ABSTRACT
Background
The atherogenic index of plasma (AIP) is a novel metabolic biomarker of atherosclerosis. Nevertheless, the association between the AIP and new-onset hypertension has not been elucidated in the Chinese population.
Methods
Prospective data were obtained from 3150 participants aged ≥ 18 years in the China Health and Nutrition Survey from 2009 to 2015. The AIP is a logarithmically transformed ratio of triglycerides to high-density lipoprotein cholesterol in molar concentration. Cox regression analysis was used to determine the association of AIP index with new-onset hypertension.
Results
After the six-year follow-up, 1054 (33.4%) participants developed new-onset hypertension. The participants were divided into AIP quartile groups (Q1-Q4). Compared with those in Q1, subjects in Q3–4 had nearly 1.35 times the risk of new-onset hypertension after full adjustment [Q3: hazard ratio (HR): 1.35, 95% confidence interval (CI): 1.13–1.62; Q4: HR: 1.35, 95% CI: 1.13–1.64]. The risks of new-onset hypertension were nearly 1.30 times higher in subjects in Q2–4 than in subjects in Q1 (p < .01) after the full adjustment when we excluded subjects with diabetes and/or chronic kidney diseases. There was a significant difference [HR (CI): 1.27 (1.04–1.54) vs. 0.90 (0.69–1.18)] when subjects were divided into two groups according to body mass index (BMI) level (<24 vs. ≥24 kg/m2).
Conclusions
The present study suggested that individuals with a higher AIP index are associated with new-onset hypertension, independent of kidney function and glucose levels. The association was stronger in subjects with normal BMI, which may provide early screening of metabolomics in hypertension prevention.
Introduction
Hypertension (HTN) has been acknowledged as the leading modified risk factor in reducing atherosclerotic cardiovascular diseases (ASCVDs) worldwide. An estimated 1.13 billion adults had hypertension by 2015, and 1.56 billion adults are predicted to have hypertension by 2025.Citation1 The death rate attributable to high blood pressure (BP) increased 34.2% from 2009 to 2019, and the actual number of deaths increased 65.3% according to data from the National Vital Statistics System (NVSS) in 2019 reported by the American Heart Association (AHA).Citation2 More than 23.2% of participants suffered from hypertension based on the China Hypertension Survey study, with low awareness, treatment, and control rates of HTN, implying a huge potential burden of future cardiovascular disease in China.Citation3 It is of great value to identify modified risk factors to predict incident hypertension.
The novel combined lipid profile, atherogenic index of plasma (AIP), calculated according to the formula log[triglycerides(TG)/high-density lipoprotein cholesterol (HDL-C)],Citation4 reflecting lipid metabolism, was introduced by Dobiásová et al. in 2000Citation5 and has been considered a prognostic biomarker of ASCVDsCitation6,Citation7 and chronic kidney damage,Citation8 as well as type 2 diabetes.Citation9,Citation10 To date, limited studies have shown the relationship between the AIP index and new-onset hypertension.Citation11 Individuals with higher AIP were reported to be significantly and positively associated with the risk of prehypertension or hypertension based on the cross-sectional analysis.Citation12 A prospective cohort study based in Taiwan indicated that the AIP revealed a significant correlation that remained after full adjustment for hypertension in subjects aged 40–64 years but not in those aged above 65 years.Citation13 A multicenter and longitudinal cohort study needs to be conducted to validate the predictive role of AIP in new-onset hypertension.
We performed an analysis based on the data from the China Health and Nutrition Survey (CHNS) cohort study, which is recognized as representative of China, to assess the relationship between AIP and new-onset hypertension with compelling evidence. We hypothesized that subjects with a higher AIP will be more likely to suffer new-onset hypertension, which would promote early detection.
