Artigo Camila Chagas

Prévia do material em texto

ARTICLE
Is phase angle associated with visceral adiposity and
cardiometabolic risk in cardiology outpatients?
Victoria Domingues Ferraz 1,4, Jarson Pedro da Costa Pereira 1,4✉, Claudia Porto Sabino Pinho Ramiro2,
Gabriela Maria Pereira Floro Arcoverde 1,2, Isa Galvão Rodrigues 2, Camila Lima Chagas 2, José Reginaldo Alves de Queiroz Jr3,
Maria Conceição Chaves de Lemos1, Alcides da Silva Diniz 1 and Ilma Kruze Grande de Arruda1
© The Author(s), under exclusive licence to Springer Nature Limited 2024
BACKGROUND/OBJECTIVES: Phase angle (PhA) serves as a prognostic marker in various clinical scenarios, reflecting oxidative
stress and cellular damage. Despite its clinical relevance, its connection with adiposity and cardiovascular risk markers remains
underexplored. Hence, our study sought to investigate the relationship between PhA and metabolic, adiposity, and cardiovascular
risk parameters among outpatients with cardiology diagnosis.
SUBJECTS/METHODS: Adults aged between 26 and 59 years, under the care of a cardiology unit, were included. Ultrasound
imaging was used to assess visceral adipose tissue (VAT). Single-frequency bioelectrical impedance analysis (BIA) [50 kHz] was
employed to calculate PhA, from BIA’s resistance and reactance measurements. Muscle strength, body mass index, waist
circumference, and waist-to-height ratio were also evaluated. Framingham’s risk score was calculated to estimate the cardiovascular
risk events. Metabolic blood samples’ results were obtained from medical records.
RESULTS: One hundred and five participants were included in our study. Low PhA was observed in 29.5% of our sample.
Higher PhA values were independently and inversely associated with both higher VAT and cardiovascular risk (adjusted OR:
0.79 [95% CI 0.69;0.91], OR: 0.74 [95% CI 0.60;0.89], respectively). Lower PhA values (≤5.59) were goodly associated with high
VAT (AUC: 0.82 pAlcohol consumption was evaluated as a
dichotomous response (yes or no) [10]. In the case of smoking, individuals
smoking at least one cigarette per day were categorized as smokers, those
who never smoked were classified as non-smokers, and individuals who
had smoked at some point in their lives but were not currently smoking at
the time of the research were identified as former smokers [11].
The presence of the following comorbidities, namely systemic arterial
hypertension (SAH) and type 2 diabetes mellitus (2DM), was assessed. Self-
reported participant diagnoses were initially obtained, and subsequent
validation was conducted by cross-referencing with medical records. This
validation encompassed confirming the utilization of antihypertensive and
hypoglycemic medications, as well as obtaining medical confirmation of
comorbidities. Furthermore, measurements of systolic blood pressure (SBP)
and diastolic blood pressure (DBP) were recorded.
Cardiometabolic parameters
Framingham Risk Score [12] was utilized to evaluate the risk of
cardiovascular diseases. The risk score was computed based on
categorized values of age, sex, total cholesterol, HDL-C, SBP, smoking
status, and 2DM. Smoking status was ascertained through self-reported
information and categorized as “current smokers” (encompassing both
current smokers and recent quitters) or “non-smokers” (encompassing
individuals who had never smoked or had quit smoking long ago). For
analytical purposes, the results of the Framingham score were stratified
into two categories: lower risk for individuals with scores between low and
intermediate (5–20%), and higher risk for those with high scores (>20%)
[12], indicating an elevated risk of cardiovascular diseases.
For the study, various metabolic parameters were deemed elevated, in
accordance with the criteria established by Faludi et al. in 2017 and Cobas
et al. in 2022 [national guidelines] [13, 14]. These parameters included
fasting blood glucose (FBG) (>100mg/dL), HbA1C (>5.7%), total cholesterol
(TC) (>190mg/dL), HDL-c (130mg/dL), and triglycer-
ides (>150mg/dL). All pertinent blood samples results were retrieved from
the medical records.
