https://orcid.org/0000-0003-4720-6674
Figures
Abstract
Introduction
Cardiovascular diseases (CVD) are the greatest threat to health worldwide and in Russia. Our study aimed to use Cox proportional hazards models to develop cardio-vascular risk scores and nomograms based on prospective data from studies conducted in Russia.
Methods
All materials used in this study were obtained from the epidemiological study “Epidemiology of Cardiovascular Diseases in the Regions of the Russian Federation” (ESSE-RF): ESSE-RF (2012-2014) and ESSE-RF2 (2017). A total of 18,454 individuals without CVD aged 25–64 years were included in our study. The participants were randomly divided into a training and testing set at a ratio of 7:3. The Russian deprivation index and its components (social, economic and environmental) were used as area-level predictors. To select the best potential predictive variables for our models, the random forests variable selection algorithm based on minimal depth was used. To predict three- and five-year CVD-free survival, four prognostic nomograms were developed from the results of multivariate analysis.
Results
The nomograms had considerable discriminative power, calibrating abilities and clinical effectiveness. The time dependent AUC was > 0.7 for the prediction of CVD-free survival in both the training and testing sets.
Conclusion
For the first time, the nomograms have been created that include area-level predictors (socio-economic and environmental) and lipid spectrum indicators (triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol) and assess the probability of fatal and non-fatal cardiovascular events among the Russian population.
Citation: Zelenina AA, Shalnova SA, Drapkina OM (2025) Development and validation of nomograms including individual- and area-level variables to predict risk of fatal and non-fatal cardiovascular diseases among Russian population. PLoS One 20(5): e0324736. https://doi.org/10.1371/journal.pone.0324736
Editor: Aleksandra Klisic, University of Montenegro-Faculty of Medicine, MONTENEGRO
Received: August 14, 2024; Accepted: April 30, 2025; Published: June 2, 2025
Copyright: © 2025 Zelenina et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data of ESSE-RF and ESSE-RF2 cannot be shared publicly in accordance with the rules of the ethics committee of the National Medical Research Center for Therapy and Preventive Medicine regulations. De-identified data will be provided on reasonable request to the corresponding author (nasty-zelenin@yandex.ru) or to the research coordinator, the leading researcher of Department of Epidemiology of Chronic Non-Communicable Diseases Galina Muromtseva (GMuromtseva@gnicpm.ru) or by the Ethics Committee of the National Medical Research Center for Therapy and Preventive Medicine, Russia, Moscow, Petroverigsky per., 10, building 3 (phone number +7 (499)-553-68-10, SecretaryNEC@gnicpm.ru). Proposals will be reviewed and approved by the researchers, local regulatory authorities, and the ethics committee of the National Medical Research Center for Therapy and Preventive Medicine. Once the proposal has been approved, data may be transferred through a secure online platform after signing a data access agreement and a confidentiality agreement.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Cardiovascular diseases (CVD) are the greatest threat to health worldwide and in Russia. In 2021, 20.5 million people died from a cardiovascular condition globally (around one-third of all global deaths) [1]. According to World Health Organization, in 2019 the top two causes of death were ischemic heart disease (385 deaths per 100,000 population) and stroke (224 deaths per 100,000 population) in Russia [2].
Undoubtedly, the most effective measure to combat cardiovascular disease is the early identification of risk factors. A number of large studies have made significant contributions to the understanding of individual behavioral and biological risk factors, for example the Framingham Study [3] and the North Karelia Project [4].
During the Framingham Study, the concept of risk factors for the development of CVD was first introduced and the theory of modified and unmodified risk factors was put forward. The study also identified risk factors for CVD, such as smoking, increased levels of low-density lipoprotein cholesterol (LDL-C), a diet high in cholesterol/animal fat, arterial hypertension, diabetes mellitus (DM), insufficient physical activity, obesity, postmenopausal status, etc. All these factors had a direct impact on the occurrence and development the disease.
Certainly, studying the influence of risk factors of the so-called direct exposure has been productive. Thus, during the implementation of the “North Karelia” project, the main attention was paid to general lifestyle changes (especially nutrition and smoking), which, when implemented as a preventive intervention, led to a decrease in mortality from coronary heart disease (CHD) by 56% in men and by 64% in women aged 35–64 years. However, the reduction in the level of risk factors explained only 53% of the total reduction in mortality from CHD, 23% of the total decline in CHD was due to treatment, and about 24% of the total decline in incidence was not explained by the risk factors included in the model. The data obtained suggested that in addition to direct impact factors, there are factors that indirectly affect health. Awareness of this fact served as an impetus for the development of concepts of social determinants of health (SDOH).
The US Centers for Disease Control and Prevention provides the following definition of SDOH: “the nonmedical factors that influence health outcomes. They are the conditions in which people are born, grow, work, live, and age. These forces and systems include a wide set of forces and systems that shape daily life such as economic policies and systems, development agendas, social norms, social policies, and political systems” [5].
The literature is replete with studies pertaining to socio-economic and environmental inequities in healthcare [6–11]. Studying the impact of social determinants on population health, they adhere to a deprivation approach, which defines inequalities in socio-economic and environmental conditions and health [12].
In the studies, individual and area-level indicators of deprivation can be identified. For example, few studies [13,14] showed the association between self‐reported indicators of deprivation and risk of atherosclerotic cardiovascular diseases (ASCVD).
Area-level indicators, in turn, are presented in studies as separate indicators [14] or as part of the index of deprivation [15].
Area-level deprivation indicators improve cardiovascular risk assessment tools in case of spatial differences in cardio-vascular health [16,17]. The use of a unified national CVD risk assessment model in such conditions will certainly lead to an inadequate assessment of risk: underestimation of risk in some environmental conditions (regions) and overestimation in others. The use of index to measure the area-level deprivation also has its advantages, as it allows one to take into account more complex mechanisms of interaction between social, economic and environmental indicators when studying their impact on health. In world scientific practice, awareness of these facts has already led to some national cardiovascular risk scores began to include environmental characteristics of residence within individual countries. For example, the New Zealand PREDICT score includes as a risk factor – New Zealand index of socioeconomic deprivation [18], the British QRISK score – Townsend deprivation index [19], the Scottish ASSIGN score – the Scottish Index of Multiple Deprivation [20].
A number of studies confirm the association of these deprivation indices with cardiovascular disorders [21–27].
In addition to cardiovascular risk score, deprivation indices are included in tools risk assessment of type 2 diabetes [28], common cancers [29], survival in patients with colorectal cancer [30], breast cancer mortality [31] lung cancer [32], oesophageal cancer [33], ischemic stroke [34], dementia [35], and asthma exacerbations [36].
In Russian medical practice used a tool for assessing fatal risk of CVD – SCORE [37] and its updated version, a tool for assessing fatal and non-fatal risk of CVD – SCORE2 [38]. Both of these tools were validated for the Russian population and include only traditional risk factors. Unfortunately, as far as we know, there is no cardiovascular risk score for the Russian population, which, in addition to the traditional risk factors, includes characteristics of areas of residence. Therefore, cardio-vascular risk score included area-level social determinants is needed for Russian adults.
Our study aimed to use Cox proportional hazards models to develop cardio-vascular risk scores and nomograms [39] based on prospective data from studies conducted in Russia. The models’ discrimination, clinical utility, calibration and internal validation will all be assessed.