Materials and methods
Study design
The China Health and Nutrition Survey (CHNS) cohort study was considered to be representative of China.Citation14 Detailed information has been described elsewhere.Citation15,Citation16 The CHNS cohort study enrolled general participants from nine provinces including Liaoning, Heilongjiang, Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi, and Guizhou, which was established in 1989 and followed up in 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, and 2015. During each follow-up, trained staff followed the standardized process to collect demographic characteristics information and blood pressure measurements. Blood samples were first collected by nurses in 2009 (http://www.cpc.unc.edu/projects/china) since the design of our study requires the baseline lipid profiles from the blood sample measurement. This study conducted the analysis based on the CHNS cohort study from 2009 to 2015, and the flow chart is shown in . We included participants who met the following criteria: (1) age ≥18 years; (2) biochemical data, especially TG and HDL-C; and (3) blood pressure measurements at baseline (2009) and during the two follow-ups (2011 and 2015). A total of 3150 participants were finally included in the analysis.
Figure 1. Flowchart of the study participants enrolled from the China health and nutrition survey (n = 3510).
The study protocol was approved by the institutional review committees of the National Institute of Nutrition and Food Safety, the Chinese Center for Disease Control and Prevention, the University of North Carolina at Chapel Hill, and the China-Japan Friendship Hospital, Ministry of Health. The study complied with the Declaration of Helsinki and each participant aged above 18 years provided written informed consent for the cohort study.
Anthropometric measurements and general data
Each staff member received standard training before the cohort study. Demographic and clinical characteristic information was collected through a standard questionnaire, which included sex, age, community type, ethnicity, marital status, education level, occupation, exercise level, history of drinking, smoking, and chronic diseases (hypertension, type 2 diabetes, ischemia disease) with related medical treatments. Height was measured by a standard right-angle device (to the nearest 0.5 cm), weight was measured without shoes and heavy clothing, and we calculated body mass index (BMI) = body weight (kg)/height (m)2. Waist circumference (WC) and hips were measured to the nearest 0.1 cm using a constant tension tape in the standing position. The waist-to-hip ratio (WHR) was calculated as waist (m)/hip (m) circumstance. The fasting blood samples were collected by trained nurses, which included major cardiovascular biomarkers (lipids, diabetes such as HbA1c, glucose, insulin, TG) and important nutrition biomarkers (transferrin, hemoglobin, and ferritin). Protocols used to collect and process blood samples are available at the website (https://www.cpc.unc.edu/projects/china/data/datasets/biomarker-data).
BP measurements
The standard blood pressure was measured three times. Participants were asked to rest for more than 5 minutes without drinking coffee and tea, and then the blood pressure was measured with appropriately sized cuffs by trained staff using a mercury manometer under the standard method. The average BP was used in the final analysis. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded with phase I and V Korotkoff sounds. Mean arterial pressure (MAP) was calculated using the following equation: 1/3 SBP + 2/3 DBP. The definition of new onset hypertension is SBP ≥ 140 and/or DBP ≥90 mmHg, which was proposed by the China Guidelines for the Prevention and Treatment of Hypertension.
Definitions
The AIP is a logarithmically transformed ratio of TG to HDL-C in molar concentration (mmol/L).Citation8 Participants were divided into four groups according to quartiles. Type 2 diabetes was diagnosed as having FBG ≥7.0 mmol/L (126 mg/dl) or HbA1c ≥ 6.5% (48 mmol/mol) by the American Diabetes Association (ADA).Citation17 Chronic kidney disease (CKD) was defined by an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2, which was calculated according to the formula eGFR = 175 × serum creatinine−1.234 × age−0.179 (×0.79 for girls/women), where serum creatinine concentration is in milligrams per deciliter and age is in years.Citation18,Citation19 According to the weight management guidelines of China, BMI was divided into two groups for analysis: underweight and normal weight (<24 kg/m2) or overweight and obese (≥24 kg/m2).Citation20,Citation21
Statistical analysis
Kolmogorov-Smirnov test was used to evaluate the normality of continuous variables. Normally distributed continuous variables are described as the mean ± standard deviation (SD). A t test was used for comparisons between two groups and one-way ANOVA was used for comparisons among three or more groups. Nonnormally distributed continuous variables are expressed as medians (interquartile ranges) and were compared with the nonparametric test. Categorical variables were described as frequencies (%), and chi-square tests or Fisher’s exact tests were conducted for comparison. The relationship between the AIP index as a categorical variable and new-onset hypertension was analyzed through Cox proportional hazard models. Multivariable models were used to adjust for confounding factors. Model 1: sex, age, marital status, smoking, alcohol consumption, province, and diabetes history; Model 2: Model 1+ further adjusted for urea, uric acid, total protein, fasting blood glucose, and eGFR. A two-tailed p value of < 0.05 was considered statistically significant in the present study. All of the analyses were performed with the statistical software SPSS 25.0 (SPSS, Inc., Chicago, IL).
