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Association between life’s crucial 9 and major eye diseases among US adults aged 40 years or older

BMC Public Health volume 25, Article number: 2504 (2025) Cite this article

Abstract

Background

Vision impairment due to eye diseases represents a significant global public health concern. There is an increasing acknowledgment of the relationship between cardiovascular health (CVH) and eye diseases. However, Life’s Crucial 9 (LC9), the latest scoring framework for CVH, has yet to be investigated in relation to major eye diseases.

Methods

This cross-sectional study included 3830 adults aged 40 years or older from the US National Health and Nutrition Examination Survey 2005–2008. We analyzed the relationship between LC9 scores and major eye diseases, including retinopathy, age-related macular degeneration, cataract, and glaucoma using weighted multivariable logistic regression, restricted cubic spline analysis, and subgroup analyses.

Results

After adjusting for covariates, the poor CVH group (LC9 < 50) exhibited significant higher risks of glaucoma (odds ratio [OR] = 2.37, 95% confidence interval [CI]: 1.11–5.08), retinopathy (OR = 2.92, 95% CI: 1.84–4.63), and any objectively confirmed ocular disease (OR = 2.25, 95% CI: 1.45–3.49) compared to the ideal CVH group (LC9 ≥ 80). Restricted cubic spline analysis demonstrated a significant inverse linear association between LC9 scores and the risk of these diseases. Subgroup analyses indicated significant interactions between LC9 score and sex concerning retinopathy and any objectively confirmed ocular disease.

Conclusions

Suboptimal CVH correlated with increased odds of several major eye diseases in adults aged 40 years or older, highlighting the potential value of CVH optimization for reducing visual impairment burden in this population. Further investigation on the potential causality is warranted.

Peer Review reports

Introduction

Vision impairment due to eye diseases constitutes a significant public health challenge globally, particularly among the middle-aged and older population [1, 2]. In this population, cataract, glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy are the leading causes of vision loss and blindness worldwide [3]. These conditions not only affect patients’ quality of life but also impose considerable economic burdens on families and society [4, 5].

In recent years, accumulating evidence has indicated a significant association between cardiovascular health (CVH) and eye diseases. For instance, shared mechanisms, including inflammation, oxidative stress, and aging, underlie both cardiovascular disorders and ocular conditions such as glaucoma, AMD, and cataract [6,7,8,9]. Furthermore, the eye is increasingly acknowledged as a unique window for cardiovascular risk stratification and disease prediction [10, 11]. To evaluate overall CVH in adults, the American Heart Association (AHA) initially defined Life’s Simple 7 (LS7) in 2010, which encompasses three modifiable health behaviors (diet, physical activity, and smoking) and four health factors (body mass index [BMI], blood glucose, blood pressure, and cholesterol) [12]. In 2022, the AHA refined LS7 to Life’s Essential 8 (LE8), incorporating sleep health as a new behavioral metric [13]. Previous studies have demonstrated that lower LE8 scores are associated with an increased risk of specific ocular diseases, such as glaucoma and diabetic retinopathy [14, 15]. However, the LE8 framework did not account for psychological well-being, an emerging but critical contributor to both CVH and ocular diseases. For example, stress has been shown to elevate intraocular pressure in glaucoma, whereas mindfulness-based stress reduction by meditation can reduce intraocular pressure and improve quality of life of glaucoma patients [16, 17]. In addition, chronic stress has also been found to be associated with an increased risk of AMD [18].

With the transition from the traditional biomedical model to the biopsychosocial medical model, there has been an increased emphasis on the significant role of psychological health in CVH [19, 20]. Building on this paradigm, the year 2024 marked the introduction of Life’s Crucial 9 (LC9), an advanced framework that incorporates psychological health into the existing LE8 metrics [21]. Unlike LE8, LC9 explicitly recognizes the interaction between mental state and CVH, making it a more comprehensive tool for studying multi-system associations. Nonetheless, the association between CVH, as assessed by LC9, and major eye diseases remains unexplored.

In this study, we performed a systematic analysis of the correlation between LC9 score and the risk of major eye diseases, utilizing data from the US National Health and Nutrition Examination Survey (NHANES).

