Chen AM, He QY, Wu YC, Chen JQ, Ma XQ, Hu LY, Wang GNY, Wang ZT, Wu ZY, Zheng ZJ, Jia YJ. Association of quantified cardiovascular health status with all-cause mortality risk in prediabetic patients. World J Diabetes 2025; 16(5): 102052 [DOI: 10.4239/wjd.v16.i5.102052]
Corresponding Author of This Article
Yi-Jie Jia, PhD, Department of Endocrinology & Metabolism, Southern Medical University, No. 1023 South Shatai Road, Baiyun District, Guangzhou 510515, Guangdong Province, China. yijie0207@126.com
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Ao-Miao Chen, Qiu-Yu He, Ling-Yuan Hu, Zhuo-Tong Wang, The First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, Guangdong Province, China
Yi-Chuan Wu, Ge-Ning-Yue Wang, School of Stomatology, Southern Medical University, Guangzhou 510515, Guangdong Province, China
Zhi-Yong Wu, College of Traditional Chinese Medicine, Southern Medical University, Guangzhou 510515, Guangdong Province, China
Co-corresponding authors: Zong-Ji Zheng and Yi-Jie Jia.
Author contributions: Chen AM, Chen JQ, Zheng ZJ, and Jia YJ designed this study. Chen AM extracted the data and performed statistical analysis; He QY, Wu YC, Chen JQ, Ma XQ, Hu LY, Wang GNY, Wu ZY and Wang ZT validated the statistical analysis; Chen AM and He QY wrote the original manuscript draft, which was revised by Wu YC, Chen JC, Wu ZY, Zheng ZJ, and Jia YJ. All authors made significant contributions to the manuscript and approved it for submission. Chen AM and He QY contributed equally to this work as co-first authors. Zheng ZJ and Jia YJ are the guarantors of this work and, as much, had full access to all the data in the study and take responsibility for the integrity of the data and accuracy of the data analysis. First, this decision reflects the significant contributions made by the two authors throughout the research process, demonstrating their equal role in the conceptualization, methodology, and implementation of the study. Second, each author brings unique expertise that is critical to the success of our research. In addition, this practice facilitates communication with potential collaborators within our hospital, providing multiple points of contact for consultation. We believe this approach recognizes the efforts and contributions of all parties involved and encourages future research collaborations.
Supported by National Natural Science Foundation of China, No. 82370818 and No. 82270862; the Natural Science Foundation of Guangdong Province, No. 2024A1515012744; the Guangzhou Science and Technology Project, No. 2025A04J3541; and the National Undergraduate Training Program for Innovation and Entrepreneurship, Southern Medical University, No. 202312121031 and No. S202312121167.
Institutional review board statement: This is a retrospective analysis using NHANES data and does not involve clinical trials. The data used in this study were obtained from the publicly available National Health and Nutrition Examination Survey (NHANES) database. In accordance with NHANES policies, all participants provided informed consent prior to the commencement of the study, and the research received ethical approval from the National Center for Health Statistics (NCHS) Institutional Review Board. Consequently, secondary analyses using the NHANES database are considered exempt from additional ethical review. All data were anonymized to protect the privacy of participants.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: There is no conflict of interest.
Data sharing statement: The datasets generated and analyzed for the current study are available in the NHANES database. More information about the NHANES can be obtained at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yi-Jie Jia, PhD, Department of Endocrinology & Metabolism, Southern Medical University, No. 1023 South Shatai Road, Baiyun District, Guangzhou 510515, Guangdong Province, China. yijie0207@126.com
Received: October 8, 2024 Revised: December 4, 2024 Accepted: March 21, 2025 Published online: May 15, 2025 Processing time: 199 Days and 22.9 Hours
Abstract
BACKGROUND
Patients with prediabetes are at increased risk of developing cardiovascular disease. The Life's Essential 8 (LE8) score, updated by the American Heart Association in 2022, is a tool used to quantify cardiovascular health (CVH). Quantifying healthy living status on the basis of the uniform standard LE8 will be useful for confirming whether health interventions can reduce the risk of death in prediabetic patients.