Subgroup analysis
Individuals with a SBP of 120 to 139 mm Hg or a DBP of 80 to 89 mm Hg should be considered as prehypertensive.Citation22 The CKD is defined as decreased kidney function shown by eGFR of less than 60 mL/min per 1·73 m.2Citation23 Previous studies showed cardiovascular risk increased especially after 50 years of age, particularly in hypertension.Citation24,Citation25 We further conducted the subgroup to identify the modification in the relationship between AIP index and new-onset hypertension, subjects were divided into two groups according to sex (male vs. female), age (<50 vs. ≥50 years), WHR level (<0.85Citation10 vs. ≥0.85), smoking habits (yes vs. no), drinking habits (yes vs. no), residence status (urban vs. rural), eGFR (<60 vs. ≥60), BMI (<24 vs. ≥24 kg/m2), SBP (<120 vs. 120- < 140 mmHg) and DBP (<80 vs. 80- < 90 mmHg) levels.
Sensitivity analysis
We conducted a sensitivity analysis to modify the potential effect of T2DM and renal function on the incidence of hypertension after excluding 263 subjects with type 2 diabetes, 168 subjects with CKD and 402 subjects with both type 2 diabetes and CKD. As the novel definition of hypertension was recommended and published by the American College of Cardiology (ACC) and American Heart Association (AHA) guidelines,Citation26 we conducted a sensitive analysis of relationship between the AIP index and new-onset hypertension by the 130/80 mmHg definition.
Results
Differences in clinical characteristics according to AIP interquartile of the study population
A total of 3150 available patients were finally analyzed in this study, and 41.8% were males. The median(interquartile range) of AIP index was −0.08 (−0.28–0.14), and the participants were divided into four groups according to the interquartile range. The comparison of clinical characteristics at baseline and follow-up is shown in . Subjects with the highest AIP were more likely to be male, with the highest rates of smoking and drinking and the highest BMI, WHR, serum uric acid (UA), total cholesterol, TG, fasting plasma glucose and alanine aminotransferase levels and the lowest HDL-C level (p < .05). No significant difference existed in age, the distribution of living residence or marital status(p > .05). Moreover, we compared the baseline characteristics between subjects with new-onset hypertension and those with normal blood pressure (Table S1). The BMI, WHR, serum UA, total cholesterol, TG, fasting plasma glucose and alanine aminotransferase levels of subjects with new-onset hypertension were significantly higher than those of subjects with normal blood pressure (p < .05).
Table 1. Characteristics of the study participants at baseline and during follow-ups.
Association of AIP index with new-onset hypertension
After six years of follow-up, 1054 (33.4%) participants developed new-onset hypertension. As the AIP index group increased from quartile 1 (Q1) to Q4 according to AIP level, the incident rates of new-onset hypertension were 27.7%, 31.1%, 37.5% and 37.5%, respectively. Compared with Q1, subjects in Q3–4 had nearly 1.35 times the risk of new-onset hypertension after full adjustment [Q3: hazard ratio (HR): 1.35, 95% confidence interval (CI): 1.13–1.62; Q4: HR: 1.35, 95% CI: 1.13–1.64] (). Additionally, the risks of new-onset hypertension increased significantly as the AIP group changed from Q1 to Q4 after the full adjustment (p < .01). When we combined subjects with Q2-Q4, similar trends existed in the predictive role of the AIP index with new-onset hypertension (p = .003). Furthermore, the Cox model showed that as the AIP index per 1.0 increase, the risk of new-onset hypertension increased 1.47 times (CI: 1.19–1.82) (p < .001).