Materials and methods

Research design and study population

NHANES is a large national survey administered by the National Center for Health Statistics (NCHS) to assess the health and nutrition status of people in the US. Since 1999, the NHANES has been conducted biennially without interruption. The survey’s protocol has been approved by the NCHS Institutional Review Board, and all participants provided written informed consent. This study was conducted in accordance with the Declaration of Helsinki. As this study utilized deidentified data from NHANES, no additional ethics approval is required. This study was conducted between March 29 and April 15 2025.

To explore the relationship between LC9 and major eye diseases, we utilized data from NHANES 2005–2008, which comprised 7081 US adults aged 40 years or older. Then the following data were excluded: incomplete LC9 data (n = 1602), lack of information for the studied eye diseases (n = 730), and lack of complete covariate information (n = 919). Finally, 3830 individuals were included in our analysis (Fig. 1).

Fig. 1
figure 1

Participant selection flowchart of the study CVH, cardiovascular health; LC9, Life’s Crucial 9

Full size image

Assessment of LC9

The LC9 scoring system comprises nine metrics, which include four health behaviors, four health factors, and psychological health [21]. The healthy behaviors include diet, physical activity, sleep health, and smoking (nicotine exposure), while the health factors include BMI, non-high-density lipoprotein cholesterol (non-HDL cholesterol), blood glucose, and blood pressure. Detailed scoring criteria for each metric are provided in Supplementary Table S1. In brief, each metric is evaluated on a scale ranging from 0 to 100, where higher scores denote a more favorable condition. The average of the scores of the nine metrics is the overall LC9 score [22]. Since there is no established classification standard specifically for LC9 at present, the demarcation point in the LE8 framework has been adopted. An LC9 score of 80 to 100 signifies ideal CVH, a score between 50 and 79 suggests moderate CVH, and a score between 0 and 49 indicates poor CVH [13, 23].

Assessment of major eye diseases

In this study, four major eye diseases were included, including retinopathy, AMD, cataract, and glaucoma. In NHANES 2005–2008, for all eligible participants aged 40 years or older, 45° non-mydriatic digital fundus images were obtained using a non-mydriatic retinal camera (Canon CR6-45NM) by trained technicians in a standardized procedure. The images were evaluated by at least two experienced graders from the University of Wisconsin. In this study, retinopathy was defined by the presence of any kind of retinopathy in at least one eye of the participant. AMD included both the early stage (defined by presence or absence of drusen and/or pigmentary abnormalities) and late stage (defined by exudative AMD signs and/or geographic atrophy) in at least one eye. Cataract was determined based on whether the participants had undergone cataract surgery in at least one eye, given the high cataract surgical coverage in the US [24]. Glaucoma was determined based on whether they had been diagnosed with glaucoma. Based on relevant studies, participants who had retinopathy and/or AMD were defined as having “any objectively determined ocular disease” [25].

Covariates

In accordance with previous publications, several covariates were adjusted in the study to enhance the robustness of the findings. These covariates included age (measured in years), sex (categorized as male or female based on the binary categorization at birth), race (classified as non-Hispanic Black, non-Hispanic White, and others), poverty income ratio, poverty level (categorized as poor, moderate, and high), education level (categorized as below high school, high school, and more than high school), alcohol consumption, and the presence of chronic kidney disease (CKD) [14]. As several commonly utilized covariates (blood pressure, BMI, blood lipid levels, and blood glucose levels, etc.) were already included in LC9 framework, additional adjustments for these covariates were not required.

Statistical analysis

To ensure the representativeness of the data, sampling weights were used in this analysis. Firstly, the data were comprehensively summarized and elaborated based on the presence of major eye diseases. Participants were categorized into three groups according to their CVH status: the ideal CVH group (LC9 ≥ 80), the moderate CVH group (50 ≤ LC9 < 80), and the poor CVH group (LC9 < 50). Chi-square test was used for comparison of categorical variables between groups, and one-way analysis of variance was used for comparison of continuous variables. Continuous and categorical variables were expressed as weighted mean ± standard error and unweighted frequency (weighted percentage), respectively. Subsequently, weighted multivariable logistic regression was conducted to examine the association between LC9 scores and major eye diseases. To ensure the rigor of the analysis, three different models were constructed: the crude model, which was unadjusted, model 1, which adjusted for age, sex, race, education level, and poverty income ratio, and model 2, which further adjusted for alcohol consumption, and CKD on the basis of model 1. Furthermore, a restricted cubic spline analysis was used to further investigate whether there was a nonlinear relationship between the overall LC9 score and each of the major eye diseases. This analysis was based on logistic regression, with four equally spaced knots (the 5th, 35th, 65th, and 95th percentiles of LC9 score). Additionally, subgroup analyses were conducted to explore the association between LC9 and each major eye disease, stratified by age, sex, race, alcohol consumption, and CKD status.

