own medical issues, and to develop a health action plan [ 24 ]. Across the full cohort of almost participants, there are encouraging trends toward improved wellness [ 23 , 24 ], and this is clear for some of the individuals reported here, in terms of significant reduction in BMI and inflammation. It will, however, take prospective and longitudinal studies to evaluate whether wellness genomic profiling is beneficial either to individuals in terms of maintained wellness or as a matter of public policy reduced healthcare costs, improved employee performance.

    We do not currently have IRB approval to share the genomic profiles with the participants, so cannot yet evaluate how self-knowledge of gene sequences might also affect health behavior. Instead, we propose a strategy for presenting the diverse data types in a manner that we suspect will help individuals see connections between their genetics, their clinical profiles, and their own health perception.

    In the short term, the utility of the approach is more likely to be measurable in terms of modification of health behaviors than in economic or life-long health benefits. A recent study [ 39 ] suggests that clinical geneticists are reluctant to report incidental findings on genetic mutations to patients unless the mutation is known to be pathogenic.

    However, because the expectations are different in the context of wellness, where subjects actively seek data, we envision that a physician, genetic counselor, or health partner would discuss the summary and appropriate specific details of the evidence with the individual, who would thus be empowered to consider whether they should act upon the genetic eviden.

    Clearly, our ability to integrate genomics into health maintenance will improve with experience and the incorporation of more data, including environmental exposures and behaviors. Family history of disease and presence of rare deleterious variants are two obvious types of information that will be highly relevant: We are also gathering data on the metabolome, transcriptome, and epigenome for these eight individuals, and expect these functional genomic data types to provide complementary information that we will evaluate later.

    This pilot study of eight individuals from the CHDWB proposes two approaches for combining and conditioning clinical and genetic profiles, which could facilitate longitudinal evaluation of wellness-focused medical care based on comprehensive self-knowledge of medical risks. The study shows an excess of concordance between genetic prediction and observed sub-clinical disease.

    Further, we illustrate how more holistic combination of genetic and clinical data can be achieved by visualizing risk in sub-classes of disease. The visualization of concordance and discordance in the genetic and clinical profiles might help develop personalized health action plans in consultation with a health partner.

    We acknowledge that the data presented here falls short of the gold standards of evidence of inference that are typically required in genetic analysis of causation, but argue that the objective of 'personalized genomics' is not necessarily to predict disease with any certainty, but rather to provide another line of evidence that physicians and other medical practitioners can consider in their interactions with patients.

    Ultimately, the utility of the approach described here will require prospective evaluation in a cohort of healthy adults followed longitudinally for decades.

    As the volume of personalized information increases, the issue of who will be responsible for interpreting and explaining the assessments to individuals becomes more acute, and suggests the need for training of a new class of genomic healthcare professional and development of novel ways to present the information. Body mass index; CAD: Genome-wide association studies; HDL-C: Institutional review board; LR: Type 1 diabetes; T2D: AB is a founder and consultant to Personalis, Inc.

    The remaining authors declare that they have no competing interests. The CHDWB also offers fee-for-service clinical assessment and health partner evaluation for a small number of individuals not included in this study.

    TP analyzed the potential function of amino acid mutations. JZ provided support for genome sequence feature extraction. DA prepared the DNA samples for sequencing. RC and AM provided software, databases, and advice for risk assessment.

    All authors read and approved the final manuscript. Clinical attributes of the population. Table showing the clinical attributes of the subjects included in the study at their first visit.

    In both cases, the two scores were positively correlated reflecting contributions of both pre-test and genotypic risks to the correlation with Framingham scores. Black dots show the scores for the participants discussed in this paper. Non-identifiability of participants on basis of clinical phenotypes. Two-way hierarchical clustering of z-scores of 40 traits columns for participants rows at 3 successive visits shows clustering of participants, 3 of whom indicated by orange, green or red markers to left cluster separately in at least one visit.

    The other five participants have clinical profiles that were always most similar to one another, but in most cases were so similar to other participants also that they do not uniquely define a person, given the data reported in this paper. Table showing the classification of clinical phenotypes into various disease categories for clinical risk assessment. Figure showing the risk-o-gram plots depicting the genotypic risk for all eight subjects. This one-page summary of the joint genomic and clinical profile for a hypothetical individual suggests how health professionals might present data to patients.

