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Chaoyang Li, E. Ford, Guixiang Zhao, L. Balluz, W. Giles (2009)Estimates of body composition with dual-energy X-ray absorptiometry in adults.
The American journal of clinical nutrition, 90 6
F. Fowkes, G. Murray, I. Butcher, C. Heald, Lee Rj, L. Chambless, A. Folsom, A. Hirsch, M. Dramaix, G. Debacker, J. Wautrecht, M. Kornitzer, A. Newman, M. Cushman, K. Sutton-Tyrrell, Amanda Lee, J. Price, R. D’Agostino, J. Murabito, P. Norman, K. Jamrozik, J. Curb, K. Masaki, B. Rodríguez, J. Dekker, L. Bouter, R. Heine, G. Nijpels, C. Stehouwer, L. Ferrucci, M. McDermott, H. Stoffers, Hooi Jd, J. Knottnerus, M. Ogren, B. Hedblad, J. Witteman, M. Breteler, M. Hunink, A. Hofman, M. Criqui, R. Langer, A. Fronek, W. Hiatt, R. Hamman, H. Resnick, J. Guralnik (2008)Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis.
JAMA, 300 2
K. Nasir, E. Guallar, A. Navas-Acien, M. Criqui, J. Lima (2005)Relationship of Monocyte Count and Peripheral Arterial Disease: Results From the National Health and Nutrition Examination Survey 1999–2002
Arteriosclerosis, Thrombosis, and Vascular Biology, 25
T. Murphy, R. Dhangana, M. Pencina, R. D'Agostino (2012)Ankle-brachial index and cardiovascular risk prediction: an analysis of 11,594 individuals with 10-year follow-up.
Atherosclerosis, 220 1
Asghar Naqvi, Roger Davis, K. Mukamal (2014)Nutrient intake and peripheral artery disease in adults: key considerations in cross-sectional studies.
Clinical nutrition, 33 3
O. Gruzdeva, D. Borodkina, E. Uchasova, Y. Dyleva, O. Barbarash (2018)Localization of fat depots and cardiovascular risk
Lipids in Health and Disease, 17
Hanna-Sofia Karcher, Robert Holzwarth, H. Mueller, A. Ludolph, R. Huber, J. Kassubek, E. Pinkhardt (2013)Body Fat Distribution as a Risk Factor for Cerebrovascular Disease: An MRI-Based Body Fat Quantification Study
Cerebrovascular Diseases, 35
Morgana Mongraw-Chaffin, M. Allison, G. Burke, M. Criqui, K. Matsushita, P. Ouyang, R. Shah, C. Shay, C. Anderson (2017)CT‐Derived Body Fat Distribution and Incident Cardiovascular Disease: The Multi‐Ethnic Study of Atherosclerosis
The Journal of Clinical Endocrinology & Metabolism, 102
B. Strasser, M. Arvandi, Evan Pasha, Andreana Haley, P. Stanforth, Hirofumi Tanaka (2015)Abdominal obesity is associated with arterial stiffness in middle-aged adults.
Nutrition, metabolism, and cardiovascular diseases : NMCD, 25 5
KB Gast, M den Heijer, JW Smit, RL Widya, HJ Lamb, A de Roos (2015)Individual contributions of visceral fat and total body fat to subclinical atherosclerosis: The NEO study
Sejung Park, Hyoung-Mo Yang, K. Seo, So-Yeon Choi, Byoung-Joo Choi, M. Yoon, G. Hwang, S. Tahk, S. Sheen, B. Choi, H. Lim (2016)The relationship between coronary atherosclerosis and body fat distribution measured using dual energy X-ray absorptiometry.
Yu Yang, Longguang Liu, Hongxiao Sun, Fengze Nie, Xinhua Hu (2019)High ankle brachial index and cardiovascular outcomes in the general population and suspected or established cardiovascular disease patients: a meta-analysis.
International angiology : a journal of the International Union of Angiology
Akihiko Kato, Junko Ishida, Yukino Endo, T. Takita, M. Furuhashi, Y. Maruyama, M. Odamaki (2011)Association of abdominal visceral adiposity and thigh sarcopenia with changes of arteriosclerosis in haemodialysis patients.
Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 26 6
Xuyu Gu, Changfeng Man, Heng Zhang, Yu Fan (2019)High ankle-brachial index and risk of cardiovascular or all-cause mortality: A meta-analysis.
F. Horber, Bruno Gruber, F. Thomi, Eric Jensen, P. Jaeger (1997)Effect of sex and age on bone mass, body composition and fuel metabolism in humans.
Nutrition, 13 6
A. Velescu, A. Clará, R. Martí, R. Ramos, S. Pérez-Fernández, L. Marcos, M. Grau, I. Dégano, J. Marrugat, R. Elosúa (2017)Abnormally High Ankle-Brachial Index is Associated with All-cause and Cardiovascular Mortality: The REGICOR Study.
European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery, 54 3
S. Jung, Jihyun Park, Y. Seo (2021)Relationship between arm-to-leg and limbs-to-trunk body composition ratio and cardiovascular disease risk factors
Scientific Reports, 11
R. Sampaio, Priscila Sampaio, M. Yamada, T. Yukutake, M. Uchida, T. Tsuboyama, H. Arai (2014)Arterial stiffness is associated with low skeletal muscle mass in Japanese community‐dwelling older adults
Geriatrics & Gerontology International, 14
R. Wildman, P. Muntner, J. Chen, K. Sutton-Tyrrell, Jiang He (2005)Relation of inflammation to peripheral arterial disease in the national health and nutrition examination survey, 1999-2002.
