Normal-Weight Obesity and Normal-Central Weight Obesity in Older Adult and Geriatric Patients with Acute Coronary Syndrome

According to the 2022 World Health Statistics Report by the World Health Organization (WHO), cardiovascular diseases and other conditions like cancer, diabetes and respiratory diseases led to around 33.2 million global deaths in 2019, marking a 28% increase from 2000. The report highlights that Ischemic Heart Disease (IHD) accounts for over 50% of premature female mortality in more than half of the world’s countries and over 75% of premature male mortality in a similar proportion of countries. Excessive adipose tissue has been identified as a significant risk factor for coronary artery disease, as it is directly linked to causative factors of cardiovascular diseases. Well-established mechanisms, such as increased insulin resistance, heightened sympathetic nervous system activity, elevated free fatty acid turnover, and higher leptin levels, contribute to obesity triggering other clinical conditions like diabetes, hypertension, dyslipidemia, and obstructive sleep apnea, thus compounding the overall cardiometabolic risk [1].

The Body Mass Index (BMI) is commonly used in clinical practice and epidemiological studies as a diagnostic method for obesity. However, the obesity definition considered the gold standard by the WHO corresponds to excessive body fat, which is not fully addressed by BMI. The BMI does not adequately account for body fat and lean body mass, both of which are determinant components for analyzing the relationship with cardiovascular disease outcomes [1]. The limitations of the BMI become further pronounced when attempting to capture the body composition distribution in older individuals. This is due to the aging process, which triggers a redistribution of adipose tissue, leading to heightened fat deposition in the central region and a decline in lean body mass. Moreover, the constraints of the BMI are compounded by factors such as reduced skeletal mass and the accompanying loss of height that come with aging [2].

Certainly, particularly in older individuals, a normal BMI range (18.5-24.9kg/m²) might not accurately indicate an appropriate body fat percentage and could obscure the presence of excessive fat. This specific scenario, where a normal BMI falls within the designated range but is coupled with a high body fat percentage, is known as Normal-Weight Obesity (NWO). This condition is linked to an increased risk of developing non-communicable chronic diseases [3]. Furthermore, when BMI falls within the normal range but there’s an accumulation of abdominal fat, it presents another risk phenotype known as Normal-Weight Central Obesity (NWCO). Consequently, some individuals might possess a normal total body fat level but still be centrally obese. This underscores that despite having a within-range total body fat, they exhibit central obesity [1]. Within this context, research that investigates the risk linked to diverse profiles of body fat composition and distribution can substantially enhance comprehension of more high-risk nutritional phenotypes. This enhanced understanding, in turn provides guidance for directing preventive and therapeutic measures. Thus, our study aimed to conduct a comparative analysis of the cardiometabolic risk associated with NWO and NWCO in patients hospitalized due to Acute Coronary Syndrome (ACS).

Study design and participants

This is a cross-sectional study with an added prospective component (assessment of in-hospital complications and length of stay). The study involves individuals aged ≥50 years, diagnosed with ACS, and having a BMI>18.5kg/m². The participants were admitted for hospitalization between May and October 2022 at a renowned university hospital specializing in Cardiology in Northeast Brazil. Participants with clinical conditions that hindered accurate anthropometric measurements (such as limb amputation, edema, bedridden status, and bed confinement) were excluded from our study. Furthermore, individuals with concomitant wasting diseases or conditions that directly influenced body composition and fat redistribution were also excluded. This encompassed individuals with New York Heart Association functional class III or IV congestive heart failure, patients undergoing dialysis for kidney disease, individuals with chronic obstructive pulmonary disease, those afflicted by HIV infection, cancer patients and those in the postoperative phase after major surgeries. The sample size calculation was performed using Epi Info software, version 6.04, specifically within the STATCALC module. This calculation took into consideration the previous year’s data on the number of ACS-related hospitalizations within the same study period (total hospitalizations in 6 months=250). With a presumed prevalence of 25% for normal weight obesity [4] a confidence level of 95%, and a standard error of 5%, the minimum required sample size was determined to be 134 patients. To account for potential losses, this sample size was increased by 10%, resulting in a total sample size of 148 participants.

