Insulin resistance-related indices, genetic risk, and the risk of cardiovascular disease in individuals with preclinical or clinical obesity: a large prospective cohort study in the UK biobank – BioMed Central

Report on Cardiovascular Disease Research and its Alignment with Sustainable Development Goals

Introduction: Contextualizing Cardiovascular Health within the SDG Framework

This report synthesizes a comprehensive body of recent scientific literature concerning cardiovascular diseases (CVDs), their risk factors, and predictive markers. The findings are analyzed through the lens of the United Nations Sustainable Development Goals (SDGs), primarily focusing on SDG 3 (Good Health and Well-being), which aims to reduce premature mortality from non-communicable diseases (NCDs). The research also intersects with SDG 2 (Zero Hunger) by addressing malnutrition in the form of obesity, and SDG 10 (Reduced Inequalities) by highlighting health disparities. The collaborative nature of this research underscores the importance of SDG 17 (Partnerships for the Goals).

The Global Burden of Cardiovascular Disease: A Direct Challenge to SDG 3

A significant portion of the reviewed literature quantifies the global prevalence and impact of CVDs, providing critical data for monitoring progress towards SDG Target 3.4 (reduce by one-third premature mortality from NCDs). These studies establish the scale of the public health challenge that must be overcome.

• Studies such as Mensah et al. (2023) and Roth et al. (2020) provide extensive data on the global burden of cardiovascular diseases and associated risks, tracking trends over several decades.
• Projections by Chong et al. (2024) forecast the future burden of CVDs, emphasizing the urgent need for preventative strategies to meet SDG targets.
• This body of work forms the evidence base for global health policy and resource allocation aimed at combating NCDs.

Key Risk Factors: Linking Obesity and Metabolic Health to SDGs 2 and 3

The research strongly associates obesity and insulin resistance with CVD, linking nutritional health directly to NCD outcomes. This connection bridges the objectives of SDG 2, which addresses all forms of malnutrition, and SDG 3, which focuses on disease prevention.

Obesity as a Primary Driver

The link between excess adiposity and cardiovascular health is well-documented, highlighting the need for policies that promote healthy diets and physical activity.

• Powell-Wiley et al. (2021) issued a scientific statement detailing the mechanisms linking obesity and cardiovascular disease.
• A pooled analysis in The Lancet (2024) presents worldwide trends in obesity from 1990 to 2022, showing a rising prevalence that threatens global health goals.
• The diagnostic criteria for clinical obesity are being refined (Rubino et al., 2025), which will improve clinical management and public health surveillance.

Insulin Resistance and the Triglyceride-Glucose (TyG) Index

The Triglyceride-Glucose (TyG) index has emerged as a crucial, cost-effective marker for insulin resistance and a powerful predictor of CVD. Its utility supports the development of accessible health monitoring tools, contributing to more equitable healthcare (SDG 10).

1. The TyG index is established as a significant marker for CVD risk and mortality in the general population (Liu et al., 2022; Tao et al., 2022).
2. Numerous studies have validated the association between the TyG index and specific cardiovascular events, including atherosclerotic disease (Xia et al., 2024), stroke (Fu et al., 2025), and heart failure (Wang et al., 2025).
3. Research has explored the TyG index’s predictive power across different populations, such as those with hypertension (Li et al., 2025; Huang et al., 2025), diabetes (Zhang et al., 2023), and metabolic syndrome (Wei et al., 2024), demonstrating its broad applicability.
4. The interaction between the TyG index and obesity indicators further strengthens its predictive value for adverse cardiovascular outcomes (Dang et al., 2024; Chen et al., 2023; Cho et al., 2022).

Advanced Risk Stratification: Integrating Genetics and Lifestyle for SDG 3

Modern research integrates genetic predisposition with lifestyle and metabolic factors to create more precise risk prediction models. This approach is fundamental to developing personalized prevention strategies that can accelerate progress towards SDG 3.

• Studies by Li et al. (2024), Li et al. (2023), and Thompson et al. (2022) demonstrate the integration of polygenic risk scores into CVD prediction, enhancing guideline-recommended models.
• The interplay between genetic susceptibility and lifestyle factors, such as smoking (Yang et al., 2025) and healthy behaviors (Li et al., 2023), is a key area of investigation.
• This research highlights that while genetic risk is important, adherence to a healthy lifestyle can significantly mitigate the risk of CVD, empowering individuals and informing public health messaging.