The American journal of cardiology, 96 11
Y. Ostchega, R. Paulose‐Ram, C. Dillon, Q. Gu, Jeffery Hughes (2007)Prevalence of Peripheral Arterial Disease and Risk Factors in Persons Aged 60 and Older: Data from the National Health and Nutrition Examination Survey 1999–2004
Journal of the American Geriatrics Society, 55
K. Gast, N. Tjeerdema, T. Stijnen, J Smit, O. Dekkers (2016)Insulin resistance and atherosclerosis : the role of visceral fat
R. Bouchi, M. Asakawa, N. Ohara, Yujiro Nakano, T. Takeuchi, Masanori Murakami, Y. Sasahara, Mitsuyuki Numasawa, Isao Minami, Hajime Izumiyama, Koshi Hashimoto, T. Yoshimoto, Y. Ogawa (2016)Indirect measure of visceral adiposity ‘A Body Shape Index’ (ABSI) is associated with arterial stiffness in patients with type 2 diabetes
BMJ Open Diabetes Research & Care, 4
Luis Eraso, E. Fukaya, E. Mohler, D. Xie, Daohang Sha, J. Berger (2014)Peripheral arterial disease, prevalence and cumulative risk factor profile analysis
European Journal of Preventive Cardiology, 21
Asghar Naqvi, Roger Davis, K. Mukamal (2012)Dietary fatty acids and peripheral artery disease in adults.
Atherosclerosis, 222 2
Background: Ankle-brachial index (ABI) is a simple, non-invasive and easy-to-obtain measure for the evaluation of atherosclerotic peripheral arterial disease (PAD). This study aimed to investigate the relationships between body fluid volumes, body composition, body fat distribution and ABI at a population perspective. Results: Using the US National Health and Nutrition Examination Survey Data (NHANES) during 1999–2000, 2001–2002, and 2003–2004, adults ≥ 40 years old were eligible for inclusion. Univariate and multivariable linear and logistic regression analyses were performed to determine the associations between ABI, body fluid volume and body composition assessed by bioelectrical impedance analysis (BIA), and body fat distribution assessed by dual-energy X-ray absorptiometry (DEXA). After exclusion, the final analytic sample contained 1535 participants who were repre - sentative of totally 28,572,458 subjects in the US. After adjustments for relevant confounders, estimated fat mass was significantly and inversely associated with ABI (beta: − 0.0009, 95% CI = − 0.0015, − 0.0003). Total percent fat (beta: − 0.0024, 95% CI = − 0.0033, − 0.0014), trunk percent fat (beta: − 0.0016, 95% CI = − 0.0023, − 0.0009), and percent fat at the four limbs were also significantly and inversely associated with ABI (p < 0.001). In addition, subjects with higher estimated fat mass, total percent fat, trunk percent fat and higher percent fat at the four limbs were all signifi- cantly more likely to have abnormal ABI < 0.9. No significant association between parameters of body fluid volume and abnormal ABI was observed. Conclusions: Estimated fat mass, total percent fat, trunk percent fat and percent fat at the four limbs were signifi- cantly and inversely associated with ABI. Subjects with abnormal ABI are more likely to have higher total percent fat, trunk percent fat and the limb fat. These findings fill the knowledge gap on the relationships between atherosclerosis and body fat distribution. Further well-designed prospective studies are needed to confirm the present findings. Keywords: Ankle-Brachial index (ABI), Atherosclerosis, Peripheral artery disease (PAD), Body fluid volume, Body fat distribution non-fatal myocardial infarction and unstable angina, 1 Background cerebrovascular accidents, and peripheral artery disease Atherosclerotic cardiovascular diseases (ASCVDs) (PAD). To date, ASCVDs are predominant causes of involves the build-up of cholesterol plaque in arter- death worldwide. An estimated 17.9 million people died ies and includes acute coronary syndrome such as fatal/ from ASCVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to heart attack and stroke . Most ASCVDs can be prevented by address- *Correspondence: firstname.lastname@example.org ing lifestyles such as tobacco use, unhealthy diet, physical Department of Geriatric Medicine, The Fourth Medical Center of PLA General inactivity, and harmful alcohol use. It is crucial to detect Hospital, No.51 Fu Cheng Road, Haidian District, Beijing, China © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Li et al. Artery Research (2022) 28:91–99 92 ASCVDs as early as possible such that proper interven- nationally representative large cohort. We hypothesized tions may be started earlier to lower the subsequent mor- that some measures of body fluid volumes and certain bidity and mortality [2–4]. body fat distribution are independently associated with PAD, in particular, is a prevalent but underdiagnosed ABI. manifestation of atherosclerosis. In most cases of PAD, atherosclerotic plaques narrow the arterial flow lumen 2 Results which restricts blood flow to the distal extremity. Proper 2.1 Study Population awareness of PAD has a significant clinical impact A total of 31,126 subjects were identified from 1999– because PAD acts as a marker for systemic atheroscle- 2000, 2001–2002, and 2003–2004 study cycles of the rosis. Moreover, patients with PAD have an equivalent NHANES, and of whom 29,402 received examinations at cardiovascular risk to those with previous myocardial the mobile examination center (MEC). There were 9145 infarction and require aggressive risk factor modification participants aged ≥ 40 years. After excluding the partici- to improve their long-term health outcomes. In clinical pants who’s data were missing regarding ABI measures