Data analysis was performed using R software (version 4.3.3), along with Zstats v1.0 (www.zstats.net). A P value of less than 0.05 was considered indicative of statistical significance.

Results

Population baseline characteristics

In this study, a total of 3830 NHANES participants, of whom 53.62% were female, were included, with a mean age of 55.64 ± 0.42 years (Table 1). The overall LC9 score for all participants was 68.40 ± 0.55. Specifically, the mean scores for the poor, moderate, and ideal CVH groups were 43.29 ± 0.35, 66.52 ± 0.25, and 85.59 ± 0.22, respectively. In general, individuals with ideal CVH tended to have the following characteristics: younger age, higher poverty income ratio (i.e., being wealthier), a higher proportion of non-Hispanic whites, higher educational attainment, as well as lower prevalence of CKD and cardiovascular diseases.

Table 1 Baseline characteristics of the study population
Full size table

Table 2 showed the prevalence of major eye diseases among participants with varying CVH status. Compared to the poor and moderate CVH groups, the ideal CVH group exhibited lower prevalence of glaucoma, retinopathy, and any objectively confirmed ocular disease (all P < 0.05). No statistically significant differences were observed in the prevalence of cataract and AMD among the three groups.

Table 2 Prevalence of major eye diseases in participants with different CVH level
Full size table

Association between LC9 and major eye diseases

The analysis results of three different weighted multivariate logistic regression models were shown in Table 3. Although different covariates were adjusted in these models, the results remained basically consistent: compared to the ideal CVH group, the poor CVH group exhibited a higher risk of glaucoma (Crude model odds ratio [OR] = 2.41, 95% confidence interval [CI]: 1.24–4.66; Model 1 OR = 2.34, 95% CI: 1.12–4.90; Model 2 OR = 2.37, 95% CI: 1.11–5.08), retinopathy (Crude model OR = 4.17, 95% CI: 2.60–6.70; Model 1 OR = 3.22, 95% CI: 2.04–5.10; Model 2 OR = 2.92, 95% CI: 1.84–4.63), and any objectively confirmed ocular disease (Crude model OR = 2.86, 95% CI: 1.82–4.49; Model 1 OR = 2.43, 95% CI: 1.57–3.74; Model 2 OR = 2.25, 95% CI: 1.45–3.49) (all P < 0.05).

Table 3 Associations between LC9 and major eye diseases
Full size table

To evaluate the linearity of the association between the LC9 score and major eye diseases, a restricted cubic spline analysis was conducted (Fig. 2). The results were consistent with the major findings, indicating that higher LC9 scores were associated with a low risk of glaucoma, retinopathy, and any objectively confirmed ocular disease (all P for overall < 0.05). No nonlinear associations observed between LC9 score and major eye diseases (all P for nonlinear > 0.05).

Fig. 2
figure 2

Association between LC9 score and major eye diseases LC9, Life’s Crucial 9 Participants who had retinopathy and/or AMD were defined as having “any objectively determined ocular disease”. The reference value of the restricted cubic spline curve is 67.2

Full size image

Subgroup analyses

In subgroup analyses, significant interactions were observed between the LC9 score and sex in terms of retinopathy and any objectively confirmed ocular disease, with female participants exhibiting higher odds than their male counterparts at comparable CVH status (both P for interaction < 0.05). Additionally, a significant interaction was observed between the LC9 score and alcohol consumption in relation to glaucoma, where current drinkers exhibited higher glaucoma risk compared to non-drinkers or former drinkers among individuals with moderate CVH (P for interaction < 0.05). No significant interactions were observed between the LC9 score and covariates in terms of AMD and cataract (P for interaction > 0.05) (Supplementary Tables S2-S6).