Ultrasound (US)
The evaluation of visceral adipose tissue (VAT) and subcutaneous adipose
tissue (SAT) involved abdominal ultrasound conducted by a single trained
observer, with participants undergoing a fasting period of at least 4 h. The
Apogee 3500 color digital ultrasound imaging system (SIUI, Shantou,
China), equipped with a 4.0 MHz convex transducer, was employed for this
procedure. The measurement of visceral fat adhered to the protocol
outlined by Mauad et al. in 2017 [15].
The thickness of the fat measured in the subcutaneous layer was
performed with a linear transducer at a frequency of 10.0 MHz. All
individuals were evaluated in the dorsal decubitus position, with the right
arm elevated. The measurement of subcutaneous fat was performed with
the transducer positioned transversely 1.0 cm above the umbilical scar, on
the xiphopubic line, without exerting pressure on the abdomen, in order
not to underestimate the measurement. The anatomical limits for
measuring subcutaneous thickness were the skin and the external
(superficial) surface of the rectus abdominis muscle, quantified in
centimeters.
The measurement of visceral fat was performed with a convex
transducer at a frequency of 4.0 MHz, positioned transversely 1.0 cm
above the umbilical scar, on the xiphopubic line, without exerting pressure
on the abdomen, in order not to underestimate the measurement. The
anatomical limits for measuring visceral fat thickness had as a reference
point the internal (deep) surface of the rectus abdominis muscle and the
anterior wall of the aorta, with the individual exhaling, quantified in
centimeters. The measurements of VAT were quantified in centimeters
(cm). In line with the recommendations of Leite et al. [16], a cutoff point of
≥9 cm for men and ≥8 cm for women was employed to identify the
presence of visceral obesity.
Figure 1 shows an example of VAT image by US obtained during our
study. A prior assessment of the reproducibility of intra-evaluator US
measurements was conducted on 10% of the sample. Intra-evaluator
reproducibility was notably high, with an Intraclass Correlation Coefficient
of 0.99 for SAT evaluation and 0.97 for VAT.
Bioelectrical impedance analysis (BIA)
A single-frequency (50 kHz) Biodynamic Body Composition Analyzer,
model 310e, from Biodynamics Corporation in Seattle, WA, USA, was
employed to assess R, Xc, and calculate PhA. Patients adhered to a 4-h
fasting period, refrained from alcohol consumption for the past 48 h, and
avoided intense physical exercise the day before the test. They received
advanced notice of these requirements at least 3 days prior. During the
assessment, patients laid in a dorsal decubitus position on a bed, free of
any metallic objects. The test involved the use of four electrodes placed on
the right wrist and ankle.
The manufacturer’s specifications highlight that these meters are
modern, digital, and capable of precisely measuring impedance, indepen-
dent of stray capacitance. Designed with a solid-state, digital architecture,
these meters remain stable without drifting and do not necessitate
recalibration. BIA 310 meter accurately captures resistance and reactance
across the entire range found in the human body. Detailed specifications
for R and Xc include a 0–1500 ohms range for R, and 0–300 ohms for Xc,
with 1 ohm resolution, 0.001 ± 0.1 ohms accuracy for R, 0.002 ± 0.1 ohms
accuracy for Xc, all measured at a frequency of 50 kHz.
Based on sex and age, participants were divided into groups according
to their derived SF-BIA PhA. PhA values were calculated using the formula
PhA= arctangent (Xc/R) × 180/π, using data acquired from a 50 kHz
frequency. Low PhA was classified according to the cutoff threshold of
visceral adiposity
and cardiovascular risk. Performance was classified based on the area under
the receiver operating characteristic curve (AUC) values: excellent (0.90–1.00),
good (0.80–0.90), fair (0.70–0.80), poor (0.60–0.70), and failed (0.50–0.60) [21].
The optimal cutoff values for PhA associated with such variables were
determined using the Youden index within the AUC.