practice, the ankle brachial index (ABI) is widely used by (n = 1573), those who had an ABI > 1.3 (n = 38), and who a variety of specialist nurses, physicians, surgeons and did not have complete information on body fluid volume, podiatrists in different settings as a surrogate indicator body composition, body fat distribution (n = 5964) and for the severity of PAD, which is believed as a simple, dietary variables (n = 34), the final study population were non-invasive, rapid, and cheap method [5, 6]. totally 1535. Using the NHANES sample weight formu- Previously, obesity, especially central obesity, was lae, this analytic sample size was representative of a total linked to large arterial stiffness. Several dietary fats, body of 28,572,458 subjects in the US. The flow chart of study composition such as low skeletal muscle and abdomi- selection process is presented as Fig. 1. nal visceral adiposity, and were also associated with increased risk for arterial stiffening in a list of prior 2.2 Characteristic of the Study Population by ABI Status study reports [7–12]. However, the potential associations The included subjects had a median age of 44.5 years, and between body fluid, body fat and ABI have not been dem - the majority were females (68.2% in abnormal group and onstrated before. 51.2% in normal group). ABI was categorized into two To fill the current knowledge gap, in this study, we groups: abnormal group: < 0.90 (n = 24) versus normal aimed to investigate whether body fluid volumes and group: 0.9–1.3 (n = 1511). Weighted means and propor- body fat distribution is associated with ABI, using a tions of body fluid volume, body composition, body fat Fig. 1 Flow diagram of study selection process Li et al. Artery Research (2022) 28:91–99 93 distribution, demographic characteristics, comorbidities, significantly ABI. No significant association between laboratory measurement, as well as nutrients intake by parameters of body fluid volume and abnormal ABI ABI groups are shown in Table 1. In specific, estimated was found (Table 3). fat mass and estimated percent body fat were significantly higher in the abnormal ABI group (p = 0.003, p < 0.001, 2.5 Multivariable Logistic Regression Analysis respectively). The parameters of body fat distribution, of the Association Between Abnormal ABI and Study including total percent fat (p = 0.003), left arm percent fat Variables (p = 0.010), right arm percent fat (p = 0.008), left leg per- In multivariable logistic analysis, after adjusting for cent fat (p = 0.007), right leg percent fat (p = 0.008) and relevant confounders, higher estimated fat mass trunk percent fat (p = 0.008) were all significantly higher (OR = 1.04, 95% CI = 1.01–1.07), estimated percent in the abnormal ABI group, while estimated extracellu- body fat (OR = 1.04, 95% CI = 1.01–1.08), total percent lar fluid volume, intracellular fluid volume, total water fat (OR = 1.09, 95%CI = 1.04–1.14), trunk percent fat body volume or fat-free mass were not significantly dif - (OR = 1.09, 95%CI = 1.03–1.16) and higher percent fat of ferent between the two groups. In addition, the abnormal the four limbs were all significantly more likely to have ABI group consisted of significantly more current smok - abnormal ABI < 0.9. No significant association between ers (52.6% vs 25.7%) but less former and never smokers parameters of body fluid volume and abnormal ABI was (3.9% vs 24.3% and 43.5% vs 50.0%, respectively) than the found (Table 4). normal ABI group (p = 0.017). Subjects in the normal ABI group had a higher proportion of diabetes than the 3 Discussion abnormal ABI group (5.2% vs 0.5%, p = 0.002). Average To date, this is the first analysis evaluating the potential SBP was higher in the abnormal ABI group. With regard relationships between abnormal ABI, body fluid volume, to laboratory parameters, serum folate and albumin level body composition and body fat distribution at a large was lower in the abnormal ABI group (p = 0.004 and population level. In this study cohort of 1535 partici- p < 0.001, respectively), while WBC (white blood cell) pants represented as 28,572,458 US subjects. The results count was higher in the abnormal ABI group (p = 0.026, showed that after adjusting for relevant confounders, respectively) (Table 1). estimated fat mass, total percent fat, trunk percent fat and percent fat of the four limbs were inversely associ- ated with ABI. Subjects with higher estimated fat mass, 2.3 Univ ariate Linear Regression Analysis total percent fat, trunk percent fat and higher percent fat of the Associations Between ABI and Study Variables of the four limbs were all significantly more likely to have Univariate linear analysis was performed to determine abnormal ABI. the associations between ABI and the parameters of In general, these results unveiled the relationships body fluid volume, body composition, body fat distribu - between parameters of body fat distribution and ABI, a tion and the other covariates. Estimated fat mass (beta: validated indicator of atherosclerosis. − 0.0014, 95% CI = − 0.0020, − 0.0008), estimated per- The ABI measure is regarded as a simple and conveni - cent body fat (beta = − 0.0018, 95% CI = − 0.0025, ent method for diagnosing lower extremity PAD. Dur- − 0.0011), total percent fat (beta: − 0.0026, 95%CI = ing the past two decades, abnormally high ABI was also − 0.0034, − 0.0018), trunk percent fat (beta: − 0.0021, continuously associated with an increased risk of cardio- 