Materials and methods
Data sources
All materials used in this study were obtained from the epidemiological study “Epidemiology of Cardiovascular Diseases in the Regions of the Russian Federation” (ESSE-RF): ESSE-RF and ESSE-RF2. The recruitment period for ESSE-RF began on September 15, 2012 and ended on June 10, 2014. The recruitment period for ESSE-RF2 was from July 3, 2017, to February 14, 2018. The ESSE-RF was registered at ClinicalTrials.gov as NCT02449460. A total of 28,817 individuals aged 25–64 years were examined. The studies were performed using a single protocol. The samples were drawn based on the Kish method [40], which provides for systematic, multi-step, random community-based sampling on the premises of medical and preventive treatment facilities. The ESSE-RF and ESSE-RF2 studies have been carried out in two phases: 1st phase – the examination of study participants to investigate CVD risk factors and 2nd – prospective observation of the vital status of the cohort examination and assessment of causes of death. Prospective monitoring the occurrence of fatal and non-fatal events are held annually, starting from next year after completion of the first phase of the studies.
Both databases disclose participant demographic information, laboratory and instrumental indicators, and survival status. Detailed data collection methods have been previously published [41,42].
The study was conducted in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines (S1 Table) [43].
Ethics statement
The study protocol was approved by the following Ethics Committees: National Medical Research Center for Therapy and Preventive Medicine, Russian Cardiology Research-and-Production Complex, and Federal Almazov North-West Medical Research Centre. All study participants provided written informed consent before starting the survey and the interview.
Exclusion criteria
Exclusion criteria was applied during the data selection process. Participants who met any of the following criteria were excluded from the study: CVD or missing information about CVD at the 1st phase of the study and unknown survival time. A positive answer to the question: “Has a doctor ever told you that you have/had the following diseases: stroke (cerebral thrombosis or hemorrhage), myocardial infarction, CHD (angina), heart rhythm disturbances, other heart diseases?” was considered an identifier of a history of CVD. Fig 1 showed the workflow of this study.
KM, Kaplan-Meier; IDI, integrated discrimination improvement; NRI, net reclassification improvement; ROC, receiver operating characteristic; CVD, cardiovascular diseases.
Individual-level independent variables
The analysis included sex, age, place of residence (rural/urban), smoking status (current/former/never), DM, body mass index (BMI), systolic blood pressure (SBP), heart rate (HR), the use of antihypertensive medications and laboratory parameters such as high-density lipoprotein cholesterol (HDL-C), LDL-C, triglycerides (TG), uric acid, creatinine and glucose.
Blood pressure and HR were measured using an automatic blood pressure monitor (Omron, Kyoto Japan) after patients were requested to rest for 5 minutes in a seated position.
The body height and weight were measured in participants with lightweight clothing and without shoes. BMI was calculated using the formula weight/height2.
Information on the use of antihypertensive medication, smoking status, place of residence, and DM were self-reported by the participants. Respondents who smoked at least one cigarette/cigarette per day or who quit smoking less than a year ago were considered to be smokers. Three groups of people were identified according to their smoking status: current, former and never.
Blood sampling for biochemical testing were collected via venipuncture after 12 hours of fasting. Blood serum was obtained by low-speed centrifugation at 900 g for 20 minutes at a temperature of +4°С. Samples of biological material were frozen and stored at a temperature not exceeding minus 20°С. Transportation of biomaterials was carried out by specialized services. Lipid spectrum indicators (the levels of TG, LDL-C and HDL-C), as well as uric acid, creatinine and glucose, were determined on an Abbot Architect c8000 autoanalyzer using diagnostic kits from Abbot Diagnostic (USA). Standardization and quality control of the analysis were carried out in accordance with the requirements of the Federal System for External Quality Assessment of Clinical Laboratory Research.
Missing data
Missing values in the dataset were: self-reported doctor-diagnosed DM (0.05%), use of antihypertensive medications (0.38%), SBP (0.29%), HR (0.3%), BMI (0.63%), HDL-C (1.52%), LDL-C (1.9%), creatinine (1.66%), blood glucose (1.53%), uric acid (1.57%), and smoking status (0.1%). Bootstrap-based Expectation-Maximization algorithm was employed to impute missing values using “Amelia” package [44].
Definitions
Survival status: The outcome of a participant without CVD at the time of examination either event or censored.
Event: CVD death (based on the International Classification of Diseases, 10th revision (ICD-10) code recorded as the underlying cause of death) or CHD (non-fatal) or a cerebrovascular accident (non-fatal).
Survival time: Measure of the follow-up time from a defined starting point from the examination of participants at the 1st phase of the study to the occurrence of the event.
Area-level deprivation index
Russian deprivation index (RDI) was used to measure level of deprivation. Indicators for the index were obtained from official statistical publications of the Federal State Statistics Service of Russia and the All-Russian Census of Population for 2010 [45]. Principal component analysis was used to create the index. RDI includes 17 indicators reflecting the socio-economic and environmental deprivation at the regional level in Russia (S2 and S3 Tables). The deprivation index and its components levels were divided into four quantiles. The deprivation effect was assessed by comparing four quantiles. The first quantile (Q1) is the least deprived area; the fourth quantile (Q4) is the most deprived area. The index measures general deprivation, and its components measure social, economic and environmental deprivation, respectively (S4–S7 Tables). Further details on the creating of RDI have been described in the previous publication [46].
Statistical analysis
All statistical analyses were conducted using R software (version 4.4.1; https://www.R‐project.org) with the R base package. The participants were randomly divided into a training and testing set at a ratio of 7:3 using the “caret” package. The training set was used to develop the models and the testing set for internal validation.
Continuous variables were described by median (Med) and the interquartile range (IQR). Categorical variables were expressed as percentages (%) and absolute numbers (n). Variables between sets were assessed by Pearson’s Chi-squared test and Wilcoxon rank sum test as appropriate. Multicollinearity was evaluated by the variance inflation factor (VIF) using package “car”. VIF > 4.0 was interpreted as indicating multicollinearity [47].
To select the best potential predictive variables for our models, the random forests variable selection algorithm based on minimal depth from the package “randomForestSRC” was used [48]. The features selected in the data set using the random forests variable selection algorithm were utilized to construct nomograms through a multivariate Cox regression analysis to predict participants’ 3-year and 5-year survival probability. These variables were then taken into multivariate Cox regression models. For the current study we used clustered data [49]: individual-level variables (e.g., sex, age, smoking status, DM, etc.) and cluster-level variables (RDI and its components) thus Cox-Proportional hazards models with Huber-White cluster sandwich estimator of variance were used [50,51] To assess the impact of the prognostic factors for 3-year and 5-year CVD-free survival (fatal/non-fatal), Hazard Ratio (HR) and 95% confidence interval (CI) were calculated using multivariate Cox proportional risk regression models. Four Cox regression models were constructed. All models include the same laboratory and anthropometric indicators, the difference lies in area-level indicators. Model 1 includes an indicator of general deprivation, model 2 – social deprivation, model 3 – economic deprivation, model 4 – environmental deprivation. Then, to predict the survival probability of CVD (fatal/no-fatal), a nomogram models were constructed using “rms” package. Model 1 used to develop nomogram A, model 2 – nomogram B, model 3 – nomogram C, model 4 – nomogram D.
