Dr. Ali Tinazli is the CEO of lifespin.health and has 15+ years in Fortune 100 corporate strategy and entrepreneurship (SONY, HP).
Obesity rates continue to climb alongside a $254.9 billion global weight loss products and services market. Supplements, smartphone weight loss apps, diet plans and exercise programs are widely available to consumers, yet the World Obesity Federation projects that 1 billion people globally will be obese by 2030.
In the U.S., nearly $480 billion in direct healthcare costs annually are related to obesity and associated comorbidities. While obesity is a complex and escalating threat, studies have shown that it may be preventable and reversible. Understanding how the body processes, stores and burns fat is vital for healthy and sustainable weight loss.
For example, the relationship between nutrition and exercise on a molecular level can set off crucial changes in specific molecules. These alterations could, in turn, lead to better health by regulating gene expression, which affects how we make insulin, burn or store fat and respond to cholesterol and lipids.
Evaluating what does and doesn’t work regarding weight loss efforts can often be the difference between frustration and success. Advancements in health technologies may allow people to understand and better address the underlying reasons behind their struggle to lose unwanted body weight.
If knowledge is power in the fight against obesity, access to real-time health information could be the way forward. Thanks to technology convergence between biology and data science, it is now possible to generate comprehensive and actionable health insights.
Precision Medicine To Diagnose And Treat Obesity
Precision medicine, for example, uses multimodal health information from an individual to prevent, diagnose and treat disease. This new toolset can help slow disease progression and increase treatment efficacy by considering individual variability. Classifying a condition based on functional changes due to the illness leads to a targeted treatment approach that can improve successful outcomes in response to interventions.
In recent years, the study of “omics” has become increasingly important to researchers in the medical field. Metabolomics is an emerging clinical modality that holds tremendous promise in precision medicine. Disturbances in healthy metabolism leave biochemical patterns in the human body easily detected in a blood sample. Through metabolomic analyses, researchers can identify potential disease markers and measure changes in metabolite profiles in response to physiological or pathological conditions.
This technique can help identify profound disruption of the metabolome in obese individuals, and metabolic signatures of their condition strongly correlate with associated metabolic comorbidities. Metabolomics makes possible the identification of different patterns between healthy obese individuals and obese individuals with obesity-associated metabolic complications.
Further, metabolomics can identify clinically significant heterogeneity in obesity—identifying a metabolic fingerprint can help phenotype a patient and aid in selecting certain patients for specific therapies.
Larger “omics” studies in the future will help scientists understand why we each respond differently to exercise. Researchers can use the data from these studies to define more-precise molecular signatures, via a blood test, to better gauge a person’s fitness level and how their body may respond to various forms of exercise.
AI-Backed Clinical Decision Making Can Mean Earlier Diagnoses
The intersection of data science and precision medicine will further the innovation trajectory in healthcare. The big data revolution provides a way to utilize artificial intelligence (AI) and machine learning algorithms for clinical decision-making for many disease treatments. Digital BMs and computational biology will pave the way to earlier diagnosis and precise treatment options.
Precision medicine couples established clinical indexes with molecular profiling to better diagnose and arrive at therapeutic strategies, meeting the specific needs of each group of patients. Therefore, precise interpretation of clinical data and omics is necessary for the evolution of the precision medicine ecosystem.
In the fight against obesity and other chronic and life-threatening diseases, there is a critical need for cost-effective, scalable, and automated testing solutions. Technological advancements mean we can expect vast improvements to the results experienced through current treatments for obesity.
These technological advancements can generate significantly more knowledge about health, well-being and diseases to increase the chances the right patient gets the personalized therapy needed at the right time.
For example, metabolomics studies combined with data science and machine learning can reduce complex high-dimensional data to a few crucial features, leaving scientists to narrow their focus on statistically significant ones. When utilizing machine learning, fluid-based metabolomics is a minimally invasive liquid biopsy method to provide people with highly accurate diagnostic values.
Challenges In The Adoption Of Precision Medicine
For precision medicine to be widely adopted, providers using the data for decision making must be able to interpret the analysis output. Tests must be accurate and easy to access by providers, with the output being easy to comprehend. Fortunately, new technology offers solutions from emerging players that promise a bio revolution to enhance precision medicine efforts.
Healthtech platforms are now robust enough to capture, interpret and share complex patient data. However, there are challenges in adopting precision medicine. For example, legal safeguards for protecting patient privacy are critical when accessing private data is required, and so stakeholders must consider the appropriate levels of transparency in collecting, sharing and using personal information.
Also, providers must manage expectations concerning associated risks and potential benefits. Precision medicine holds tremendous promise—however, despite the many incredible advances in recent years, there are scientific limitations. Therefore, providers must communicate openly and successfully with patients before treatment to ensure realistic expectations.
Technology’s Role In The Era Of Precision Medicine
New tools at the intersection of data science, health and consumer technologies bring real promise for eradicating the global obesity epidemic. Together, big data and predictive analytics can identify characteristics of processes that correlate with optimal outcomes. These insights are vital for providers using precision medicine to foster innovation, hone processes and improve outcomes for a cycle of continuous improvement.
Big data compiles research and treatments from around the world, and AI neatly organizes the scientific information so providers can develop personalized treatments. Data-driven healthcare tech promises cost-effective scalability and accessibility of precision medicine-based tools essential to democratize better health and well-being while reducing the public healthcare cost burden.