Methodology and Global Collaboration: The Spirit of SDG 17

The advancement in understanding CVD is heavily reliant on large-scale, collaborative data resources and sophisticated analytical methods. These efforts exemplify SDG 17 (Partnerships for the Goals), showcasing how international cooperation and open data sharing are essential for tackling complex global challenges.

• Many of the cited studies utilize data from major biobanks and population studies, such as the UK Biobank (Sudlow et al., 2015; Bycroft et al., 2018) and the National Health and Nutrition Examination Survey (NHANES).
• The application of advanced statistical techniques, including Cox regression (Hua et al., 2025), restricted cubic splines (Discacciati et al., 2025), and causal mediation analysis (Shi et al., 2021), allows for a deeper understanding of the complex relationships between risk factors and disease.
• These collaborative platforms enable researchers worldwide to investigate the causes of disease and develop solutions, fostering a global partnership for sustainable development.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

   SDG 3: Good Health and Well-being

   • The article’s references extensively discuss the “global burden of cardiovascular diseases” (References 1, 2, 3), risk factors like obesity and insulin resistance (References 4, 8), and mortality associated with these conditions. This directly aligns with SDG 3, which aims to ensure healthy lives and promote well-being for all at all ages, with a specific focus on combating non-communicable diseases (NCDs).

   SDG 2: Zero Hunger

   • This goal is relevant because it aims to “end all forms of malnutrition.” The references highlight obesity as a major public health issue and a risk factor for cardiovascular disease (Reference 4). Reference 5, “Worldwide trends in underweight and obesity from 1990 to 2022,” explicitly addresses both ends of the malnutrition spectrum. The SDG framework recognizes obesity as a form of malnutrition, connecting the article’s themes to the broader goals of SDG 2.

2. What specific targets under those SDGs can be identified based on the article’s content?

   Target 3.4

   • “By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.”
   • The article’s references are centered on understanding and predicting mortality from cardiovascular diseases, which are a primary category of NCDs. Numerous titles refer to “cardiovascular mortality” (References 13, 19), “all-cause and cause-specific mortality” (Reference 16), and the “global burden” of these diseases (References 1, 2), which directly relates to the goal of reducing premature mortality from NCDs.

   Target 2.2

   • “By 2030, end all forms of malnutrition…”
   • The article’s focus on obesity as a critical risk factor for cardiovascular disease (Reference 4) and the analysis of global trends in both underweight and obesity (Reference 5) align with this target. By studying the prevalence and health impact of obesity, the research contributes to the knowledge base needed to address this form of malnutrition.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

   Indicator 3.4.1

   • “Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease.”
   • This indicator is directly implied throughout the references. Studies such as “The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients” (Reference 15) and “Association between the triglyceride glucose index and cardiovascular mortality in obese population” (Reference 19) are examples of research that provides the data and analysis necessary to track this specific mortality rate.

   Implied Indicator: Prevalence of Obesity

   • While the official SDG indicator (2.2.2) focuses on malnutrition in children, the principle of tracking the prevalence of all forms of malnutrition is central to Target 2.2. The article’s references strongly imply the use of obesity prevalence as a key health indicator. Reference 5, “Worldwide trends in underweight and obesity from 1990 to 2022,” is a direct example of the research used to measure this. The consistent link drawn between obesity and adverse cardiovascular outcomes (References 4, 12, 20) underscores its importance as a metric for public health and sustainable development.

4. SDGs, Targets, and Indicators Table

   SDGs	Targets	Indicators
   SDG 3: Good Health and Well-being	Target 3.4: Reduce by one-third premature mortality from non-communicable diseases.	Indicator 3.4.1: Mortality rate attributed to cardiovascular disease. The references analyze mortality rates and risk factors associated with cardiovascular diseases, directly contributing to the data for this indicator.
   SDG 2: Zero Hunger	Target 2.2: End all forms of malnutrition.	Implied Indicator: Prevalence of obesity. The references study worldwide trends in obesity (Reference 5) and its role as a major risk factor for disease (Reference 4), making its prevalence a key metric for tracking this form of malnutrition.

Source: cardiab.biomedcentral.com