Report on the Association Between TyG, TyG/HDL-c, and Diabetes Risk in NAFLD Patients with Emphasis on Sustainable Development Goals (SDGs)
1. Introduction
Non-alcoholic fatty liver disease (NAFLD) is a metabolic liver disorder marked by lipid accumulation in hepatocytes, with a rising global prevalence contributing significantly to chronic liver diseases. NAFLD is closely associated with metabolic conditions such as cardiovascular diseases, chronic kidney disease, and notably increases the risk of type 2 diabetes mellitus (T2DM). Insulin resistance (IR) is a key factor in NAFLD progression and the transition to T2DM. Early identification of effective indicators to assess T2DM risk in NAFLD patients is critical for timely intervention and aligns with SDG 3: Good Health and Well-being, aiming to reduce premature mortality from non-communicable diseases.
Recent research highlights heterogeneity in the pathophysiological links among NAFLD, insulin resistance, and cardiovascular risk, influenced by genetic, metabolic, and inflammatory factors. Conventional predictors may not fully capture this complexity; thus, indices such as the triglyceride-glucose index (TyG) and the TyG to HDL cholesterol ratio (TyG/HDL-c) have emerged as integrative markers of glucose-lipid metabolism, potentially improving risk stratification in NAFLD patients.
1.1 The Triglyceride-Glucose Index (TyG) and TyG/HDL-c
- TyG is calculated from fasting plasma glucose and triglycerides and serves as a surrogate marker for insulin sensitivity.
- TyG/HDL-c incorporates HDL cholesterol, an anti-atherosclerotic lipoprotein linked to insulin sensitivity, offering a comprehensive metabolic risk assessment.
- These indices have shown predictive value for T2DM risk but require further validation specifically in NAFLD populations.
1.2 Study Objectives
- To investigate the association between TyG, TyG/HDL-c, and prospective T2DM risk in NAFLD patients.
- To explore potential nonlinear relationships and threshold effects.
- To evaluate predictive performance across subgroups stratified by gender, age, and metabolic status.
2. Materials and Methods
2.1 Data Source and Study Design
A retrospective cohort study was conducted at The Central Hospital of Shaoyang, enrolling 611 NAFLD patients between February 2019 and February 2022, with a 3-year follow-up for diabetes incidence. After exclusions, 457 eligible patients were analyzed, with 21.66% developing diabetes. Diagnosis criteria followed the 2023 American Diabetes Association standards. Ethical approval was obtained, with informed consent waived due to the retrospective design.
2.2 Variables
2.2.1 Exposure Factors
- TyG index: Calculated as ln[(Fasting Plasma Glucose (mg/dL) × Triglycerides (mg/dL))/2].
- TyG/HDL-c ratio: Calculated as TyG divided by HDL cholesterol (mg/dL).
2.2.2 Covariates
- Demographics: age, sex, marital status.
- Lifestyle: smoking status (non-smoker, ex-smoker, smoker), alcohol consumption (yes/no).
- Clinical: blood pressure, hypertension, coronary heart disease.
- Laboratory: liver and kidney function tests, blood cell counts.
2.2.3 Outcome
Incident T2DM during follow-up, defined by ADA 2023 criteria.
2.3 Statistical Analysis
- Multiple imputation addressed missing data; SMOTE balanced class distribution.
- Multivariate logistic regression assessed associations.
- Restricted cubic spline (RCS) analysis explored nonlinear relationships and thresholds.
- Subgroup analyses evaluated effect modification by gender, age, and metabolic status.
3. Results
3.1 Baseline Characteristics
Among 457 NAFLD patients, 99 developed diabetes. Significant differences between diabetic and non-diabetic groups included higher TyG and TyG/HDL-c levels, increased hypertension prevalence, and marital status. These findings underscore metabolic and social determinants of health, relevant to SDG 3 and SDG 10: Reduced Inequalities.
3.2 Principal Component Analysis
SMOTE balancing preserved the overall data structure, ensuring robustness of subsequent analyses.
3.3 Association of TyG and TyG/HDL-c with Diabetes Risk
- Both indices showed significant positive dose-response relationships with diabetes risk.
- Highest quartiles of TyG and TyG/HDL-c were associated with 12.93 and 7.57 times greater diabetes risk, respectively, compared to lowest quartiles.
- Findings were consistent across balanced and unbalanced datasets.
3.4 Subgroup Analyses
- Gender significantly modified the association; females exhibited stronger predictive associations.
- Obesity status also influenced risk magnitude, with higher ORs in obese individuals.
- No significant interactions observed for marital status, smoking, alcohol use, hypertension, or coronary heart disease.
3.5 Nonlinear Relationships and Thresholds
- RCS analysis revealed nonlinear associations between TyG, TyG/HDL-c, and diabetes risk.
