TO THE EDITOR
Metabolic-associated fatty liver disease (MAFLD), previously referred to as non-alcoholic fatty liver disease, has become a significant global health issue, affecting an estimated 25%-38% of the global population[1]. MAFLD is strongly associated with various metabolic disorders, including obesity, type 2 diabetes, and cardiovascular diseases, contributing substantially to morbidity and mortality worldwide[2]. In patients with MAFLD, around 25% progress to a more severe condition known as metabolic dysfunction-associated steatohepatitis (MASH). MASH is characterized by hepatocyte damage resulting from inflammation and ballooning of liver cells, which can lead to liver fibrosis. If left untreated, MASH can progress to cirrhosis, decompensated cirrhosis, hepatocellular carcinoma, necessitating liver transplantation for affected individuals[3]. Despite its increasing prevalence, there are currently limited effective treatment options for MAFLD, underscoring the urgent need for preventive strategies and early identification of individuals at risk.
Cardiovascular health (CVH) has been recognized as an important factor influencing the development and progression of MAFLD[2]. The American Heart Association has established two primary metrics for evaluating CVH: Life’s Simple 7 (LS7) and the updated Life’s Essential 8 (LE8)[4,5]. These metrics include a variety of health behaviors (such as diet, physical activity, smoking, and sleep) and health factors (including body mass index, blood pressure, cholesterol, and glucose levels), providing a holistic assessment of an individual’s CVH[4,5]. Recent research indicates that greater adherence to LS7 and LE8 is linked to a lower risk of developing MAFLD, suggesting that these tools could be valuable for early identification and prevention of liver disease[6]. Despite the explored relationship between CVH metrics and MAFLD, several gaps in the existing literature persist. Most studies have employed cross-sectional designs, which limit the ability to determine causal relationships[7-11]. Additionally, many investigations have failed to adequately address potential confounding factors, such as dietary habits, medication usage, and genetic predispositions, all of which may affect both CVH and the risk of MAFLD. Furthermore, the applicability of findings to diverse populations, particularly those in low - and middle-income countries, remains uncertain.
A recent study by Fu et al[12] provided valuable insights into the relationship between CVH metrics, as measured by LE8 and LS7, and the prevalence of MAFLD. The authors’ exploration of CVH metrics in predicting the risk of MAFLD is a notable effort. Nonetheless, several methodological limitations, unaddressed confounding factors, and potential biases that could impact the interpretation of their findings should be considered. Additionally, we offer suggestions for future research aimed at addressing these concerns.
METHODOLOGICAL LIMITATIONS
The study design was based on cross-sectional, which inherently restricts its ability to establish causal relationships between CVH metrics and MAFLD. Although the authors identified certain associations, the temporal relationship between CVH and MAFLD remains unclear. Additionally, this design complicates the evaluation of changes in CVH metrics over time that could affect the risk of developing MAFLD.
Furthermore, the study characterized MAFLD using vibration-controlled transient elastography and controlled attenuation parameters. However, these methodologies have limitations regarding their sensitivity and specificity, particularly when differentiating MAFLD from other liver diseases. Future research should adopt longitudinal designs to improve understanding of the causal links between CVH metrics and MAFLD, enabling repeated assessments of CVH metrics over time and offering a more nuanced view of their influence on the development of MAFLD. Additionally, integrating liver biopsy data or advanced imaging techniques could significantly improve diagnostic accuracy in future studies.
STATISTICAL LIMITATIONS AND UNADDRESSED CONFOUNDERS
The authors accounted for several confounding variables; however, their analysis did not include important factors such as dietary intake, genetic predisposition, and medication usage. Future research should incorporate a broader range of confounders, especially those related to genetics and lifestyle[13]. In addition, the study utilized logistic regression models to investigate the relationship between LE8, LS7, and MAFLD. However, the authors did not sufficiently address potential multicollinearity among the CVH components. For example, body mass index, blood pressure, and glucose levels were interconnected, and their collective impact on MAFLD may not be comprehensively reflected in the current analysis[14]. Future investigations should use advanced statistical methods, such as structural equation modeling or machine learning algorithms, to better capture the complex interactions among CVH components[15]. Additionally, conducting sensitivity analyses to evaluate the robustness of findings while adjusting for multicollinearity would be beneficial. While the study identified a non-linear association, it did not thoroughly explore possible threshold effects or interactions[15]. Further stratified analyses and interaction testing are required to confirm the validity of the non-linear relationship. Furthermore, the complexity of the models raises concerns about overfitting, especially considering the modest sample size. Sensitivity analyses with external validation cohorts should be implemented to strengthen the findings.