Discussion

To our knowledge, this is the first study to analyze the association between LC9 and major eye diseases. We found a significant inverse linear association between LC9 score and the risk of retinopathy, glaucoma, and any objectively confirmed ocular disease. The subgroup analyses suggested that the inverse associations between LC9 score and the risk of retinopathy and any objectively confirmed ocular disease were was more pronounced in females. These findings not only enriched our understanding of the relationship between CVH and eye diseases, but also provided insights for promoting eye health in the middle-aged and elderly population.

Overall, the observed association between LC9 scores and major eye diseases align with established theories linking CVH and ocular health. For example, glaucoma, a disease characterized by progressive optic nerve damage and degeneration of retinal ganglion cells, are often associated with elevated intraocular pressure and impaired ocular blood perfusion [26]. Multiple studies have demonstrated that several LC9 metrics, e.g., blood pressure, lipid level, and glycemic status, can influence these mechanisms [27,28,29]. Moreover, numerous research has suggested that healthy dietary patterns, another LC9 metric, were associated with a lower risk of glaucoma [30]. Hypertension and diabetes were also widely recognized as risk factors for several retinopathies like hypertensive retinopathy and epiretinal membrane [31,32,33].

Notably, no significant association between LC9 and cataract was observed in this study. Although consistent with some previous studies, this finding should be interpreted with caution. In the Beaver Dam Eye Study, researchers found that the influence of cardiovascular disease and its risk factors on the incidence of age-related cataract was quite limited [34]. Similarly, no significant association was noticed between LE8 score and cataract risk [14]. However, the lower scores in behavioral domain of LE8 showed significant correlation with cataract, aligning with previous findings [14]. Therefore, it is important to note that as a broad measure of CVH, the lack of a significant association between cataract and overall LC9 scores does not imply that all components of LC9 have no impact on cataract. It should also be pointed out that, in this study cataract diagnosis relied on self-reported history of cataract surgery, which could introduce bias, particularly in uninsured or underserved populations whose access to surgical care is limited. The association between cardiovascular disease and AMD has long been controversial. In this study, no significant association was found between CVH and AMD. This finding is consistent with some previous studies but differs from others [8, 35, 36]. Given the complexity of these two diseases and the existing controversies, further research is warranted. In summary, both cataract and AMD are characterized by multifactorial etiologies and heterogeneous phenotypes, with their respective pathogenetic mechanisms remaining incompletely understood. While NHANES provides a large-scale dataset, the null findings for cataract and AMD may be influenced by sample size limitation. For instance, the event counts for cataract and AMD in this study were relatively low, potentially affecting statistical power to detect associations. Future studies with larger sample sizes are needed to validate these results and exclude the possibility of insufficient statistical power.

Notably, the subgroup analyses showed significant interactions between LC9 score and sex, especially regarding the risk of retinopathy and any objectively confirmed ocular disease, suggesting that females have a higher risk of certain ocular disease compared to males of similar CVH levels. One possible explanation for this could be hormonal differences. In females, ovarian hormones play a significant role in various physiological processes, whereas loss of these hormones (typically occurs naturally with aging) can increase the incidence and mortality of cardiovascular diseases [37]. Given the close association between the cardiovascular system and the eye, this connection appears to be reasonable [38, 39]. After menopause, women’s ovarian function declines, and one of the important characteristics is that the level of estradiol will drop significantly [40]. A recent study has demonstrated that estradiol has a protective effect on retinopathy in diabetic model mice: ovariectomized female diabetic mice (whose estradiol levels were reduced) had more signs of early diabetic vascular damage than the controls (female diabetic mice with preserved ovaries) [41]. Similarly, more and more evidence suggests that exposure to estrogen may have a protective effect on other ocular diseases, such as glaucoma and AMD (for example, postmenopausal hormone therapy is associated with a later age of onset of glaucoma and a lower risk of AMD) [42,43,44,45,46]. However, further research is needed to confirm this finding. Another contributing factor might lie in the realm of lifestyle. Females often carry multiple social and family responsibilities, which can lead to increased stress, an important LC9 component [47]. Besides, high-stress environments may disrupt sleep patterns, another LC9 component [48].