Table 2. The association of AIP index with new-onset hypertension.
Subgroup analysis
Stratified analyses were conducted to further identify the association between AIP index and the risk of new-onset hypertension in various subgroups, and subjects were divided into two groups according to sex, age, WHR level, smoking habits, drinking habits, residence status, eGFR, BMI, SBP and DBP levels. As shown in , there was a significant association [HR(CI):1.27(1.04–1.54) vs. 0.90 (0.69–1.18)] when subjects were divided into two groups according to BMI level (<24 vs. ≥24 kg/m2), and the P interaction term in the model was 0.036. The other variables had no effect on the modification in the relationship (P for interaction > 0.05).
Figure 2. Stratified analyses by potential modifiers of the association between AIP index and new-onset hypertension. AIP, atherogenic index of plasma; Q, quartile; HR hazard ratio; CI, confidence interval; ref, reference; WHR, waist hip ratio; CKD, chronic kidney disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.
Sensitive analysis
To assess the effect of confounding factors, we excluded subjects with diabetes and/or CKD in Table S2, which showed that the risks of new-onset hypertension were nearly 1.30 times higher in subjects in Q2–4 than subjects in AIP Q1 (p < .01) after the full adjustment. In addition, we diagnosed hypertension by the 130/80 mmHg standard in accordance with the new ACC and AHA guidelines, and only subjects in Q4 had significantly higher risks of incident hypertension (Q4 vs. Q1, HR, 1.23; 95% CI 1.02–1.48), and there was no significant relationship between subjects in Q2–4 and those in Q1 (p > .05, Table S3).
Discussion
In this large-scale population-based prospective study with a six-year follow-up, our results indicated the predictive role of the AIP index in new-onset hypertension after full adjustment. A positive association existed in the subgroup analysis and in the sensitivity analysis when excluding subjects with type 2 diabetes and/or CKD. This study was the first to identify the relationship between the AIP index and incident hypertension through a nationwide and representative Chinese population, which may confer an important implication for the prevention of hypertension and become a potential effective index for the hypertension risk assessment.
Compelling evidence has acknowledged that the risks of hypertension include race and ethnicity, social determinants, genetic/family history, obesity and abnormal lipid metabolism.Citation2 The incidence of new-onset hypertension was 33.5% (n = 1054) in this study, and the incident rate was 62.0% per 1000 person-years of follow-up in accordance with the 140/90 mmHg definition. After we redefined hypertension, the incidence rate was 96.9% per 1000 person-years of follow-up. These results were different from the incidence rate of previous studies based on the CARDIA study in different races and sexesCitation27 and the KaiLuan study in Tangshan City, Hebei Province, China.Citation28 Differences in ethnicity, sample size, and hypertension definition may affect the incidence rates. There is an urgent need to identify modified risk factors to prevent the progression of hypertension with this high incidence rates across this general Chinese population.
The AIP index is recognized as a novel biomarker in the early diagnosis of CVD events and even beyond traditional risk factors.Citation6,Citation29 In terms of the association between the AIP index and hypertension, the results were inconsistent by a comprehensive review.Citation30 Most studies focused on atherosclerosis outcome as a result of pathogenic conditions created by high AIP levels always accompanied by high TG and low HDL-C, frequently accompanied by concomitant hypertension.Citation31 Our study provides independent evidence on the positive association between the AIP index and new-onset hypertension after adjusting for several confounding factors, including glucose level and kidney function. The results were consistent with a cross-sectional study based on 15 453 normoglycemic participants, which reported a positive association of AIP with prehypertension or hypertension.Citation12 Another study showed the predictive role of AIP in high blood pressure only after adjusting for age and C-reactive protein.Citation11 Contrary to recent studies, AIP levels did not show any positive association with either SBP or DBP levels,Citation32,Citation33 which may be due to the heterogeneity of the selected population with type 2 diabetes and other arterial stiffness diseases. In this study, the prediction of high AIP levels in hypertension proposed new insights, suggesting that the priority of lipid control as an upstream prevention and treatment target of cardiovascular diseases is crucial for the prevention and treatment of cardiometabolic diseases.