The reliability and accuracy of the nomograms were assessed via Harrell’s concordance-index (C-index), receiver operating characteristic (ROC) analysis and calibration curves. The area under the time-dependent ROC curve (AUC) was used to evaluate the prediction discrimination of the nomograms. In the regression model, AUC value was similar to C-index, and AUC value > 0.7 was considered to have better discrimination ability [52]. Calibration curves were plotted using the R software “rms” package to evaluate the calibration of the nomograms. To further estimate the clinical usefulness of the nomograms, decision curve analysis (DCA) was conducted using the “dcurves” package.
The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to calculate the performance improvement of the nomogram A compared to the nomograms B, C and D.
Additionally, the total score for all participants was computed utilizing these nomograms and two cut-off points for these total scores were determined using package “rolr” [53]. The participants were divided into the low-risk, moderate-risk and high-risk groups. To evaluate the prognostic differences between the three risk groups, the Kaplan-Meier (KM) method was used for survival analysis employing the log-rank test with Bonferroni correction. P < 0.05 for both sides was considered significant.
Results
Variable selection
VIF was assessed among the independent variables (sex, age, place of residence (rural/urban), smoking status (current/former/never), DM, BMI, SBP, HR, HDL-C, LDL-C, TG, uric acid, creatinine, glucose, use of antihypertensive medications, and values of RDI (its components)). All variables had VIF < 4.0 and were included in random forest variable selection analysis.
The results of random forest selection process (the tree minimal depth approach) indicated the following independent variables: sex, age, smoking status (current/former/never), DM, BMI, SBP, HR, HDL-C, LDL-C, TG, uric acid, creatinine, glucose, and values of RDI (its components) as important variables and therefore they were included in final Cox regression models. Place of residence (rural/urban) and use of antihypertensive medications were excluded from the analysis (Fig 2).
Vertical dashed line indicates the maximal minimal depth for important variables. Indicators for nomogram A (A), nomogram B (B), nomogram C (C), nomogram D (D). SBP, systolic blood pressure; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol.
Baseline characteristics
Table 1 illustrated the basic demographic, anthropometric, and laboratory information of the eligible participants. We divided all participants into the training set (n = 12,919) and the testing set (n = 5,535). There were no significant differences in LDL-C, HDL-C, TG, age, BMI, SBP, serum uric acid, creatinine, HR between the two sets (p > 0.05). There were significant differences among participants who living in general deprived areas. As for social, economic and environmental deprived areas, the difference between the training set and the testing set was not statistically significant (p > 0.05).
S8 Table showed the baseline characteristics of the two sets by CVD events. The participants with CVD (fatal/nonfatal) had higher age, TG, BMI, SBP, fasting glucose, serum uric acid, creatinine in the training and testing sets (p < 0.05). And there was no statistically significant difference in HDL-C, heart rate in testing set (p > 0.05).
Multivariate Cox proportional hazard regression analysis
Multivariate analysis showed that smoking status, age, sex, SBP, BMI, LDL-C, HDL-C, and DM were individual-level prognostic factors associated with CVD-free survival in all models (S9 Table). In model 1 general deprivation (p < 0.032, Q2 vs. Q1 (HR 1.19; 95% CI: 1.01–1.39), p < 0.032, Q3 vs. Q1 (HR 1.56; 95% CI: 1.04–2.33)) was associated with CVD-free survival. In model 2 social deprivation (p < 0.003, Q2 vs. Q1 (HR 1.75; 95% CI: 1.21–2.54)) was associated with CVD-free survival. However, in model 3 and 4 economic and environmental deprivation was no statistically significant associated with CVD-free survival.
Construction of nomograms
To predict 3- and 5-year CVD-free survival, four prognostic nomograms were developed from the results of multivariate analysis using the training set (Figs 3–6).
Validation of the nomograms
The C-indices of the training and testing sets were 0.79 (95% CI: 0.75–0.82) and 0.78 (95% CI: 0.74–0.82), respectively, indicating the great discriminative ability of the nomogram A (Table 2). Other C-index values of the training and testing sets also indicated that nomogram B, C and D had good discriminative ability. The time dependent AUC was > 0.7 for the prediction of CVD-free survival in both the training and testing sets (Fig 7), indicating favorable discrimination by the nomograms. Calibration curves with 1000 bootstrap resamples for 3- and 5-year CVD-free survival were close to the 45-degree diagonal, indicating that the predicted survival probabilities were generally consistent with the observed probabilities (Figs 8–11). Moreover, DCA curves confirmed the validity of the nomograms (Fig 12). In sum, the nomograms for 3- and 5-year CVD-free survival had considerable predictive accuracy and clinical effectiveness.
(A) Three-year and (B) five-year CVD-free (fatal/non-fatal) survival in the training set. (C) Three-year and (D) five-year CVD-free survival in the testing set.
(A) Three-year and (B) five-year CVD-free (fatal/non-fatal) survival in the training set. (C) Three-year and (D) five-year CVD-free survival in the testing set.
(A) Three-year and (B) five-year CVD-free (fatal/non-fatal) survival in the training set. (C) Three-year and (D) five-year CVD-free survival in the testing set.
(A) Three-year and (B) five-year CVD-free (fatal/non-fatal) survival in the training set. (C) Three-year and (D) five-year CVD-free survival in the testing set.
The DCA for three- (A), five-year (B), CVD-free survival prediction in training set and for three - (C) and five-year (D) CVD-free survival prediction in the testing set.
Comparison of the nomogram A with nomogram B, C and D
ROC curves were plotted to compare the predictive accuracy of the nomogram A with the nomogram B, C, and D. The comparison of the predictive abilities of the nomogram A with the nomogram B, C and D showed that the AUC values of nomogram A for 3- and 5-year CVD-free survival in the training set (81.2 and 79.3, respectively) were close to the AUC values of nomogram B (81.5 and 79.8, respectively), nomogram C (80.2 and 78.3, respectively), and nomogram D (79.8 and 78.1, respectively) (Fig 13). The results for the testing set also showed that the AUC values of the nomogram A for 3- and 5-year CVD-free survival (80.8 and 77.9, respectively) close to the AUC values for nomogram B (80.9 and 78.7, respectively) nomogram C (79.8 and 76.4, respectively), and nomogram D (79.6, and 76.1, respectively) (Fig 14).
In the accuracy analyses of NRI and IDI, the nomogram A showed similar performance with nomogram B, C and D (Table 3). In the training and testing set, the 3- and 5-year NRI of the nomograms were zero or close to zero. These results together demonstrated that the nomogram A had a same predictive ability with compared to the nomogram B, C and D.
Risk group stratification
Beyond the development and validation of the nomograms, all participants in the training set were sorted based on their nomogram scores and classified into three CVD (fatal/non-fatal) risk groups using “rolr” package: low-risk group (total nomogram A points of 0–211, total nomogram B points of 0–216, total nomogram C points of 0–209, total nomogram D points of 0–216), moderate‐risk group (total nomogram A points of 212–244, total nomogram B points of 217–254, total nomogram C points of 210–255, and total nomogram D points of 217–261) and high-risk group (total nomogram A points of 245–400, total nomogram B points of 255–400, total nomogram C points of 256–400, and total nomogram D points of 262–400).