95% CI = − 0.0029, − 0.0014) as well as percent fat of the vascular events, cerebrovascular events, and even death four limbs were all significantly associated with ABI. No from any cause [13–17]. significant association between parameters of body fluid On the other hand, body fat distribution is also closely volume and ABI was observed (Table 2). related to cardiovascular diseases. A previous study cor- related truncal fat distribution measured using DEXA to 2.4 Multiv ariable Linear Regression Analysis the extent of coronary atherosclerosis in Korean patients, of the Association Between ABI and Study Variables and concluded that truncal fat distribution were more In multivariable linear analysis, after adjusting for rel- clinically relevant to atherosclerosis compared with total evant confounders, participants with estimated fat body fat or BMI . The percentage of visceral adipose mass (beta: − 0.0009, 95%CI = − 0.0015, − 0.0003), tissue by itself had been regarded as a risk factor for both estimated percent body fat (beta: − 0.0008, 95%CI = small vessel cerebrovascular disease and cerebral ath- − 0.0016, 0.0000), total percent fat (beta: − 0.0024, erosclerosis of the large-to-medium-sized arteries . 95%CI = − 0.0033, − 0.0014), trunk percent fat (beta: Another prior study in Europe reported that visceral − 0.0016, 95% CI = − 0.0023, − 0.0009) and percent adipose tissue contributed beyond overall adiposity to fat of the four limbs remained to be associated with a subclinical atherosclerosis, particularly in women . Li et al. Artery Research (2022) 28:91–99 94 Table 1 Characteristics of study population by ABI (n = 1535) Study variables Total (n = 1535) ABI p value Abnormal (< 0.9) (n = 24) Normal (0.9–1.3) (n = 1511) Body fluid volume Estimated extracellular fluid volume (L) 17.4 (17.1,17.6) 18.1 (16.1,20.0) 17.4 (17.1,17.6) 0.657 Estimated intracellular fluid volume (L) 23.1 (22.7,23.5) 23.2 (20.7,25.7) 23.1 (22.7,23.5) 0.946 Estimated total water body volume (L) 40.5 (39.8,41.1) 41.3 (36.9,45.7) 40.5 (39.8,41.1) 0.803 Body composition Estimated fat mass (kg) 27.5 (26.8,28.3) 34.7 (32.7,36.7) 27.4 (26.7,28.2) 0.003 Estimated fat-free mass (kg) 54.3 (53.5,55.2) 55.3 (49.4,61.1) 54.3 (53.5,55.2) 0.828 Estimated percent body fat (kg) 33.4 (32.9,34.0) 38.6 (35.6,41.5) 33.4 (32.8,33.9) < 0.001 Body fat distribution Total percent fat 34.0 (33.4,34.6) 40.5 (39.1,41.8) 33.9 (33.3,34.5) 0.003 Left arm percent fat 34.4 (33.6,35.1) 41.1 (39.5,42.8) 34.3 (33.5,35.0) 0.010 Right arm percent fat 34.4 (33.6,35.1) 41.2 (40.3,42.0) 34.3 (33.5,35.0) 0.008 Left leg percent fat 35.4 (34.7,36.2) 41.8 (40.1,43.6) 35.3 (34.6,36.1) 0.007 Right leg percent fat 35.8 (35.1,36.5) 41.8 (40.3,43.3) 35.8 (35.0,36.5) 0.008 Trunk percent fat 33.8 (33.2,34.4) 40.7 (38.6,42.7) 33.7 (33.1,34.3) 0.008 Demography Age 44.5 (44.3,44.7) 45.5 (44.3,46.7) 44.5 (44.3,44.7) 0.290 Gender 0.282 Male 765 (48.6) 7 (31.8) 758 (48.8) Female 770 (51.4) 17 (68.2) 753 (51.2) Race/ethnicity 0.068 White 683 (77.1) 7 (29.2) 676 (46.0) Hispanic 464 (11.3) 5 (11.6) 459 (11.3) Black 348 (11.6) 12 (29.3) 336 (11.4) Others 40 0 40 SBP 121.2 (120.0,122.4) 133.1 (122.7,143.5) 121.0 (119.8,122.3) 0.004 DBP 76.2 (75.4,77.0) 75.3 (73.0,77.5) 76.2 (75.4,77.0) 0.611 Smoking status 0.017 Never 773 (49.9) 12 (43.5) 761 (50.0) Former 332 (24.1) 2 (3.9) 330 (24.3) Current 430 (26.0) 10 (52.6) 420 (25.7) Comorbidity Diabetes 101 (5.1) 1 (0.5) 100 (5.2) 0.002 Arthritis 255 (18.7) 6 (27.1) 249 (18.6) 0.417 Hypertension 293 (19.0) 13 (34.6) 280 (18.9) 0.138 Laboratory measurement Serum folate (nmol/L) 31.0 (29.5,32.5) 24.1 (21.4,26.7) 31.1 (29.5,32.6) 0.004 Serum albumin (g/dL) 43.5 (43.2,43.8) 41.8 (40.8,42.7) 43.5 (43.3,43.8) < 0.001 CRP (mg/dl) 0.4 (0.3,0.4) 0.5 (0.4,0.6) 0.4 (0.3,0.4) 0.342 WBC count (SI) 7.2 (7.0,7.3) 8.6 (7.0,10.2) 7.2 (7.0,7.3) 0.026 Serum total bilirubin (umol/L) 11.9 (11.6,12.3) 10.5 (7.9,13.2) 12.0 (11.6,12.3) 0.295 Total cholesterol (mmol/L) 5.3 (5.3,5.4) 5.3 (4.9,5.7) 5.3 (5.3,5.4) 0.832 Triglycerides (mmol/L) 1.7 (1.6,1.8) 2.4 (0.0,4.8) 1.7 (1.6,1.8) 0.225 Hemoglobin (g/dl) 14.5 (14.4,14.7) 14.9 (13.7,16.1) 14.5 (14.4,14.7) 0.602 Serum vitamin B12 (pmol/L) 373.5 (355.5,391.4) 407.8 (330.4,485.1) 373.1 (355.2,390.9) 0.308 Homocysteine (umol/L) 8.4 (8.2,8.7) 7.8 (6.8,8.9) 8.4 (8.2,8.7) 0.221 Avoid not applicable due to one cell with zero count, not include the analysis p < 0.05 are shown in bold. Continuous variables are shown as weighted mean and 95% CI; categorical variables are shown as unweighted counts (weighted %) ABI ankle brachial index; CRP C-reactive protein; DBP diastolic blood pressure; SBP systolic blood pressure; SI, standard unit; WBC white blood cell Li et al. Artery Research (2022) 28:91–99 95 Table 2 Univariate linear regression analysis of the associations Table 2 (coninted) between ABI and study variables Estimate (95% CI) p value Estimate (95% CI) p value Hypertension − 0.0166 (− 0.0306, 0.022 − 0.0026) Body fluid volume Laboratory measurement Estimated extracellular fluid 0.0026 (0.0011, 0.0041) < 0.001 Serum folate (nmol/L) 0.0003 (− 0.0001, 0.099 volume (L) 0.0006) Estimated intracellular fluid 0.0015 (0.0006, 0.0024) 0.002 volume (L) Serum albumin (g/dL) 0.0045 (0.0026, 0.0064) < 0.001 Estimated total water body 0.0010 (0.0004, 0.0016) < 0.001 CRP (mg/dl) − 0.0282 (− 0.0424, < 0.001 volume (L) − 0.0140) WBC count (SI) − 0.0064 (− 0.0092, < 0.001 Body composition − 0.0037) Estimated fat mass (kg) − 0.0014 (− 0.0020, < 0.001 