A medical team providing free blood pressure and blood sugar measurements for elderly residents at a nursing home in Sichuan Province in October 2024 Photo: IC PHOTO

China is undergoing rapid population aging, with projections indicating that by 2035, the population aged 60 and above will exceed 400 million, accounting for over 30% of the total population. This transition to a highly-aging society presents both challenges and opportunities for strategic policy planning. Consequently, research on population aging and health has become a pressing priority.

Health outcomes are generally influenced by two primary factors: genetic predisposition and external environmental conditions. While genetic factors serve as a foundational determinant, environmental influences play a crucial regulatory role. More importantly, genetic predispositions interact dynamically with environmental factors—a phenomenon known as gene-environment interaction. Though pervasive, this interplay has often been overlooked in traditional social science research, which primarily focuses on individuals as social beings and neglects their biological attributes. As a result, conventional social science models frequently fall short in fully explaining shifts in outcomes and phenomena.

Over the past decade, the international academic community has increasingly emphasized the integration of biological perspectives into social science research, examining how the interplay between genetic predispositions and environmental factors shapes human behavior and social phenomena. In the context of China’s rapidly aging population, sociogenomics offers novel insights into health governance while driving a paradigm shift in social science research.

Precision health interventions in dementia

Dementia prevention and treatment exemplify how sociogenomics can contribute to health governance in aging societies.

Dementia presents a formidable challenge for aging societies, imposing substantial economic and caregiving burdens at individual, family, and societal levels. Alzheimer’s disease, the most prevalent form, is often described as “the most gentle terminal illness.” Currently, no definitive cure exists, making early detection and intervention the primary strategies for delaying disease progression. While the medical community continues to search for effective pharmaceutical treatments, social scientists have traditionally focused on the role of environmental and social determinants of health. Yet both disciplines confront the same question: Why do individuals living in similar environments experience vastly different health trajectories?

Traditional social science approaches to cognitive health primarily emphasize external environmental factors while often overlooking the role of genetics. Data from the Chinese Longitudinal Healthy Longevity Survey indicates that incorporating genetic factors into analytical models to explore gene-environment interactions not only provides new insights into targeted health interventions for older adults but also underscores the value of sociogenomics as an emerging interdisciplinary field.

Dietary habits represent one of the most accessible and cost-effective behavioral factors for health intervention. However, empirical research on the cognitive benefits of dietary modifications remains limited. While previous studies have theorized that increased protein intake may enhance cognitive health, definitive empirical evidence remains lacking. Sociogenomic research has contributed a critical breakthrough by incorporating gene-environment interaction analysis. Findings indicate that when genetic factors are excluded, no significant correlation is observed between dietary protein intake frequency and cognitive decline. However, when gene-diet interactions are examined, a compelling pattern emerges: older adults with a higher genetic risk for dementia experience a significantly slower rate of cognitive decline when maintaining a high-frequency protein intake diet. This observation aligns with the Social Compensation Theory, which posits that specific environmental conditions exert a stronger protective effect on individuals with higher genetic susceptibility. This insight not only advances precision-based dementia interventions but also underscores the pivotal role of the social environment in modulating genetic expression.

Sociogenomics also broadens explanatory power when examining other daily activities among older adults. Leisure activities were categorized into cognitive, social, and physical domains. While all three demonstrated a general protective effect against cognitive decline, only cognitive activities—such as reading books or newspapers—provided significant benefits for individuals with elevated genetic risks. This suggests that for high-risk individuals, interventions should prioritize cognitively stimulating activities rather than generalized recommendations for increased socialization or physical activity.

This micro-level gene-environment interaction mechanism offers a new framework for understanding macro-social phenomena: When public health policies overlook genetic heterogeneity within populations, they risk resource misallocation and ineffective interventions.

From black box mechanisms to quantitative, molecular genetics

The early research paradigm of sociogenomics was primarily rooted in traditional behavioral genetics, relying on twin and family study designs as its core methodology. By comparing phenotypic similarities across individuals with varying degrees of genetic relatedness—such as monozygotic and dizygotic twins, parent-child pairs, and siblings—researchers demonstrated that genetic factors significantly influence human social behavior. Estimates suggest that approximately 30% to 60% of behavioral differences can be attributed to genetic influences. However, due to technological limitations at the time, researchers could only quantify genetic contributions statistically, without uncovering the underlying biological mechanisms. This left genetic influence as something of a “black box” in which inputs and outputs could be observed, but internal processes remained elusive.