The results of the KM survival analysis with the log-rank test showed that there existed different CVD-free survival in three risk groups of participants (p < 0.001) (Fig 15). There were also significant differences in pairwise comparisons adjusted by the Bonferroni method among three risk groups.
Development of a web-based calculator
We utilized “shiny” package to develop a free online calculator based on our nomograms. By using the calculator, the probabilities of 3- and 5-year CVD-free (fatal/non-fatal) survival could be easily obtained. Furthermore, this tool calculates the nomogram total score and determines the risk group. The online calculator can be accessed at https://nomcvdepriv.shinyapps.io/Cardio/
Discussion
The results of our study demonstrated that area-level indicators were associated with CVD and can be considered as risk factors and included in models for assessing the risk of CVD. As mentioned above, some studies included area-level socio-economic indices as an independent risk factor in their models for assessing CVD risk. In our study has developed the nomogram that includes RDI with both socio-economic (e.g., unemployment rate, low income, etc.), and environmental variables (e.g., fire forest incidence, transport-related emissions from stationary sources: NO2, SO2, CO, etc.) and the nomograms that include separately the social, economic and environmental components of the index as independent predictors.
Numerous systematic reviews confirmed the association of air pollution indicators with CVD [54–59]. Basic pathophysiological mechanisms, which are activated by exposure to air pollutants and promotes adverse cardiovascular effects are systemic inflammation, oxidative stress, thrombosis, coagulation, vascular dysfunction and atherosclerosis [60–64].
Important is the fact that nomograms created in our study were validated in the Russian population. As mentioned above, we know only two CVD risk assessment scores that have been validated in the Russian population – SCORE and SCORE-2. There are not area-level independent predictors in both scores. Consequently, our study is the first to create nomograms that included individual-level and area-level independent predictors and validated in Russian population. In addition, our nomograms, unlike these CVD risk assessment scores where total cholesterol is used as an independent predictor, included lipid spectrum indicators, such as TG, HDL-C and LDL-C. Our nomograms demonstrated that low levels of HDL-C and high levels of TG and LDL-C contribute significantly to the risk of CVD. This result is similar to the findings of Bartlett et al. [65], who examined the association of low levels of HDL-C with a risk of CVD against the background of increased levels of TG and LDL-C and found that CVD risk was higher when low HDL-C was accompanied by LDL-C ≥ 100 mg/dL (OR 1.3, 95% CI: 1.0–1.6), TG ≥ 100 mg/dL (OR 1.3, 95% CI: 1.1–1.5) or both (OR 1.6, 95% CI: 1.2–2.2). Moreover, the study shown that high levels of TG increase the risk of developing CVD, regardless of the level of HDL-C and LDL-C. Another study [66] also found that increasing TG levels increased the risk of CVD, regardless of HDL-C levels, and in men there was an increase in the risk of CVD with increasing TG levels to 100 mg/dL, and in women to 200 mg/dL. It follows that hypertriglyceridemia is a powerful risk factor for adverse cardiovascular events. This fact may be taken into account when prescribing lipid-lowering medications [67–69]. Therefore, our nomograms can not only assess the risk of CVD, but also contribute to the development of more effective recommendations aimed at preventing CVD, in contrast to the scores that include only total cholesterol.
Our nomograms demonstrated that with increasing BMI also increases the risk of CVD. Kim et al. [70] conducted an umbrella review that included 12 systematic reviews with 53 meta-analyses, which covering more than 501 cohorts and 30 million participants, also confirmed that an increase in BMI was associated with a higher risk of developing all specific CVD.
Our nomograms include blood glucose levels, which allows us to assess the impact of prediabetes on the occurrence of CVD. Moreover, the results of our study demonstrated an increase in the risk of CVD with increasing glucose levels. Our findings is supported by a prospective study [71] conducted in northeast China among 15,557 participants without diabetes aged over 40 years, which found that multivariate-adjusted hazard ratios for CVD mortality in groups with prediabetes and hypertension were 2.28 (95% CI: 1.50–3.47) for those diagnosed by fasting plasma glucose (FPG) < 5.6 mmol/L, 2.18 (95% CI: 1.53–3.10) for those diagnosed by FPG 5.6–6.0 mmol/L and 2.35 (95% CI: 1.65–3.35) for those diagnosed by FPG 6.1–6.9 mmol/L compared with the reference group.
Another study [72] also found that prediabetes (126 mg/dL > FPG ≥ 100 mg/dL) was associated with a 1.95-fold (95% CI: 1.43–2.52, p < 0.0001) increased risk for hypertension. Huang et al. [73] conducted a systematic review with meta-analysis and confirmed that prediabetes was associated with an increased risk of cardiovascular disease. Another systematic review with meta-analysis [74] included total of 129 studies involving 10,069,955 individuals for analysis revealed that in the general population, prediabetes was associated with an increased risk of composite CVD (relative risk (RR) 1.15, 95% CI: 1.11–1.18), CHD (RR 1.16, 95% CI: 1.11–1.21), and stroke (RR 1.14, 95% CI: 1.08–1.20). In patients with ASCVD, prediabetes was associated with an increased risk of composite CVD (RR 1.37, 95% CI: 1.23–1.53), and CHD (RR 1.15, 95% CI: 1.02–1.29). Cao et al. [75] found that progression from prediabetes to diabetes within a 3-year period was associated with higher risks of CVD-related death (HR 1.61, 95% CI: 1.12–2.33) compared with persistent prediabetes. Kim et al. [76] found that individuals with normal fasting glucose (< 5.5 mmol/L) at baseline, who were subsequently newly diagnosed with diabetes (fasting glucose: ≥ 7.0 mmol/L) and prediabetes (fasting glucose: 5.5–6.9 mmol/L), had increased CVD incidence (HR 1.13, (95% CI: 1.05–1.23) and HR 1.04 (95% CI: 1.01–1.07), respectively).
In our nomograms, lowering uric acid levels increases the risk of CVD. Although hyperurecemia is believed to be a risk factor for CVD, the role of uric acid as an independent predictor remains controversial. These contradictions may be associated with pathophysiological processes involving uric acid, which lead to the occurrence and development of CVD [77]. On the one hand, uric acid acts as an antioxidant; on the other hand, uric acid represents a pro-oxidant agent [78]. Acting as pro-oxidant agent a high level of uric acid triggers inflammation and oxidation process (oxidative stress), resulting in endothelial dysfunction, which precedes the development of atherosclerosis [79]. However, uric acid as an antioxidant has protective properties in relation to the vascular endothelium and low levels of uric acid can also activate mechanisms leading to endothelial dysfunction. These assumptions are confirmed by numerous studies. For instance, a study conducted in Japan [80] found U-shaped association between serum uric acid level and risk of CVD mortality. The multivariable adjusted HRs for CVD mortality comparing uric acid < 2.5 mg/dL with uric acid of 3.5–4.4 mg/dL were 3.96 (95% CI: 1.37–11.47) in women. At the same time, in women with uric acid concentrations of ≥ 8.5 mg/dL also had a greater risk for mortality from CVD (HR 11.44, 95% CI: 2.74–47.68), in comparison to women with uric acid concentrations of 3.5–4.4 mg/dL. Another study [81] included 127,771 adults aged 65 years or older also established that SUA ≥ 8 mg/dL (HR 1.13, 95% CI: 1.06–1.21) or < 4 mg/dL (HR 1.16, 95% CI: 1.07–1.25) compared with the reference SUA strata of 4 to < 5 mg/dL independently predicted higher CVD‐related mortality. Some studies also revealed U-shaped or non-linear association between serum uric acid level and risk of CVD mortality [82–84], hypertensive heart failure [85], ischemic stroke [86], and other cardiovascular events [87,88] Also, a number of studies found that low uric acid level predicted poor functional outcomes in acute stroke [89,90]. In any case, the influence of uric acid levels on the occurrence and development of CVD requires further investigation.
