https://orcid.org/0000-0003-3172-1540
ABSTRACT
Background
Obesity, especially visceral obesity, plays an important role in the progression of cardiovascular disease (CVD). The body roundness index (BRI) is a new measure of obesity that is considered to reflect visceral obesity more comprehensively than other measures. This study aims to evaluate the relationship between BRI and CVD risk in hypertensive patients with obstructive sleep apnea (OSA) and explore its superiority in predicting CVD.
Methods
The Cox proportional hazards model was used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CVD. The area under the curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to assess which measures of obesity had the best predictive value for CVD risk.
Results
During a median follow-up period of 6.8 years, 324 participants suffered a CVD event. After multivariable adjustment, compared with the reference group (the first tertile), the HRs (95% CI) of CVD were 1.25 (95% CI, 0.93–1.70) and 1.74 (95% CI, 1.30–2.33) for subjects in the tertile 2 and tertile 3 groups, respectively. Compared with other measurement indicators, BRI has the highest predictive value for CVD risk [AUC: 0.627, 95% CI: 0.593–0.661]. The addition of the BRI to the fully adjusted multivariate model improved the predictive power for CVD, which was validated in the continuous NRI and the IDI (all P < .05).
Conclusions
BRI was significantly associated with the risk of CVD in hypertensive patients with OSA. Furthermore, BRI may improve CVD risk prediction in hypertensive patients with OSA.
Introduction
Cardiovascular disease (CVD) is a serious systemic disease that is an important cause of death and serious complications, accounting for about 40% of deaths in China (Citation1,Citation2). Especially in the past 20 to 30 years, the incidence of CVD has increased significantly in China, which has become a substantial public health and economic burden (Citation3). Hypertension is an exceedingly common condition. Current research suggests that hypertensive patients with obstructive sleep apnea (OSA) are at increased risk of developing CVD (Citation4–6).
Previous studies have shown that obesity is a major factor causing CVD (Citation7–9). However, most previous studies have chosen body mass index (BMI) as a measure of obesity. As obesity is a metabolic disease, it is insufficient to define obesity using BMI alone. Body fat distribution is not accurately reflected by BMI, and abdominal obesity is more strongly associated with increased CVD risk (Citation10). More recently, a new body measure, the body roundness index (BRI), was proposed by Thomas et al. in 2013 (Citation11). It models the shape of the human body as an ellipse or oval to obtain body circumference relative to height or roundness and uses eccentricity to predict visceral fat and total body fat percentage. Compared with other measurement methods, BRI combines height, waist circumference (WC), weight, and other indicators, which can more comprehensively reflect the proportion and distribution of visceral fat (Citation12,Citation13). Previous findings have also demonstrated that BRI shows better predictive power than BMI, WC, and hip circumference as a superior measure of obesity in assessing diabetes risk in hypertensive patients with obesity (Citation14).
Several recent studies have shown that BRI levels are correlated with carotid atherosclerosis, metabolic syndrome (MetS), and heart failure in the general population (Citation15–18). In particular, BRI is a good predictor of CVD risk in the general population. In a large cross-sectional study, Li et al. reported that BRI was more associated with risk factors for CVD and better predicted risk stratification for CVD compared with BMI (Citation19). Contemporaneously, a study found that over time, BRI trajectories were significantly associated with an increased risk of CVD (Citation15). In addition, BRI was associated with adverse cardiovascular outcomes, and higher BRI levels were significantly associated with an increased risk of cardiovascular death and all-cause mortality (Citation20,Citation21). However, the relationship between BRI and incident CVD has not been confirmed in hypertensive patients with OSA. Moreover, it is unclear whether measuring BRI is a better predictor of CVD compared with traditional adiposity measures. Therefore, in this study, we aimed to evaluate the relationship between BRI and CVD risk in hypertensive patients with OSA and explore its superiority in predicting CVD. Investigating the relationship between BRI and CVD will enable the development of better prevention strategies to delay the occurrence of CVD events in hypertensive patients with OSA.
Material and methods
Study design and participants
The detailed procedures and main outcomes of the UROSAH have been published elsewhere (Citation22–25). Briefly, the UROSAH study was aimed at evaluating the long-term cardiovascular outcomes and risk factors of hypertensive patients with OSA. From 2011 to 2013, we gathered data from hypertensive patients with suspected OSA who visited the People’s Hospital of Xinjiang Uygur Autonomous Region. This study had a total of 3605 participants. By January 2021, 276 participants had been lost to follow-up. We included 2585 participants with OSA diagnosed by polysomnography (PSG). Furthermore, we excluded participants with a history of CVD at baseline or with missing BRI data at baseline. Finally, the sample of participants included in the analysis was 2265 (Figure S1).
This study, which complies with the Declaration of Helsinki, has been approved by the ethics committee (No. 2019030662). Consent was given voluntarily by all participants. The study adhered to the STROBE guidelines.
Data collection and definitions
Demographic, clinical, lifestyle, physical examination, medication history, and laboratory data were collected by electronic medical records. Trained research nurses performed all anthropometric measurements. Height, weight, WC, and blood pressure were measured according to standard procedures and are described in detail in the Supplementary Material. BMI was calculated using the formula BMI = weight (kg)/height (m2). Waist-to-height-ratio (WHtR) = WC/height. BRI was calculated using the formula BRI = 364.2–365.5 × (1 – [WC/2π]2/[0.5 × height2)½. Smoking and drinking habits were classified as never, past, or current. Diabetes was defined as the use of diabetes medication, a fasting glucose level ≥7.0 mmol/L, or a self-reported physician diagnosis of diabetes. As previously reported, continuous positive airway pressure (CPAP) treatment and oral appliance treatment were classified as regular and irregular. After an overnight fast (at least eight hours), blood samples were drawn in the morning. Fasting plasma glucose (FPG), high-density lipoprotein (HDL-C), total cholesterol (TC), low-density lipoprotein (LDL-C), and triglycerides (TG) were measured in the central laboratory. Estimated glomerular filtration rate (eGFR) computed by the CKD-EPI equation.
