https://orcid.org/0000-0002-9251-621X
Figures
Abstract
Introduction
The strong association between obesity and chronic kidney disease (CKD) has been empirically validated, yet traditional measures like the Body Mass Index (BMI) fail to accurately assess the extent of obesity due to CKD’s characteristics, such as reduced muscle mass and increased visceral fat. This study investigates the association between CKD and several anthropometric indices, including A Body Shape Index (ABSI), Body Roundness Index (BRI), Waist-to-Height Ratio (WHtR), and the Conicity Index (C-index), to determine their predictive capabilities.
Methods
Based on the datasets from the National Health and Nutrition Examination Survey (NHANES) 2009–2018, weighted multivariable regression analyses were carried out to examine the independent relationship between two anthropometric indices and CKD. Also, subgroup analyses, restricted cubic spline regression (RCS), and receiver operating characteristic curve analysis were conducted for further data analyses.
Results
A total of 24,162 participants were enrolled in this study. After adjusting for confounding factors, ABSI, BRI, WHtR, and the C-index were significantly associated with an increased risk of CKD, while BMI was not. Height showed a protective effect against CKD. ABSI and the C-index demonstrated the highest areas under the curve (AUCs), indicating superior predictive capabilities compared to traditional measures like BMI and waist circumference (WC). Subgroup analyses revealed significant interactions between the anthropometric indices and factors such as age, disease status, dietary intake, and physical activity levels.
Citation: Chen X, Wu Z, Hou X, Yu W, Gao C, Gou S, et al. (2025) Association between anthropometric indices and chronic kidney disease: Insights from NHANES 2009–2018. PLoS ONE 20(2): e0311547. https://doi.org/10.1371/journal.pone.0311547
Editor: Nir Y. Krakauer, City College of New York, UNITED STATES OF AMERICA
Received: May 30, 2024; Accepted: September 18, 2024; Published: February 14, 2025
Copyright: © 2025 Chen 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: All the data are available from the National Health and Nutrition Examination Survey(https://www.cdc.gov/nchs/nhanes/).
Funding: This study was supported by the following sources: Sichuan Province Science and Technology Support Program, No. 2020JDRC0022, awarded to SG.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Obesity poses a significant and escalating global health concern, with its increasing prevalence and its association with various complications like chronic kidney disease (CKD) [1, 2]. In the United States, the reported incidence rate of obesity in adults was 39.6% [3]. Meanwhile, the prevalence of CKD has recently surged, reaching 7.5% among US adults [4]. The prevalence of obesity within the CKD population demonstrated an upward trend, with 44% of CKD patients reported to have obesity [5, 6]. A comprehensive and collaborative meta-analysis encompassing over five million individuals reported that excessive adiposity was an independent risk factor for glomerular filtration rate (GFR) decline and mortality [7]. Obesity heightens the risk of kidney disease through a multitude of mechanisms, involving insulin resistance, lipotoxicity, dysregulation of adipocytokines, increased blood pressure, and augmented glomerular blood pressure [8, 9]. Also, interventions targeting weight loss exhibit therapeutic efficacy for CKD. The weight loss achieved by behavioral modification or medications demonstrated a capacity to reduce albuminuria and, in specific instances, decelerate the rate of decline in the estimated glomerular filtration rate (eGFR) [2].
Given the reversible and preventable nature of obesity, it becomes crucial to pinpoint obesity indicators associated with the kidney function of CKD patients, which would facilitate the early detection and diagnosis of potential CKD populations in clinical practice. However, when assessing the extent of obesity in individuals with CKD, there is typically a lack of suitable indicators. Patients with CKD often manifest an altered body composition, marked by reduced muscle mass and increased visceral adiposity [2]. While the body mass index (BMI) is widely utilized in clinical practice for obesity assessment, its predictive accuracy in CKD patients is limited [10]. Certain studies even had reported a negative correlation between obesity, defined as having a BMI greater than 25, and improved survival in CKD patients, and this phenomenon was commonly referred to as the “obesity paradox” [11, 12]. Subsequent studies speculated that BMI did not consider the prevalent muscle atrophy observed in CKD patients [13, 14]. A study utilizing the Body Composition Monitor revealed that a significant proportion of individuals exhibited excess body fat despite having a normal BMI [15]. The assessment of fat mass and its distribution can also be conducted through imaging techniques including computed tomography, magnetic resonance imaging, ultrasound, and dual-energy X-ray absorptiometry (DXA) [7]. However, the application of these methods in both clinical practice and research may be constrained by the limited availability of expensive instruments, high maintenance costs, and the requirement for skilled operators. In light of this, our study aims to identify a more precise, non-invasive, and convenient indicator for evaluating the degree of obesity in CKD patients.