Serum total bilirubin (umol/L) 0.0022 (0.0010, 0.0034) < 0.001 − 0.0008) Total cholesterol (mmol/L) − 0.0043 (− 0.0099, 0.135 Estimated fat-free mass (kg) 0.0007 (0.0003, 0.0012) < 0.001 0.0014) Estimated percent body fat (kg) − 0.0018 (− 0.0025, < 0.001 Triglycerides (mmol/L) − 0.0017 (− 0.0042, 0.182 − 0.0011) 0.0008) Body fat distribution Hemoglobin level (g/dl) 0.0030 (− 0.0014, 0.180 Total percent fat − 0.0026 (− 0.0034, < 0.001 0.0075) − 0.0018) Serum Vitamin B12 (pmol/L) 0.0000 (0.0000, 0.0000) 0.676 Left arm percent fat − 0.0018 (− 0.0024, < 0.001 Homocysteine (umol/L) − 0.0006 (− 0.0026, 0.575 − 0.0012) 0.0014) Right arm percent fat − 0.0019 (− 0.0025, < 0.001 − 0.0013) P < 0.05 are shown in bold Left leg percent fat − 0.0022 (− 0.0029, < 0.001 ABI ankle brachial index; CRP C-reactive protein; CI confidence interval; DBP − 0.0016) diastolic blood pressure; OR odds ratio; SBP systolic blood pressure; SI standard unit; WBC white blood cell Right leg percent fat − 0.0021 (− 0.0027, < 0.001 Unstandardized beta coefficients are reported − 0.0015) Trunk percent fat − 0.0021 (− 0.0029, < 0.001 − 0.0014) Demography Age − 0.0005 (− 0.0029, 0.715 Visceral fat, but not subcutaneous fat, is significantly 0.0020) associated with increased risk for CVD in a multi-eth- Gender nic cohort . These studies together imply a specific Male Ref role of body fat distribution in the early development of Female − 0.0351 (− 0.0488, < 0.001 atherosclerosis. − 0.0213) Interestingly, a previous study had reported that the Race/ethnicity higher the fat mass of the legs compared to the arms, fat- White Ref free mass of the arms compared to the legs, and fat mass Hispanic − 0.0081 (− 0.0239, 0.309 0.0078) or fat-free mass of the limbs compared to the trunk, the Black − 0.0505 (− 0.0623, < 0.001 lower the prevalence of CVD-risk factors . − 0.0388) Despite the relationships of body fat distribution and SBP − 0.0009 (− 0.0014, < 0.001 CVDs were demonstrated, no previous study has yet − 0.0004) directly associated ABI measures and body fat distribu DBP − 0.0005 (− 0.0012, 0.132 tion. In the present analysis we attempted to determine the 0.0002) links between DEXA-measured body fat distribution and Smoking status ABI measures, and found that both truncal fat and limb Never Ref fat were associated with abnormal ABI. Although directly Former 0.0179 (0.0069, 0.0290) 0.002 comparisons between the findings of ours and the prior Current − 0.0289 (− 0.0450, < 0.001 studies cannot be made, the results seem consistent with − 0.0127) the previous ones that linked truncal fat with CVD risks. Comorbidity In addition to trunk fat, the present study also found Diabetes − 0.0226 (− 0.0504, 0.108 0.0051) significant associations between higher limb fat and Arthritis − 0.0153 (− 0.0359, 0.143 abnormal ABI. As mentioned above, ABI < 0.9 is a good 0.0054) indicator for PAD in the primary care settings, and PAD is a condition where a build-up of fatty deposits in the Li et al. Artery Research (2022) 28:91–99 96 Table 3 Multivariable linear regression analysis of the associations Table 4 Multivariable logistic regression analysis of the between ABI and body fluid volume, body composition and body associations between abnormal ABI (< 0.9) and body fluid fat distribution volume, body composition and body fat distribution Estimate (95% CI) p value aOR (95% CI) p value Body fluid volume Body fluid volume Estimated extracellular fluid − 0.0015 (− 0.0041, 0.227 Estimated extracellular fluid volume (L) 1.05 (0.82,1.35) 0.669 volume (L) 0.0010) Estimated intracellular fluid volume (L) 1.00 (0.91,1.11) 0.938 Estimated intracellular fluid − 0.0011 (− 0.0025, 0.099 Estimated total water body volume (L) 1.01 (0.94,1.09) 0.812 volume (L) 0.0002) Body composition Estimated total water body − 0.0007 (− 0.0017, 0.121 Estimated fat mass (kg) 1.04 (1.01,1.07) 0.018 volume (L) 0.0002) Estimated fat-free mass (kg) 1.01 (0.95,1.06) 0.836 Body composition Estimated percent body fat (kg) 1.04 (1.01,1.08) 0.025 Estimated fat mass (kg) − 0.0009 (− 0.0015, 0.002 − 0.0003) Body fat distribution Estimated fat-free mass (kg) − 0.0005 (− 0.0012, 0.115 Total percent fat 1.09 (1.04,1.14) < 0.001 0.0001) Left arm percent fat 1.05 (1.01,1.09) 0.010 Estimated percent body fat (kg) − 0.0008 0.053 Right arm percent fat 1.05 (1.01,1.09) 0.009 (− 0.0016, > 0.0001) Left leg percent fat 1.07 (1.02,1.12) 0.009 Body fat distribution Right leg percent fat 1.06 (1.01,1.11) 0.009 Total percent fat − 0.0024 (− 0.0033, < 0.001 Trunk percent fat 1.09 (1.03,1.16) 0.003 − 0.0014) Left arm percent fat − 0.0019 (− 0.0028, < 0.001 p < 0.05 are shown in bold − 0.0010) aOR adjusted odds ratio; CI confidence interval Right arm percent fat − 0.0019 (− 0.0027, < 0.001 Each measure of body fluid and fat distribution was performed in separate − 0.0011) multivariable model using logistic regression, adjusted for race and SBP Left leg percent fat − 0.0026 (− 0.0036, < 0.001 − 0.0015) of atherosclerosis such as serum lipids was observed Right leg percent fat − 0.0022 (− 0.0032, < 0.001 − 0.0012) between the two groups. The major strengths of this study were the usage of the Trunk percent fat − 0.0016 (− 0.0023, < 0.001 − 0.0009) nationally representative database with a large multi- Each measure of body fluid and fat distribution was performed in separate ethnic population sample, with a number of important multivariable model using linear regression, adjusted for gender, race, smoking socio-demographic, behavioral