Ethical aspects

Data collection occurred within a 72-hour window following patient admission to the ward. The initiation of data collection was contingent upon obtaining approval for the research protocol from the Human Research Ethics Committee (Protocol 5.371.274/2022). Comprehensive informed consent was obtained from all patients through the signing of the appropriate documentation.

Normal-weight obesity and normal-weight central obesity assessment

NWO was defined by the simultaneous presence of a BMI falling within the normal range (18.5-24.9kg/m²) (WHO, 2000) and an elevated body fat percentage (%BF) (>30% in men and >40% in women) [5]. The identification of NWCO was based on the coexistence of a normal BMI (18.5-24.9kg/m²) (WHO, 2000) and an abdominal circumference ≥102cm for men and ≥88cm for women (NCEP ATP III, 2002). The BMI was calculated as the quotient of weight divided by the square of height (Weight/Height²) (WHO, 2000).

The %BF was obtained through tetrapolar Bioelectrical Impedance Analysis (BIA) [Byodinamics® model 310]. The following preparation protocol was observed: fasting for a minimum of 4 hours prior to the test; urination 30 minutes before the test; abstaining from caffeine-containing foods (such as coffee, colabased sodas, chocolates, cocoa beverages, and teas) for 24 hours before the test [6-8]. For %BF classification in both genders, the cutoff points suggested by Donini et al. [5] were employed, wherein a %BF of ≥30% for men and ≥40% for women served as thresholds.

Cardiometabolic risk

For the assessment of cardiometabolic risk, the following factors were considered: fasting glucose and lipid profile alterations (total cholesterol, fractions, and triglycerides), elevated C-Reactive Protein (CRP), Metabolic Syndrome (MS), along with troponin levels, the need for coronary angioplasty or myocardial revascularization surgery during hospitalization, complications during hospitalization (requirement for Intensive Care Unit referral), outcomes and length of hospital stay (in days). Metabolic Syndrome (MS) was defined according to the criteria proposed by the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP-ATP III, 2002) [9] which diagnoses its presence when three or more of the following criteria are met: central obesity (waist circumference ≥102cm for men and ≥88cm for women); elevated triglycerides (greater than 150mg/dL); low levels of HDL-C (less than 40mg/dL for men and less than 50mg/dL for women); elevated Blood Pressure (BP) (values equal to or greater than 135/85mmHg or the use of medication to lower BP); and elevated fasting glucose (values equal to or greater than 100mg/ dL) or diabetes mellitus. In the analysis of blood samples, reference thresholds were employed: total cholesterol (TC) <190mg/ dL, HDL-C >40mg/dL, triglycerides (TG) <150mg/dL, and LDLC< 70mg/dL, adhering to the guidelines delineated in the Brazilian Dyslipidemia and Atherosclerosis Prevention Guideline [10]. Elevated C-Reactive Protein (CRP) concentrations were regarded as significant for values exceeding 4.9mg/dL and troponin levels surpassing 0.014ng/mL were deemed noteworthy, consistent with the parameters endorsed by the institution’s laboratory standards.

Demographic, clinical and behavioral data

In terms of sociodemographic variables, the dataset included information on gender, age, education (measured in years of schooling) and race. Race was self-reported by patients and categorized as white, black, or mixed race in accordance with the classification provided by the Brazilian Institute of Geography and Statistics [11]. Within the realm of clinical variables, consideration was given to the presence of comorbidities, encompassing conditions such as Diabetes Mellitus (DM), Systemic Arterial Hypertension (SAH) and Chronic Kidney Disease (CKD). Turning to behavioral variables, we assessed reported smoking status, categorized as smoker (regular consumption), non-smoker (individuals who never smoked or ceased smoking over 10 years ago) [12] and ex-smoker (those who quit smoking within the last 10 years). Physical activity was also assessed with classifications of sedentary (individuals abstaining from physical exercise for more than six months) and active (individuals engaging in regular sports activities at least three times a week) based on the American College of Sports Medicine (ACSM) [13].