Sleep study
All participants underwent standard procedures and attended overnight PSG (Compumedics E Series, Australia) in our sleep laboratory. Sleep studies were scored by a registered polysomnographic technologist according to established criteria. The details of the overnight sleep study and scoring criteria are described in the Supplementary Material. The severity of OSA was defined by the apnea-hypopnea index (AHI): mild OSA (AHI ≥5 but < 15); moderate OSA (AHI ≥15 but < 30); and severe OSA (AHI ≥30).
Outcome ascertainment
The primary outcome in the present study was the first occurrence of CVD events (CHD and stroke). CHD was defined as a fatal or nonfatal myocardial infarction, unstable angina, and coronary revascularization. Strokes included ischemic and hemorrhagic strokes. As previously mentioned, we defined CVD events. Definitions of CVD are detailed in the Supplementary Material. Follow-up results were obtained through inpatient medical records, outpatient examinations, or telephone interviews. All clinical endpoints, including all deaths, were independently adjudicated by a blinded clinical event committee. Person-years of follow-up were determined for each participant as the time between the date of the initial exam and the date of the first CVD event, the date of death, or the date of the final follow-up visit, whichever came first.
Statistical analysis
Participants were grouped into three categories based on the tertile of BRI. The Kaplan-Meier method and the log-rank test were used to determine the incidence rate of CVD. Variance inflation factors were applied to check for multicollinearity (Table S1). Schoenfeld residuals were used to test the proportional hazards assumptions (Figure S2). Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by Cox regression. The tertile median values were used to generate the P-values for linear trends. Additionally, the dose-response relationship was evaluated using multivariable restricted cubic splines. In addition, to ensure the reliability of our findings, we carried out a number of additional analyses. Subgroup analyses were done, and differences were investigated using tests for interaction. At the same time, we also conducted a large number of comparative analyses on the superiority of BRI in predicting the risk of CVD in hypertensive patients with OSA. Details on statistical analysis are provided in the Supplemental Material. All statistical tests were performed as two-sided tests with a significance level of 0.05. Analyses were conducted using R (version 4.1.1).
Results
Baseline characteristics
The current study included 2265 participants, as shown in the flowchart (Figure S1). The study’s participants were 49.62 years old on average (SD, 10.77). There were 1558 participants overall, and 68.79% of them were male. BRI on average was 4.59 (SD, 1.14). lists the study cohort’s characteristics based on the BRI tertiles. From the lowest to highest tertile, participants were progressively younger, had higher BMI, WC, DBP, serum lipids (excluding HDL-C or TC), AHI, and higher rates of diabetes. Also, they were more likely to have had antiplatelet, lipid-lowering, glucose-lowering, regular oral appliance treatment, and regular CPAP treatment. For SBP, eGFR, drinking, and smoking, there was no discernible trend.
Table 1. Baseline characteristics according to tertiles of body roundness index.
Association between BRI and CVD risk
The median follow-up duration was 6.80 (interquartile range, 5.90–8.00) years. During this period, 324 (14.30%) participants suffered a CVD, 201 of whom had a CHD, and the remaining had a stroke. The Kaplan-Meier curves showed the participants in the highest quartile of BRI had a higher cumulative incidence for CVD compared with those of other groups over follow-up time (log-rank test, P < .05; ). displays the relationships between BRI and CVD risk, CHD risk, and stroke risk. Overall, there were significant positive correlations between BRI and the CVD risk () (per SD increment; HR, 1.27, 95% CI: 1.12–1.43); the CHD risk () (per SD increment; HR, 1.22, 95% CI: 1.04–1.43); and the stroke risk () (per SD increment; HR, 1.35, 95% CI: 1.11–1.65). After multivariable adjustment, compared with the reference group (the first tertile), the HRs of CVD were 1.25 (95% CI, 0.93–1.70) in the second tertile and 1.74 (95% CI, 1.30–2.33) in the third tertile. The BRI was positively associated with CVD risk in a dose-dependent manner in Model 1 (P for trend < .05). The relationship remained largely unchanged in Models 2 and 3. Similar findings were observed for the secondary outcomes.
Figure 1. Cumulative incidence curves stratified by BRI tertiles.
Table 2. Associations between the body-roundness index and study outcomes.
Figure 2. Dose-response associations of BRI with incident study outcomes. (a) CVD. (b) CHD. (c) stroke. The independent variable (horizontal axis) is the BRI level, and the dependent variable (vertical axis) is the hazard ratio for the outcome. Point estimates (solid lines) and 95% confidence intervals (dashed lines) were estimated by restricted cubic splines analysis with knots placed at the 10th, 50th, and 90th percentiles (the median as the reference).
Subgroup and sensitivity analysis
In subgroup analyses, the outcomes were similar when analyses were stratified by sex, age, BMI, smoking status, drinking status, SBP, DBP, or AHI, although several of the relationships did not approach statistical significance, mostly due to reduced power (). No discernible interactions between BRI and these stratification factors were found. However, among participants without diabetes, the associations were more obvious (P-interaction = 0.002 for CHD; ). In sensitivity analyses that eliminated cases with less than 2 years of follow-up, the results held up well (Table S2). The results of the analysis using the competing-risks models were similar (Table S3). Similar results were found in patients who were not treated with OSA (Table S4). However, further adjustment for the use of medications during the follow-up period did not substantially change the association between BRI and the risk of CVD (Table S5). In addition, E values indicated that residual confounding resulting from unmeasured confounding factors is probably mild (Table S6).