The study addressed several demographic and clinical confounders but overlooked other important factors that may influence the results. Specifically, dietary patterns, medication use (such as statins and antihypertensives), and genetic predisposition to MAFLD are not accounted for[16]. These elements could significantly impact both CVH measures and the risk of developing MAFLD, potentially leading to biased outcomes. The study sample is also drawn from the National Health and Nutrition Examination Survey, which may not adequately represent diverse ethnic and socioeconomic groups. It is also worth noting that certain variables, such as physical activity and dietary intake, depend on self-reported data, thereby introducing the possibility of measurement bias[17]. Comprehensive information regarding dietary habits, medication use, and genetic factors must be incorporated to improve future research[18]. This would enable a more holistic understanding of the interplay between CVH and MAFLD and facilitate the identification of specific subgroups that might benefit from targeted interventions. Furthermore, utilizing multi-center cohorts would improve the external validity of the findings. Although the findings align with established cardiovascular and metabolic health guidelines, it is important to acknowledge the potential bias in reporting significant associations.
KEY INSIGHTS
The study population was based on data from the National Health and Nutrition Examination Survey, which, although representative of the United States population, may not apply to other populations with varying genetic, cultural, or socioeconomic characteristics. Consequently, the results may not extend to groups with a higher prevalence of MAFLD or differing CVH profiles. Additionally, the manuscript suggested a causal relationship, which may be misleading given the study's cross-sectional design. While the authors relate their findings to previous research, they did not fully address the inconsistencies in study populations and methodologies.
Future investigations should focus on the mechanisms behind these paradoxical associations. Using qualitative or mixed-methods approaches could improve understanding of how specific health behaviors impact MAFLD risk across diverse populations[19]. It is recommended that the language used in the study be adjusted to emphasize association rather than causation. A more comprehensive discussion of the methodological differences with prior studies is needed. Future research should strive to replicate these findings in various populations, including those from low- and middle-income countries where the prevalence of MAFLD is rising[20]. This would assist in evaluating the global applicability of the LE8 and LS7 as predictive tools for MAFLD. Integrating these metrics into standard cardiovascular risk screening and connecting them with electronic health records can enhance automated MAFLD risk stratification. This integration could promote early interventions and enable personalized lifestyle recommendations for patients. Additionally, a detailed investigation into paradoxical findings, such as the absence of a protective effect from physical activity observed in certain studies, is essential. Possible underlying mechanisms for these findings include inflammatory responses, gut microbiota alterations, and metabolic compensation effects.
Furthermore, to elucidate the paradoxical findings related to certain health behaviors, such as physical activity, mechanistic studies should delve into the biological pathways that connect CVH metrics to MAFLD[21]. This may involve exploring the roles of inflammation, oxidative stress, and gut microbiota in the pathogenesis of MAFLD. Integrating multi-omics data, which includes genomics, proteomics, and metabolomics, represents another promising avenue for future research[22]. Epigenetic modifications, such as DNA methylation and histone acetylation, have been implicated in metabolic disorders[23]. Future studies should explore gene-environment interactions to identify subpopulations that may benefit from targeted interventions. In addition, identifying biomarkers that may mediate the relationship between CVH and MAFLD could lead to the development of personalized prevention and treatment strategies. Lastly, intervention studies are necessary to evaluate the effectiveness of improving CVH metrics in reducing the risk of MAFLD. Randomized controlled trials may allow for assessing the impacts of lifestyle interventions - such as diet, exercise, and sleep hygiene - on CVH and MAFLD outcomes.
As the prevalence of MAFLD continues to rise, public health initiatives need to focus on improving CVH as a key strategy for preventing this condition. Community-based lifestyle interventions, national health screening programs that include metrics such as the LS7 and LE8, and targeted campaigns promoting dietary changes and physical activity are strongly recommended to address this. Furthermore, nations with proven success in reducing cardiovascular disease, like Finland and Japan, can serve as valuable examples for incorporating these effective strategies into efforts to lower MAFLD risk[13].
CONCLUSION
The study by Fu et al[12] sheds light on the relationship between CVH metrics and MAFLD. However, notable methodological limitations and unaddressed confounders could influence the results. It is advisable to implement longitudinal study designs, include a broader range of data on potential confounding variables, and apply advanced statistical methods to strengthen future research. Future studies should consider incorporating advanced statistical methods, exploring genetic factors, and implementing policy-driven health interventions to enhance their understanding of the relationship between CVH and MAFLD. Addressing these concerns would improve the validity and generalizability of the findings. These improvements are essential for developing more effective prevention and management strategies for MAFLD. The contributions of the authors are commendable, and their work paves the way for further advancements in this critical area of study.
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: United States
Peer-review report’s classification
Scientific Quality: Grade A, Grade B
Novelty: Grade A, Grade B
Creativity or Innovation: Grade A, Grade B
Scientific Significance: Grade A, Grade B
P-Reviewer: Giangregorio F; Kargbo DA S-Editor: Bai Y L-Editor: A P-Editor: Wang WB