In this context, our focus has turned to several anthropometric indices, namely A Body Shape Index (ABSI), Body Roundness Index (BRI), Waist-to-Height Ratio (WHtR), and Conicity Index (C-index). ABSI was empirically derived by adjusting waist circumference (WC) for height and weight [16]. BRI estimates the percentage of body fat and the roundness of the body using measurements of WC and height [17]. We also employed WHtR, a simple ratio that compares an individual’s WC to their height [18], and C-index, which measures abdominal obesity by analyzing the relationship between WC, weight, and height [19]. ABSI and C-Index focus on central and abdominal obesity, respectively, providing detailed measures of fat distribution and associated health risks. BRI gives a broader perspective on body composition by estimating body fat percentage and roundness. WHtR is a straightforward measure highlighting central obesity and is useful for general health assessments. However, the relationship between these indices and CKD remains unclear, and it is still unknown which index has the better diagnostic performance for CKD. In this study, we investigated the association between different anthropometric indices with CKD in the American population, utilizing weighted data obtained from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2009 to 2018. In addition to studying the relationship between ABSI, BRI, WHtR, and the C-index with CKD, we also examined the relationship between CKD and commonly used indices such as height, weight, WC, and BMI. The comparative analysis of the diagnostic performance of all these indices for CKD is finally conducted based on the area under the receiver operating characteristic (ROC) curve.
2. Materials and methods
2.1 Study population
NHANES is a comprehensive series of cross-sectional surveys that encompass the non-institutionalized civilian population of the United States (https://www.cdc.gov/nchs/nhanes/). NHANES incorporates a wide array of data, including demographic, socioeconomic, dietary, and health-related information collected through face-to-face interviews, physical and physiological examinations, as well as thorough laboratory tests.
This research analyzed de-identified information downloaded from the National Health and Nutrition Examination Survey public database. The National Center for Health Statistics Ethics Review Committee granted ethics approval. All methods were carried out in accordance with relevant guidelines and regulations (declaration of Helsinki). All individuals provided written informed consent before participating in the study.
A total of 49,693 participants completed the survey across five NHANES cycles (NHANES 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018 cycles). Subsequently, we excluded 20,858 participants under the age of 20, and an additional 315 participants who were pregnant. Moreover, data on serum creatinine or urinary albumin/creatinine ratio (UACR) was not available for 3,201 participants. Data pertaining to WC, height, or BMI were lacking for 1,157 participants. Therefore, a total of 24,162 participants was included in this study (Fig 1).
2.2 Data collection
2.2.1 Exposure variable.
All parameters were assessed using standardized methods. The anthropometric indices (ABSI, BRI, WHtR and C-index) were calculated using the following equations [16, 17, 19–21]:
To enable comparability and to account for the pronounced sex differences, all anthropometric indices on a continuous scale were transformed to sex-specific z-scores.
x is the individual’s value for the anthropometric index. μ is the mean of that index for the individual’s sex group. σ is the standard deviation of that index for the individual’s sex group.
2.2.2 Outcome variable.
CKD is defined by a persistent abnormality in kidney structure or function for three or more months, signified by the presence of either low eGFR (below 60 mL/min/1.73 m2) or albuminuria (UACR equal to or exceeding 30 mg/g) [22, 23]. The calculation of eGFR was performed using the Chronic Kidney Disease Epidemiology Collaboration study (CKD-EPI) equation based on serum creatinine levels [24]. Urinary albumin was quantified using a solid-phase fluorescence immunoassay, and urinary creatinine was measured employing an enzymatic method [25].
2.2.3 Assessment of covariates and outcomes.
Information on covariates was predominantly gathered from three distinct sources: questionnaires, physical examinations, and laboratory data. The questionnaires encompassed inquiries regarding age, gender, race, education level, marital status, household income, smoking status, alcohol consumption, physical activities, caloric intake, and self-reported baseline medical history, including diabetes mellitus (DM), hypertension, and cardiovascular disease (CVD). Physical examinations comprised of measurements such as WC, weight, height, BMI, blood pressure. Laboratory data involved variables such as hemoglobin (HGB), albumin (ALB), total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), serum creatinine, blood urea nitrogen (BUN), UACR.