Data analysis

The data underwent statistical analysis utilizing the Statistical Package for the Social Sciences (SPSS) version 13.0 (SPSS Inc., Chicago, IL, USA). Continuous quantitative variables were subjected to normality testing using the Kolmogorov-Smirnov test. Variables exhibiting a normal distribution were summarized with mean and standard deviation, while those with non-normal distribution were summarized with median and interquartile range. Categorical variables were depicted through frequency distribution, and the Pearson Chi-Square test was applied to assess potential associations between exposure and outcome variables. Mean comparisons across multiple groups were executed using the one-way ANOVA test (for more than two means), while the Kruskal-Walli’s test (for more than two medians) was employed in the case of skewed data distributions. Statistical significance was acknowledged for values of p <0.05.

Within the study timeframe, 174 eligible patients were enrolled. However, 30 patients were subsequently excluded due to factors such as refusal to participate, data inconsistencies, or missing records, resulting in a final sample size of 144 patients. The mean age of the participants was 65.9±8.4 years, with a predominant male representation (59.0%) and a significant proportion falling within the geriatric category (79.9%). Among clinical diagnoses, the most prevalent was ST-Elevation Myocardial Infarction (STEMI) (45.2%) and 45.1% of the patients had a concurrent diagnosis of diabetes. Notably, a substantial portion of the cohort exhibited limited educational attainment (68.9%) and identified as sedentary (74.4%) (Table 1). Excessive weight was identified in 65.0% of the sample, while excess body fat and central obesity were evident in 54.9% and 59.7%, respectively. The prevalence of Normal-Weight Obesity (NWO) was 10.4% and the prevalence of Normal-Weight Central Obesity (NWCO) was 5.6%. Increased weight with normal body fat was observed in 38.9% of the patients (Table 2). When considering only patients with normal weight, NWO was present in 27.8% of the sample (data not shown in tables).

Table 1:Sociodemographic, clinical and lifestyle characteristics of hospitalized patients with Acute Coronary Syndrome (N=144). Recife, PE, 2022.

Table 2:Normal-Weight Obesity (NWO) and Normal- Weight Central Obesity (NWCO) in hospitalized patients with Acute Coronary Syndrome (N=144). Recife-PE, 2022.

Abbreviations: BMI: Body Mass Index; BIA: Bioelectrical Impedance Analysis; WC: Waist Circumference; NWO: Normal Weight Obesity; NWCO: Normal-Weight Central Obesity

NWO was more frequent in men (2.74 times higher; p=0.031), in hypertensive individuals (6.5 times higher; p=0.047) and sedentary individuals (p=0.024) (Table 3). The frequency of NWCO was greater among women (3 times higher; p<0.001), with a tendency to be higher among ex-smoker patients (probability value near the threshold of statistical significance (p=0.055) (Table 4). In comparison with other groups (normal weight and body fat, excessive weight and normal body fat, and excessive weight with increased body fat), mortality was more frequent in individuals with NWO (p=0.038). However, the need for surgical intervention was higher in the group where excessive weight and body fat coexisted (p=0.033). MS and serum levels of TC and TG were higher in the group of individuals with excessive weight and normal body fat (p<0.05) (Table 5). MS was more frequent in individuals with NWCO phenotype and in individuals whom central obesity coexisted with excessive weight (p<0.05). Levels of TC and troponin were higher in individuals with NWCO (p<0.05). Individuals with excessive weight and central obesity had higher serum levels of TG (Table 6).

Table 3:Sociodemographic, clinical and lifestyle factors associated with Normal-Weight Obesity (NWO) in hospitalized patients with Acute Coronary Syndrome (N=144). Recife-PE, 2022.

Abbreviations:Qui Quadrado; UA: Unstable Angina; STEMI: ST-Elevation Myocardial Infarction; NSTEMI: Non-STElevation Myocardial Infarction; SAH: Systemic Arterial Hypertension; T2DM: Type 2 Diabetes Mellitus; PCI: Percutaneous Coronary Intervention; ICU: Intensive Care Unit. NWO: Normal-Weight Obesity

Table 4:Sociodemographic, clinical, and lifestyle factors associated with Normal-Weight Central Obesity (NWCO) in hospitalized patients with Acute Coronary Syndrome (N=144). Recife-PE, 2022.

