The Case for Whole-Person Integrative Care

A review of whole-medical systems and holistic care approach for type 2 diabetes and associated metabolic syndrome

Whole-person care and holistic care approach has been proposed for complementary and integrative health care for type 2 diabetes mellitus. However, some doubts still exist on the feasibility of replicating processes followed in clinical trials and observational studies in real-world settings. This narrative literature review summarized and assessed existing clinical evidence (clinical trials, observational studies, and case reports) describing holistic and integrated care approach in adult and adolescent individuals with type 2 diabetes mellitus in clinical practice. The goal was to highlight existing evidence for implementation and outcomes of whole-medical systems and holistic integrated care approach for type 2 diabetes mellitus. A nonsystematic literature search was performed on Google Scholar, PubMed, Web of Science, ProQuest and ScienceDirect to identify clinical evidence from different parts of the world, evaluating the use of whole-medical systems and/or holistic care interventions in clinical practice for management of type 2 diabetes mellitus. Relevant keywords were used in the search. Data were analyzed using content analysis and simple descriptive statistics (percentages). Most of the studies (64%) were mainly conducted in Eastern countries (India, China and Israel) while 36% of the studies were conducted in the Western countries (USA, Netherlands, Canada and Mexico). Lifestyle medicine and integrated naturopathy were shown to be the commonly used whole-medical systems for type 2 diabetes mellitus management. Significant improvements in type 2 diabetes parameters, medication use, other symptoms, and overall feeling of wellness were observed in all studies. This review study revealed limited utilization and/or documentation of whole-medical systems or holistic care treatments for type 2 diabetes mellitus in regions of the world other than eastern countries. Lifestyle medicine, naturopathy, yoga, Ayurveda and traditional Chinese medicine were shown to be effective for type 2 diabetes mellitus, either as an alternative or as a complementary therapy. Please cite this article as: Makoni L, Manduna IT, Mbiriri AL. A review of whole-medical systems and holistic care approach for type 2 diabetes and associated metabolic syndrome. J Integr Med. 2024; 22(3): 199-209.

Integrative medicine for musculoskeletal pains - A proposed model based on clinical experience

With an increasing number of chronic diseases and related disabilities, modern medicine is falling short of offering definitive, cost-effective, long-term solutions. As we realize contemporary medicine's limitations and side effects, we are gravitating towards whole-person care. Taking learnings from integrative medicine treatment of musculoskeletal pains, I comprehended that a paradigm shift is needed in thinking about etiopathogenesis and diagnosing disease. Different healing possibilities can be identified in humans if we shift our focus from reductionist science to whole-person care. Each health condition is an outcome of disturbed homeostasis involving multiple body systems. Based on homeostatic balancing principles and clinical experience, five interrelated and interdependent pathways are discussed. Based on these pathways, a working model of integrative medicine is presented. It can act as a template for medical practitioners to start integrative medicine practice for the benefit of patients. To make integrative medicine mainstream, there is a need to standardize care, frame regulations, train and educate the practitioners. At the same, it shall be put to rigorous scientific experimentation, research, and scrutiny.

Achieving Whole Health for Veterans and the Nation: A National Academies of Sciences, Engineering, and Medicine Report

This Viewpoint discusses the Whole Health System of care led by the US Department of Veterans Affairs.

Whole Health in Parts: Omissions from National Data Sets

Background: The Whole Health model is a holistic approach to facilitate whole health practices by addressing (1) the physical, mental, and social health of individuals and (2) associated support systems. Several national organizations such as the Institute for Healthcare Improvement's (IHI) Age-Friendly Health Systems (AFHS) movement and, the U.S. Department of Veterans Affairs have implemented whole health frameworks with many common elements and promoted whole health practice and skills. However, implementing a Whole Health model across communities and health systems will require evidence of effectiveness. Generating evidence on the effectiveness of the Whole Health model's effect on health outcomes requires data-driven intelligence. Methods: We identified the national public-use data sets that are most often used in health research with a machine-assisted literature search of PubMed and Scopus for peer-reviewed journal articles published from 2010 through the end of 2021, including preprints, using Python [3.7]. We then assessed if the 8 most commonly used datasets include variables associated with whole health. Results: The number of publications examining whole health has increased annually in the last decade, with more than 2800 publications in 2020 alone. Since 2010, 24,811 articles have been published using 1 of these data sets. However, we also found a lack of data (ie, data set includes all of the whole health variables) to examine whole health in national data sets. Conclusions: We support a call to expand data collection and standardization of critical measures of whole health.

Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures

Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multi-chemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer's disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems.