Applications of systems science in biomedical research regarding obesity and noncommunicable chronic diseases: opportunities, promise, and challenges

Disparities of obesity and non-communicable disease burden between the Tibetan Plateau and developed megacities in China

Background

Non-communicable diseases (NCDs) including risk factors, e.g., obesity, are the major causes of preventable deaths in China, yet NCD disparities in China remain under-studied.

Objective

This study aimed to compare the determinants and burden of NCDs within four selected provinces in mainland China: the least developed Qinghai-Tibet Plateau group (PG, Tibetan Autonomous Region [TAR] and Qinghai Province) and most developed megacity group (MCG, Shanghai, and Beijing).

Methods

Studies, reports, and other official sources with comparable data for NCD burden and related determinants for the four provinces were searched. Geographic, demographic, socioeconomic, and dietary characteristics and selected health indicators (e.g., life expectancy) were extracted from the China Statistical Yearbook and China Health Statistics Yearbook. Data on NCD burdens were extracted from the National Chronic Disease and Risk Factor Surveillance Study and other nationally representative studies.

Results

The overall NCD mortality rates and prevalence of metabolic risk factors including obesity, hypertension, and diabetes in mainland China have increased in the past 20 years, and this trend is expected to continue. The PG had the highest level of standardized mortality rates (SMRs) on NCDs (711.6-896.1/100,000, 6th/6-level); the MCG had the lowest (290.6-389.6/100,000, 1st/6-level) in mainland China. The gaps in SMRs were particularly high with regard to chronic respiratory diseases (PG 6th^/6-level, MCG 1st/6-level) and cardiovascular diseases (6th/6 and 4th/6 in TAR and Qinghai; 1st/6-level and 2nd/6-level in Shanghai and Beijing). In contrast, the prevalence rates of obesity, hypertension, and diabetes were generally higher or comparable in MCG compared to PG. Diabetes prevalence was particularly high in MCG (5th/5-level, 13.36-14.35%) and low in PG (1st/5-level, 6.20-10.39%). However, awareness, treatment, and control of hypertension were poor in PG. Additionally, PG had much lower and severely inadequate intakes of vegetables, fruits, and dairy products, with additional indicators of lower socioeconomic status (education, income, etc.,) compared with MCG.

Conclusion

Evidence showed large disparities in NCD burden in China's provinces. Socioeconomic disparity and dietary determinants are probably the reasons. Integrated policies and actions are needed.

Implementation of Nutrigenetics and Nutrigenomics Research and Training Activities for Developing Precision Nutrition Strategies in Malaysia

Nutritional epidemiological studies show a triple burden of malnutrition with disparate prevalence across the coexisting ethnicities in Malaysia. To tackle malnutrition and related conditions in Malaysia, research in the new and evolving field of nutrigenetics and nutrigenomics is essential. As part of the Gene-Nutrient Interactions (GeNuIne) Collaboration, the Nutrigenetics and Nutrigenomics Research and Training Unit (N2RTU) aims to solve the malnutrition paradox. This review discusses and presents a conceptual framework that shows the pathway to implementing and strengthening precision nutrition strategies in Malaysia. The framework is divided into: (1) Research and (2) Training and Resource Development. The first arm collects data from genetics, genomics, transcriptomics, metabolomics, gut microbiome, and phenotypic and lifestyle factors to conduct nutrigenetic, nutrigenomic, and nutri-epigenetic studies. The second arm is focused on training and resource development to improve the capacity of the stakeholders (academia, healthcare professionals, policymakers, and the food industry) to utilise the findings generated by research in their respective fields. Finally, the N2RTU framework foresees its applications in artificial intelligence and the implementation of precision nutrition through the action of stakeholders.

The Application of Systems Science in Nutrition-Related Behaviors and Outcomes Implementation Research: A Scoping Review

Use of systems science can improve the dissemination and implementation (D&I) process. However, little is known about use of systems science in nutrition D&I research. The purpose of this article is to synthesize the ways in which systems science methodology is applied in nutrition D&I research. Scoping review methodology involved searching 6 academic databases for full-text, peer-reviewed, English articles published between 1970 and 2020 that employed systems science within nutrition D&I research. Data extraction included intervention type, population, study aim, methods, theoretical approach, outcomes, and results. Descriptive statistics and qualitative thematic analysis followed. Thirty-four retained articles qualitatively identified benefits (successful planning and organization of complex interventions) and challenges (limited resources, trainings, and lack of knowledge) to utilizing systems science in nutrition D&I research. Future research should work toward building knowledge capacity among nutrition practitioners by increasing available trainings and resources to enhance the utilization of systems science in nutrition D&I research.

Health policy and public health implications of obesity in China

China has experienced many drastic social and economic changes and shifts in people's lifestyles since the 1990s, in parallel with the fast rising prevalence of obesity. About half of adults and a fifth of children have overweight or obesity according to the Chinese criteria, making China the country with the highest number of people with overweight or obesity in the world. Assuming that observed time trends would continue in the future, we projected the prevalence of and the number of people affected by overweight and obesity by 2030, and the associated medical costs. The rising incidence of obesity and number of people affected, as well as the related health and economic consequences, place a huge burden on China's health-care system. China has made many efforts to tackle obesity, including the implementation of relevant national policies and programmes. However, these measures are inadequate for controlling the obesity epidemic. In the past decade, China has attached great importance to public health, and the Healthy China 2030 national strategy initiated in 2016 provides a historical opportunity to establish comprehensive national strategies for tackling obesity. China is well positioned to explore an effective model to overcome the obesity epidemic; however, strong commitment and leadership from central and local governments are needed, as well as active participation of all related society sectors and individual citizens. TRANSLATION: For the Chinese translation of the paper see Supplementary Materials section.

Spatial Technologies in Obesity Research: Current Applications and Future Promise

Geographic Information Systems (GIS), Global Positioning Systems (GPS), and remote sensing (RS) are revolutionizing obesity-related research. The primary applications of GIS have included visualizing obesity outcomes and risk factors, constructing obesogenic environmental indicators, and detecting geographical patterns of obesity prevalence and obesogenic environmental features. GPS was mainly used to delineate individual activity space and combined with other devices to measure obesogenic behaviors. RS has been understated for its role as important sources of data about natural and built environments. These spatial technologies, collectively called the 3S technologies, will be useful in measuring more facets of obesogenic environments and individual environmental exposure at finer levels and studying obesity etiology and interventions.

Applications of systems modelling in obesity research

Obesity is a complex system problem involving a broad spectrum of policy, social, economic, cultural, environmental, behavioural, and biological factors and the complex interrelated, cross-sector, non-linear, dynamic relationships among them. Systems modelling is an innovative approach with the potential for advancing obesity research. This study examined the applications of systems modelling in obesity research published between 2000 and 2017, examined how the systems models were developed and used in obesity studies and discussed related gaps in current research. We focused on the applications of two main systems modelling approaches: system dynamics modelling and agent-based modelling. The past two decades have seen a growing body of systems modelling in obesity research. The research topics ranged from micro-level to macro-level energy-balance-related behaviours and policies (19 studies), population dynamics (five studies), policy effect simulations (eight studies), environmental (10 studies) and social influences (15 studies) and their effects on obesity rates. Overall, systems analysis in public health research is still in its early stages, with limitations linked to model validity, mixed findings and its actual use in guiding interventions. Challenges in theory and modelling practices need to be addressed to realize the full potential of systems modelling in future obesity research and interventions.

Obesity trend in the United States and economic intervention options to change it: A simulation study linking ecological epidemiology and system dynamics modeling

OBJECTIVES

To study the country-level dynamics and influences between population weight status and socio-economic distribution (employment status and family income) in the US and to project the potential impacts of socio-economic-based intervention options on obesity prevalence.

STUDY DESIGN

Ecological study and simulation.

METHODS

Using the longitudinal data from the 2001-2011 Medical Expenditure Panel Survey (N = 88,453 adults), we built and calibrated a system dynamics model (SDM) capturing the feedback loops between body weight status and socio-economic status distribution and simulated the effects of employment- and income-based intervention options.

RESULTS

The SDM-based simulation projected rising overweight/obesity prevalence in the US in the future. Improving people's income from lower to middle-income group would help control the rising prevalence, while only creating jobs for the unemployed did not show such effect.

CONCLUSIONS

Improving people from low- to middle-income levels may be effective, instead of solely improving reemployment rate, in curbing the rising obesity trend in the US adult population. This study indicates the value of the SDM as a virtual laboratory to evaluate complex distributive phenomena of the interplay between population health and economy.

Using Systems Science to Inform Population Health Strategies in Local Health Departments: A Case Study in San Antonio, Texas

OBJECTIVES

Because of state and federal health care reform, local health departments play an increasingly prominent role leading and coordinating disease prevention programs in the United States. This case study shows how a local health department working in chronic disease prevention and management can use systems science and evidence-based decision making to inform program selection, implementation, and assessment; enhance engagement with local health systems and organizations; and possibly optimize health care delivery and population health.

METHODS

The authors built a systems-science agent-based simulation model of diabetes progression for the San Antonio Metropolitan Health District, a local health department, to simulate health and cost outcomes for the population of San Antonio for a 20-year period (2015-2034) using 2 scenarios: 1 in which hemoglobin A1c (HbA1c) values for a population were similar to the current distribution of values in San Antonio, and the other with a hypothetical 1-percentage-point reduction in HbA1c values.

RESULTS

They projected that a 1-percentage-point reduction in HbA1c would lead to a decrease in the 20-year prevalence of end-stage renal disease from 1.7% to 0.9%, lower extremity amputation from 4.6% to 2.9%, blindness from 15.1% to 10.7%, myocardial infarction from 23.8% to 17.9%, and stroke from 9.8% to 7.2%. They estimated annual direct medical cost savings (in 2015 US dollars) from reducing HbA1c by 1 percentage point ranging from $6842 (myocardial infarction) to $39 800 (end-stage renal disease) for each averted case of diabetes complications.

CONCLUSIONS

Local health departments could benefit from the use of systems science and evidence-based decision making to estimate public health program effectiveness and costs, calculate return on investment, and develop a business case for adopting programs.

Towards a systems approach for chronic diseases, based on health state modeling

Rising pressure from chronic diseases means that we need to learn how to deal with challenges at a different level, including the use of systems approaches that better connect across fragments, such as disciplines, stakeholders, institutions, and technologies. By learning from progress in leading areas of health innovation (including oncology and AIDS), as well as complementary indications (Alzheimer's disease), I try to extract the most enabling innovation paradigms, and discuss their extension to additional areas of application within a systems approach. To facilitate such work, a Precision, P4 or Systems Medicine platform is proposed, which is centered on the representation of health states that enable the definition of time in the vision to provide the right intervention for the right patient at the right time and dose. Modeling of such health states should allow iterative optimization, as longitudinal human data accumulate. This platform is designed to facilitate the discovery of links between opportunities related to a) the modernization of diagnosis, including the increased use of omics profiling, b) patient-centric approaches enabled by technology convergence, including digital health and connected devices, c) increasing understanding of the pathobiological, clinical and health economic aspects of disease progression stages, d) design of new interventions, including therapies as well as preventive measures, including sequential intervention approaches. Probabilistic Markov models of health states, e.g. those used for health economic analysis, are discussed as a simple starting point for the platform. A path towards extension into other indications, data types and uses is discussed, with a focus on regenerative medicine and relevant pathobiology.