Abbreviations:Qui Quadrado; UA: Unstable Angina; STEMI: ST-Elevation Myocardial Infarction; NSTEMI: Non-STElevation Myocardial Infarction; SAH: Systemic Arterial Hypertension; T2DM: Type 2 Diabetes Mellitus; PCI: Percutaneous Coronary Intervention; ICU: Intensive Care Unit. NWCO: Normal-weight central obesity.

Table 5:Cardiometabolic factors associated with Normal-Weight Obesity (NWO) in hospitalized patients with Acute Coronary Syndrome (N=144). Recife-PE, 2022.

Abbreviations:Qui Quadrado; aKruskall Wallis Test bOne-way ANOVA Test; SD: Standard deviation; QI: Quartile Interval; NWO: Normal-weight obesity; CRP: C-reactive Protein; PCI: Percutaneous Coronary Intervention (Angioplasty); CABG: Coronary Artery Bypass Grafting (Coronary Artery Bypass Surgery); ICU: Intensive Care Unit; LOS: Length of Stay; TIMI: Thrombolysis in Myocardial Infarction; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol

Table 6:Cardiometabolic Factors Associated with Normal-Weight Central Obesity (NWCO) in Hospitalized Patients with Acute Coronary Syndrome (N=144). Recife-PE, 2022.

Qui Quadrado; ^aKruskall Wallis Test ^bOne-way ANOVA Test; SD: Standard deviation; QI: Quartile Interval; NWCO: Normalweight central obesity; WC: Waist circumference; CRP: C-reactive Protein; PCI: Percutaneous Coronary Intervention (Angioplasty); CABG: Coronary Artery Bypass Grafting (Coronary Artery Bypass Surgery); ICU: Intensive Care Unit; LOS: Length of Stay; TIMI: Thrombolysis in Myocardial Infarction; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol

The outcomes of our study revealed a significant prevalence of NWO in individuals with ACS. Approximately 1 in 10ACS patients exhibit this NOW phenotype. Notably, when narrowing our focus to patients falling within the normal weight range, this obesity phenotype was detected in almost 30% of them. These findings underscore the notion that relying solely on a normal BMI may not adequately exclude patients from the scrutiny of healthcare practitioners concerning potential associated risks. To the best of our knowledge, this is the first study that has specifically assessed NWO in patients with ACS. Investigations conducted in other population groups, which evaluated NWO and utilized BIA as a method for assessing body fat percentage, have reported lower prevalence. For instance, Coelho et al. [14] examined Brazilian adults aged 23 to 39 years and reported a prevalence of 6%. Another study conducted by Jia et al. [15] found a prevalence of 7.46% for NWO in Chinese adults aged over 20 years, with a mean age of 44.88±13.49 years. Berg et al. [16] demonstrated that 9% of Swedish adults aged 25 to 74 years were classified with NWO. Mannisto et al. also studied individuals aged 25 to 74 years and found similar results in their male population (10%), but a higher prevalence in females (19%).

Divergent findings with higher prevalence rates have also been documented in other studies. Kapoor et al., Correa-Rodriguez and He et al. [17-19] reported prevalence rates of 31.7%, 29.1%, and 17.0% for NWO, respectively. These investigations encompassed individuals aged between 30 and 60 years, 18 and 30 years, and 20 and 80 years, respectively. It’s noteworthy that the age ranges of the participants in these studies are considerably wider leading to a notable diversity in the age distribution of patients. Furthermore, inconsistencies in defining the NWO phenotype, particularly regarding the specific body fat percentage cutoffs and their variation across sexes, along with the variability of ethnicities included in different studies, are factors that contribute to the variations in prevalence rates observed in the existing literature.

Regarding NWCO, the prevalence described in the literature exhibits considerable variability, making direct comparisons challenging, primarily due to the lack of standardized criteria for defining abdominal circumference cutoff points. In our study of ACS patients, we identified a lower prevalence of NWCO (5.6%) compared to NWO. A prior study by Mohamed et al. [20] reported a much higher prevalence of 21.2% for individuals aged 40 to 60. However, it’s important to consider that this study employed a different waist circumference cutoff (waist circumference > 94cm for men and > 80cm for women) to define central obesity. Conversely, Kalvari et al. using the same waist circumference cutoff points as our study, found an NWCO prevalence of 10% in. Excessive abdominal fat, encompassing both visceral and subcutaneous adipose tissue, constitutes a predisposing factor for an elevated mortality risk, even in individuals with a normal BMI. Visceral fat behaves differently from subcutaneous fat, largely due to its metabolically active nature. Those with OCPN exhibit a limited capacity to store lipids subcutaneously in comparison to overweight and obese individuals. This circumstance could potentially place them at a disadvantage in terms of mortality risk, particularly among those with higher levels of visceral adipose tissue [21].