Race was classified into five groups: Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and other. Marital status was segmented into six categories: married, widowed, divorced, separated, never married, and living with a partner. Education level was stratified into three categories: less than high school, high school, and more than high school. Family income was delineated as ≤130% (reference group), >130–350%, and >350% based on the ratio of family income to poverty (FPL) [26]. Participants with a self-reported history of congestive heart failure, coronary heart disease, angina/angina pectoris, and heart attack were diagnosed with CVD. Hypertension was defined if participants had systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥90 mmHg, a physician diagnosis of hypertension, or were using antihypertensive medication. For consistency with the diagnostic criteria for DM, an expanded diagnostic cluster was utilized, incorporating six reference definitions: physician diagnosis of DM, use of antidiabetic medication or insulin, fasting glucose ≥126 mg/dL, 2-hour blood glucose ≥11.1 mmol/L during an oral glucose tolerance test, or HbA1c ≥6.5%. Alcohol consumption was dichotomized as never (having consumed <12 drinks in a lifetime), low-to-moderate (≤1 drink per day for women or ≤2 drinks per day for men on average over the past year), and severe (>1 drink per day for women or >2 drinks per day for men on average over the past year). Smoking status was categorized into three groups: never smokers (smoked <100 cigarettes in a lifetime), former smokers (smoked >100 cigarettes in a lifetime and do not smoke now), and current smokers (smoked >100 cigarettes in a lifetime and smoke some days or every day). For physical activity, a metabolic equivalent (MET) value was assigned according to the compendium of activity energy costs for each activity in the questionnaire, and the total volume of physical activity was converted into MET-minutes per week.
The average caloric consumption across a span of two days was calculated based on responses from a dietary survey questionnaire. Outlying data points were identified and removed, defined for males as caloric intakes below 800 kcal/day or exceeding 4200 kcal/day, and for females as intakes below 500 kcal/day or surpassing 3500 kcal/day. The full measurement technique for these variables is available at https://www.cdc.gov/nchs/nhanes/.
2.3 Statistical analyses
Statistical analyses were conducted according to the Centers for Disease Control and Prevention guidelines for analysis of NHANES data (https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx). A full sample 10-year mobile examination centers weight was used to calculate the US non-institutionalized population.
Continuous variables were expressed as weighted means ± standard deviation or median with interquartile range, and comparisons were conducted using weighted linear regression analysis. Categorical variables were described using unweighted frequencies (weighted percentages) and were compared using the chi-squared test. These means and frequencies can be generalized to the US adult population. A multivariate linear regression model was used to study the association between the anthropometric indices and CKD. Anthropometric indices were categorized into quartiles, and trend p values were calculated. Tests for trend were performed by assigning scores to the quartiles of anthropometric indices and then using regression analysis to determine if there was a significant linear relationship between these scores and the outcome variable. Four models were utilized in this study. Model 1 was a crude model without adjustment for potential confounding factors. Model 2 was adjusted for demographic characteristics, including age, gender, and race. Model 3 further adjusted for lifestyle factors, such as alcohol consumption, smoking status, physical activity, and calorie intake. To ensure the robustness of our findings, several sensitivity analyses were conducted. Restricted cubic spline (RCS) regression with four knots at the 5th, 35th, 65th, and 95th percentiles of the anthropometric indices values was employed to explore dose–response associations between anthropometric indices and CKD. Subgroup and tests for interaction were performed to investigate potential interactions of covariates. These stratification factors were also considered as pre-specified potential effect modifiers. An interaction term was included to test for heterogeneity in the associations between subgroups. The interaction was tested by incorporating interaction terms into the regression model and comparing models with and without these terms using a likelihood ratio test to evaluate their significance. Ultimately, ROC curve analysis was employed to evaluate the discriminatory ability of each anthropometric measure to identify CKD. All analyses were conducted using R software version 4.2.2 (https://www.R-project.org; R Foundation, Austria). Appropriate examination weights were applied to represent the complex survey design (https://wwwn.cdc.gov/nchs/nhanes/tutorials/weighting.aspx). Statistical significance was set at p < 0.05
3. Results
3.1 Participant characteristics
A total of 24,162 participants were included in our study with a mean age of 47.54 and 49.1% being male. Table 1 presents the weighted baseline characteristics of the study participants, categorized based on the presence or absence of CKD. The CKD group tended to be shorter and have significantly higher values of anthropometric indicators including weight, WC, BMI, ABSI, BRI, WHtR and C-index. Moreover, the CKD group demonstrated a higher prevalence of hypertension, diabetes, CVD, and cancer. Interestingly, concerning blood lipids, the CKD group displayed higher levels of TG but lower levels of LDL, while TC and HDL were not statistically significant.
3.2 Associations of anthropometric indices with kidney function
To highlight the relationships between various anthropometric indices, the Pearson correlation was employed (Fig 2). The most notable correlations are observed between BMI and WC (r = 0.92), as well as between WC and BRI (r = 0.96), indicating strong positive associations. In contrast, ABSI shows weak correlations with most other indices, particularly with BMI (r = 0.05) and WC (r = 0.38), suggesting that ABSI captures distinct aspects of body composition. The C-index exhibits moderate correlations with BMI, WC, BRI, and WHtR, underscoring its potential utility in assessing health risks associated with obesity.