The findings of this study have significant implications for public health. Given the aging trend of the global population, the prevalence of both cardiovascular diseases and eye diseases is projected to increase [49, 50]. Promoting lifestyle modifications and enhancing overall health status is associated with higher LC9 scores, which in turn correlate with lower risks of cardiovascular and ocular conditions. Consequently, healthcare providers should recognize the interconnection between CVH and ocular well-being. During routine evaluations, they can assess LC9-related factors and offer tailored recommendations and referrals. This integrated healthcare approach may not only alleviate the burden of ocular disease but also enhance the CVH and long-term quality of life among the middle-aged and older population.

This study has some limitations. First of all, a limitation is the exclusion of over 40% of eligible participants due to incomplete LC9 or covariate data, which may introduce selection bias. Second, cataract diagnosis was determined through self-reported surgical history. Although this method has been validated as a reliable indicator of clinically significant cataract, it may lead to underestimation of the cataract prevalence in the study cohort. Third, the reliance on self-reported glaucoma diagnosis may underestimate the true prevalence, as early-stage glaucoma may lack noticeable symptoms and remain undiagnosed in community settings. These differences in diagnosis likelihood could bias results. Also, The study does not account for health care access, insurance status, or frequency of eye care utilization, which may confound the observed associations. Finally, the correlation between the LC9 score and major eye diseases suggested that cardiovascular diseases share common factors with the occurrence of these eye diseases. However, due to the cross-sectional design of this study, the causal relationship between the two cannot be inferred, making further research necessary.

Conclusions

Suboptimal CVH correlated with increased odds of several major eye diseases among adults aged 40 years or older, suggesting that optimizing CVH may be correlated with reduced visual impairment burden in this population. Further investigation of the causal relationship between CVH and ocular diseases is warranted.

Data availability

The datasets used in this study can be found in the National Health and Nutrition Examination Surveys database [https://www.cdc.gov/nchs/nhanes/index.htm].

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Acknowledgements

We want to thank all the participants and investigators of NHANES.

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers 82070972 and 82271093). The study sponsor/funder was not involved in the design of the study; the collection, analysis, and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report.

Author information

Author notes

  1. Yang Meng, Zongbiao Tan, and Yuan Liu contributed equally to this work as co-first authors.

Authors and Affiliations

  1. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510060, China

    Yang Meng, Yuan Liu, Yuan Ma, Ziye Chen, Lan Jiang & Tao Li

  2. Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, 430060, China

    Zongbiao Tan

Authors
  1. Yang Meng
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  2. Zongbiao Tan
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  4. Yuan Ma
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  6. Lan Jiang
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  7. Tao Li
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Contributions

YM1, ZT, YL, YM2, ZC, LJ, and TL contributed to study design and interpretation of data. YM1, ZT, YL, and TL did the statistical analysis and wrote the manuscript. TL obtained the funding. All authors read and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Tao Li.

Ethics declarations

Ethics approval and consent to participate

The NHANES protocol has been approved by the NCHS Institutional Review Board, and all participants provided written informed consent. This study was conducted in accordance with the Declaration of Helsinki. Since this study used deidentified data from NHANES, no additional ethics approval is required.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Supplementary Information

12889_2025_23704_MOESM1_ESM.pdf

Supplementary Material 1. Supplementary Table S1. Quantification of cardiovascular health based on Life's Crucial 9. Supplementary Table S2. Subgroup analysis on the association between Life's Crucial 9 and the presence of glaucoma. Supplementary Table S3. Subgroup analysis on the association between Life's Crucial 9 and the presence of retinopathy. Supplementary Table S4. Subgroup analysis on the association between Life's Crucial 9 and the presence of cataract. Supplementary Table S5. Subgroup analysis on the association between Life's Crucial 9 and the presence of age-related macular degeneration. Supplementary Table S6. Subgroup analysis on the association between Life's Crucial 9 and the presence of any objectively confirmed ocular disease.

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Meng, Y., Tan, Z., Liu, Y. et al. Association between life’s crucial 9 and major eye diseases among US adults aged 40 years or older. BMC Public Health 25, 2504 (2025). https://doi.org/10.1186/s12889-025-23704-1

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BMC Public Health

ISSN: 1471-2458

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