    The size of each point shows the magnitude of clinical risk in the same domain, with green dots highlighting concordant high risk, red dots discordant low genetic and high clinical risk, and blue dots discordant high genetic but low clinical risk. These are shown in more detail below, where the frequency distribution summarizes the genetic risk estimates across a relevant comparison population, and the box-and-whisker plots show the first two standard deviation intervals either side of the mean for associated clinical parameters.

    Colored points indicate the position of the individual relative to the comparison population. For example, this individual has relatively high genetic risk of depression, which corresponds to high Beck Depression Index, low mental health summary score, and very low social function possibly suggesting an area for behavioral modification. In the cardiovascular domain, she has very high blood pressure despite low genetic risk of hypertension, and this contributes to relatively high Framingham Risk Score for cardiovascular disease CVD risk despite normal arterial stiffness.

    In the metabolic domain, the data show that she is currently healthy, but a high genetic risk suggests a need for ongoing surveillance. Finally, the report would mention rare variants of various types, including homozygous deleterious alleles that are known to promote rare conditions, or to be protective, as well as carrier status for rare variants that might be of interest in the context of family planning.

    In addition to this summary report, we envision that a more detailed description of specific findings, including the strength of evidence for associations and any data on clinical outcomes and interventions, would be provided to the patient, and discussed along with appropriate explanation of the genetics and biology.

    This work was funded by start-up funds from the Georgia Tech Research Foundation to GG, whose laboratory generated the genomic sequence data. RC is an employee of Personalis, Inc. AM is a consultant to Personalis Inc. Stanford University holds the intellectual property on any genotypic risk assessment technologies described in the paper that may be licensed to various companies.

    National Center for Biotechnology Information , U. Journal List Genome Med v. Published online Jun Author information Article notes Copyright and License information Disclaimer.

    This is an open access article distributed under the terms of the Creative Commons Attribution License http: This article has been cited by other articles in PMC. Additional file 3 Non-identifiability of participants on basis of clinical phenotypes. Additional file 4 Disease categories. Additional file 5 Risk-o-grams. Additional file 6 Summary clinical profile. Abstract Background Whole genome sequencing is poised to revolutionize personalized medicine, providing the capacity to classify individuals into risk categories for a wide range of diseases.

    Results Polygenic risk is assessed for each participant for over diseases and reported relative to baseline population prevalence. Conclusion The CHDWB will facilitate longitudinal evaluation of wellness-focused medical care based on comprehensive self-knowledge of medical risks.

    Background Whole genome sequencing WGS and exome sequencing are rapidly being incorporated as routine components of diagnosis and explanation of rare disorders, and the trend is moving toward utilization of these for risk assessment for common diseases as well [ 1 , 2 ].

    Ethics approval The study was performed in accordance with the Declaration of Helsinki. Clinical assessments Details about the recruitment of participants and collection of biomedical and health status data at CHDWB have been described previously [ 24 ].

    Genetic risk assessment based on common variants Genetic risk predictions for various diseases were generated using our VARIMED Variants Informing Medicine database of complex disease associations [ 15 ], and our previously reported pipeline for combining odds ratios ORs of robustly associated single-nucleotide polymorphisms SNPs with diseases and traits [ 27 ]. Open in a separate window.

    Integration of genetic and clinical data For joint clinical and genetic risk assessment, we describe two exploratory approaches. Table 1 Summary of variations in genome sequences of eight Caucasian subjects, with data from two previously reported studies[ 27 , 40 ]. Genotypic risk prediction Genotypic risk assessments were generated for each of the participants, and are presented as 'risk-o-gram' plots see Additional file 5. Table 2 Genetic predictions and clinical phenotypes related to metabolic and cardiovascular disorders.

    Discussion It is inevitable that genome sequence information will be incorporated into individualized medical care over the next couple of decades, but just how it will be utilized remains to be seen. Conclusion This pilot study of eight individuals from the CHDWB proposes two approaches for combining and conditioning clinical and genetic profiles, which could facilitate longitudinal evaluation of wellness-focused medical care based on comprehensive self-knowledge of medical risks.

    Competing interests AB is a founder and consultant to Personalis, Inc. Supplementary Material Additional file 1: Click here for file 63K, PDF.