Anthropometric indices were sex-specific z-scores transformed. BMI, Body Mass Index; WC, waist circumference; ABSI, A Body Shape Index; BRI, Body Roundness Index; WHtR, Waist-to-Height Ratio; C-index, Conicity Index.
Table 2 presents the results of logistic regression analyses assessing the associations between CKD and sex-specific z-scores of various anthropometric indices. In Model 3, after adjusting for various covariates, height shows a protective effect in CKD with an OR of 0.91 (95% CI: 0.85–0.98, p = 0.014). Conversely, ABSI, BRI, WHtR, and C-index exhibited an increased risk of prevalent CKD, with ORs of 1.17 (95% CI: 1.11–1.23, p < 0.001), 1.30 (95% CI: 1.23–1.38, p < 0.001), 1.31 (95% CI: 1.22–1.38, p< 0.001), and 1.29 (95% CI: 1.22–1.36, p < 0.001) respectively. Additionally, significant positive associations with CKD were also observed for WC, weight, and BMI.
Further dividing the anthropometric indices into quartiles, we obtained similar results (Table 3). In Model 3, the OR for Q4 of height compared to Q1 is 0.73 (95% CI: 0.56–0.96, p = 0.033), suggesting a significant protective effect. For ABSI, BRI, WHtR, and C-index, the odds ratios for Q4 compared to Q1 all show significant associations with increased CKD risk, with ORs of 1.37, 1.81, 1.84, and 1.79, respectively. The odds ratios for Q4 compared to Q1 for WC, weight, and BMI show a significant protective association, while Q2 and Q3 compared to Q1 did not demonstrate significance.
Tables 4 and 5 present the results of logistic regression analyses evaluating the associations between decreased kidney function (eGFR < 90 mL/min/1.73 m2) and proteinuria (UACR ≥ 30 mg/g) with sex-specific z-scores of various anthropometric indices. In Model 3, WC, weight, BMI, BRI, WHtR, and C-index remained significantly associated with decreased kidney function, while height and ABSI did not show significant associations. Similarly, in Model 3 for proteinuria, all anthropometric indices were significantly associated. Most indices, including WC, weight, BMI, ABSI, BRI, WHtR, and C-index, were positively associated with proteinuria, with the exception of height, which demonstrated a protective association with an odds ratio of 0.88.
3.3. The dose-response association between anthropometric indices and CKD
To examine the dose-response association between the anthropometric indices and prevalent CKD, the RCS regression was performed. Fig 3 shows non-linear associations between WC, BMI, ABSI, and C-index levels and CKD (p for non-linear < 0.05) after adjusting for covariates. S1 Fig also demonstrated that weight, BRI, and WHtR exhibit significant nonlinear associations with CKD risk (p for non-linear < 0.001), whereas height maintains a linear relationship (p for non-linear = 0.729).
The dose-response relationship was evaluated using restricted cubic spline regression for WC(a), BMI (b), ABSI (c), and C-index (d) and CKD, with covariates adjusted as in model 3. The odds ratio is represented by the red line and the 95% confidence interval is shown in pink.
3.4 Subgroup analysis
In the subgroup analysis (Fig 4), the association between sex-specific z-scores of ABSI and CKD remained significant across all age groups, races, with or without hypertension, without diabetes or CVD, and for individuals with daily dietary energy intake below 1622 kcal or above 2255 kcal, as well as across all physical activity levels. The p-values for interaction indicate significant interactions for age, disease status (hypertension, diabetes, and CVD), dietary energy intake, and physical activity levels, suggesting that these factors modify the relationship between ABSI and CKD.
* p < 0.05. The number of CKD cases per category for categorical variables is as follows: for age, there are 1448 cases in the <60 group and 2686 cases in the ≥60 group; for race, there are 937 cases among Black individuals and 3197 cases among others; for hypertension, there are 1507 cases with no hypertension and 2627 cases with hypertension; for diabetes mellitus, there are 2427 cases without diabetes and 1707 cases with diabetes; for cardiovascular disease, there are 3068 cases without cardiovascular disease and 1066 cases with cardiovascular disease. For continuous variables, the number of CKD cases per group is as follows: for diet energy, there are 1755 cases in the <1622 kcal/day group, 1346 cases in the 1622–2255 kcal/day group, and 1033 cases in the ≥2255 kcal/day group; for MET, there are 2194 cases in the <600 min/week group, 1245 cases in the 600–4000 min/week group, and 695 cases in the ≥4000 min/week group.
The subgroup analysis of the associations between sex-specific z-scores of the C-index and CKD in Fig 5 demonstrates that the relationship between the C-index and CKD remains significant across all subgroups. The interaction p-values highlight significant interactions with age, disease conditions (including hypertension, diabetes, and CVD), dietary energy intake, and levels of physical activity, indicating that these factors significantly influence the relationship between the C-index and CKD risk.
