Cardiovascular disease and dementia: burden, demographics, and shared risk factors
Cardiovascular disease (CVD) and dementia rank among the leading causes of death globally for women. Alzheimer’s disease affects over 6 million people in the U.S., two-thirds of whom are women. Despite this disparity, the underlying causes of sex differences in dementia remain poorly understood, particularly regarding the emerging vascular contributions. To address this gap, the UC Davis Center for Women’s Cardiovascular and Brain Health [Reference Villablanca, Dugger and Nuthikattu1] applies a convergence science framework – integrating insights across disciplines – to enhance health outcomes for women.
A growing body of evidence links CVD and its risk factors (e.g., hyperlipidemia, hypertension, obesity, and diabetes mellitus) to cognitive decline. These conditions often result from cumulative exposures throughout life. Women’s elevated dementia risk appears to exceed effects explained by longevity alone, with sex-specific factors – such as menopause-related hormonal changes in midlife – also implicated [Reference Janicki and Schupf2]. Identifying modifiable risk and protective factors across women’s lifespans offers promise for lowering their dementia burden.
Type 2 diabetes mellitus (T2DM), a prevalent but preventable risk factor for both CVD and dementia [Reference Saeedi, Petersohn and Salpea3], and gestational diabetes mellitus (GDM) – a pregnancy-specific risk factor for later T2DM and CVD – are increasingly explored for their potential role in dementia risk. This communication highlights the intersection of these metabolic conditions with CVD and dementia risk, emphasizing opportunities for transdisciplinary and translational convergence science.
Pregnancy as a window for lifespan prevention of CVD and dementia: a focus on GDM and type 2 diabetes
T2DM, a major health issue in the U.S. and globally, stems from both biological and behavioral factors such as overweight/obesity and sedentary lifestyles. Critically, women with T2DM face roughly double the risk of cardiovascular disease and 60% greater likelihood of developing dementia compared to those without [Reference Chatterjee, Peters and Woodward4]. Moreover, early adulthood metabolic conditions such as GDM, a common pregnancy complication, forecast markedly elevated lifetime risk for T2DM [Reference Dennison, Chen and Green5] and cognitive decline – including a 67% higher risk of dementia [Reference Zhang, Gao and Gao6]. Yet these risks are modifiable, and calls to intervene are gaining increased attention. Indeed, meta-analyses show that postpartum lifestyle interventions reduce T2DM risk by 19–24% in women with prior GDM (even with modest postpartum weight reduction), with greater benefits in high-risk groups [Reference Bracco, Reichelt and Alves7].
Figure 1 identifies opportunities for preventive intervention by illustrating how early-adulthood risk factors (i.e., obesity, poor diet, and sedentary behavior) heighten the likelihood of developing GDM, which in turn amplifies metabolic vulnerability across the life course. Although the elevated dementia risk associated with a history of GDM is only partially mediated by progression to T2DM [Reference Zhang, Gao and Gao6], few studies have investigated these upstream mechanisms.
Figure 1. Accumulation of Dementia and CVD risk over the lifespan. Created in biorender.com.
Importantly, exposure to maternal GDM also predisposes offspring to elevated risks of childhood obesity and metabolic dysregulation, establishing cardiometabolic vulnerability from infancy [Reference Catalano8]. Thus, critical windows exist throughout life where intervention may reduce the risk of CVD and dementia in both women and their children.
Thus, GDM should be recognized as an early sentinel and a pivotal opportunity for preventive intervention. Lifestyle interventions, such as dietary modification, physical activity, and pharmacotherapy (where appropriate) implemented before, during, or after pregnancy can substantially mitigate long-term cardiometabolic and cognitive risks for both generations [Reference Kartchner, Dunn and Taylor9].
Disparities and the impact of social determinants of health
Disparities in CVD, T2DM, and dementia are well-established. For example, women – especially from racial and ethnic minority groups – experience higher CVD mortality than men [Reference Kyalwazi, Loccoh and Brewer10]; African Americans and Caribbean Latinos have elevated dementia rates compared to non-Hispanic White individuals [Reference Mehta and Yeo11]; and progression to T2DM is significantly higher among Black women with GDM as compared to non-Hispanic White women [Reference Janevic, McCarthy and Liu12].