and laboratory param- status and SBP and unstandardized beta coefficients are reported eters being adjusted. However, this study has several p < 0.05 are shown in bold limitations. First, the NHANES is a cross-sectional aOR adjusted odds ratio; CI confidence interval dataset, and thus no causal inference could be made. Second, information regarding duration and severity of the comorbidities were lacking. Third, some variables arteries restricts blood supply to leg muscles. Therefore, included in the analyses were based on the interview it is not surprising that subjects with an abnormal ABI (questionnaire) data and are subject to potential recall had a greater percent limb fat. Moreover, it was previ- bias or misunderstanding of the question. ously reported each local fat depot can be considered an independent endocrine organ that actively produces bio- 4 Conclusion logically active molecules, such as pro- and anti-inflam - US adults with abnormal ABI are more likely to have matory cytokines and adipokines . Consequently, higher total percent fat or trunk percent fat but not the accumulating evidence suggests the development of CVD limb fat or body fluid volume. These findings fill the may be mediated through the regional distribution of knowledge gap on the relationships between atheroscle- adipose tissue . Importantly, the pro-inflammatory rosis and body fat distribution. Further well-designed effect of excessively deposited body adipose tissue partly prospective studies are needed to confirm the present explains the relationships between higher trunk fat and findings. limb fat determined by BIA and abnormal ABI in the pre- sent study, although no difference of traditional markers Li et al. Artery Research (2022) 28:91–99 97 5 Materials and Methods (MEC). Participants lied supine on the exam table dur- 5.1 Data Source ing the exam. Systolic pressure is measured on the right This study was a secondary analysis of data from The arm (brachial artery) and both ankles (posterior tibial National Health and Nutrition Examination Survey arteries). Systolic blood pressure is measured twice at (NHANES) database, which was collected by the Cent- each site for participants aged 40–59 years and once ers for Disease Control and Prevention (CDC), National at each site for participants aged 60 years and older. Center for Health Statistics (NCHS) in the USA. (http:// Participants are excluded from the exam if they have a www. cdc. gov/ nchs/ nhanes/). The NHANES program bilateral amputation or weigh over 400 pounds (due to began in the United States in the early 1960s, and has equipment limitations). Participants was categorized been conducted as a series of surveys focusing on dif- into two groups by ABI measures: abnormal group ferent population groups and health topics. Samples (ABI < 0.9) and normal group (ABI 0.9–1.3) for further for the NHANES surveys are selected to represent the comparison. United States population of all ages. NHANES used a multi-stage, stratified, clustered, probability sampling 5.5 Assessment of Body Fluid Volume, Body Composition design to identify a nationally representative sample of and Body Fat Distribution non-institutionalized civilians in the US Weights are In this NHANES database, body fluid measures includ - created in NHANES to account for the complex survey ing extracellular fluid volumes, intracellular fluid vol - design (including oversampling), survey non-response, umes, total water body volumes, and body fat including and post-stratification adjustment to match total popula - estimated fat mass, fat-free mass and percent body fat tion counts from the Census Bureau. When a sample is were determined by Bioelectrical impedance analysis weighted in NHANES, it is representative of the US civil- (BIA). The NHANES bio-impedance spectroscopy (BIS) ian non-institutionalized resident population. A sample multi-frequency measurements were collected in the weight is assigned to each sample person. Further infor- BIA examination. A small alternating current was passed mation about background, design, and protocols of the through surface electrodes placed on the right hand and NHANES are available on the NHANES website (http:// foot and the impedance to the current flow was meas - wwwn. cdc. gov/ nchs/ nhanes). ured by different electrodes placed adjacent to the injec - tion electrodes. The voltage drop between electrodes 5.2 Ethics Statement provided a measure of impedance, or opposition to the NHANES was reviewed and approved through the flow of the electric current. NCHS Research Ethics Review Board, and informed Data of body fat distribution in the present analysis consent was provided by each participant. Please check were obtained from Dual-energy x-ray absorptiometry the NHANES website for NCHS Research Ethics Review (DXA), which is the most widely accepted method of Board Approval (https:// www. cdc. gov/ nchs/ nhanes/ measuring body composition, due in part to its speed, irba98. htm). Since all of the NHANES data are de-iden- ease of use, and low radiation exposure. The whole body tified, the analysis of the data does not require Institu - DXA scans were administered in the NHANES MEC. tional Review Board approval (IRB) or further informed In particular, DEXA scans were administered to eligible consent. survey participants 8 years of age and older. Pregnant females, self-reported history of radiographic contrast 5.3 Study Population material use in past 7 days, nuclear medicine studies in Data of adults ≥ 40 years old in the NHANES database the past 3 days, and weight over 300 pounds or height between 1999 and 2004 were extracted. The participants over 6’5’’ were excluded from the examination. Total per- with incomplete data for ABI measures and other main cent fat, percent fat of the limbs and truck were included study variables were excluded from the study cohort. in the analysis. 