AIM
To investigate the associations between all-cause mortality risk and CVH status (as quantified by the LE8 score) in prediabetic patients.
METHODS
This study included 5344 participants with prediabetes (age: 52.9 ± 15.8 years; 51.6% men). The LE8 score includes four health indicators and four health behaviors. Cox proportional hazard ratios were calculated for all-cause mortality in the high CVH (LE8 ≥ 80), low CVH (LE8 ≤ 50), and moderate CVH (LE8 50-79) subgroups, and restricted cubic spline analyses were performed. Separate analyses of the associations of all-cause mortality risk with each LE8 component and CVH health behaviors and indicators were also performed.
RESULTS
In the median follow-up period of 8.33 years, 658 deaths occurred. Compared with those among participants with high CVH, the covariate-adjusted HRs (95% confidence intervals) for mortality among participants with moderate and low CVH were 2.55 (1.23-5.31) and 3.92 (1.70-9.02), respectively. There was a linear relationship between an improvement in CVH status and a reduction in all-cause mortality risk (P-overall < 0.0001, P-nonlinear = 0.7989). Improved CVH health behaviors had a more significant protective effect on patients with prediabetes than did the improvement in CVH health indicators.
CONCLUSION
High CVH status (as quantified by the LE8 score) is significantly associated with reduced mortality risk in prediabetic adults in the United States.
Core Tip: This study addresses an important and timely topic by exploring the relationship between quantified cardiovascular health, as defined by Life’s Essential 8, and all-cause mortality risk in prediabetic patients. The research is relevant and supported by comprehensive data analysis.
Citation: Chen AM, He QY, Wu YC, Chen JQ, Ma XQ, Hu LY, Wang GNY, Wang ZT, Wu ZY, Zheng ZJ, Jia YJ. Association of quantified cardiovascular health status with all-cause mortality risk in prediabetic patients. World J Diabetes 2025; 16(5): 102052
Prediabetes is defined as a glucose-related metabolic state that is indicative of a high risk of developing diabetes[1]. It is estimated that by the year 2030, 470 million people will suffer from prediabetes worldwide[2,3], and more than 70% of prediabetic patients will eventually develop diabetes[4]. Moreover, prior guidelines and two meta-analyses involving more than one million people have shown that patients with prediabetes have an increased risk of developing cardiovascular disease (CVD)[2,5,6].
Previous guidelines and numerous large cohort studies have focused on identifying effective health interventions for diabetes, which are essential to reduce the medical burden and improve survival outcomes[7-10]. However, previous studies that performed health assessments with independent cutoffs may produce completely opposite results. For example, the physical activity requirement for healthy young adults is an additional 280 minutes of moderate-intensity activity per week in the Da Qing Diabetes Prevention Outcomes Study (DQDPOS) and 150 minutes per week in the DPPOS[9,11]. Therefore, quantifying healthy living status using the uniform standard Life’s Essential 8 (LE8) score will be useful for determining whether health interventions can preventatively reduce the risk of death in prediabetic patients.
The LE8 score, updated by the American Heart Association in 2022, is a tool used to quantify cardiovascular health (CVH). Compared with the earlier health metric Life's Simple 7 (LS7), the LE8 is an optimized assessment that combines the original seven components [physical activity status, blood glucose, lipids, blood pressure, smoking status, body mass index (BMI), and diet] with a sleep score. In addition, it is scored on a scale of 0-100, which makes the LE8 a more sensitive and intuitive system for interindividual differences, highlighting factors that maintain or improve CVH[12]. Owing to its efficacy in health assessment, the LE8 score has been widely used in prevention- and prognosis-related studies[13-16]. However, to our knowledge, few studies have investigated the association between the LE8 score and all-cause mortality risk in patients with type 2 diabetes mellitus (T2DM)[17]. Our study concluded that CVH intervention should be strengthened as early as possible in T2DM patients and prediabetic patients who are at risk of death and CVD. Early health interventions can often achieve better results, especially in prediabetic patients[4]. However, in medical practice, many prediabetic patients with CVD are forced to change their lifestyle because of functional limitations or for psychological reasons[18-20]. These patients have more difficulty achieving high CVH (LE8 ≥ 80) or even moderate CVH (LE8 ≥ 50), as defined by the AHA, which highlights the need to clarify whether there is a linear relationship between the LE8 score and mortality risk.