The higher prevalence of NWO in men contrasts with some previous findings that indicated a higher occurrence of this nutritional phenotype in females. Marques-Vidal et al. [22] reported a prevalence of 2.8% in men and 5.4% in women, while Correa-Rodriguez et al. [18] similarly noted a greater prevalence in females (2.0% in men and 46% in women). Conversely, Jia et al. [15] reported higher prevalence among men (6.71% to 10.16% in men and 1.73% to 6.64% in women), aligning with our observations. Nonetheless, like the overall NWO prevalence, these disparities can be attributed to variations in the cutoff points for percentage of body fat, ethnicities and age groups. Additionally, our study encompassed a distinct patient profile from the studies, focusing solely on ACS patients. On the other hand, NWCO was more prevalent in women, as has been demonstrated by other authors [20]. Kouvari et al. [23] indicated a fourfold higher number of NWCO cases in females. Shirasawa et al. [24], using the waist-tohip ratio as a criterion for central obesity, also reported a higher prevalence in women (30.2% vs. 15.6%). Two significant factors related to fat distribution in females might explain these findings. Firstly, women with normal weight, despite having smaller adipocytes than men, have a greater number of adipocytes per unit of mass in the abdominal subcutaneous adipose tissue, suggesting hyperplastic expansion of subcutaneous abdominal adipose tissue [25]. Moreover, during menopause, a phase that corresponds to the age range of our patient profile, there is a substantial reduction in estrogen levels accompanied by an increase in adipose tissue mass. This transition is also associated with a change in fat distribution, shifting from a predominant gluteal-femoral subcutaneous adipose tissue to abdominal subcutaneous adipose tissue, resulting in a more central fat distribution pattern [26-28].

The higher prevalence of systemic arterial hypertension in individuals with NWO aligns with the findings presented by Correa- Rodrigues et al. [18], Jia et al. [15] and Romero-Corral et al. [29] when analyzing cardiometabolic risks in individuals aged >20 years. Kapoor et al. [17] investigating individuals between 20 and 60 years with diabetes risk, also found significantly elevated blood pressure levels in those with NWO when compared to individuals with normal BMI and percent body fat, as well as to individuals with obesity. Well-established mechanisms such as increased sodium reabsorption by the Henle loop, insulin resistance, heightened sympathetic nervous system activity, activation of the renin-angiotensin-aldosterone system and altered vascular function explain the relationship between elevated blood pressure and obesity. It is postulated that individuals with NWO have sufficient adipose tissue to trigger these mechanisms, elucidating the similar or even higher blood pressure levels observed in these individuals compared to the obese and overweight counterparts [30,15]. Furthermore, certain insights attained suggest that the cardiovascular risk in individuals with NWO can be attributed to compromised systolic and diastolic function of the left ventricle, which has been found in these patients. This impairment is secondary to abdominal fat deposition, pro-fibrotic and proinflammatory states, as well as increased insulin resistance. This pre-clinical myocardial dysfunction can even progress gradually to manifest cardiomyopathy [31] The higher number of sedentary individuals within the NWO group compared to all other analyzed phenotypes underscores that physical inactivity contributes to an inadequate nutritional profile. Even if an individual maintains their weight within the normal range, an unfavorable body composition with excess fat can still prevail. Prior studies have demonstrated that an active lifestyle imparts a more favorable body composition profile [32] while sedentary behavior favors the NWO phenotype [33].