5.4 Assessment of ABI 5.6 Demographic and Socioeconomic Status ABI is the ratio of the blood pressure at the ankle to The Family and Sample Person Demographics ques - the blood pressure in the upper arm (brachium). It is tionnaires were collected in the participants’ homes by usually regarded as an indicator for PAD in asymp- trained interviewers using the Computer-Assisted Per- tomatic individuals. In the NHANES, the ABI exam sonal Interviewing (CAPI) system. Age, sex, and race/ was performed by trained health technicians in a spe- ethnicity were recorded using interviewer-administered cially equipped room in the mobile examination center questionnaires. Li et al. Artery Research (2022) 28:91–99 98 5.7 Laboratory Measurement MIANALYZE procedure to provide accurate estimates Blood specimens were collected at NHANES Mobile of standard error. Examination Centers (MECs). Whole blood specimens were processed, stored, and shipped to the Division of Abbreviations Laboratory Sciences, National Center for Environmental ABI: Ankle-brachial index; PAD: Peripheral arterial disease; NHANES: National Health, and Centers for Disease Control and Prevention Health and Nutrition Examination Survey Data; BIA: Bioelectrical impedance analysis; DEXA: Dual-energy X-ray absorptiometry; ASCVDs: Atherosclerotic for analysis. Complete descriptions of the collection and cardiovascular diseases; MEC: Mobile examination center. analytical methods are available in the Laboratory data section of NHANES database. Individual’s laboratory Acknowledgements The authors acknowledge the efforts of the United States National Center data such as serum albumin, total bilirubin, hemoglobin for Health Statistics (NCHS) in creation of the National Health and Nutrition level, C-reactive protein (CRP), homocysteine, folate and Examination Survey (NHANES) Data. The interpretation and reporting of these vitamin B12, level of total cholesterol and triglycerides, data are the sole responsibility of the authors. as well as white blood cell counts were identified and Author Contributions included in the analysis. LL: acquisition of data; analysis and interpretation of data; drafting of the manuscript; guarantor of integrity of the entire study. JZ: acquisition of data; drafting of the manuscript; statistical analysis. LW: acquisition of data; critical 5.8 Statistical Analysis revision of the manuscript; statistical analysis. ZZ: analysis and interpretation To take complex sampling design of NHANES data of data; critical revision of the manuscript. YX: conception and design; criti- cal revision of the manuscript; guarantor of integrity of the entire study. All into account, all analyses were performed using SAS authors read and approved the final manuscript. survey analysis procedures to generate nationally rep- resentative estimates (SAS Institute Inc., Cary, NC, Funding None. USA). Weighted mean and 95% confidence inter- val (CI) were presented for continuous variables; Availability of Data and Materials unweighted number and weighted proportion were The datasets used during the current study are available from the correspond- ing author on reasonable request. presented for categorical variables. Since three cycles of data were combined in the current study, sample Declarations weights across survey cycles were constructed accord- ing to analytic guidelines published by National Center Conflict of Interest for Health Statistics. The authors declare that they have no competing interests. Differences in means between groups of ankle bra - Ethical Approval and Consent to Participate chial index (ABI) were compared using SURVEYREG NHANES was reviewed and approved through the NCHS Research Ethics procedure for continuous variables, while Rao-Scott Review Board, and informed consent was provided by each participant. Please check the NHANES website for NCHS Research Ethics Review Board Approval chi-square test was performed to examine difference (https:// www. cdc. gov/ nchs/ nhanes/ irba98. htm). Since all of the NHANES data in the proportions between ABI groups using SUR- are de-identified, the analysis of the data does not require Institutional Review VEYFREQ procedure for categorical variables. Linear Board approval (IRB) or further informed consent. regression analysis and binary logistic regression analy- Consent for Publication sis were performed to evaluate the association of ABI Not applicable. with body fluid and fat, as well as potential covariates Received: 24 January 2022 Accepted: 17 May 2022 such as socioeconomic status, biomarkers, comorbid- Published online: 9 June 2022 ity, behaviors, and intake of nutrients. Probabilities modeled are cumulated over the lower Ordered Values. Variables with p-value less than 0.05 in univariate anal- References ysis were considered as potential confounding factors. 