Therefore, in the present study, we investigated the associations and linear relationships between CVH status measured by the LE8 score and all-cause mortality risk in patients with prediabetes via data from the National Health and Nutrition Examination Survey (NHANES).
MATERIALS AND METHODS
Study design and population
This is a retrospective analysis using NHANES data and does not involve clinical trials. The data used in this study were obtained from the publicly available NHANES database. In accordance with NHANES policies, all participants provided informed consent prior to the commencement of the study, and the research received ethical approval from the National Center for Health Statistics (NCHS) Institutional Review Board. Consequently, secondary analyses using the NHANES database are considered exempt from additional ethical review. All the data were anonymized to protect the privacy of the participants.
The data and guidelines used in this analysis are freely available from the NCHS: https://www.cdc.gov/nchs/nhanes/index.htm. The NHANES is a nationally representative study that was designed to investigate nutritional status and health in the United States. The research design and methods have been described in detail (http://www.cdc.gov/nchs/nhanes/about_nhanes.htm).
Five NHANES cycles from 2007-2016 with 29201 adult participants were included at baseline. To ensure the robustness of the results, participants with insufficient data on diabetes-related biochemical markers (fasting blood glucose, glycosylated hemoglobin type A1C (HbA1c) and self-reported physician-diagnosed prediabetic) (n = 16295), serum creatinine (n = 160), other data to calculate LE8 (n = 2333), mortality follow-up (n = 5), weight data (n = 317), and 10091 participants were included in the analysis.
In accordance with the ADA criteria and previous studies[21-23], prediabetes was defined as a self-reported physician-diagnosed prediabetic state or an HbA1c level ranging from 5.7% to 6.4% or a fasting blood glucose level ranging from 100 mg/dL to 125 mg/dL. Because this study targeted a prediabetic population, 5344 participants were included in this study after screening.
Assessment of CVH
CVH was assessed via the LE8 score[18], which consists of physical activity status, dietary health, nicotine exposure, BMI, sleep health, blood glucose, blood lipids, and blood pressure. LE8 scores were calculated as the average of the eight component scores, ranging from 100 to 0. According to recommendations from the AHA, CVH status was classified according to the LE8 score: 80-100 as high CVH, 0-49 as low CVH, and 50-79 as moderate CVH.
Eight components were scored according to different metrics (Supplementary Table 1). The Healthy Eating Index 2020 was used to calculate diet score[24], and dietary information was obtained via a self-reported food frequency questionnaire. Information on self-reported physical activity, nicotine exposure, and sleep duration was collected via the NHANES questionnaire. Height, weight, and blood pressure were measured via standard instruments at the examination centers. Non-high-density lipoprotein (HDL) cholesterol was used to calculate the lipid score. Blood glucose was scored by means of fasting plasma glucose or HbA1c measured via standard methods.
Covariate assessment
Covariates included demographic variables, health-related exposures, and underlying diseases, according to previous studies[25-27]. The demographic variables include years of age, sex, ethnicity, income and poverty rate. Health-related exposures included drinking, smoking, BMI (kg/m2), and total cholesterol. Although LE8 includes these factors, scoring for the stratification of these factors leads to a loss of statistical power, so they were included as covariates (continuous) to increase the reliability of the results. Basic diseases included diabetes, hypertension, CVD (myocardial infarction, congestive heart failure, angina attack, or coronary heart disease, as diagnosed by a physician), and anemia.
Mortality assessment
Baseline data from the NHANES from 2007 through 2016 were linked to the National Death Index through December 31, 2019 to determine survival.
Statistical analysis
All analyses accounted for the complex sampling design of the NHANES and used fasting subsample weights (wtsaf10yr) to ensure nationally representative results. CVH status was classified according to LE8: 80-100 as high CVH, 0-49 as low CVH, and 50-79 as moderate CVH status[12].