Figure 3. Association between BRI (per SD increment) and study outcomes in various subgroups. (a) CVD. (b) CHD. (c) stroke.
Comparative analysis of the prediction of CVD by four measurement indicators
We used receiver operating curve (ROC) analysis to determine the predictive value for CVD risk with the BMI, WC, WHtR, and BRI (Figure S3). In terms of the area under the curve (AUC) of the CVD, the BRI has the largest AUC [0.627, 95% CI: 0.593–0.661] compared with other measures (Figure S3A). We also found similar results in the AUC for CHD and stroke (Figures S3B and S3C). Moreover, we also evaluated the value of various measurement indicators for the incremental prediction of CVD. Adding BRI to the fully adjusted Model 3 significantly improved the risk reclassification of functional outcomes (Table S7). The continuous net reclassification improvement (NRI) was 0.133 (95% CI 0.042–0.190, P < .001) and the corresponding integrated discrimination improvement (IDI) was 0.019 (95% CI 0.005–0.037, P = .007). Also, the predictive performance is further demonstrated by the maximum R-squared value after adding BRI to Model 3. In addition, we assessed clinical utility using decision curve analysis (DCA), and visual-based DCA results confirmed that using BRI to predict CVD achieved a greater net benefit than other measures (Figure S4A).
Discussion
To our knowledge, this is the first study to investigate the relationship between BRI and CVD in hypertensive patients with OSA. In this large retrospective study, we found a significant association between BRI and CVD risk. This relationship persisted after adjustment for numerous potential confounders. Meanwhile, the BRI shows stronger predictive power compared to other indicators. Therefore, the results of this study demonstrate that early and rational BRI control may be beneficial to prevent or delay the onset of CVD in hypertensive patients with OSA.
Obesity is a strong risk factor for the development of CVD (Citation7). Visceral obesity, as opposed to subcutaneous fat, has been shown in several studies to be a more significant risk factor for CVD (Citation26,Citation27). It has been established that visceral fat varies by race and ethnicity (Citation28). According to some research, compared to other racial or ethnic groups, Asians have a higher body fat percentage and more visceral adipose tissue for a given BMI (Citation28,Citation29). Previous studies have also shown that the discriminatory power of BMI is not ideal because this calculation does not distinguish between adipose tissue and lean body mass and does not truly reflect visceral obesity, so it may not be a good predictor of CVD risk (Citation30,Citation31). Compared to visceral fat imaging, WC is often used as a simple and easy method to assess obesity and is associated with a higher absolute risk of metabolic and CVD (Citation32). However, the main disadvantage of WC is that it does not take into account height, which may lead to underestimation or overestimation of abdominal obesity in short or taller individuals (Citation33,Citation34). In addition, differences in WC thresholds by race, gender, and age are important issues to consider (Citation35). Meanwhile, WHtR is often used as a better indicator for assessing obesity and cardiometabolic disease than WC and BMI (Citation34). However, WHtR also has limitations, being susceptible to age and other factors, and is not superior to WC and BMI in overweight and obese young adults (Citation36). BRI is a novel measurement that combines height and WC and calculates eccentricity by means of human modeling (Citation11). The BRI provides a more comprehensive picture of body shape and visceral fat distribution characteristics than these traditional measurements (Citation13). Andrei et al. found that BRI was significantly better than BMI and WC in predicting MetS and MetS components (Citation37). Another study also confirmed that BRI also showed more significant superiority than WC in predicting dyslipidemia (Citation38). Therefore, the BRI was used as a measure of obesity in this study.
Our study is a cohort study in which we explored the relationship between BRI levels and CVD risk in hypertensive patients with OSA. The results of the present study are in agreement with the existing evidence that elevated BRI due to visceral fat accumulation and abdominal obesity is a significant risk factor for CVD (Citation39,Citation40). A recent large-scale cohort study in northern China demonstrated an association between BRI and CVD risk. The result showed that participants with higher BRI levels had a greater risk of CVD and that the relationship was more significant in younger people (Citation15). Another cohort study also found similar results, showing a non-linear correlation between BRI and cardiovascular mortality. In particular, when BRI > 4.99, BRI levels show a significant positive correlation with CVD mortality (Citation21). In addition, a large cross-sectional study further found that BRI was significantly associated with cumulative cardiometabolic risk factors (Citation41). However, the above-mentioned studies may also have some limitations. These include the relatively short follow-up period, the consideration of only the general population and not the so-called highly exposed population groups, and the fact that no comparative analysis of various indicators of obesity was performed. Compared with previous studies, this study focused on the impact of BRI on the long-term prognosis of hypertensive patients with OSA and demonstrated the predictive value of BRI for CVD in this high-risk population. The results presented here throw new light on the need for clinicians to advise this population to lose weight as soon as possible, especially to reduce abdominal obesity. According to the 2019 American Heart Association Guidelines for Primary Cardiovascular Disease Prevention, which suggest that obese individuals with a BMI ≥30 kg/m2 need to lose weight to prevent the risk of CVD, our results provide further support and complement this recommendation (Citation42).