The increased mortality observed in individuals with NWO aligns with findings presented by Romero-Corral et al. [29] who also demonstrated an independent association between cardiovascular mortality and NWO in women. These findings can be elucidated based on the various cardiovascular alterations present in individuals with NWO, including increased subclinical vascular inflammation, subclinical atherosclerosis with vulnerable plaques, greater vascular stiffness, and asymptomatic impairment of left ventricular systolic and diastolic function [31,34,35]. Furthermore, we also noted that patients with NWO exhibited a higher need for coronary artery bypass graft surgery. However, given the absence of previous studies reporting NWO in ACS patients, data regarding adverse outcomes secondary to ACS are still lacking in the literature. The higher frequency of MS in patients with NWCO, but not in individuals with NWO, can be attributed to the fact that waist circumference is included in the diagnostic criteria for metabolic syndrome [9]. Notably, excessive abdominal fat, especially visceral adipose tissue, has been associated with insulin resistance, dyslipidemia and inflammation, which are predisposing factors for MS [36]. Another important aspect to consider regarding the metabolic risk associated with NWCO is the limited muscle mass in these patients. Like NWO, individuals with this phenotype of obesity may have sarcopenic obesity, as having a normal weight with a high percentage of body fat makes them strong candidates for having limited amounts of muscle mass, particularly among the older individuals who constitute a significant portion of our studied patient profile [37].

The observed higher troponin levels in patients with NWCO can be attributed to the fact that troponin serves as a marker for myocardial injury. The accumulation of abdominal fat, in and of itself, possesses the potential to induce detrimental effects on myocardial function through a cascade of mechanisms involving inflammatory cytokines, activation of the renin-angiotensinaldosterone system, insulin resistance, hyperinsulinemia and the process of lipotoxicity due to lipid buildup in cardiac tissue. These interconnected factors collectively contribute to the impairment of myocardial energy utilization, apoptosis of myocardial cells and an increase in fibrotic processes [38,39].

Several limitations should be acknowledged in this study. First, the cross-sectional design inherently prevents us from establishing causality between variables. Second, our study was conducted within a single hospital center, which may limit the generalizability of our findings due to the non-representative sample of ACS patients. Third, the use of BIA for assessing body fat may have limitations, particularly concerning hydration issues often observed in older individuals, potentially leading to an underestimation of body fat and an overestimation of fat-free mass. Furthermore, the chosen cutoff points for %GC in this investigation correspond to elevated levels of body fat, exceeding those employed in prior studies. Nevertheless, it is important to note that the adopted cutoff was suggested by prominent scientific institutions (ESPEN and EASO) [5]. Another aspect to be considered is that important risk markers that could be added to the study of the topic were not evaluated, such as sirtuina, a promising marker in the study of obesity profiles and metabolic changes [40]. Despite these limitations, the key strength of our study lies in being, to the best of our knowledge, the first to explore the discussed obesity phenotypes within a cohort of ACS patients. This underscores the significance of employing alternative nutritional assessment measures beyond BMI for diagnosing and evaluating potential cardiometabolic risks [41-56].

Individuals with ACS exhibited a high prevalence of overweight, excessive body fat and central obesity. Furthermore, a substantial proportion of individuals displayed the NWO phenotype and, to a lesser extent, the NWCO phenotype. The NWO phenotype was more prevalent in men, individuals with hypertension, and those leading sedentary lifestyles, while the NWCO phenotype was more common among women. NWO was linked to increased mortality, and NWCO was associated with MS and higher levels of serum TC and troponin. The connection between NWO and NWCO with adverse outcomes and metabolic risk underscores the importance of characterizing distinct nutritional phenotypes. The combination of parameters used for their identification could serve as a screening tool for poor prognosis among ACS patients.