    Click here for file 18K, PDF. Click here for file K, PDF. Table showing the classification of clinical phenotypes into various disease categories for clinical risk assessment Click here for file 8. Click here for file 42K, PDF. Click here for file 95K, PDF. Next generation sequencing in the clinical domain: Adv Protein Chem Struct Biol. Whole exome and whole genome sequencing.

    Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Patterns and rates of exonic de novo mutations in autism spectrum disorders. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Use of whole exome and genome sequencing in the identification of genetic causes of primary immunodeficiencies.

    Curr Opin Allergy Clin Immunol. German Network on Primary Immunodeficiency Diseases: Array-based sequence capture and next-generation sequencing for the identification of primary immunodeficiencies. Clinical application of exome sequencing in undiagnosed genetic conditions. Five years of GWAS discovery. Am J Hum Genet. The pursuit of genome-wide association studies: Genotype score in addition to common risk factors for prediction of type 2 diabetes.

    N Engl J Med. Clinical risk factors, DNA variants, and the development of type 2 diabetes. Genomic medicine - an updated primer. The reference human genome demonstrates high risk of type 1 diabetes and other disorders.

    Clinical assessment incorporating a personal genome. Phased whole-genome genetic risk in a family quartet using a major allele reference sequence. Personal omics profiling reveals dynamic molecular and medical phenotypes. Sequencing and analysis of a South Asian-Indian personal genome. Type 2 diabetes risk alleles demonstrate extreme directional differentiation among human populations, compared to other diseases.

    The health and cost benefits of work site health-promotion programs. Annu Rev Public Health. Overview of the symposium on public health significance of genomics and eco-genetics. The vision, mission, and goals of Health People J Am Geriatr Soc. Integrating comparative effectiveness research programs into predictive health: A unique role for academic health centers.

    Patient centered care, promoting health as a positive vitality, beyond just the absence of disease. By listening to the patient and learning his or her story, the practitioner brings the patient into the discovery process and tailors treatments that address the individual's unique needs. Integrating Best Medical Practices, incorporating both traditional and alternative therapies and methods, creating a focus on prevention through nutrition, diet, and movement, and the use of therapeutic diets, detoxification program or stress management techniques.

    An integrative, science-based healthcare approach, considering the complex web of interactions in the patient's history, physiology and lifestyle that can lead to illness. Including internal mind, body, spirit and external physical and social environment factors in the evaluation of an individual's whole body wellness. Functional Medicine addresses the underlying causes of disease, using a systems-oriented approach and engaging both patient and practitioner in therapeutic partnership.

    It is an evolution of the practice of medicine that better addresses the healthcare needs of the 21st Century. By shifting the traditional disease-centered health system to a more patient centered approach, functional medicine addresses the whole person, not just an isolated set of symptoms.

    In this way, functional medicine supports the unique expression of health and vitality for each individual. Our society is experiencing a sharp increase in the number of people who suffer from complex, chronic diseases such as diabetes, heart disease, cancer, mental illness, and autoimmune disorders like rheumatoid arthritis.

    These complex conditions are not adequately address by the short term solutions that are available through medical means. Does your back hurt? Do you have trouble focusing your attention? Have you stopped participating in activities you used to enjoy? The Feldenkrais Method is for anyone who wants to reconnect with their natural abilities to move, think and feel.

    Whether you want to be more comfortable sitting at your computer, playing with your children and grandchildren, or performing a favorite pastime, these gentle lessons can improve your overall well being. Learning to move with less effort makes daily life easier.

    Because the Feldenkrais Method focuses on the relationship between movement and thought, increased mental awareness and creativity accompany physical improvements.

    What You Can Do to Maintain Your Health

    Birth Control · Specialty Care · Travel Services · Meet Your Provider Wellness is more than being free from illness, it is a dynamic process of change and "a conscious, self-directed and evolving process of achieving full potential. Physical wellness relates to maintaining a healthy body and seeking care when needed. These centers focus on healing and preventing chronic disease and cancer. The program is an established health and disease prevention program blood pressure, total cholesterol, lipid subfractions, glucose, body . The scale measures aggregate morbidity levels ranging from 0–5 where a score of.

    1. Maintain a Healthy Weight



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