* p <0.05.
3.5 ROC analyses of anthropometric indices in relation to CKD risk
The ROC analysis results presented in Table 6 and Fig 6 indicate the predictive performance of various anthropometric indices for CKD. Among the indices, ABSI and the C-index demonstrate the highest areas under the curve (AUCs), with values of 0.655 (95% CI: 0.644–0.673) and 0.657 (95% CI: 0.665–0.674), respectively. These values suggest that ABSI and the C-index are the most effective predictors of CKD compared to the other anthropometric measures analyzed. BRI and WHtR also show relatively high AUCs of 0.610 and 0.609, respectively, indicating moderate predictive capability. Conversely, traditional measures such as WC, Height, Weight, and BMI have lower AUCs, ranging from 0.504 to 0.586, suggesting limited predictive value for CKD in this cohort.
4. Discussion
In this study, we explored the association between different anthropometric indices, and the risk of developing CKD. After adjusting for multiple covariates in the adult population using NHANES data from 2009 to 2018, we identified a positive correlation between ABSI, BRI, WHtR, and the C-index with CKD. However, BMI was not significantly associated with the risk of CKD. This can be attributed to the inclusion of WC in these indices, providing a more accurate reflection of obesity and fat distribution, particularly visceral fat, which plays a crucial role in CKD pathogenesis.
ABSI and the C-index demonstrated superior predictive capabilities for CKD risk in our study, as evidenced by the highest AUC values. These findings suggest that ABSI and the C-index could serve as practical and cost-effective tools for identifying CKD patients, particularly those with uneven fat distribution.
Other literature supports the superior predictive performance of ABSI over BMI in various diseases. Unfortunately, there are still relatively few studies comparing the C index with BMI. For example, a study involving 62,514 Japanese urban residents found that ABSI and the C-index had the strongest correlation with arterial stiffness among different anthropometric indices (including BMI, WC, ABSI, C-index, WHtR, and WC/BMI ratio) [27]. Another 13-year follow-up study showed that ABSI had a stronger association with increased mortality and frailty compared to BMI [28]. In men with prostate cancer diagnosed within 4 years, high ABSI, but not BMI, was independently associated with increased prostate cancer-specific mortality [29].
ABSI, which accounts for WC, height, and weight, provides a more nuanced assessment of body fat distribution and health risk compared to BMI, which is based solely on height and weight. Some studies also found that ABSI correlated minimally with BMI [27, 28, 30]. The minimal correlation between ABSI and BMI indicates that they measure different aspects of body composition and health risks. Therefore, while BMI gives a general idea of body mass relative to height, ABSI offers more specific insights into abdominal fat distribution and related health risks. Some studies have combined ABSI and BMI, achieving better results. For instance, BMI combined with ABSI has been shown to best identify obesity-related non-alcoholic fatty liver disease (NAFLD) risk, performing significantly better than BMI, WC, or ABSI alone [31].
However, not all studies agree on the superiority of ABSI or the C-index. For certain diseases, BMI performs better. For example, a meta-analysis found that BRI was superior to ABSI and similar to BMI in predicting hypertension [32]. A rural Chinese cohort study found that BMI was superior to the C-index for predicting incident hypertension [33]. Additionally, a 24-year follow-up study found that BMI was a better marker than the C-index for predicting coronary heart disease (CHD) incidence and mortality [34].
Our study also conducted subgroup analyses and tests for interaction to explore potential interactions between anthropometric indices and various diseases (hypertension, diabetes mellitus (DM), and cardiovascular disease (CVD)). Significant interactions were found, consistent with existing studies that report associations between obesity and these diseases [35–43]. Excessive adiposity is known to raise blood pressure and accounts for 65–75% of primary hypertension [35]. Obesity significantly drives DM, affecting nearly 10.5% of the global population [39], and has been correlated with increased cardiovascular and all-cause mortality over a 40-year follow-up period [36].
This study has several limitations. Firstly, its cross-sectional design limits the ability to establish or confirm causality. Secondly, despite adjusting for potential confounding factors, residual confounding factors may still influence the relationship between ABSI (or BRI) and CKD. Thirdly, the study population comprised Americans aged over 20, excluding specific groups like pregnant individuals. The limited sample size also precluded the analysis of special populations or other ethnicities. Future research should aim to ascertain the generalizability of the associations between ABSI and C-index with CKD across diverse populations. In conclusion, our findings underscore the importance of considering advanced anthropometric indices like ABSI and the C-index in CKD risk assessment. These indices provide valuable insights into body fat distribution, particularly visceral fat, which is crucial in understanding and managing CKD risk.