1. Ostchega Y, Paulose-Ram R, Dillon CF, Gu Q, Hughes JP. Prevalence Multivariable models were then constructed by adding of peripheral arterial disease and risk factors in persons aged 60 and significant covariate pertaining to socioeconomic sta - older: data from the National Health and Nutrition Examination Survey 1999–2004. J Am Geriatr Soc. 2007;55(4):583–9. tus, biomarkers/comorbidity/examination and behav- 2. Wildman RP, Muntner P, Chen J, Sutton-Tyrrell K, He J. Relation of inflam- ior/nutrients intake sequentially. Each measure of body mation to peripheral arterial disease in the national health and nutrition fluid and fat was performed in separate multivariable examination survey, 1999–2002. Am J Cardiol. 2005;96(11):1579–83. 3. Nasir K, Guallar E, Navas-Acien A, Criqui MH, Lima JA. Relationship of model. Since fat was measured by dual energy X-ray monocyte count and peripheral arterial disease: results from the National absorptiometry in which multiple imputation was per- Health and Nutrition Examination Survey 1999–2002. Arterioscler Thromb formed to deal with missing data, all analyses in terms Vasc Biol. 2005;25(9):1966–71. 4. Naqvi AZ, Davis RB, Mukamal KJ. Nutrient intake and peripheral artery of fat distribution were performed separately by each disease in adults: key considerations in cross-sectional studies. Clin Nutr. of the five imputed datasets and then combined using 2014;33(3):443–7. Li et al. Artery Research (2022) 28:91–99 99 5. Bouchi R, Asakawa M, Ohara N, Nakano Y, Takeuchi T, Murakami M, et al. Indirect measure of visceral adiposity “A Body Shape Index” (ABSI) is asso- ciated with arterial stiffness in patients with type 2 diabetes. BMJ Open Diabetes Res Care. 2016;4(1): e000188. 6. Eraso LH, Fukaya E, Mohler ER 3rd, Xie D, Sha D, Berger JS. Peripheral arte- rial disease, prevalence and cumulative risk factor profile analysis. Eur J Prev Cardiol. 2014;21(6):704–11. 7. Naqvi AZ, Davis RB, Mukamal KJ. Dietary fatty acids and peripheral artery disease in adults. Atherosclerosis. 2012;222(2):545–50. 8. Strasser B, Arvandi M, Pasha EP, Haley AP, Stanforth P, Tanaka H. Abdomi- nal obesity is associated with arterial stiffness in middle-aged adults. Nutr Metab Cardiovasc Dis. 2015;25(5):495–502. 9. Sampaio RA, Sewo Sampaio PY, Yamada M, Yukutake T, Uchida MC, Tsuboyama T, et al. Arterial stiffness is associated with low skeletal muscle mass in Japanese community-dwelling older adults. Geriatr Gerontol Int. 2014;14(Suppl 1):109–14. 10. Li C, Ford ES, Zhao G, Balluz LS, Giles WH. Estimates of body composi- tion with dual-energy X-ray absorptiometry in adults. Am J Clin Nutr. 2009;90(6):1457–65. 11. Kato A, Ishida J, Endo Y, Takita T, Furuhashi M, Maruyama Y, et al. Associa- tion of abdominal visceral adiposity and thigh sarcopenia with changes of arteriosclerosis in haemodialysis patients. Nephrol Dial Transpl. 2011;26(6):1967–76. 12. Horber FF, Gruber B, Thomi F, Jensen EX, Jaeger P. Eec ff t of sex and age on bone mass, body composition and fuel metabolism in humans. Nutrition. 1997;13(6):524–34. 13. Murphy TP, Dhangana R, Pencina MJ, D’Agostino RB Sr. Ankle-brachial index and cardiovascular risk prediction: an analysis of 11,594 individuals with 10-year follow-up. Atherosclerosis. 2012;220(1):160–7. 14. Gu X, Man C, Zhang H, Fan Y. High ankle-brachial index and risk of cardiovascular or all-cause mortality: a meta-analysis. Atherosclerosis. 2019;282:29–36. 15. Yang Y, Liu L, Sun H, Nie F, Hu X. Relation between high ankle-brachial index and cardiovascular outcomes in the general population and cardiovascular disease: a meta-analysis. Int Angiol. 2020;39(2):131–8. 16. Velescu A, Clara A, Martí R, Ramos R, Perez-Fernandez S, Marcos L, et al. Abnormally high ankle-brachial index is associated with all-cause and cardiovascular mortality: the REGICOR study. Eur J Vasc Endovasc Surg. 2017;54(3):370–7. 17. Ankle Brachial Index Collaboration, Fowkes FG, Murray GD, Butcher I, Heald CL, Lee RJ, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA. 2008;300(2):197–208. 18. Park SJ, Yang HM, Seo KW, Choi SY, Choi BJ, Yoon MH, et al. The rela- tionship between coronary atherosclerosis and body fat distribution measured using dual energy X-ray absorptiometry. Atherosclerosis. 2016;248:190–5. 19. Karcher HS, Holzwarth R, Mueller HP, Ludolph AC, Huber R, Kas- subek J, et al. Body fat distribution as a risk factor for cerebrovascular disease: an MRI-based body fat quantification study. Cerebrovasc Dis. 2013;35(4):341–8. 20. Gast KB, den Heijer M, Smit JW, Widya RL, Lamb HJ, de Roos A, et al. Individual contributions of visceral fat and total body fat to subclinical atherosclerosis: The NEO study. Atherosclerosis. 2015;241(2):547–54. 21. Mongraw-Chaffin M, Allison MA, Burke GL, Criqui MH, Matsushita K, Ouyang P, et al. CT-derived body fat distribution and incident cardiovas- cular disease: the multi-ethnic study of atherosclerosis. J Clin Endocrinol Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : Metab. 2017;102(11):4173–83. 22. Jung S, Park J, Seo YG. Relationship between arm-to-leg and limbs-to- fast, convenient online submission trunk body composition ratio and cardiovascular disease risk factors. Sci thorough peer review by experienced researchers in your ﬁeld Rep. 2021;11(1):17414. 23. Gruzdeva O, Borodkina D, Uchasova E, Dyleva Y, Barbarash O. Localization rapid publication on acceptance of fat depots and cardiovascular risk. 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Artery Research – Springer Journals
Published: Sep 1, 2022
Keywords: Ankle-Brachial index (ABI); Atherosclerosis; Peripheral artery disease (PAD); Body fluid volume; Body fat distribution
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