With the advent of molecular genetic technologies, sociogenomics has undergone a paradigm shift. Genome-wide association studies (GWAS) have enabled the deconstruction of the genetic architecture underlying complex social outcomes—not only for medical conditions like hypertension but also behavioral traits such as educational attainment, number of births, and age at first childbirth. The introduction of polygenic scores (PGS) has further enabled researchers to quantify individual genetic predispositions and incorporate them into empirical social science research.

The central contribution of sociogenomics lies in expanding the explanatory boundaries of traditional social science research. Gene-environment interaction studies not only uncover social environmental effects previously overlooked in conventional sociological analyses but also identify populations most susceptible to environmental influences. Traditional social science research often neglects the role of genetic variation. However, such variation objectively exists, and the findings of conventional studies often represent an average effect across genetically diverse individuals. This average effect may appear minimal, masking substantial heterogeneity.

For instance, in health research of the elderly, integrating genetic variables reveals variation in the effectiveness of environmental interventions. The relationship between dietary protein intake and cognitive health, for example, remains undetectable using traditional analytical methods. Gene-environment interaction analysis uncovers these hidden effects, prompting a reassessment of the role of social variables: the environment is not merely an external factor but a crucial mechanism regulating biological traits.

By systematically analyzing the interplay between genetic and environmental influences, sociogenomics challenges both genetic determinism and environmental determinism, presenting a more comprehensive and profound understanding.

Additionally, sociogenomics provides valuable insights for policy design and intervention strategies. Recognizing the combined influence of genes and the environment allows policymakers to craft more targeted, effective health interventions that account for both individual genetic predispositions and the broader social environment. This integrated approach supports the development of more equitable and impactful health policies.

New paradigm for health research in aging society

In the context of China’s rapidly aging population, sociogenomics has introduced a transformative approach to health governance. Managing health in an aging society is not solely the domain of medical research—sociology also plays a vital role. While medical sciences primarily focus on disease treatment, sociology emphasizes the creation of environments conducive to health. Compared to medical approaches, sociological perspectives facilitate unique policy recommendations aimed at improving the social environment and reducing health disparities. Sociogenomics further reinforces the centrality of social factors in health governance, highlighting that while genetic predisposition offers important insights into health risks, environmental factors—such as access to healthcare resources, social support, and economic conditions—ultimately shape health outcomes. Therefore, health governance must integrate genetic information with strategies for improving the social environment, such as facilitating community-based genetic testing services while strengthening social security systems to ensure comprehensive medical and psychological support for older adults.

Addressing health challenges in an aging society requires transcending traditional models by integrating advances in genomics. Two key dimensions—gene-environment interactions and gene-environment correlations—are particularly critical for understanding health outcomes in later life. The application of PGS offers a novel tool for predicting and intervening in age-related diseases. As genetic technologies advance, the cost of obtaining genetic information continues to decline, making precision health interventions increasingly viable. On one hand, genetic risk assessments enable earlier disease prevention, shifting public health efforts from reactive treatment to proactive prevention. On the other hand, identifying genetically sensitive populations for targeted environmental interventions enhances resource allocation efficiency, thereby mitigating the economic and caregiving burdens associated with aging-related diseases. Additionally, by accounting for individuals’ inherent biological diversity, gene-environment interaction analysis not only improves the precision of social science research but also enhances the effectiveness and equity of social policy implementation.

Sociogenomics has propelled social science research into a new phase characterized by the co-evolution of biological and social systems. At the theoretical level, it bridges micro-level genetic mechanisms with macro-level societal dynamics. At the practice level, its findings provide scientific support for the Healthy China Initiative. As genetic testing costs fall and big data technologies develop, the establishment of early warning systems for precision health and the design of personalized intervention programs will likely become key areas of future research.

It is important to emphasize that the ultimate goal of sociogenomics is not genetic optimization, but rather the development of a more inclusive, precise health governance system grounded in a deeper understanding of biological-social interactions. This represents not only a transcendence of the traditional social science paradigm but also a scientifically informed response to the challenges posed by population aging.

Zhang Yun is an associate professor from the School of Sociology and Anthropology at Sun Yat-sen University.