Data were analyzed using IBM SPSS Statistics version 20 (SPSS Inc.,
Chicago, IL, United States), and MedCalc version 22.0.0.9 software
(MedCalc, Mariakerke, Belgium). The level of statistical significance was
set at pe
Ph
A
0.
02
(0
.0
0;
0.
70
)
0.
03
3
0.
96
(0
.9
2;
0.
99
)
0.
04
3
0.
08
(0
.0
0;
8.
29
)
0.
28
1
0.
11
(0
.0
0;
2.
58
)
0.
16
8
0.
52
(0
.3
7;
0.
74
)
for continued exploration of PhA’s
value, especially in the context of multimodal interventions aimed
at improving health outcomes.
By monitoring changes in PhA, we may gain valuable insights
into the effectiveness of interventions and individuals’ recovery of
cardiometabolic health status. We highlight the importance of
future studies involving both similar and diverse clinical popula-
tions, utilizing our cutoff values and comparing findings. This
approach will contribute to a more comprehensive understanding
of the relationship between PhA and cardiometabolic
assessments.
DATA AVAILABILITY
Requests for the datasets generated during and/or analyzed will be considered based
on reasonable request to the corresponding author.
REFERENCES
1. Marra M, Sammarco R, De Lorenzo A, Iellamo F, Siervo M, Pietrobelli A, et al.
Assessment of body composition in health and disease using bioelectrical
impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA): a critical
overview. Contrast Media Mol Imaging. 2019;2019:1–9. https://doi.org/10.1155/
2019/3548284.
2. Gonzalez MC, Heymsfield SB. Bioelectrical impedance analysis for diagnosing
sarcopenia and cachexia: what are we really estimating?: editorial. J Cachexia
Sarcopenia Muscle. 2017;8:187–9. https://doi.org/10.1002/jcsm.12159.
3. Gonzalez MC, Barbosa-Silva TG, Heymsfield SB. Bioelectrical impedance analysis
in the assessment of sarcopenia. Curr Opin Clin Nutr Metab Care. 2018;21:366–74.
https://doi.org/10.1097/MCO.0000000000000496.
4. Felipe De Oliveira Guedes F, Matias De Sousa I, Cunha De Medeiros GO, Gonzalez
MC, Trussardi Fayh AP. Is there a difference in the parameters of the bioelectrical
impedance obtained from devices from different manufacturers? A cross-
sectional study in hospitalized cancer patients. Clin Nutr ESPEN. 2023;56:120–6.
https://doi.org/10.1016/j.clnesp.2023.05.010.
5. Prado CM, Ford KL, Gonzalez MC, Murnane LC, Gillis C, Wischmeyer PE, et al.
Nascent to novel methods to evaluate malnutrition and frailty in the surgical
patient. J Parenter Enteral Nutr. 2023;47:S54–68. https://doi.org/10.1002/
jpen.2420.
6. Garlini LM, Alves FD, Ceretta LB, Perry IS, Souza GC, Clausell NO. Phase angle and
mortality: a systematic review. Eur J Clin Nutr. 2019;73:495–508. https://doi.org/
10.1038/s41430-018-0159-1.
7. Souza NC, Avesani CM, Prado CM, Martucci RB, Rodrigues VD, De Pinho NB, et al.
Phase angle as a marker for muscle abnormalities and function in patients with
colorectal cancer. Clin Nutr. 2021;40:4799–806. https://doi.org/10.1016/
j.clnu.2021.06.013.
8. Kawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dys-
function in obesity. Am J Physiol Cell Physiol. 2021;320:C375–91. https://doi.org/
10.1152/ajpcell.00379.2020.
9. Saglietto A, Manfredi R, Elia E, D’Ascenzo F, De Ferrari GM, Biondi-Zoccai G, et al.
Cardiovascular disease burden: Italian and global perspectives. Minerva Cardiol
Angiol. 2021;69:231–240. https://doi.org/10.23736/S2724-5683.21.05538-9.