The results of our study shown that high creatinine levels increase the risk of CVD. Schytz et al. [91] demonstrated that increasing creatinine from 0 to 30% increased the risk of cardiovascular event among men aged 50–79 years. Chen et al. [92] shown that serum creatinine levels were positively associated with 10-year cardiovascular risk (β = 0.432, p < 0.001) in total participants.
Our results, as well as previous numerous studies [93–96], confirmed the harmful effects of tobacco on the cardiovascular system. The mechanisms by which smoking results in cardiovascular events have been well studied [97]. It has now been established that there is a genetic predisposition to smoking [98–100]. It can be assumed that this fact is one of the reasons for the ineffectiveness of many strategies aimed at preventing and reducing tobacco use [101–103].
Strengths and limitations
This study has some limitations. First, information about medications, including diuretics, lipid-lowering and urate-lowering agents, was not included in this study. Secondly, there is no data available to estimate the 10-year probability CVD-free survival. Nevertheless, our study has several strengths. For the first time, nomograms have been created that include area-level predictors (socio-economic and environmental) and lipid spectrum indicators (TG, HDL-C and LDL-C) and assess the probability of fatal and non-fatal cardiovascular events among the Russian population. Finally, our nomograms were validated using data of population-based nationwide study – ESSE-RF that included the largest representative sample of the general population of Russia.
Conclusions
It is widely understood that preventing and detecting the disease at an early stage of its development are the most effective measures to fight against it. Our nomograms can provide an early prediction for the risk of new-onset cardiovascular disorders as well as timely intervention measures to prevent or delay the occurrence of CVD, and ultimately reduce the adverse cardiovascular prognosis among Russian adults.
Supporting information
S1 Table. TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) checklist.
https://doi.org/10.1371/journal.pone.0324736.s001
(DOCX)
S2 Table. Components of Russian deprivation index.
https://doi.org/10.1371/journal.pone.0324736.s002
(DOCX)
S3 Table. Definitions of deprivation indicators.
https://doi.org/10.1371/journal.pone.0324736.s003
(DOCX)
S4 Table. The federal subjects of Russia stratified by level of general deprivation.
https://doi.org/10.1371/journal.pone.0324736.s004
(DOCX)
S5 Table. The federal subjects of Russia stratified by level of social deprivation.
https://doi.org/10.1371/journal.pone.0324736.s005
(DOCX)
S6 Table. The federal subjects of Russia stratified by level of economic deprivation.
https://doi.org/10.1371/journal.pone.0324736.s006
(DOCX)
S7 Table. The federal subjects of Russia stratified by level of environmental deprivation.
https://doi.org/10.1371/journal.pone.0324736.s007
(DOCX)
S8 Table. Baseline characteristics for the training and testing sets by CVD event (fatal/non-fatal).
https://doi.org/10.1371/journal.pone.0324736.s008
(DOCX)
S9 Table. Multivariate analysis of CVD-free survival in the training set.
https://doi.org/10.1371/journal.pone.0324736.s009
(DOCX)
Acknowledgments
The authors would like to thank Galina Fedotova for the linguistic revision of this paper.
References
- 1.
World Heart Report 2023: Confronting the World’s Number One Killer [Internet]. Geneva, Switzerland: World Heart Federation; 2023 [cited 2024 Jun 5]. Available from: https://world-heart-federation.org/resource/world-heart-report-2023/
- 2.
Russian Federation – WHO Data [Internet] [cited 2024 Aug 1]. Available from: https://data.who.int/countries/643
- 3. Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet. 2014;383(9921):999–1008. pmid:24084292
- 4. Puska P, Vartiainen E, Tuomilehto J, Salomaa V, Nissinen A. Changes in premature deaths in Finland: successful long-term prevention of cardiovascular diseases. Bull World Health Organ. 1998;76(4):419–25. pmid:9803593
- 5.
U.S. Centers for Disease Control and Prevention, Social Determinants of Health (SDOH) [Internet] [cited 2024 Jun 20]. Available from: https://www.cdc.gov/about/priorities/why-is-addressing-sdoh-important.html
- 6. Kamp I van, Loon J van, Droomers M, Hollander A de. Residential Environment and Health: A Review of Methodological and Conceptual Issues. Rev Environ Health. 2021;19(3–4):381–401. pmid:34058091
- 7. Satapathy P, Khatib MN, Gaidhane S, Zahiruddin QS, Gaidhane AM, Rustagi S, et al. Association of neighborhood deprivation and hypertension: A systematic review and meta-analysis. Curr Probl Cardiol. 2024;49(4):102438. pmid:38301916
- 8. Jalili F, Hajizadeh M, Mehrabani S, Ghoreishy SM, MacIsaac F. The association between neighborhood socioeconomic status and the risk of incidence and mortality of colorectal cancer: A systematic review and meta-analysis of 1,678,582 participants. Cancer Epidemiol. 2024;91:102598. pmid:38878681
- 9. van Vuuren CL, Reijneveld SA, van der Wal MF, Verhoeff AP. Neighborhood socioeconomic deprivation characteristics in child (0-18 years) health studies: a review. Health Place. 2014;29:34–42. pmid:24954613
- 10. Ingram E, Ledden S, Beardon S, Gomes M, Hogarth S, McDonald H, et al. Household and area-level social determinants of multimorbidity: a systematic review. J Epidemiol Community Health. 2021;75(3):232–41. pmid:33158940
- 11. MacRae C, Fisken HW, Lawrence E, Connor T, Pearce J, Marshall A, et al. Household and area determinants of emergency department attendance and hospitalisation in people with multimorbidity: a systematic review. BMJ Open. 2022;12(10):e063441. pmid:36192100
- 12. Schervish PG. Peter Townsend. Poverty in the United Kingdom: A Survey of Household Resources and Standards of Living. Pp. 1216. Berkeley, CA: University of California Press, 1979. $37.50. Paperbound, $15.95. Ann Am Acad Political Social Sci. 1981;456(1):182–3.