These inequities reflect broader social determinants of health (SDOH) – including socioeconomic status, education, healthcare access, and environment – which powerfully influence lifestyle risk factors and thus may contribute to disparate long-term outcomes in CVD and dementia risk. Structural barriers – such as limited access to preventive health services – can further exacerbate these disparities and limit opportunities for lifestyle modification. Addressing these inequities requires transdisciplinary structural and policy-level strategies.
The imperative for convergence science: integrating transdisciplinary and translational research
Advancing public health demands innovation through collaborative, cross-disciplinary approaches. Convergence science embodies this by deeply integrating expertise across disciplines into frameworks that drive translational impact and innovation focused on problem solving [Reference Villablanca, Dugger and Nuthikattu1]. Convergence science matters in four domains: 1) solving societal challenges that are vexing as it centers on integration that is problem-driven – bringing together expertise across disciplines to foster novel innovations tailored to societal needs and novel frameworks; 2) generating innovation at the interdisciplinary interface by breaking down disciplinary silos, creating new conceptual models and methodologies, and enabling mutual learning to achieve solutions and advances; 3) bridging real-world application and research by supporting the translation of knowledge from basic discovery to impact that is tangible; and 4) holistically reimaging population health by enabling more equity-centered, effective strategies. These concepts are further expanded upon in key resources [13–16].
While convergence science bridges foundational and clinical research, translational research typically remains within domains that are biomedical and focuses on translating discoveries in basic science into practical applications. In comparison, convergence science also harnesses diverse methodologies – including basic science, behavioral, and implementation science – to forge novel solutions and paradigms (Figure 2).
Figure 2. Convergence of behavioral and basic science across the lifespan to address women’s health disparities in gestational diabetes, cardiovascular disease, Type 2 diabetes, and Dementia. Adapted from National Center for Advancing Translational Sciences.
Unlike the fusion of disciplinary methods to create new frameworks in convergence science, team science centers on effective collaboration amongst diverse professionals on a shared challenge. Team science emphasizes interdisciplinary, structured collaboration to generate clinically meaningful solutions and new knowledge. It underscores communication, shared vision, processes, and joint attribution throughout the research lifecycle.
Thus, convergence science drives innovative integration of knowledge and methods, and translational research bridges human applications of basic science, while team science ensures that collaborations are intentional and managed – fostering trust, leveraging collective strengths, and achieving impact greater than siloed efforts alone.
Leveraging transdisciplinary disease expertise and translational science methods: a convergence science approach to address diabetes-associated dementia and cardiovascular disease risk
Examples from basic, behavioral, and implementation science are provided below to further support the potential of convergence science to advance the intersection of T2DM, GDM, CVD, and dementia research.
Basic science
Preclinical murine models are vital for dissecting molecular mechanisms that have been shown to link T2DM and dementia. For example, leptin receptor-deficient diabetic (db/db) mice exhibit increased blood–brain barrier (BBB) permeability, characterized by disrupted tight junction proteins and inflammatory markers across the hippocampal microvasculature [Reference Stamatovic, Johnson, Keep and Andjelkovic17]. In addition, dysregulated expression of adhesion molecules (e.g., ICAM-1, VCAM-1), matrix metalloproteinases involved in extracellular matrix degradation (e.g., MMP-9), and neuronal survival, growth, and differentiation (e.g., S100b) is observed in the db/db brain [Reference Alshammari, Alshehri and Alqahtani18]. Beyond structural dysfunction, db/db mice demonstrate cognitive impairment – most notably in spatial learning and memory – alongside altered hippocampal gene expression, including changes in inflammatory and angiogenic pathways [Reference Milenkovic, Nuthikattu, Norman and Villablanca19]. Moreover, antidiabetic agents restore abnormal amyloid-β transport across the BBB and attenuate memory deficits [Reference Chen, Dai and Hu20], further supporting shared pathogenic pathways between T2DM and dementia.
Behavioral science
Behavioral medicine translates mechanistic insights into interventions that foster health behaviors for treatment and prevention – such as healthy eating, physical activity, weight control, medication adherence, and disease screening. For example, research identifying a novel link between motivational factors and postpartum weight retention can inform the development of lifestyle interventions to reduce maternal obesity [Reference Brown, Kiernan and Hedderson21]. Emerging research at the intersection of medicine, healthcare delivery, and behavioral science has identified critical barriers to maintaining glycemic control during a GDM-complicated pregnancy (e.g., limited workplace support, perceptions of treatment intrusiveness), which point toward targets for intervention at the individual, healthcare system, and community levels. Large-scale behavioral pragmatic trials are also examining strategies to encourage women with overweight/obesity, prior GDM, and prediabetes to engage in weight management and evidence-based care for diabetes prevention [Reference Madievsky, Vu and Cheng22]. Among many examples, these behavioral science investigations illustrate the power of convergence science to identify, design, and test effective interventions – supported by mechanistic evidence – for prevention and health promotion.