A significant finding in the subgroup analysis was that the relationship between BRI and CHD risk was more significant in non-diabetic patients compared to diabetic patients. The relationship between obesity and CVD risk in patients with diabetes is complex (Citation43,Citation44). Several previously published studies have suggested a phenomenon termed the “obesity paradox” (Citation43,Citation45–47). A study of US adults indicated that CVD mortality decreased significantly with increasing BMI in diabetic patients, while the opposite was true for non-diabetic patients, similar to our results (Citation48). Consistently, a prospective study in the UK found that obese patients with type 2 diabetes (T2D) had a significantly lower risk of CVD than those of normal weight (Citation49). Furthermore, mortality was lower in overweight or obese patients with T2D and cardiovascular comorbidities than in normal-weight patients, and weight loss was associated with higher mortality and morbidity (Citation45). However, the effect of diabetes combined with obesity on CVD remains controversial. This phenomenon might be due to the following reasons. First, leaner individuals with more severe diseases may lose more weight due to disease wastage than those who are overweight or obese, leading to more frailty and poorer outcomes (Citation48,Citation50). Second, patients with diabetes and obesity may have more caloric reserves and greater cardiorespiratory fitness than those who are normal weight or lean (Citation51–53). Third, diabetics with comorbid obesity are considered at high risk of CVD and may receive or seek treatment earlier in the disease course, altering the natural course of the disease compared to lean patients (Citation54). Finally, diabetes itself leads to changes in blood lipids that significantly increase the risk of CHD, which may attenuate or mask the effect of visceral obesity on CHD (Citation55,Citation56). However, detailed mechanisms remain unclear and warrant further investigations.
However, the mechanism of increased BRI levels causing CVD risk in hypertensive patients with OSA remains unclear, there exist several possible explanations. First, visceral adipose tissue regulates the uptake and release of fatty acids, but in the obese state, the fatty acid metabolism of visceral adipose tissue becomes disturbed, leading to excessive accumulation of fatty acids in visceral fat and other tissues. The excessive accumulation of fatty acids can lead to insulin resistance and fatty acid peroxidation, further increasing the risk of cardiovascular disease (Citation57–60). Second, visceral adipose tissue is an active endocrine tissue capable of producing a variety of inflammatory mediators, such as tumor necrosis factor-alpha and interleukin-6. The excessive release of these inflammatory mediators can trigger a systemic chronic inflammatory response that damages vascular endothelial cells and leads to the development of cardiovascular disease (Citation61–64). Finally, inflammatory mediators and hormones released from visceral adipose tissue can cause abnormalities in vascular endothelial function. This leads to increased angiotensin production and decreased nitric oxide production, contributing to vasoconstriction and inflammatory responses, further increasing the risk of cardiovascular disease (Citation65–67).
Several advantages of this study deserve special attention. First, this study provides a unique perspective on the potential association between BRI and CVD risk in hypertensive patients with OSA. Second, extensive adjustments and sensitivity analyses of potential confounding factors were performed in this study. The relationship between BRI and CVD risk remains robust, suggesting that our result is relatively reliable. However, a few limitations merit consideration when interpreting the results of this study. First, BRI was only evaluated at the baseline, which would not fully reflect the long-term status. Second, while multivariable adjustment and multiple sensitivity analyses were performed, unmeasured confounders cannot be excluded. Third, as an observational study, it can only prove association, not causation. Fourth, the study sample consisted of hypertensive patients with OSA in China, which may limit the general extrapolation of our result. Fifth, it may be difficult to determine the exact timing of CVD due to its prolonged development period. However, the primary aim of this analysis was to determine the association between BRI and CVD incidence, not the exact timing of the event. Lastly, the median follow-up period of 6.8 years may not be sufficient to assess the impact of BRI on CVD risk in relatively young patients.
Conclusion
In conclusion, this study found that BRI, as a novel indicator of obesity, was significantly associated with CVD risk in hypertensive patients with OSA and may also provide a new reference indicator for early prevention of CVD in this high-risk group.
Author contributions
Xintian Cai and Shuaiwei Song did complete data analysis and wrote the manuscript. Junli Hu and Qing Zhu helped perform the analysis with constructive discussions. Wenbo Yang, Jing Hong, Xiaoguang Yao, and Qin Luo contributed to the conception of the study. Nanfang Li gave guidance to the whole research process.
Supplemental Material
Download MS Word (2.6 MB)Disclosure statement
The study’s authors affirm that there were no financial or commercial ties that may be viewed as having a possible conflict of interest.
Data availability statement
The manuscript contains all the evidence that supports the findings. On reasonable request, the associated author will provide more in-depth information and raw data.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/10641963.2023.2259132.
Additional information
Funding
References
- Zhou M, Wang H, Zhu J, Chen W, Wang L, Liu S, Li Y, Wang L, Liu Y, Yin P, et al. Cause-specific mortality for 240 causes in China during 1990–2013: a systematic subnational analysis for the global burden of disease study 2013. The Lancet. 2016 Jan 16;387(10015):251–10. doi:10.1016/S0140-6736(15)00551-6. Cite in: PMID: 26510778
- Weiwei C, Runlin G, Lisheng L, Manlu Z, Wen W, Yongjun W, Zhaosu W, Huijun L, Zhe Z, Lixin J, et al. Outline of the report on cardiovascular diseases in China, 2014. Eur Heart J Suppl. 2016 May;18(Suppl F):F2–F11. doi:10.1093/eurheartj/suw030. Cite in: PMID: 28533724
- Zhao D, Liu J, Wang M, Zhang X, Zhou M. Epidemiology of cardiovascular disease in China: current features and implications. Nat Rev Cardiol. 2019 Apr;16(4):203–12. doi:10.1038/s41569-018-0119-4. Cite in: PMID: 30467329.