- 13. Colantonio LD, Richman JS, Carson AP, Lloyd-Jones DM, Howard G, Deng L, et al. Performance of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations by Social Deprivation Status. J Am Heart Assoc. 2017;6(3):e005676. pmid:28314800
- 14. Xia M, An J, Safford MM, Colantonio LD, Sims M, Reynolds K, et al. Cardiovascular Risk Associated With Social Determinants of Health at Individual and Area Levels. JAMA Netw Open. 2024;7(4):e248584. pmid:38669015
- 15. Lang S-J, Abel GA, Mant J, Mullis R. Impact of socioeconomic deprivation on screening for cardiovascular disease risk in a primary prevention population: a cross-sectional study. BMJ Open. 2016;6(3):e009984. pmid:27000783
- 16. Singh M, Nag A, Gupta L, Thomas J, Ravichandran R, Panjiyar BK. Impact of Social Support on Cardiovascular Risk Prediction Models: A Systematic Review. Cureus. 2023;15(9):e45836. pmid:37881384
- 17. Teshale AB, Htun HL, Owen A, Gasevic D, Phyo AZZ, Fancourt D, et al. The Role of Social Determinants of Health in Cardiovascular Diseases: An Umbrella Review. J Am Heart Assoc. 2023;12(13):e029765. pmid:37345825
- 18. Grey C, Wells S, Riddell T, Kerr A, Gentles D, Pylypchuk R, et al. A comparative analysis of the cardiovascular disease risk factor profiles of Pacific peoples and Europeans living in New Zealand assessed in routine primary care: PREDICT CVD-11. N Z Med J. 2010;123(1309):62–75. pmid:20186243
- 19. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ. 2007;335(7611):136. pmid:17615182
- 20. Woodward M, Brindle P, Tunstall-Pedoe H; SIGN group on risk estimation. Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart. 2005;93(2):172–6.
- 21. Ford MM, Highfield LD. Exploring the Spatial Association between Social Deprivation and Cardiovascular Disease Mortality at the Neighborhood Level. PLoS One. 2016;11(1):e0146085. pmid:26731424
- 22. Janghorbani M, Jones RB, Nelder R. Neighbourhood deprivation and excess coronary heart disease mortality and hospital admissions in Plymouth, UK: an ecological study. Acta Cardiol. 2006;61(3):313–20. pmid:16869453
- 23. Stjärne MK, Ponce de Leon A, Hallqvist J. Contextual effects of social fragmentation and material deprivation on risk of myocardial infarction--results from the Stockholm Heart Epidemiology Program (SHEEP). Int J Epidemiol. 2004;33(4):732–41. pmid:15155706
- 24. Jackson CA, Jones NRV, Walker JJ, Fischbacher CM, Colhoun HM, Leese GP, et al.; Scottish Diabetes Research Network Epidemiology Group. Area-based socioeconomic status, type 2 diabetes and cardiovascular mortality in Scotland. Diabetologia. 2012;55(11):2938–45. pmid:22893029
- 25. Bijman LAE, Chamberlain RC, Clegg G, Kent A, Halbesma N. Association of socioeconomic status with 30-day survival following out-of-hospital cardiac arrest in Scotland, 2011-2020. Eur Heart J Qual Care Clin Outcomes. 2024;10(4):305–13. pmid:37727980
- 26. Riddell T. Heart failure hospitalisations and deaths in New Zealand: patterns by deprivation and ethnicity. N Z Med J. 2004;118(1208):U1254. pmid:15682203
- 27. Hippisley-Cox J, Coupland C. Development and validation of risk prediction equations to estimate future risk of heart failure in patients with diabetes: a prospective cohort study. BMJ Open. 2015;5(9):e008503. pmid:26353872
- 28. Hippisley-Cox J, Coupland C. Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study. BMJ. 2017;359:j5019. pmid:29158232
- 29. Hippisley-Cox J, Coupland C. Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: prospective cohort study. BMJ Open. 2015;5(3):e007825. pmid:25783428
- 30. Hippisley-Cox J, Coupland C. Identifying patients with suspected colorectal cancer in primary care: derivation and validation of an algorithm. Br J Gen Pract. 2012;62(594):e29-37. pmid:22520670
- 31. Clift AK, Collins GS, Lord S, Petrou S, Dodwell D, Brady M, et al. Predicting 10-year breast cancer mortality risk in the general female population in England: a model development and validation study. Lancet Digit Health. 2023;5(9):e571–81. pmid:37625895
- 32. Liao W, Coupland CAC, Burchardt J, Baldwin DR, DART initiative, Gleeson FV, et al. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models. Lancet Respir Med. 2023;11(8):685–97. pmid:37030308
- 33. Hippisley-Cox J, Mei W, Fitzgerald R, Coupland C. Development and validation of a novel risk prediction algorithm to estimate 10-year risk of oesophageal cancer in primary care: prospective cohort study and evaluation of performance against two other risk prediction models. Lancet Reg Health Eur. 2023;32:100700. pmid:37635924
- 34. Hippisley-Cox J, Coupland C, Brindle P. Derivation and validation of QStroke score for predicting risk of ischaemic stroke in primary care and comparison with other risk scores: a prospective open cohort study. BMJ. 2013;346:f2573. pmid:23641033
- 35. Walters K, Hardoon S, Petersen I, Iliffe S, Omar RZ, Nazareth I, et al. Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data. BMC Med. 2016;14:6. pmid:26797096
- 36. Kallis C, Calvo RA, Schuller B, Quint JK. Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England. Pragmat Obs Res. 2023;14:111–25. pmid:37817913
- 37. Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al.; SCORE project group. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003. pmid:12788299
- 38. SCORE2 working group and ESC Cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021;42(25):2439–54. pmid:34120177
- 39. Wang X, Lu J, Song Z, Zhou Y, Liu T, Zhang D. From past to future: Bibliometric analysis of global research productivity on nomogram (2000-2021). Front Public Health. 2022;10:997713. pmid:36203677
- 40.
Kish L. Survey sampling. New York: John Wiley & Sons. 1965.
- 41. Research organizing committee of the ESSE-RF project. Epidemiology of cardiovascular diseases in different regions of Russia (ESSE-RF). The rationale for and design of the study. J Profilakticheskaya meditsina. 2013;6:25–34. Russian.
- 42. Kapustina AV, Evstifeeva SE, Muromtseva GA, Konstantinov VV, Balanova YuA, Shalnova SA, et al. The study of the vital status and non-fatal cardiovascular events in the prospective cohort study stage, “Epidemiology of cardiovascular diseases and their risk factors in different regions of the Russian Federation”. J Profilakticheskaya meditsina. 2014;6:26–31. Russian.
- 43. Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1-73. pmid:25560730
- 44. Honaker J, King G, Blackwell M. AmeliaII: A Program for Missing Data. J Stat Soft. 2011;45(7):1–47.
- 45.
The Russian Federal State Statistics Service [Internet]. The All-Russian Census of Population for 2010 [cited 2024 Jun 27]. Available from: https://rosstat.gov.ru/free_doc/new_site/m-sotrudn/eng_site/sensus.html
- 46. Zelenina A. Russian subject-level index of multidimensional deprivation and its association with all-cause and infant mortality. J Prev Med Hyg. 2022;63(4):E533–40. pmid:36890998
- 47.
James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning: with Applications in R. Springer Texts in Statistics: Springer, New York, NY, 2021. https://doi.org/10.1007/978-1-0716-1418-1
- 48.
Ishwaran H, Kogalur UB. Random Forests for Survival, Regression and Classification (RF-SRC). 2016. R package version 2.4.1. [cited 2024 Jul 11]. Available from: https://cran.r-project.org/package=randomForestSRC
- 49. Knox KL, Bajorska A, Feng C, Tang W, Wu P, Tu XM. Survival analysis for observational and clustered data: an application for assessing individual and environmental risk factors for suicide. Shanghai Arch Psychiatry. 2013;25(3):183–94. pmid:24991156
- 50.