Notably, except for BMI status，the relationship between the AIP index and new-onset hypertension was not modified by most of the variables in our subgroup analysis, indicating the generalization and stability of this result. The association of the AIP index with new-onset hypertension was stronger in subjects with normal BMI (<24 kg/m2). Compelling evidence has been reported for the link between overweight/obesity and incident hypertension.Citation34,Citation35 Excessive adiposity was reported to raise blood pressure and accounts for 65–75% of primary hypertension.Citation36 Another perspective may suggest that early intervention or control of low AIP levels in patients with normal BMI has important clinical significance for the prevention of new-onset hypertension.
The underlying pathological mechanisms linking AIP and hypertension progression are not fully understood fully and are mostly considered to be related to serum lipid concentrations.Citation34 High AIP is driven by high TG and low HDL-C, and it is acknowledged that excess TG is stored ectopically in other depots, such as visceral adipose tissue,Citation37 which is related to increased adipokine production and insulin resistance.Citation38 Vascular endothelial and autonomic dysfunction are other mechanisms related to hypertension induced by visceral adipose tissue.Citation39,Citation40 HDL-C was reported to remove cholesterol from peripheral cells and transfer it to the liver for bile acid synthesis and is recognized as a hormonal regulator of platelet, vascular endothelial and smooth muscle cell interactions,Citation41,Citation42 and decreased HDL-C may induce endothelial dysfunction by reducing the bioavailability of endothelium-derived nitric oxide.Citation43 Sympathetic tone, impaired arterial compliance and increased arterial stiffness may be involved in the pathological mechanisms.Citation44,Citation45
The study has several strengths, including the prospective and large-scale study design with relatively long-term follow-up and a comprehensive investigation of metabolic profiles. However, certain limitations in the present study should be noted. First, although we modified potential confounders, including eGFR, glucose and demographic characteristics, the data about exercise activity and salt intake are lacking in the analysis, which may affect the AIP level and the progression of blood pressure. Another limitation may be that causality could not be explained by observational studies. Finally, this study was limited to the Chinese general population, and more ethnic and racial cohort studies will be needed to replicate and validate our findings.
Conclusion
In summary, the present study suggested that individuals with a higher AIP index are associated with the incidence of new-onset hypertension, independent of age, eGFR and glucose levels. The association was stronger in subjects with normal BMI status, which may provide early screening of metabolomics in hypertension prevention since AIP is easily available. Indeed, more attention should be given to the detection and prevention of hypertension in patients with high AIP.
List of abbreviations
| AIP | = | atherogenic index of plasma |
| CHNS | = | China Health and Nutrition Survey |
| TG | = | Triglycerides |
| HDL-C | = | high-density lipoprotein-cholesterol |
| HR | = | hazard ratio |
| HTN | = | Hypertension |
| ASCVDs | = | atherosclerotic cardiovascular diseases |
| BP | = | blood pressure |
| NVSS | = | National Vital Statistics System |
| AHA | = | American Heart Association |
| BMI | = | body mass index |
| WC | = | Waist circumference |
| WHR | = | Waist to hip ratio |
| SBP | = | Systolic blood pressure |
| DBP | = | diastolic blood pressure |
| MAP | = | Mean arterial pressure |
| ADA | = | American Diabetes Association |
| CKD | = | Chronic kidney disease |
| eGFR | = | estimated glomerular filtration rate |
| SD | = | standard deviation |
| ACC | = | American College of Cardiology |
| UA | = | uric acid |
Authors’ contributions
Yue Yuan collected the data, performed the statistical analysis and was charge of writing, drafting and preparation of the manuscript. Wei Sun and Xiangqing Kong were charge of conception or design, interpretation of data and revision of the manuscript. All the authors approved the final version of the manuscript.
Supplemental Material
Download Zip (51.4 KB)Acknowledgments
Data in this research were from the China Health and Nutrition Survey (CHNS). The authors are grateful to all subjects who participated in the nationwide population-based study. We also thank the National Institute for Nutrition and Health, China Center for Disease Control and Prevention.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/10641963.2024.2303999
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References
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