- Salman LA, Shulman R, Cohen JB. Obstructive sleep apnea, Hypertension, and cardiovascular risk: epidemiology, pathophysiology, and management. Curr Cardiol Rep. 2020 Jan 18;22(2):6. doi:10.1007/s11886-020-1257-y. Cite in: PMID: 31955254
- Drager LF, Bortolotto LA, Krieger EM, Lorenzi-Filho G. Additive effects of obstructive sleep apnea and hypertension on early markers of carotid atherosclerosis. Hypertension. 2009 Jan;53(1):64–69. doi:10.1161/HYPERTENSIONAHA.108.119420. Cite in: PMID: 19015401.
- Cai X, Hu J, Wen W, Wang J, Wang M, Liu S, Zhu Q, Hong J, Dang Y, Yao X, et al. Associations of the cardiometabolic index with the risk of cardiovascular disease in patients with Hypertension and obstructive sleep apnea: results of a longitudinal cohort study. Oxidative Medicine And Cellular Longevity. 2022 Jun 23;2022: 1–15. doi:10.1155/2022/4914791. Cite in: PMID: 35783191
- Marinou K, Tousoulis D, Antonopoulos AS, Stefanadi E, Stefanadis C. Obesity and cardiovascular disease: from pathophysiology to risk stratification. Int J Cardiol. 2010 Jan 7;138(1):3–8. doi:10.1016/j.ijcard.2009.03.135. Cite in: PMID: 19398137
- Carey RM, Whelton PK. New wrinkles in hypertension management 2022. Curr Opin Cardiol. 2022 Jul 1;37(4):317–25. doi:10.1097/HCO.0000000000000980. Cite in: PMID: 3573167
- Kotsis V, Tsioufis K, Antza C, Seravalle G, Coca A, Sierra C, Lurbe E, Stabouli S, Jelakovic B, Redon J, et al. Obesity and cardiovascular risk: a call for action from the European society of hypertension working group of obesity, diabetes and the high-risk patient and European Association for the study of obesity: part B: obesity-induced cardiovascular disease, early prevention strategies and future research directions. J Hypertens. 2018 Jul;36(7):1441–55. doi:10.1097/HJH.0000000000001731. Cite in: PMID: 29652731
- Hirokawa W, Nakamura K, Sakurai M, Morikawa Y, Miura K, Ishizaki M, Yoshita K, Kido T, Naruse Y, Nakagawa H. Mild metabolic abnormalities, abdominal obesity and the risk of cardiovascular diseases in middle-aged Japanese men. J Atheroscler Thromb. 2010 Sep 30;17(9):934–43. doi:10.5551/jat.4549. Cite in: PMID: 20610894
- Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, Maeda Y, McDougall A, Peterson CM, Ravussin E, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring). 2013 Nov;21(11):2264–71. doi:10.1002/oby.20408. Cite in: PMID: 2351995.
- Nkwana MR, Monyeki KD, Lebelo SL. Body roundness index, a body shape index, conicity index, and their Association with nutritional status and cardiovascular risk factors in South African rural young adults. Int J Environ Res Public Health. 2021 Jan 1;18(1):281. doi:10.3390/ijerph18010281. Cite in: PMID: 3340150
- Tian S, Zhang X, Xu Y, Dong H. Feasibility of body roundness index for identifying a clustering of cardiometabolic abnormalities compared to BMI, waist circumference and other anthropometric indices: the China Health and nutrition survey, 2008 to 2009. Med. 2016 Aug;95(34):e4642. doi:10.1097/MD.0000000000004642. Cite in: PMID: 2755996.
- Liu Y, Liu X, Guan H, Zhang S, Zhu Q, Fu X, Chen H, Tang S, Feng Y, Kuang J. Body roundness index is a superior obesity index in predicting diabetes risk among hypertensive patients: a prospective cohort study in China. Front Cardiovasc Med. 2021 Nov 18;8:736073. doi:10.3389/fcvm.2021.736073. Cite in: PMID: 3486963.
- Wu M, Yu X, Xu L, Wu S, Tian Y. Associations of longitudinal trajectories in body roundness index with mortality and cardiovascular outcomes: a cohort study. Am J Clin Nutr. 2022 Mar 4;115(3):671–78. doi:10.1093/ajcn/nqab412. Cite in: PMID: 34918019
- Wang J, Wu M, Wu S, Tian Y. Relationship between body roundness index and the risk of heart failure in Chinese adults: the Kailuan cohort study. ESC Heart Fail. 2022 Apr;9(2):1328–37. doi:10.1002/ehf2.13820. Cite in: PMID: 3510404.
- Rico-Martín S, Calderón-García JF, Sánchez-Rey P, Franco-Antonio C, Martínez Alvarez M, Sánchez Muñoz-Torrero JF. Effectiveness of body roundness index in predicting metabolic syndrome: a systematic review and meta-analysis. Obes Rev. 2020 Jul;21(7):e13023. doi:10.1111/obr.13023. Cite in: PMID: 32267621.
- Geraci G, Zammuto M, Gaetani R, Mattina A, D’Ignoto F, Geraci C, Noto D, Averna M, Cottone S, Mulè G. Relationship of a body shape index and body roundness index with carotid atherosclerosis in arterial hypertension. Nutr Metab Cardiovasc Dist. 2019 Aug;29(8):822–29. doi:10.1016/j.numecd.2019.04.013. Cite in: PMID: 31204196.