Harrell FE. Regression Modelling Strategies with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Second. New York: Springer; 2015.
- 51. Desai M, Bryson SW, Robinson T. On the use of robust estimators for standard errors in the presence of clustering when clustering membership is misspecified. Contemp Clin Trials. 2013;34(2):248–56. pmid:23220255
- 52. Hartman N, Kim S, He K, Kalbfleisch JD. Pitfalls of the concordance index for survival outcomes. Stat Med. 2023;42(13):2179–90. pmid:36977424
- 53.
Crowley J, Mitchell A, Qu P, Morgan G, Barlogie B. Optimal Three-Group Splits Based on a Survival Outcome. In: Matsui S, Crowley J. (eds) Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer, Singapore; 2017. https://doi.org/10.1007/978-981-10-0126-0_14
- 54. Mustafic H, Jabre P, Caussin C, Murad MH, Escolano S, Tafflet M, et al. Main air pollutants and myocardial infarction: a systematic review and meta-analysis. JAMA. 2012;307(7):713–21. pmid:22337682
- 55. Jia Y, Lin Z, He Z, Li C, Zhang Y, Wang J, et al. Effect of Air Pollution on Heart Failure: Systematic Review and Meta-Analysis. Environ Health Perspect. 2023;131(7):76001. pmid:37399145
- 56. Shah AS, Lee KK, McAllister DA, Hunter A, Nair H, Whiteley W, et al. Short term exposure to air pollution and stroke: systematic review and meta-analysis. BMJ. 2015;350:h1295. ; Erratum in: BMJ. 2016 Sep 06;354:i4851.
- 57. Zhang D, Chen W, Cheng C, Huang H, Li X, Qin P, et al. Air pollution exposure and heart failure: A systematic review and meta-analysis. Sci Total Environ. 2023;872:162191. pmid:36781139
- 58. Niu Z, Liu F, Yu H, Wu S, Xiang H. Association between exposure to ambient air pollution and hospital admission, incidence, and mortality of stroke: an updated systematic review and meta-analysis of more than 23 million participants. Environ Health Prev Med. 2021;26(1):15. pmid:33499804
- 59. Barros B, Oliveira M, Morais S. Continent-based systematic review of the short-term health impacts of wildfire emissions. J Toxicol Environ Health B Crit Rev. 2023;26(7):387–415. pmid:37469022
- 60. Miller MR. The cardiovascular effects of air pollution: Prevention and reversal by pharmacological agents. Pharmacol Ther. 2022;232:107996. pmid:34571110
- 61. Chin MT. Basic mechanisms for adverse cardiovascular events associated with air pollution. Heart. 2015;101(4):253–6. pmid:25552258
- 62. Dehghani S, Vali M, Jafarian A, Oskoei V, Maleki Z, Hoseini M. Ecological study of ambient air pollution exposure and mortality of cardiovascular diseases in elderly. Sci Rep. 2022;12(1):21295. pmid:36494401
- 63. Palacio LC, Pachajoa DC, Echeverri-Londoño CA, Saiz J, Tobón C. Air Pollution and Cardiac Diseases: A Review of Experimental Studies. Dose Response. 2023;21(4):15593258231212793. pmid:37933269
- 64. Brook RD, Franklin B, Cascio W, Hong Y, Howard G, Lipsett M, et al.; Expert Panel on Population and Prevention Science of the American Heart Association. Air pollution and cardiovascular disease: a statement for healthcare professionals from the Expert Panel on Population and Prevention Science of the American Heart Association. Circulation. 2004;109(21):2655–71. pmid:15173049
- 65. Bartlett J, Predazzi IM, Williams SM, Bush WS, Kim Y, Havas S, et al. Is Isolated Low High-Density Lipoprotein Cholesterol a Cardiovascular Disease Risk Factor? New Insights From the Framingham Offspring Study. Circ Cardiovasc Qual Outcomes. 2016;9(3):206–12. pmid:27166203
- 66. Aberra T, Peterson ED, Pagidipati NJ, Mulder H, Wojdyla DM, Philip S, et al. The association between triglycerides and incident cardiovascular disease: What is “optimal”? J Clin Lipidol. 2020;14(4):438-447.e3. pmid:32571728
- 67.
Karanchi H, Muppidi V, Wyne K. Hypertriglyceridemia. [Updated 2023 Aug 14]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan- [cited 2025 Mar 28]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK459368/
- 68. Hernandez P, Passi N, Modarressi T, Kulkarni V, Soni M, Burke F, et al. Clinical Management of Hypertriglyceridemia in the Prevention of Cardiovascular Disease and Pancreatitis. Curr Atheroscler Rep. 2021;23(11):72. pmid:34515873
- 69. Drexel H, Tamargo J, Kaski JC, Lewis BS, Saely CH, Fraunberger P, et al. Triglycerides revisited: is hypertriglyceridaemia a necessary therapeutic target in cardiovascular disease?. Eur Heart J Cardiovasc Pharmacother. 2023;9(6):570–82. pmid:37328424
- 70. Kim MS, Kim WJ, Khera AV, Kim JY, Yon DK, Lee SW, et al. Association between adiposity and cardiovascular outcomes: an umbrella review and meta-analysis of observational and Mendelian randomization studies. Eur Heart J. 2021;42(34):3388–403. pmid:34333589
- 71. Yue L, Tian Y, Ma M, Jing L, Sun Q, Shi L, et al. Prevalence of prediabetes and risk of CVD mortality in individuals with prediabetes alone or plus hypertension in Northeast China: insight from a population based cohort study. BMC Public Health. 2024;24(1):475. pmid:38360567
- 72. Geva M, Shlomai G, Berkovich A, Maor E, Leibowitz A, Tenenbaum A, et al. The association between fasting plasma glucose and glycated hemoglobin in the prediabetes range and future development of hypertension. Cardiovasc Diabetol. 2019;18(1):53. pmid:31029146
- 73. Huang Y, Cai X, Mai W, Li M, Hu Y. Association between prediabetes and risk of cardiovascular disease and all cause mortality: systematic review and meta-analysis. BMJ. 2016;355:i5953. pmid:27881363
- 74. Cai X, Zhang Y, Li M, Wu JH, Mai L, Li J, et al. Association between prediabetes and risk of all cause mortality and cardiovascular disease: updated meta-analysis. BMJ. 2020;370:m2297. pmid:32669282
- 75. Cao Z, Li W, Wen CP, Li S, Chen C, Jia Q, et al. Risk of Death Associated With Reversion From Prediabetes to Normoglycemia and the Role of Modifiable Risk Factors. JAMA Netw Open. 2023;6(3):e234989. pmid:36976559
- 76. Kim SM, Lee G, Choi S, Kim K, Jeong S-M, Son JS, et al. Association of early-onset diabetes, prediabetes and early glycaemic recovery with the risk of all-cause and cardiovascular mortality. Diabetologia. 2020;63(11):2305–14. pmid:32820349