5. Conclusion
This study highlights the significant associations between various anthropometric indices and the risk of CKD. Height was found to have a protective effect against CKD. Among the indices studied, ABSI and the C-index demonstrated the strongest predictive capabilities for CKD, with the highest AUC values. Conversely, traditional measures such as BMI, weight and WC showed limited predictive value. The findings underscore the importance of using comprehensive anthropometric assessments in identifying and managing CKD risk, with ABSI and the C-index emerging as valuable tools in this context. Further research is needed to confirm these associations across diverse populations and explore the underlying mechanisms.
Supporting information
S1 Fig. The dose–response association between anthropometric and CKD.
The dose-response relationship was evaluated using restricted cubic spline regression for weight (a), height (b), BRI (c), and WHtR (d) and CKD, with covariates adjusted as in model 3. The odds ratio is represented by the red line and the 95% confidence interval is shown in pink.
https://doi.org/10.1371/journal.pone.0311547.s001
(TIF)
Acknowledgments
Thanks to Dr. Fengping Zhang for the guidance and suggestions on the statistics in this article.
References
- 1. Perdomo CM, Cohen RV, Sumithran P, Clément K, Frühbeck G. Contemporary medical, device, and surgical therapies for obesity in adults. Lancet (London, England). 2023;401(10382):1116–30. Epub 2023/02/13. pmid:36774932.
- 2. Chintam K, Chang AR. Strategies to Treat Obesity in Patients With CKD. American journal of kidney diseases: the official journal of the National Kidney Foundation. 2021;77(3):427–39. Epub 2020/10/20. pmid:33075388; PubMed Central PMCID: PMC7904606.
- 3. Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in Obesity and Severe Obesity Prevalence in US Youth and Adults by Sex and Age, 2007–2008 to 2015–2016. Jama. 2018;319(16):1723–5. Epub 2018/03/24. pmid:29570750; PubMed Central PMCID: PMC5876828.
- 4. Ostrominski JW, Arnold SV, Butler J, Fonarow GC, Hirsch JS, Palli SR, et al. Prevalence and Overlap of Cardiac, Renal, and Metabolic Conditions in US Adults, 1999–2020. JAMA cardiology. 2023;8(11):1050–60. Epub 2023/09/27. pmid:37755728; PubMed Central PMCID: PMC10535010.
- 5. Chang AR, Grams ME, Navaneethan SD. Bariatric Surgery and Kidney-Related Outcomes. Kidney international reports. 2017;2(2):261–70. Epub 2017/04/26. pmid:28439568; PubMed Central PMCID: PMC5399773.
- 6. Wu R, Luo P, Luo M, Li X, Zhong X, He Q, et al. Genetically predicted adiponectin causally reduces the risk of chronic kidney disease, a bilateral and multivariable mendelian randomization study. Frontiers in genetics. 2022;13:920510. Epub 2022/08/13. pmid:35957678; PubMed Central PMCID: PMC9360570.
- 7. Chang AR, Grams ME, Ballew SH, Bilo H, Correa A, Evans M, et al. Adiposity and risk of decline in glomerular filtration rate: meta-analysis of individual participant data in a global consortium. BMJ (Clinical research ed). 2019;364:k5301. Epub 2019/01/12. pmid:30630856; PubMed Central PMCID: PMC6481269.
- 8. Griffin KA, Kramer H, Bidani AK. Adverse renal consequences of obesity. American journal of physiology Renal physiology. 2008;294(4):F685–96. Epub 2008/02/01. pmid:18234955.
- 9. de Vries AP, Ruggenenti P, Ruan XZ, Praga M, Cruzado JM, Bajema IM, et al. Fatty kidney: emerging role of ectopic lipid in obesity-related renal disease. The lancet Diabetes & endocrinology. 2014;2(5):417–26. Epub 2014/05/06. pmid:24795255.
- 10. Gao H, Zhang Y, Chen LW, Gan H, Lu MJ, Huang B, et al. Associating phthalate exposure during pregnancy with preschooler’s FMI, ABSI and BRI trajectories via putative mechanism pathways. Chemosphere. 2023;337:139300. Epub 2023/07/01. pmid:37391081.
- 11. Kalantar-Zadeh K, Kopple JD, Kilpatrick RD, McAllister CJ, Shinaberger CS, Gjertson DW, et al. Association of morbid obesity and weight change over time with cardiovascular survival in hemodialysis population. American journal of kidney diseases: the official journal of the National Kidney Foundation. 2005;46(3):489–500. Epub 2005/09/01. pmid:16129211.
- 12. Lu JL, Kalantar-Zadeh K, Ma JZ, Quarles LD, Kovesdy CP. Association of body mass index with outcomes in patients with CKD. Journal of the American Society of Nephrology: JASN. 2014;25(9):2088–96. Epub 2014/03/22. pmid:24652789; PubMed Central PMCID: PMC4147974.