First, the Kaplan-Meier method was used to analyze and illustrate the cumulative mortality of prediabetic patients with different CVH status categories. Multivariable Cox proportional hazards models were constructed to assess the association between CVH status and all-cause mortality on the basis of follow-up time. The model passed Schoenfeld residual tests for statistical compliance (Supplementary Figure 1), and C statistics were calculated. Demographic variables, health-related exposures, and basic diseases were included as covariates to adjust for confounding factors.
Next, because the restricted cubic spline (RCS) model more accurately captured the true dose-response relationship by allowing the curve to bend at the node, RCSs with 3 knots (90th, 50th, and 10th percentiles) were applied to analyze the linear relationship between all-cause mortality risk and the total LE8 CVH score in prediabetic patients. Models were evaluated according to the Akaike information criterion (AIC) and tested for nonlinearity. To test the generalizability and sensitivity of this relationship, RCS models were repeated, stratified according to age, sex, income and poverty ratio, and the presence or absence of CVD or anemia. For the second sensitivity analysis, we removed adults who survived less than one year of follow-up to evaluate whether the results were influenced by reverse causation. In the third sensitivity analysis, all participants with CVD were excluded because the disease itself may force patients to make lifestyle changes, such as physical activity restrictions.
Finally, to investigate the best protective factors for patients with prediabetes and to analyze the protective effects of health behaviors and health indicators, each LE8 component score (grouped by whether it was 50 or higher) and the mean score for health behaviors and health indicators were included as additional covariates in the Cox proportional hazards model. R software was used to perform all the analyses (Version: R 4.2.1; https://www.r-project.org/). Two-sided P values of less than 0.05 were considered to indicate statistical significance.
RESULTS
Baseline characteristics
The baseline characteristics of the total cohort of participants is described in Table 1. Only 7.0% of the participants were classified as having high CVH. Prediabetic adults with better CVH status were more likely to be male, white people with higher family income levels, do not drink alcohol or smoke, have better control of blood lipids, blood glucose, and blood pressure, and not have basic diseases.
Table 1 Baseline characteristics of the weighted study population classified by cardiovascular health status.
Associations between CVH status and mortality risk in patients with prediabetes
During a median follow-up period of 8.33 years [95% confidence interval (CI): 8.25-8.50], 658 deaths occurred. As shown in Figure 1, the Kaplan-Meier survival curves differed by CVH status (P < 0.001, log-rank test). The survival curves of the high CVH group were significantly greater than those of the moderate and low CVH groups.
Figure 1 Association between different cardiovascular health statuses and cumulative mortality risk in prediabetic patients.
As the number of months of follow-up increases (X-axis, 140 months in 2007 for National Health and Nutrition Examination Survey patients), survival (Y-axis) varies by cardiovascular health status. Notably, only the original cumulative mortality data are shown in Figure 1, and the covariate-adjusted mortality risk data are shown in Table 2. The solid lines indicate cumulative mortality, and the solid lines indicate confidence intervals.
Associations between CVH status and the risk of mortality in patients with prediabetes
We developed multivariable Cox proportional hazards models, as shown in Table 2, in which worsening CVH status led to an increased risk of death. After adjusting for all potential covariates (Model 3, C statistic: 0.8252166), the risk of death in the moderate CVH group and the low CVH group was 2.55 times (HR: 2.55, 95%CI: 1.23-5.31) and 3.92 times (HR: 3.92, 95%CI: 1.70-9.02) greater, respectively, than that in the high CVH group. Clinically, these results indicate that maintaining a high LE8 score would help reduce the risk of death in prediabetic patients.
Table 2 Hazard ratios of cardiovascular health status to all-cause mortality in prediabetes.