- 77. Ndrepepa G. Uric acid and cardiovascular disease. Clin Chim Acta. 2018;484:150–63. pmid:29803897
- 78. Du L, Zong Y, Li H, Wang Q, Xie L, Yang B, et al. Hyperuricemia and its related diseases: mechanisms and advances in therapy. Signal Transduct Target Ther. 2024;9(1):212. pmid:39191722
- 79. Kimura Y, Tsukui D, Kono H. Uric Acid in Inflammation and the Pathogenesis of Atherosclerosis. Int J Mol Sci. 2021;22(22):12394. pmid:34830282
- 80. Cho SK, Chang Y, Kim I, Ryu S. U-Shaped Association Between Serum Uric Acid Level and Risk of Mortality: A Cohort Study. Arthritis Rheumatol. 2018;70(7):1122–32. pmid:29694719
- 81. Tseng W-C, Chen Y-T, Ou S-M, Shih C-J, Tarng D-C, Taiwan Geriatric Kidney Disease (TGKD) Research Group. U-Shaped Association Between Serum Uric Acid Levels With Cardiovascular and All-Cause Mortality in the Elderly: The Role of Malnourishment. J Am Heart Assoc. 2018;7(4):e007523. pmid:29440009
- 82. Cang Y, Xu S, Zhang J, Ju J, Chen Z, Wang K, et al. Serum Uric Acid Revealed a U-Shaped Relationship With All-Cause Mortality and Cardiovascular Mortality in High Atherosclerosis Risk Patients: The ASSURE Study. Front Cardiovasc Med. 2021;8:641513. pmid:34109223
- 83. Chang D-Y, Wang J-W, Chen M, Zhang L-X, Zhao M-H. Association between serum uric acid level and mortality in China. Chin Med J (Engl). 2021;134(17):2073–80. pmid:34320567
- 84. Chen J. Serum uric acid level and all-cause and cardiovascular mortality in Chinese elderly: A community-based cohort study in Shanghai. Nutr Metab Cardiovasc Dis. 2021;31(12):3367–76. pmid:34629247
- 85. Xu H, Wang Q, Liu Y, Meng L, Long H, Wang L, et al. U-Shaped Association Between Serum Uric Acid Level and Hypertensive Heart Failure: A Genetic Matching Case-Control Study. Front Cardiovasc Med. 2021;8:708581. pmid:34957229
- 86. Wang L, Hu W, Miao D, Zhang Q, Wang C, Pan E, et al. Relationship between serum uric acid and ischemic stroke in a large type 2 diabetes population in China: A cross-sectional study. J Neurol Sci. 2017;376:176–80. pmid:28431608
- 87. Li Q, Wu C, Kuang W, Zhan X, Zhou J. Correlation analysis of low-level serum uric acid and cardiovascular events in patients on peritoneal dialysis. Int Urol Nephrol. 2021;53(11):2399–408. pmid:34101100
- 88. Mannarino MR, Pirro M, Gigante B, Savonen K, Kurl S, Giral P, et al.; IMPROVE study group. Association Between Uric Acid, Carotid Intima-Media Thickness, and Cardiovascular Events: Prospective Results From the IMPROVE Study. J Am Heart Assoc. 2021;10(11):e020419. pmid:33998285
- 89. Wu H, Jia Q, Liu G, Liu L, Pu Y, Zhao X, et al. Decreased uric acid levels correlate with poor outcomes in acute ischemic stroke patients, but not in cerebral hemorrhage patients. J Stroke Cerebrovasc Dis. 2014;23(3):469–75. pmid:23735371
- 90. Wu S, Pan Y, Zhang N, Jun WY, Wang C, Investigators for the Survey on Abnormal Glucose Regulation in Patients With Acute Stroke Across China (ACROSS-China). Lower serum uric acid level strongly predict short-term poor functional outcome in acute stroke with normoglycaemia: a cohort study in China. BMC Neurol. 2017;17(1):21. pmid:28143422
- 91. Schytz PA, Nissen AB, Torp-Pedersen C, Gislason GH, Nelveg-Kristensen KE, Hommel K, et al. Creatinine increase following initiation of antihypertensives is associated with cardiovascular risk: a nationwide cohort study. J Hypertens. 2020;38(12):2519–26. pmid:32694338
- 92. Chen X, Jin H, Wang D, Liu J, Qin Y, Zhang Y, et al. Serum creatinine levels, traditional cardiovascular risk factors and 10-year cardiovascular risk in Chinese patients with hypertension. Front Endocrinol (Lausanne). 2023;14:1140093. pmid:37008918
- 93. Pan B, Jin X, Jun L, Qiu S, Zheng Q, Pan M. The relationship between smoking and stroke: A meta-analysis. Medicine (Baltimore). 2019;98(12):e14872. pmid:30896633
- 94. Gallucci G, Tartarone A, Lerose R, Lalinga AV, Capobianco AM. Cardiovascular risk of smoking and benefits of smoking cessation. J Thorac Dis. 2020;12(7):3866–76. pmid:32802468
- 95. Khan SS, Ning H, Sinha A, Wilkins J, Allen NB, Vu THT, et al. Cigarette Smoking and Competing Risks for Fatal and Nonfatal Cardiovascular Disease Subtypes Across the Life Course. J Am Heart Assoc. 2021;10(23):e021751. pmid:34787470
- 96. Banks E, Joshy G, Korda RJ, Stavreski B, Soga K, Egger S, et al. Tobacco smoking and risk of 36 cardiovascular disease subtypes: fatal and non-fatal outcomes in a large prospective Australian study. BMC Med. 2019;17(1):128. pmid:31266500
- 97. Benowitz NL, Burbank AD. Cardiovascular toxicity of nicotine: Implications for electronic cigarette use. Trends Cardiovasc Med. 2016;26(6):515–23. pmid:27079891
- 98. Larsson SC, Mason AM, Bäck M, Klarin D, Damrauer SM, Million Veteran Program, et al. Genetic predisposition to smoking in relation to 14 cardiovascular diseases. Eur Heart J. 2020;41(35):3304–10. pmid:32300774
- 99. Geng T, Chang X, Wang L, Liu G, Liu J, Khor CC, et al. The association of genetic susceptibility to smoking with cardiovascular disease mortality and the benefits of adhering to a DASH diet: The Singapore Chinese Health Study. Am J Clin Nutr. 2022;116(2):386–93. pmid:35551603
- 100. Levin MG, Klarin D, Assimes TL, Freiberg MS, Ingelsson E, Lynch J, et al.; VA Million Veteran Program. Genetics of Smoking and Risk of Atherosclerotic Cardiovascular Diseases: A Mendelian Randomization Study. JAMA Netw Open. 2021;4(1):e2034461. pmid:33464320
- 101. Tantry US, Kundan P, Gurbel PA. Lack of smoking cessation in patients with coronary artery disease: a common worldwide problem. Pol Arch Intern Med. 2022;132(12):16364. pmid:36546475
- 102. Lampridou S. Smoking cessation: why is it a persistent problem in patients with peripheral artery disease?. Br J Nurs. 2023;32(20):958–62. pmid:37938990
- 103. Patel KK, Jones PG, Ellerbeck EF, Buchanan DM, Chan PS, Pacheco CM, et al. Underutilization of Evidence-Based Smoking Cessation Support Strategies Despite High Smoking Addiction Burden in Peripheral Artery Disease Specialty Care: Insights from the International PORTRAIT Registry. J Am Heart Assoc. 2018;7(20):e010076. pmid:30371269