- Identified thresholds: TyG at 2.39 and TyG/HDL-c at 2.20, beyond which diabetes risk increased sharply.
- Gender-specific patterns indicated complex risk dynamics, particularly in females.
3.6 Predictive Performance
- ROC curve analysis showed moderate predictive ability for TyG (AUC=0.694) and TyG/HDL-c (AUC=0.693).
- Combined use improved prediction (AUC=0.703), supporting integrated metabolic risk assessment.
4. Discussion
4.1 Implications for Sustainable Development Goals
- SDG 3 (Good Health and Well-being): Early identification of diabetes risk in NAFLD patients supports prevention of non-communicable diseases and reduces healthcare burdens.
- SDG 10 (Reduced Inequalities): Consideration of gender and social factors in risk assessment promotes equitable healthcare delivery.
- SDG 9 (Industry, Innovation and Infrastructure): Use of advanced statistical methods and biomarkers enhances precision medicine capabilities.
4.2 Clinical and Biological Insights
- TyG reflects insulin resistance and may contribute to diabetes pathogenesis via impaired insulin signaling and chronic inflammation.
- TyG/HDL-c integrates lipid metabolism, with HDL-c’s antioxidant and anti-inflammatory roles critical in preserving insulin sensitivity.
- Nonlinear risk patterns highlight the importance of threshold-based interventions to prevent diabetes progression.
- Gender differences may be explained by hormonal influences on metabolism, emphasizing personalized medicine approaches.
4.3 Study Strengths and Limitations
- Strengths: Large NAFLD cohort, 3-year follow-up, robust data handling (imputation, SMOTE), nonlinear modeling.
- Limitations: Single-center retrospective design, potential unmeasured confounders, limited alcohol consumption data granularity.
- Future research should explore mechanistic pathways and validate findings in diverse populations.
5. Conclusion
This study confirms that TyG and TyG/HDL-c are significant, nonlinear predictors of diabetes risk in NAFLD patients, with gender-specific predictive variations. Incorporating these indices into clinical risk assessments can enhance early detection and personalized intervention strategies, contributing to the achievement of SDG 3 by improving metabolic health outcomes and reducing the burden of diabetes.
6. Recommendations for Future Research and Policy
- Expand multi-center prospective studies to validate and generalize findings.
- Integrate gender-specific metabolic factors into diabetes risk models.
- Develop public health strategies targeting metabolic health in NAFLD populations.
- Promote awareness and screening programs aligned with SDG targets to reduce non-communicable disease incidence.
1. Sustainable Development Goals (SDGs) Addressed or Connected to the Issues Highlighted in the Article
- SDG 3: Good Health and Well-being
- The article focuses on non-alcoholic fatty liver disease (NAFLD), type 2 diabetes mellitus (T2DM), and associated metabolic disorders, which are critical health issues.
- It emphasizes early screening, intervention, and risk assessment to reduce the burden of chronic diseases.
- SDG 2: Zero Hunger (Indirectly)
- Although not directly discussed, the metabolic disorders linked to diet and nutrition imply relevance to nutrition-related goals.
- SDG 10: Reduced Inequalities
- The article discusses heterogeneity in disease progression and the importance of precision medicine, which can help reduce health disparities.
2. Specific Targets Under Those SDGs Identified Based on the Article’s Content
- Under SDG 3: Good Health and Well-being
- 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.
- Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services, and access to safe, effective, quality, and affordable essential medicines and vaccines for all.
- Target 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries.
- Under SDG 10: Reduced Inequalities
- Target 10.2: Empower and promote the social, economic and political inclusion of all irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.
3. Indicators Mentioned or Implied in the Article to Measure Progress Towards the Identified Targets
- Indicators Related to SDG 3
- Incidence and prevalence of type 2 diabetes mellitus (T2DM) among patients with NAFLD, measured by fasting plasma glucose (FPG), HbA1c levels, and clinical diagnosis.
- Triglyceride-glucose index (TyG) and TyG/HDL-c ratio as biomarkers for insulin resistance and predictors of T2DM risk.
- Rates of early screening and intervention for diabetes risk in NAFLD patients, implied by the study’s focus on predictive tools.
- Mortality and morbidity rates from chronic liver diseases and diabetes, implied through risk assessment.
- Indicators Related to SDG 10
- Disaggregated data by gender, age, and metabolic status to assess differential risk and tailor interventions, supporting inclusion and equity.
- Use of precision medicine approaches to address heterogeneity in disease progression and treatment response.
4. Table of SDGs, Targets, and Indicators Relevant to the Article
SDGs | Targets | Indicators |
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SDG 3: Good Health and Well-being |
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SDG 10: Reduced Inequalities |
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Source: frontiersin.org