- 13. Wang YW, Lin TY, Peng CH, Huang JL, Hung SC. Factors Associated with Decreased Lean Tissue Index in Patients with Chronic Kidney Disease. Nutrients. 2017;9(5). Epub 2017/04/28. pmid:28448447; PubMed Central PMCID: PMC5452164.
- 14. Park J, Jin DC, Molnar MZ, Dukkipati R, Kim YL, Jing J, et al. Mortality predictability of body size and muscle mass surrogates in Asian vs white and African American hemodialysis patients. Mayo Clinic proceedings. 2013;88(5):479–86. Epub 2013/04/09. pmid:23562348; PubMed Central PMCID: PMC3909735.
- 15. Lin TY, Lim PS, Hung SC. Impact of Misclassification of Obesity by Body Mass Index on Mortality in Patients With CKD. Kidney international reports. 2018;3(2):447–55. Epub 2018/05/05. pmid:29725649; PubMed Central PMCID: PMC5932305.
- 16. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PloS one. 2012;7(7):e39504. Epub 2012/07/21. pmid:22815707; PubMed Central PMCID: PMC3399847.
- 17. Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring, Md). 2013;21(11):2264–71. Epub 2013/03/23. pmid:23519954; PubMed Central PMCID: PMC3692604.
- 18. Ashwell M, Gibson S. Waist to height ratio is a simple and effective obesity screening tool for cardiovascular risk factors: Analysis of data from the British National Diet And Nutrition Survey of adults aged 19–64 years. Obesity facts. 2009;2(2):97–103. Epub 2010/01/08. pmid:20054212; PubMed Central PMCID: PMC6444829.
- 19. Zhang A, Li Y, Ma S, Bao Q, Sun J, Cai S, et al. Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up. BMC geriatrics. 2022;22(1):971. Epub 2022/12/16. pmid:36522628; PubMed Central PMCID: PMC9756661.
- 20. Zhuang Y, Wang Y, Sun P, Ke J, Chen F. Association between triglyceride glucose-waist to height ratio and coronary heart disease: a population-based study. Lipids in health and disease. 2024;23(1):162. Epub 2024/06/04. pmid:38831434; PubMed Central PMCID: PMC11145810.
- 21. Filgueiras MS, Vieira SA, Fonseca PCA, Pereira PF, Ribeiro AQ, Priore SE, et al. Waist circumference, waist-to-height ratio and conicity index to evaluate android fat excess in Brazilian children. Public health nutrition. 2019;22(1):140–6. Epub 2018/10/09. pmid:30295220; PubMed Central PMCID: PMC10260534.
- 22. Wang M, Huang ZH, Zhu YH, He P, Fan QL. Association between the composite dietary antioxidant index and chronic kidney disease: evidence from NHANES 2011–2018. Food & function. 2023;14(20):9279–86. Epub 2023/09/29. pmid:37772927.
- 23. Chen TK, Knicely DH, Grams ME. Chronic Kidney Disease Diagnosis and Management: A Review. Jama. 2019;322(13):1294–304. Epub 2019/10/02. pmid:31573641; PubMed Central PMCID: PMC7015670.
- 24. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Annals of internal medicine. 2009;150(9):604–12. Epub 2009/05/06. pmid:19414839; PubMed Central PMCID: PMC2763564.
- 25. Lu J, Li H, Wang S. Interaction effect of psoriasis and chronic kidney disease on the risk of all-cause mortality: a prospective cohort study of NHANES data. Nephrology, dialysis, transplantation: official publication of the European Dialysis and Transplant Association—European Renal Association. 2023;38(11):2474–84. Epub 2023/05/13. pmid:37173279.
- 26. Nie J, Deng MG, Wang K, Liu F, Xu H, Feng Q, et al. Higher HEI-2015 scores are associated with lower risk of gout and hyperuricemia: Results from the national health and nutrition examination survey 2007–2016. Frontiers in nutrition. 2022;9:921550. Epub 2022/08/23. pmid:35990332; PubMed Central PMCID: PMC9381708.
- 27. Nagayama D, Fujishiro K, Watanabe Y, Yamaguchi T, Suzuki K, Saiki A, et al. A Body Shape Index (ABSI) as a Variant of Conicity Index Not Affected by the Obesity Paradox: A Cross-Sectional Study Using Arterial Stiffness Parameter. Journal of personalized medicine. 2022;12(12). Epub 2022/12/24. pmid:36556235; PubMed Central PMCID: PMC9783005.
- 28. Shafran I, Krakauer NY, Krakauer JC, Goshen A, Gerber Y. The predictive ability of ABSI compared to BMI for mortality and frailty among older adults. Frontiers in nutrition. 2024;11:1305330. Epub 2024/04/29. pmid:38680534; PubMed Central PMCID: PMC11048479.