10. Simão A, Precoma D, Andrade J, Correa Filho H, Saraiva J, Oliveira G, et al. I
Diretriz Brasileira de Prevenção Cardiovascular. Arq Brasileiros de Cardiol.
2013;101:1–63. https://doi.org/10.5935/abc.2013S012.
11. Pinho CPS, Diniz ADS, Arruda IKGD, Batista Filho M, Coelho PC, Sequeira LADS,
et al. Prevalência e fatores associados à obesidade abdominal em indivíduos na
faixa etária de 25 a 59 anos do Estado de Pernambuco, Brasil. Cad Saúde Pública.
2013;29:313–24. https://doi.org/10.1590/S0102-311X2013000200018.
12. Sullivan LM, Massaro JM, D’Agostino RB. Presentation of multivariate data for
clinical use: the Framingham Study risk score functions. Statist Med.
2004;23:1631–60. https://doi.org/10.1002/sim.1742.
13. Faludi A, Izar M, Saraiva J, Chacra A, Bianco H, Afiune Neto A, et al. Atualização da
diretriz brasileira de dislipidemias e prevenção da aterosclerose - 2017. Arq
Brasileiros de Cardiol. 2017;109:1–76. https://doi.org/10.5935/abc.20170121.
14. Sociedade Brasileira de Diabetes. COBAS, Roberta et al. Diagnóstico do diabetes e
rastreamento do diabetes tipo 2. Diretriz Oficial da Sociedade Brasileira de Dia-
betes, p. 557-753. 2022-2.
15. Mauad FM, Chagas-Neto FA, Benedeti ACGS, Nogueira-Barbosa MH, Muglia VF,
Carneiro AAO, et al. Reproducibility of abdominal fat assessment by ultrasound
and computed tomography. Radiol Bras. 2017;50:141–7. https://doi.org/10.1590/
0100-3984.2016.0023.
16. Leite CC, Matsuda D, Wajchenberg BL, Cerri GG, Halpern A. Correlação da medida
de espessura intra-abdominal medida pela ultra-sonografia com os fatores de
risco cardiovascular. Arq Bras Endocrinol Metab. 2000;44:49–56. https://doi.org/
10.1590/S0004-27302000000100009.
17. Barbosa-Silva MC, Barros AJ, Larsson E. Phase angle reference values for Brazilian
population. Int J Body Compos Res. 2008;6:67–68.
18. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sar-
copenia: revised European consensus on definition and diagnosis. Age and
Ageing. 2019;48:16–31. https://doi.org/10.1093/ageing/afy169.
19. World Health Organization. et al. Physical status: the use of and interpretation of
anthropometry, Report of a WHO Expert Committee. World Health Organization;
1995.
20. Mukaka MM. Statistics corner: a guide to appropriate use of correlation coeffi-
cient in medical research. Malawi Med J. 2012;24:69–71.
21. Harrell FE. Regression modeling strategies: with applications to linear models,
logistic and ordinal regression, and survival analysis. Second edition. Cham
Heidelberg New York: Springer; 2015.
22. Raji S, Sadat Bahrami L, Arabi SM, Ebrahimi Miandehi E, Sobhani SR, Ataee Z, et al.
The relationship between phase angle measurements and cardiovascular risk
factor in adults: Insights from an epidemiologic cohort study. Clin Nutr Open Sci.
2023;51:26–34. https://doi.org/10.1016/j.nutos.2023.07.004.
23. Da Silva BR, Orsso CE, Gonzalez MC, Sicchieri JMF, Mialich MS, Jordao AA, et al.
Phase angle and cellular health: inflammation and oxidative damage. Rev Endocr
Metab Disord. 2023;24:543–62. https://doi.org/10.1007/s11154-022-09775-0.
24. Langer RD, Larsen SC, Ward LC, Heitmann BL. Phase angle measured by bioe-
lectrical impedance analysis and the risk of cardiovascular disease among adult
Danes. Nutrition. 2021;89:111280. https://doi.org/10.1016/j.nut.2021.111280.