- Li Y, He Y, Yang L, Liu Q, Li C, Wang Y, Yang P, Wang J, Chen Z, Huang X. Body roundness index and waist–hip ratio result in better cardiovascular disease risk stratification: results from a large Chinese cross-sectional study. Front Nutr. 2022 Mar 10;9: 801582. doi:10.3389/fnut.2022.801582. Cite in: PMID: 35360688
- Ding J, Chen X, Shi Z, Bai K, Shi S. Association of body roundness index and its trajectories with all-cause and cardiovascular mortality among a Chinese middle-aged and older population: a retrospective cohort study. Front Public Health. 2023 Mar 23;11: 1107158. doi:10.3389/fpubh.2023.1107158. Cite in: PMID: 37033022
- Zhou D, Liu X, Huang Y, Feng Y. A nonlinear association between body roundness index and all-cause mortality and cardiovascular mortality in general population. Public Health Nutr. 2022 Nov;25(11):3008–15. doi:10.1017/s1368980022001768. Cite in: PMID: 35983642.
- Yang W, Cai X, Hu J, Wen W, Mulalibieke H, Yao X, Yao L, Zhu Q, Hong J, Li N, et al. The metabolic score for insulin resistance (METS-IR) predicts cardiovascular disease and its subtypes in patients with Hypertension and obstructive sleep apnea. CLEP. 2023 Feb 15;15:177–89. doi:10.2147/CLEP.S395938. Cite in: PMID: 36815173
- Hu J, Cai X, Li N, Zhu Q, Wen W, Hong J, Zhang D, Yao X, Luo Q, Sun L. Association between triglyceride glucose index-waist circumference and risk of first myocardial infarction in Chinese hypertensive patients with obstructive sleep apnoea: an observational cohort study. Nat Sci Sleep. 2022 May 19;14: 969–80. doi:10.2147/NSS.S362101. Cite in: PMID: 35615442
- Cai X, Li N, Hu J, Wen W, Yao X, Zhu Q, Heizhati M, Hong J, Sun L, Tuerxun G, et al. Nonlinear relationship between Chinese visceral adiposity index and new-onset myocardial infarction in patients with Hypertension and obstructive sleep apnoea: insights from a cohort study. J Inflamm Res. 2022 Feb 2;15:687–700. doi:10.2147/JIR.S351238. Cite in: PMID: 35140499
- Luo Q, Li N, Zhu Q, Yao X, Wang M, Heizhati M, Cai X, Hu J, Abulimiti A, Yao L, et al. Non-dipping blood pressure pattern is associated with higher risk of new-onset diabetes in hypertensive patients with obstructive sleep apnea: UROSAH data. Front Endocrinol. 2023 Feb 16;14:1083179. doi:10.3389/fendo.2023.1083179.Cite. in: PMID: 36875466
- Chartrand DJ, Murphy-Després A, Alméras N, Lemieux I, Larose E, Després JP. Overweight, obesity, and CVD risk: a focus on visceral/Ectopic fat. Curr Atheroscler Rep. 2022 Apr;24(4):185–95. doi:10.1007/s11883-022-00996-x. Cite in: PMID: 35235165.
- Ballin M, Nordström P, Niklasson J, Nordström A. Associations of visceral adipose tissue and skeletal muscle density with incident stroke, myocardial infarction, and all-cause mortality in community-dwelling 70-year-old individuals: a prospective cohort study. J Am Heart Assoc. 2021 May 4;10(9):e020065. doi:10.1161/JAHA.120.020065. Cite in: PMID: 33870709
- Lim U, Ernst T, Buchthal SD, Latch M, Albright CL, Wilkens LR, Kolonel LN, Murphy SP, Chang L, Novotny R, et al. Asian women have greater abdominal and visceral adiposity than caucasian women with similar body mass index. Nutr & Diabetes. 2011 May 9;1(5):e6. doi:10.1038/nutd.2011.2. Cite in: PMID: 2344938
- Lim U, Monroe KR, Buchthal S, Fan B, Cheng I, Kristal BS, Lampe JW, Hullar MA, Franke AA, Stram DO, et al. Propensity for Intra-abdominal and hepatic adiposity varies among ethnic groups. Gastroenterology. 2019 Mar;156(4):966–75.e10. doi:10.1053/j.gastro.2018.11.021. Cite in: PMID: 3044501
- Nevill AM, Stewart AD, Olds T, Holder R. Relationship between adiposity and body size reveals limitations of BMI. Am J Phys Anthropol. 2006 Jan;129(1):151–56. doi:10.1002/ajpa.20262. Cite in: PMID: 16270304.
- Maessen MF, Eijsvogels TM, Verheggen RJ, Hopman MT, Verbeek AL, de Vegt F, Gong Y. Entering a new era of body indices: the feasibility of a body shape index and body roundness index to identify cardiovascular health status. PloS One. 2014 Sep 17;9(9):e107212. doi:10.1371/journal.pone.0107212. Cite in: PMID: 25229394
- Wang L, Lee Y, Wu Y, Zhang X, Jin C, Huang Z, Wang Y, Wang Z, Kris-Etherton P, Wu S, et al. A prospective study of waist circumference trajectories and incident cardiovascular disease in China: the Kailuan cohort study. Am J Clin Nutr. 2021 Feb 2;113(2):338–47. doi:10.1093/ajcn/nqaa331. Cite in: PMID: 33330917
- Nishida C, Ko GT, Kumanyika S. Body fat distribution and noncommunicable diseases in populations: overview of the 2008 WHO expert consultation on waist circumference and waist-hip ratio. Eur J Clin Nutr. 2010 Jan;64(1):2–5. doi:10.1038/ejcn.2009.139. Cite in: PMID: 19935820.
- Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012 Mar;13(3):275–86. doi:10.1111/j.1467-789X.2011.00952.x. Cite in: PMID: 22106927.
- Zeng Q, He Y, Dong S, Zhao X, Chen Z, Song Z, Chang G, Yang F, Wang Y. Optimal cut-off values of BMI, waist circumference and waist: height ratio for defining obesity in Chinese adults. Br J Nutr. 2014 Nov 28;112(10):1735–44. doi:10.1017/s0007114514002657. Cite in: PMID: 25300318
- Sijtsma A, Bocca G, L’Abée C, Liem ET, Sauer PJ, Corpeleijn E. Waist-to-height ratio, waist circumference and BMI as indicators of percentage fat mass and cardiometabolic risk factors in children aged 3-7 years. Clin Nutr. 2014 Apr;33(2):311–15. doi:10.1016/j.clnu.2013.05.010. Cite in: PMID: 23768783.
- Stefanescu A, Revilla L, Lopez T, Sanchez SE, Williams MA, Gelaye B. Using a body shape index (ABSI) and body roundness index (BRI) to predict risk of metabolic syndrome in Peruvian adults. J Int Med Res. 2020 Jan;48(1):300060519848854. doi:10.1177/0300060519848854. Cite in: PMID: 3114454.
- Zhang K, Zhao Q, Li Y, Zhen Q, Yu Y, Tao Y, Cheng Y, Liu Y. Feasibility of anthropometric indices to identify dyslipidemia among adults in Jilin province: a cross-sectional study. Lipids Health Dis. 2018 Jan 22;17(1):16. doi:10.1186/s12944-017-0648-6. Cite in: PMID: 29357896
- Xue R, Li Q, Geng Y, Wang H, Wang F, Zhang S. Abdominal obesity and risk of CVD: a dose-response meta-analysis of thirty-one prospective studies. Br J Nutr. 2021 Nov 14;126(9):1420–30. doi:10.1017/S0007114521000064. Cite in: PMID: 33431092
- Pérez Pérez A, Ybarra Muñoz J, Blay Cortés V, de Pablos Velasco P. Obesity and cardiovascular disease. Public Health Nutr. 2007 Oct;10(10A):1156–63. doi:10.1017/S1368980007000651. Cite in: PMID: 17903325.
- Xu J, Zhang L, Wu Q, Zhou Y, Jin Z, Li Z, Zhu Y. Body roundness index is a superior indicator to associate with the cardio-metabolic risk: evidence from a cross-sectional study with 17,000 eastern-China adults. BMC Cardiovasc Disord. 2021 Feb 16;21(1):97. doi:10.1186/s12872-021-01905-x. Cite in: PMID: 33593274
- Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, Himmelfarb CD, Khera A, Lloyd-Jones D, McEvoy JW, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American college of cardiology/American Heart Association task force on clinical practice guidelines. Circulation. 2019 Sep 10;140(11):e596–e646. doi:10.1161/CIR.0000000000000678. Cite in: PMID: 30879355
- Carnethon MR, De Chavez PJ, Biggs ML, Lewis CE, Pankow JS, Bertoni AG, Golden SH, Liu K, Mukamal KJ, Campbell-Jenkins B, et al. Association of weight status with mortality in adults with incident diabetes. Jama. 2012 Aug 8;308(6):581–90. doi:10.1001/jama.2012.9282. Cite in: PMID: 22871870
- McLarnon A. Diabetes: weight status affects mortality risk. Nat Rev Endocrinol. 2012 Oct;8(10):567. doi:10.1038/nrendo.2012.152. Cite in: PMID: 22926099.
- Doehner W, Erdmann E, Cairns R, Clark AL, Dormandy JA, Ferrannini E, Anker SD. Inverse relation of body weight and weight change with mortality and morbidity in patients with type 2 diabetes and cardiovascular co-morbidity: an analysis of the PROactive study population. Int J Cardiol. 2012 Dec 15;162(1):20–26. doi:10.1016/j.ijcard.2011.09.039. Cite in: PMID: 22037349
- Kennedy LM, Dickstein K, Anker SD, Kristianson K, Willenheimer R, OPTIMAAL Study Group. The prognostic importance of body mass index after complicated myocardial infarction. J Am Coll Cardiol. 2005 Jan 4;45(1):156–58. doi:10.1016/j.jacc.2004.10.001. Cite in: PMID: 15629390.
- Romero-Corral A, Montori VM, Somers VK, Korinek J, Thomas RJ, Allison TG, Mookadam F, Lopez-Jimenez F. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet. 2006 Aug 19;368(9536):666–78. doi:10.1016/S0140-6736(06)69251-9. Cite in: PMID: 16920472
- Jackson CL, Yeh HC, Szklo M, Hu FB, Wang NY, Dray-Spira R, Brancati FL. Body-mass index and all-cause mortality in US adults with and without diabetes. J Gen Intern Med. 2014 Jan;29(1):25–33. doi:10.1007/s11606-013-2553-7. Cite in: PMID: 2392921.