Implementation science
Despite advances in developing interventions, their integration into clinical practice remains sluggish – only about 14% of original research is adopted, with an average lag of 17 years before widespread uptake [Reference Balas and Boren23]. This translation gap undermines the impact of translational research on health outcomes. Implementation science addresses this challenge by systematically identifying strategies to integrate the delivery of novel interventions in real-world settings [Reference Proctor, Bunger and Lengnick-Hall24]. Core implementation metrics include intervention acceptability, feasibility, adoption, and appropriateness, along with fidelity, cost, penetration, reach, and sustainability [Reference Proctor, Bunger and Lengnick-Hall24]. While implementation science is at an early stage of application to GDM and dementia research, this approach holds strong potential for translating effective interventions into real-world healthcare and community settings to advance women’s health.
By collaborating across basic, behavioral, clinical, and implementation domains, convergence science can accelerate the adoption of effective interventions, ultimately reducing lifelong cardiometabolic and cognitive health burdens in women.
Knowledge gaps and future directions
While our understanding of diabetes, CVD, and dementia interrelationships is expanding, several critical gaps and research opportunities persist:
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Women’s health remains underprioritized, with persistent disparities in research funding and representation in clinical trials.
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A convergence science approach is needed to unify research across disciplines and accelerate progress in these intersecting domains.
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A transdisciplinary, lifespan-informed strategy is essential to uncover links between GDM and dementia.
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Greater emphasis is required on how SDOH influences GDM and T2DM management and future risk for CVD and dementia.
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Implementation science techniques remain underutilized. There is a need for hybrid effectiveness-implementation trials and evaluations of real-world uptake and sustainability of interventions to reduce CVD and dementia risk among women.
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Although animal models of GDM exist [Reference Grupe and Scherneck25], more refined experimental systems – especially those modeling GDM’s transient effects and long-term dementia risks – are necessary for a deeper mechanistic understanding.
Conclusion
A convergence science framework – integrating multiomics, behavioral science, and implementation science – provides a comprehensive approach to addressing the complexity of chronic diseases. This transdisciplinary model facilitates the identification of early biomarkers and the development of targeted interventions for CVD and dementia prevention across the lifespan. By fostering collaboration among researchers, clinicians, and policymakers, convergence science enhances the translation of scientific discoveries into effective public health strategies. Ultimately, this integrated approach aims to mitigate the burden of CVD and improve health outcomes through early detection, personalized interventions, and sustain behavioral change for both CVD and dementia risk reduction attributable to GDM and T2DM.
Author contributions
Amparo C. Villablanca: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing-original draft, Writing-review & editing; Bridgette P. Smith: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing-original draft, Writing-review & editing; Brooke E. Wickman: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing-original draft, Writing-review & editing; Jennifer E. Norman: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing-original draft, Writing-review & editing; Siedah L. Garrison: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing-original draft, Writing-review & editing; Saivageethi Nuthikattu: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing-original draft, Writing-review & editing; Dragan Milenkovic: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing-original draft, Writing-review & editing; Susan D. Brown: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing-original draft, Writing-review & editing.
Funding statement
This work was funded in part by the UC Davis Center for Women’s Cardiovascular and Brain Health and the HEAL-HER (Heart, BrEast, and BrAin Heath Equity Research) Program supported by residual class settlement funds in the matter of April Krueger v. Wyeth, Inc., Case No. 03-cv-2496 (US District Court, SD of Calif.) awarded to Dr Amparo C. Villablanca. This work was also supported by the Frances Lazda Endowed Chair in Women’s Cardiovascular Medicine (Dr. Amparo C. Villablanca). This work was additionally supported by National Institutes of Health grants K26DK138246, P30DK092924, R01HL142996, and R01DK122087 (Dr. Susan D. Brown).
Competing interests
The authors declare no competing interests.
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