25. Portugal MRC, Canella DS, Curioni CC, Bezerra FF, Faerstein E, Neves MF, et al.
Bioelectrical impedance analysis–derived phase angle is related to risk scores of a
V.D. Ferraz et al.
6
European Journal of Clinical Nutrition
https://doi.org/10.1155/2019/3548284
https://doi.org/10.1155/2019/3548284
https://doi.org/10.1002/jcsm.12159
https://doi.org/10.1097/MCO.0000000000000496
https://doi.org/10.1016/j.clnesp.2023.05.010
https://doi.org/10.1002/jpen.2420
https://doi.org/10.1002/jpen.2420
https://doi.org/10.1038/s41430-018-0159-1
https://doi.org/10.1038/s41430-018-0159-1
https://doi.org/10.1016/j.clnu.2021.06.013
https://doi.org/10.1016/j.clnu.2021.06.013
https://doi.org/10.1152/ajpcell.00379.2020
https://doi.org/10.1152/ajpcell.00379.2020
https://doi.org/10.23736/S2724-5683.21.05538-9
https://doi.org/10.5935/abc.2013S012
https://doi.org/10.1590/S0102-311X2013000200018
https://doi.org/10.1002/sim.1742
https://doi.org/10.5935/abc.20170121
https://doi.org/10.1590/0100-3984.2016.0023
https://doi.org/10.1590/0100-3984.2016.0023
https://doi.org/10.1590/S0004-27302000000100009
https://doi.org/10.1590/S0004-27302000000100009
https://doi.org/10.1093/ageing/afy169
https://doi.org/10.1016/j.nutos.2023.07.004
https://doi.org/10.1007/s11154-022-09775-0
https://doi.org/10.1016/j.nut.2021.111280
first cardiovascular event in adults. Nutrition. 2020;78:110865. https://doi.org/
10.1016/j.nut.2020.110865.
26. Cancello R, Brunani A, Brenna E, Soranna D, Bertoli S, Zambon A, et al. Phase
angle (PhA) in overweight and obesity: evidence of applicability from diagnosis
to weight changes in obesity treatment. Rev Endocr Metab Disord.
2023;24:451–64. https://doi.org/10.1007/s11154-022-09774-1.27. Brunani A, Perna S, Soranna D, Rondanelli M, Zambon A, Bertoli S, et al. Body
composition assessment using bioelectrical impedance analysis (BIA) in a wide
cohort of patients affected with mild to severe obesity. Clin Nutr.
2021;40:3973–81. https://doi.org/10.1016/j.clnu.2021.04.033.
28. Barrea, Muscogiuri, Laudisio, Somma, Salzano, Pugliese, et al. Phase angle: a
possible biomarker to quantify inflammation in subjects with obesity and 25(OH)
D deficiency. Nutrients. 2019;11:1747. https://doi.org/10.3390/nu11081747.
29. Skrzypek M, Szponar B, Drop B, Panasiuk L, Malm M. Anthropometric, body
composition and behavioural predictors of bioelectrical impedance phase angle
in Polish young adults—preliminary results. Ann Agric Environ Med.
2020;27:91–8. https://doi.org/10.26444/aaem/118153.
30. Streb AR, Hansen F, Gabiatti MP, Tozetto WR, Del Duca GF. Phase angle asso-
ciated with different indicators of health-related physical fitness in adults with
obesity. Physiol Behav. 2020;225:113104. https://doi.org/10.1016/
j.physbeh.2020.113104.
31. Bellido D, García-García C, Talluri A, Lukaski HC, García-Almeida JM. Future lines of
research on phase angle: strengths and limitations. Rev Endocr Metab Disord.
2023;24:563–83. https://doi.org/10.1007/s11154-023-09803-7.
32. Stranahan AM. Visceral adiposity, inflammation, and hippocampal function in
obesity. Neuropharmacology. 2022;205:108920. https://doi.org/10.1016/
j.neuropharm.2021.108920.