CVH status (LE8 score)
N
Model 1
Model 2
Model 3
HR (95%CI)
P value
HR (95%CI)
P value
HR (95%CI)
P value
High (80-100)
199
Reference
Reference
Reference
Moderate (50-79)
3943
4.32 (2.10,8.91)
< 0.001
2.68 (1.34, 5.36)
0.005
2.55 (1.23, 5.31)
0.012
Low (0-49)
1202
8.39 (4.03,17.48)
< 0.001
4.75 (2.27, 9.93)
< 0.001
3.92 (1.70, 9.02)
0.001
Linear relationship between the CVH total score (LE8) and all-cause mortality risk in prediabetic patients
RCS modeling and visualization were adopted to investigate the linear relationship in prediabetic patients. The multivariable adjusted RCS models revealed a significant linear association between the LE8 score and all-cause mortality risk (P-overall < 0.0001, P-nonlinear = 0.7989; Figure 2A). AIC evaluation revealed that the model had good statistical power, with an AIC = 19.00. We calculated that for every 10-point increase in the average LE8 score, the risk of all-cause mortality decreased by up to 30%.
Figure 2 Dose-response relationship between the total cardiovascular health score of the Life's Essential 8 and all-cause mortality risk in patients with prediabetes.
A: All participants; B: Stratified analysis was performed according to age group (older than 60 years or younger); C: Stratified analysis was performed according to sex (male or female); D: Stratified analysis was performed according to anemia status (defined as hemoglobin < 12 mg/dL in men and < 11 mg/dL in women); E: A stratified analysis was performed according to the income-to-poverty ratio (higher than the study population median of 2.06 or not); F: Stratified analysis was performed according to the presence of cardiovascular disease (defined as any myocardial infarction, congestive heart failure, angina attack, or coronary heart disease, as diagnosed by a physician).
Stratified analyses revealed that the LE8 score was linearly associated with all-cause mortality risk across all strata (Figure 2B-F). In addition, after all CVD patients were eliminated, the linear relationship between the LE8 score and all-cause mortality risk was still significant (n = 4783, P-overall < 0.0001, P-nonlinear = 0.8749; Supplementary Figure 2A). To avoid the effect of reverse causality, we also examined the association of the LE8 score with all-cause mortality risk in the subgroup of patients who had at least 1 year of follow-up, and the results were generally consistent with those in the overall population (n = 5295, P-overall < 0.0001, P-nonlinear = 0.7198; Supplementary Figure 2B).
Associations of individual components of CVH, health behaviors, and health indicators with all-cause mortality risk in patients with prediabetes
We included individual components of CVH status as covariates in the multivariable Cox regression model, and the results are shown in the forest plot (Figure 3A). We found that among health behaviors, higher scores of sleep (HR: 0.76, 95%CI: 0.62-0.92), exercise (HR: 0.49, 95%CI: 0.38-0.62) and smoking (HR: 0.73, 95%CI: 0.54-0.98) were associated with a lower risk of all-cause mortality. However, no significant association was found for the diet score. In terms of health indicators, we found that prediabetic patients with higher scores, such as BMI scores (HR: 1.37, 95%CI: 1.13-1.66) and lipid scores (HR: 1.21, 95%CI: 1.01-1.44), were at greater risk of all-cause mortality. Moreover, we observed a protective effect of high glucose scores (HR: 0.63, 95%CI: 0.49-0.80) but no significant association with high blood pressure scores.
Figure 3 Associations of individual cardiovascular health component scores, health behaviors and indicators with all-cause mortality risk in patients with prediabetes.
A: Association between individual cardiovascular health component scores and all-cause mortality risk in patients with prediabetes (high for ≥ 50 points, otherwise low); B: Association of the mean score of health behaviors or indicators with all-cause mortality risk in patients with prediabetes. Covariates included age (greater than 60 years or not), sex (male or female), ethnicity (black, white, or other people), income and poverty rate (higher than the study population median 2.06 or not), cardiovascular disease (defined as any one of myocardial infarction, congestive heart failure, angina attack, or coronary heart disease, as diagnosed by a physician), and anemia (defined as hemoglobin < 12 mg/dL in men and < 11 mg/dL in women). ALQ110: At least 12 servings of alcohol in a lifetime; CVD: Cardiovascular disease.