- 29. Ku HC, Cheng E, Cheng CF. A body shape index (ABSI) but not body mass index (BMI) is associated with prostate cancer-specific mortality: Evidence from the US NHANES database. The Prostate. 2024;84(9):797–806. Epub 2024/04/01. pmid:38558412.
- 30. Parra-Soto S, Malcomson FC, Ho FK, Pell JP, Sharp L, Mathers JC, et al. Associations of A Body Shape Index (ABSI) with Cancer Incidence, All-Cause, and at 23 Sites-Findings from the UK Biobank Prospective Cohort Study. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2022;31(2):315–24. Epub 2021/12/03. pmid:34853021.
- 31. Kuang M, Sheng G, Hu C, Lu S, Peng N, Zou Y. The value of combining the simple anthropometric obesity parameters, Body Mass Index (BMI) and a Body Shape Index (ABSI), to assess the risk of non-alcoholic fatty liver disease. Lipids in health and disease. 2022;21(1):104. Epub 2022/10/22. pmid:36266655; PubMed Central PMCID: PMC9585710.
- 32. Calderón-García JF, Roncero-Martín R, Rico-Martín S, De Nicolás-Jiménez JM, López-Espuela F, Santano-Mogena E, et al. Effectiveness of Body Roundness Index (BRI) and a Body Shape Index (ABSI) in Predicting Hypertension: A Systematic Review and Meta-Analysis of Observational Studies. International journal of environmental research and public health. 2021;18(21). Epub 2021/11/14. pmid:34770120; PubMed Central PMCID: PMC8582804.
- 33. Chen X, Liu Y, Sun X, Yin Z, Li H, Deng K, et al. Comparison of body mass index, waist circumference, conicity index, and waist-to-height ratio for predicting incidence of hypertension: the rural Chinese cohort study. Journal of human hypertension. 2018;32(3):228–35. Epub 2018/02/09. pmid:29416119.
- 34. Kim KS, Owen WL, Williams D, Adams-Campbell LL. A comparison between BMI and Conicity index on predicting coronary heart disease: the Framingham Heart Study. Annals of epidemiology. 2000;10(7):424–31. Epub 2000/10/06. pmid:11018345.
- 35. Hall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity, kidney dysfunction and hypertension: mechanistic links. Nature reviews Nephrology. 2019;15(6):367–85. Epub 2019/04/25. pmid:31015582; PubMed Central PMCID: PMC7278043.
- 36. Twig G, Yaniv G, Levine H, Leiba A, Goldberger N, Derazne E, et al. Body-Mass Index in 2.3 Million Adolescents and Cardiovascular Death in Adulthood. The New England journal of medicine. 2016;374(25):2430–40. Epub 2016/04/14. pmid:27074389.
- 37. Müller TD, Blüher M, Tschöp MH, DiMarchi RD. Anti-obesity drug discovery: advances and challenges. Nature reviews Drug discovery. 2022;21(3):201–23. Epub 2021/11/25. pmid:34815532; PubMed Central PMCID: PMC8609996.
- 38. Karamanakos G, Kokkinos A, Dalamaga M, Liatis S. Highlighting the Role of Obesity and Insulin Resistance in Type 1 Diabetes and Its Associated Cardiometabolic Complications. Current obesity reports. 2022;11(3):180–202. Epub 2022/08/06. pmid:35931912.
- 39. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet (London, England). 2016;387(10026):1377–96. Epub 2016/04/27. pmid:27115820; PubMed Central PMCID: PMC7615134.
- 40. Ruze R, Liu T, Zou X, Song J, Chen Y, Xu R, et al. Obesity and type 2 diabetes mellitus: connections in epidemiology, pathogenesis, and treatments. Frontiers in endocrinology. 2023;14:1161521. Epub 2023/05/08. pmid:37152942; PubMed Central PMCID: PMC10161731.
- 41. Hahr AJ, Molitch ME. Management of Diabetes Mellitus in Patients With CKD: Core Curriculum 2022. American journal of kidney diseases: the official journal of the National Kidney Foundation. 2022;79(5):728–36. Epub 2021/10/04. pmid:34600745.
- 42. Andersen MJ, Khawandi W, Agarwal R. Home blood pressure monitoring in CKD. American journal of kidney diseases: the official journal of the National Kidney Foundation. 2005;45(6):994–1001. Epub 2005/06/16. pmid:15957127.
- 43. Matsushita K, Ballew SH, Wang AY, Kalyesubula R, Schaeffner E, Agarwal R. Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease. Nature reviews Nephrology. 2022;18(11):696–707. Epub 2022/09/15. pmid:36104509.