33. Kammerlander AA, Lyass A, Mahoney TF, Massaro JM, Long MT, Vasan RS, et al.
Sex differences in the associations of visceral adipose tissue and cardiometabolic
and cardiovascular disease risk: the Framingham Heart Study. JAHA.
2021;10:e019968. https://doi.org/10.1161/JAHA.120.019968.
34. Lukaski HC, Kyle UG, Kondrup J. Assessment of adult malnutrition and prognosis
with bioelectrical impedance analysis: phase angle and impedance ratio. Curr
Opin Clin Nutr Metabolic Care. 2017;20:330–9. https://doi.org/10.1097/
MCO.0000000000000387.
AUTHOR CONTRIBUTIONS
VD Ferraz and JPC Pereira equally contributed to the conception and design of the
research; acquisition, analysis and interpretation of the data and drafted the
manuscript; CPS Pinho Ramiro contributed to the to the conception and design of
the research; critical revision of the manuscript and data interpretation; GMF
Arcoverde, IG Rodrigues, and CL Chagas equally contributed to design of the design
of the research; acquisition, and interpretation of the data. JRA Queiroz Júnior
contributed to the design of the research, data analysis and drafted the manuscript.
AS Diniz contributed to the design of the research and data analysis; IKG Arruda
contributed to conception and design, data analysis and interpretation and critically
revised the manuscript. All authors critically revised the manuscript, agree to be fully
accountable for ensuring the integrity and accuracy of the work, and read and
approved the final manuscript.
FUNDING
This study was partially funded by the Fundação de Amparo à Ciência e Tecnologia
de Pernambuco (FACEPE) and by the Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior (CAPES), Brazil (Finance Code 001). The supporting source had no
involvement or restrictions regarding this publication.
COMPETING INTERESTS
The authors declare no competing interests.
ETHICAL APPROVAL
This study was performed in accordance with the Declaration of Helsinki and was
approved by the Hospital Ethics Committee (CAAE: 47444121.1.0000.5208). Informed
consent was obtained from all participants.
ADDITIONAL INFORMATION
Correspondence and requests for materials should be addressed to
Jarson Pedro da Costa Pereira.
Reprints and permission information is available at http://www.nature.com/
reprints
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims
in published maps and institutional affiliations.
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to
this article under a publishing agreement with the author(s) or other rightsholder(s);
author self-archiving of the accepted manuscript version of this article is solely
governed by the terms of such publishing agreement and applicable law.
V.D. Ferraz et al.
7
European Journal of Clinical Nutrition
https://doi.org/10.1016/j.nut.2020.110865
https://doi.org/10.1016/j.nut.2020.110865
https://doi.org/10.1007/s11154-022-09774-1
https://doi.org/10.1016/j.clnu.2021.04.033
https://doi.org/10.3390/nu11081747
https://doi.org/10.26444/aaem/118153
https://doi.org/10.1016/j.physbeh.2020.113104
https://doi.org/10.1016/j.physbeh.2020.113104
https://doi.org/10.1007/s11154-023-09803-7
https://doi.org/10.1016/j.neuropharm.2021.108920
https://doi.org/10.1016/j.neuropharm.2021.108920
https://doi.org/10.1161/JAHA.120.019968
https://doi.org/10.1097/MCO.0000000000000387
https://doi.org/10.1097/MCO.0000000000000387
http://www.nature.com/reprints
http://www.nature.com/reprints
	Is phase angle associated with visceral adiposity and cardiometabolic risk in cardiology outpatients?
	Introduction
	Subjects and methods
	Study�design
	Clinical and covariates
	Cardiometabolic parameters
	Ultrasound�(US)
	Bioelectrical impedance analysis�(BIA)
	Muscle strength and anthropometry
	Statistical analyses
	Results
	Patients’ characteristics
	Correlation and association analyses
	ROC curve analysis
	Discussion
	Strengths and limitations
	Clinical implications and future research
	References
	Author contributions
	Funding
	Competing interests
	Ethical approval
	ADDITIONAL INFORMATION