Because of the aforementioned heterogeneity in the associations between scores for health behaviors and health indicators, we further explored the associations between the mean scores for health behaviors and health indicators and all-cause mortality by constructing a multivariate Cox regression model (Figure 3B). Compared to participants with high average health behavior scores, participants with medium and low average health behavior scores had 1.93 times (HR: 1.93, 95%CI: 1.15-3.24) and 3.92 times (HR: 3.06, 95%CI: 1.89-4.98) greater risks of death, respectively. However, the protective effect of higher mean scores on health indicators was no longer significant (Figure 3B).
DISCUSSION
On the basis of a nationally representative sample from the NHANES, we found that CVH status quantified by the LE8 score had a significant effect on preventing mortality outcomes in patients with prediabetes. In terms of the linear relationship, the mortality risk in prediabetic patients decreased by 30% for every 10-point increase in the LE8 score, on average. Improving CVH health behaviors has a more significant protective effect on prediabetic patients than improving CVH health indicators. Owing to the strong accessibility of health behavior assessment in patients' daily lives, the CVH status based on the LE8 score may greatly improve the quality of life and prognosis of patients with prediabetes.
There has been controversy in previous large cohort studies about whether lifestyle interventions are effective in reducing all-cause mortality risk in diabetic patients. We applied a uniform CVH quantification criterion based on the LE8 score and showed that high CVH status was significantly associated with reduced all-cause mortality risk in prediabetic patients. Effective health interventions are important for improving survival prognosis and reducing the medical burden of prediabetes, and the results of the DQDPOS support our view.
Previous studies have shown that quantified CVH has a protective effect on diabetes incidence, quality of life and mortality[16,28-31]. In a follow-up cohort of 309789 participants, Sun et al[17] reported that higher CVH status was associated with reduced premature mortality among T2DM patients (HR = 0.42, 95%CI: 0.39-0.45). On the basis of the guidelines of the American Association of Clinical Endocrinology and considering the value of early-life interventions, interventions for patients with prediabetes may achieve better prognostic outcomes; therefore, we further studied patients with prediabetes and found a significant protective effect. In a meta-analysis of 193126 participants, Geidl et al[32] reported that a 1-point improvement in CVH status on the basis of the LS7 score was associated with an average 11% reduction in all-cause mortality risk (HR = 0.89, 95%CI: 0.86-0.93), with significant linear characteristics. However, with the LS7 score, CVH is quantified by simply adding the categorical scores of the seven factors, which may result in insufficient statistical power for linear associations[16]. Therefore, we used the LE8 score from the mean score of the hundred-mark scale for the eight health factors for the analysis and detected significant linear associations (P-overall < 0.0001, P-nonlinear = 0.7989). Every 10-point increase in the LE8 score was associated with a 30% reduction in all-cause mortality risk in prediabetic patients, which means that even a slight improvement in CVH status may have a significant preventive effect against mortality in prediabetic patients. Given the accessibility of the various assessments of LE8 in the daily life of patients, we focused on the health behavior component of CVH. The results showed that health behaviors could significantly improve the survival prognosis of patients. However, after adjustments, the health indicator scores have no significant protective effect, which may be due to a more fundamental effect of health behaviors. Our results suggest that physicians should strengthen health behavior education for patients with prediabetes. Marteau et al[33] highlighted that promoting diet, physical activity and other behaviors in the healthiest state remains a major challenge for cardiometabolic disease prevention efforts, which further supports our view.