- Pagidipati NJ, Zheng Y, Green JB, McGuire DK, Mentz RJ, Shah S, Aschner P, Delibasi T, Rodbard HW, Westerhout CM, et al. Association of obesity with cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease: insights from TECOS. American Heart Journal query. 2020 Jan;219:47–57. doi:10.1016/j.ahj.2019.09.016. Cite in: PMID: 31707324
- Habbu A, Lakkis NM, Dokainish H. The obesity paradox: fact or fiction? Am J Cardiol. 2006 Oct 1;98(7):944–48. doi:10.1016/j.amjcard.2006.04.039. Cite in: PMID: 16996880
- McAuley PA, Kokkinos PF, Oliveira RB, Emerson BT, Myers JN. Obesity paradox and cardiorespiratory fitness in 12,417 male veterans aged 40 to 70 years. Mayo Clin Proc. 2010 Feb;85(2):115–21. doi:10.4065/mcp.2009.0562. Cite in: PMID: 2011838.
- Schmidt DS, Salahudeen AK. CARDIOVASCULAR and SURVIVAL PARADOXES in DIALYSIS PATIENTS: obesity-survival paradox-still a controversy? Semin Dial. 2007 Nov-Dec;20(6):486–92. doi:10.1111/j.1525-139X.2007.00349.x. Cite in: PMID: 17991192.
- Oreopoulos A, Kalantar-Zadeh K, Sharma AM, Fonarow GC. The obesity paradox in the elderly: potential mechanisms and clinical implications. Clin Geriatr Med. 2009 Nov;25(4):643–59. doi:10.1016/j.cger.2009.07.005. viii Cite in: PMID: 19944265.
- Uretsky S, Messerli FH, Bangalore S, Champion A, Cooper-Dehoff RM, Zhou Q, Pepine CJ. Obesity paradox in patients with hypertension and coronary artery disease. Am J Med. 2007 Oct;120(10):863–70. doi:10.1016/j.amjmed.2007.05.011. Cite in: PMID: 17904457.
- Katsiki N, Tentolouris N, Mikhailidis DP. Mikhailidis, dyslipidaemia in type 2 diabetes mellitus: bad for the heart. Current Opinion In Cardiology query. 2017 Jul;32(4):422–29. doi:10.1097/HCO.0000000000000407. Cite in: PMID: 28362666.
- Márk L, Dani G. Diabetic dyslipidaemia and the atherosclerosis. Orv Hetil. 2016 May 8;157(19):746–52. doi:10.1556/650.2016.30441. Cite in: PMID: 27133274
- Lytle KA, Bush NC, Triay JM, Kellogg TA, Kendrick ML, Swain JM, Gathaiya NW, Hames KC, Jensen MD. Adipocyte proteins and storage of endogenous fatty acids in visceral and subcutaneous adipose tissue in severe obesity. Obesity (Silver Spring). 2021 Jun;29(6):1014–21. doi:10.1002/oby.23149. Cite in: PMID: 33893721.
- Preston KJ, Rom I, Vrakas C, Landesberg G, Etwebi Z, Muraoka S, Autieri M, Eguchi S, Scalia R. Postprandial activation of leukocyte-endothelium interaction by fatty acids in the visceral adipose tissue microcirculation. FASEB J. 2019 Nov;33(11):11993–2007. doi:10.1096/fj.201802637RR. Cite in: PMID: 31393790.
- Tchernof A, Després JP. Pathophysiology of human visceral obesity: an update. Physiol Rev. 2013 Jan;93(1):359–404. doi:10.1152/physrev.00033.2011. Cite in: PMID: 23303913.
- Neeland IJ, Hughes C, Ayers CR, Malloy CR, Jin ES. Effects of visceral adiposity on glycerol pathways in gluconeogenesis. Metabolism. 2017 Feb;67:80–89. doi:10.1016/j.metabol.2016.11.008. Cite in: PMID: 28081781.
- Unamuno X, Gómez-Ambrosi J, Rodríguez A, Becerril S, Frühbeck G, Catalán V. Adipokine dysregulation and adipose tissue inflammation in human obesity. Eur J Clin Invest. 2018 Sep;48(9):e12997. doi:10.1111/eci.12997. Cite in: PMID: 29995306.
- Anthony SR, Guarnieri AR, Gozdiff A, Helsley RN, Phillip Owens A, Tranter M. Mechanisms linking adipose tissue inflammation to cardiac hypertrophy and fibrosis. Clin Sci (Lond). 2019 Nov 29;133(22):2329–44. doi:10.1042/cs20190578. Cite in: PMID: 31777927
- Kawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol-Cell Ph. 2021 Mar 1;320(3):C375–C91. doi:10.1152/ajpcell.00379.2020. Cite in: PMID: 33356944
- Kurowska P, Mlyczyńska E, Dawid M, Jurek M, Klimczyk D, Dupont J, Rak A. Review: vaspin (SERPINA12) expression and function in endocrine cells. Cells. 2021 Jul 6;10(7):1710. doi:10.3390/cells10071710. Cite in: PMID: 34359881
- Shan B, Shao M, Zhang Q, Hepler C, Paschoal VA, Barnes SD, Vishvanath L, An YA, Jia L, Malladi VS, et al. Perivascular mesenchymal cells control adipose-tissue macrophage accrual in obesity. Nat Metab. 2020 Nov;2(11):1332–49. doi:10.1038/s42255-020-00301-7. Cite in: PMID: 33139957
- Lee CH, Lui DTW, Lam KSL. Adipocyte fatty acid-binding protein, cardiovascular diseases and mortality. Front Immunol. 2021 Mar 19;12: 589206. doi:10.3389/fimmu.2021.589206. Cite in: PMID: 33815359
- Vasamsetti SB, Natarajan N, Sadaf S, Florentin J, Dutta P. Regulation of cardiovascular health and disease by visceral adipose tissue-derived metabolic hormones. Journal Of Physiology. 2023 Jun;601(11):2099–120. doi:10.1113/jp282728. Cite in: PMID: 3566136.