Among CVH health behaviors, we noticed that increased physical activity was the top protective factor in reducing the risk of all-cause mortality. The protective effects of physical activity against mortality have been widely demonstrated[28], and our analysis validates the recent scoring method of physical activity in LE8 in the prediabetic population. Physical activity has been shown to improve vascular endothelial function, reduce sympathetic load, reduce inflammation, and increase insulin sensitivity, helping to avoid the development of diabetes complications[34]. We also found that nicotine exposure was the second most significant contributor to mortality risk in prediabetic patients. Cohort study data show that smoking cessation is associated with a reduced risk of all-cause mortality[35]. Our findings confirm this association and emphasize the protective benefits of long-term smoking cessation in prediabetic patients with a history of smoking, based on the LE8-based scoring method for the duration of smoking cessation. Previous studies have shown that smoking is associated with increased insulin resistance and inflammatory status and can also lead to an increase in low-density lipoprotein and a decrease in HDL, which can exacerbate diabetic comorbidities[36,37]. Previous evidence has shown that unhealthy sleep duration (< 7 or > 8 hours) is associated with increased all-cause mortality[38]. Our results showed that the risk of all-cause mortality significantly decreased with an increase in sleep index score. The pathological mechanism underlying the effects of sleep deprivation may involve reduced AKT phosphorylation and testosterone and melatonin secretion, leading to insulin resistance in human adipocytes[39] and, ultimately, to cardiovascular (CV) death[40]. Factors including exercise, smoking, and sleep can all affect the inflammatory response of patients, which in turn affects the prognosis of diabetes. Therefore, in the process of diabetes management, it is necessary to further control the inflammatory response[41].
Interestingly, BMI and blood lipid scores greater than 50 were significantly positively associated with all-cause mortality risk in prediabetic patients. This may be related to the obesity paradox, where obesity is a risk factor for disease incidence but has a protective effect in patients with severe disease[42-45]. Similarly, in a meta-analysis of 161,984 participants, Liu et al[46] reported the protective effect of overweight status in patients with type 2 diabetes. However, the opposite result was observed in a follow-up study with an average of 15.8 years, and Tobias et al[47] suggested that the obesity paradox was due to an insufficient follow-up time. Our findings may provide new evidence in this controversial area, and further studies are needed to confirm possible interactions in the prediabetic population. Our results revealed that the relationship between a dietary health score ≥ 50 and all-cause mortality risk was not significant. Because few participants had a score of 80 or higher in each category, we used a cutoff of 50 or higher to improve the statistical power. This cutoff may not have met the health requirements for improved outcomes in patients with prediabetes, given that diet quality is a major determinant of diabetes risk [48,49]. In a cohort study based on a United States population, Fretts et al[31] reported a similar phenomenon. A similar reason may explain the nonsignificant results for blood pressure scores.
To our knowledge, this is the first study to examine the association between quantified CVH and all-cause mortality risk in prediabetic patients. The strengths of this study are that it considers both the need to prevent prediabetes and the need to mitigate the impact of CVD comorbidities on prediabetic patients. In addition, we designed a study with a relatively large sample size and long-term follow-up, used the new LE8 score recommended by the AHA to accurately quantify CVH status, and performed covariate adjustment and sensitivity analysis during the data analysis, which made our results more reliable.
However, several potential limitations are worth considering. First, this study was an observational cohort study, so causality should be interpreted with caution. Second, the four CVH health behaviors (diet, physical activity, nicotine exposure, and sleep duration) included in the LE8 score were all self-reported, which may lead to recall bias. More objective monitoring would be beneficial to improve the accuracy of these four health behaviors. Third, we obtained the CVH score only once at baseline, but it may have changed during long-term follow-up. Fourth, owing to the small number of NHANES data years available to calculate the LE8 score and the small sample of cause-specific deaths, such as CV- and diabetes-related deaths, we failed to analyze the association between the LE8 score and cause-specific deaths. More cohort studies with longer follow-up times, larger sample sizes, or more outcomes (such as diabetes incidence, CVD onset, and cause-specific death) should be conducted to obtain more comprehensive evidence of the association between LE8 scores and prognosis in the prediabetic population. Finally, our study population included only United States adults, and caution should be taken when generalizing the results to other populations.
CONCLUSION
Our research suggests that a better CVH status on the basis of the LE8 score, especially better CVH health behaviors, has a significant protective effect against mortality in prediabetic adults. There was a linear association between an increased LE8 score and reduced all-cause mortality risk. Our findings highlight the public health value of focusing on CVH improvement in prediabetic patients, especially those with more accessible health behaviors, such as quitting smoking and alcohol consumption, increasing physical activity and improving their sleep duration, because even small improvements can yield substantial reductions in the risk of all-cause mortality.
ACKNOWLEDGEMENTS
This research was conducted via the NHANES resources. We are grateful to the participants and staff of the NHANES for their valuable contributions.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
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