1. Romero-Corral A, Somers VK, Sierra-Johnson J, Jensen MD, Thomas RJ et al. (2007) Diagnostic performance of body mass index to detect obesity in patients with coronary artery disease. European Heart Journal 28(17): 2087-2093.
2. Bowman K, Delgado J, Henley WE, Masoli JA, Kos K, et al. (2017) Obesity in older people with and without conditions associated with weight loss: follow-up of 955,000 primary care patients. Journals Of Gerontology Series A: Biomedical Sciences and Medical Sciences 72(2): 203-209.
3. Franco LP, Morais CC, Cominetti C (2016) Normal-weight obesity syndrome: diagnosis, prevalence, and clinical implications. Nutrition Reviews 74(9): 558-570.
4. Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, et al. (2008) The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: Prevalence and correlates of 2 phenotypes among the us population (NHANES 1999-2004). Archives Of Internal Medicine 168(15): 1617-1624.
5. Donini LM, Busetto L, Bischoff SC, Cederholm T, Ballesteros-Pomar MD, et al. (2022) Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement. Obesity facts 15(3): 321-335.
6. National Institutes of Health (US), Office of Medical Applications of Research (1994) Bioelectrical impedance analysis in body composition measurement: National Institutes of health technology assessment conference statement. US Department of Health and Human Services, Public Health Service, National Institutes of Health, Office of Medical Applications of Research 64(3): 524s-532s.
7. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, et al. (2004) Bioelectrical impedance analysis-part I: Review of principles and methods. Clinical Nutrition 23(5): 1226-1243.
8. Hicks VL, Stolarczyk LM, Heyward VH, Baumgartner RN (2000). Validation of near infrared interactance and skinfold methods for estimating body composition of American Indian women. Medicine And Science in Sports and Exercise 32(2): 531-539.
9. National Cholesterol Education Program (US) (2002) Expert panel on detection & treatment of high blood cholesterol in adults. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report 106(25): 3143-421.
10. Faludi AA, Izar MCDO, Saraiva JFK, Chacra APM, Bianco HT, et al. (2017) Atualização da diretriz brasileira de dislipidemias e prevenção da aterosclerose-2017. Arquivos Brasileiros de Cardiologia 109(2): 1-76.
11. IBGE II (2019) Pesquisa nacional por amostra de domicílios contí Rio de Janeiro: IBGE-Coordenação de Trabalho e Rendimento.
12. Silva MADD, Sousa AG, Schargodsky H (1998) Risk factors for acute myocardial infarction in Brazil (Africa study). Arquivos Brasileiros De Cardiologia 71(5): 667-675.
13. American College of Sports Medicine (1991) Guidelines for exercise testing and prescription.
14. Da Silva Coelho CCN, Bragança MLBM, De Oliveira BR, Bettiol H, Barbieri MA, et al. (2021) Incidence of metabolic syndrome in adults with healthy weight, normal weight obesity, and overweight/obesity. Nutrition 85: 111134.
15. Jia A, Xu S, Xing Y, Zhang W, Yu X, et al. (2018) Prevalence and cardiometabolic risks of normal weight obesity in chinese population: a nationwide study. Nutrition, Metabolism and Cardiovascular Diseases 28(10): 1045-1053.
16. Berg C, Strandhagen E, Mehlig K, Subramoney S, Lissner L, et al. (2015) Normal weight adiposity in a Swedish population: How well is cardiovascular risk associated with excess body fat captured by BMI? Obesity Science and Practice 1(1): 50-58.
17. Kapoor N, Lotfaliany M, Sathish T, Thankappan KR, Thomas N, et al. (2020) Prevalence of normal weight obesity and its associated cardio-metabolic risk factors-results from the baseline data of the Kerala Diabetes Prevention Program (KDPP). PloS One 15(8): e0237974.
18. Correa-Rodríguez M, González-Ruíz K, Rincón-Pabón D, Izquierdo M, García-Hermoso A, et al. (2020) Normal-weight obesity is associated with increased cardiometabolic risk in young adults. Nutrients 12(4): 1106.
19. He H, Pan L, Liu F, Ma J, Hu Z, et al. (2019) Expanded normal weight obesity and blood pressure in chinese adults: A community-based cross-sectional study. Australian Journal of Primary Health 25(3): 256-263.
20. Mohamed SF, Haregu TN, Khayeka-Wandabwa C, Muthuri SK, Kyobutungi C (2019) Magnitude and predictors of normal-weight central obesity–the AWI-Gen study findings. Global Health Action 12(1): 1685809.
21. Porter SA, Massaro JM, Hoffmann U, Vasan RS, O'Donnel CJ et al. (2009) Abdominal subcutaneous adipose tissue: A protective fat depot? Diabetes Care 32(6): 1068-1075.
22. Marques-Vidal P, Pécoud A, Hayoz D, Paccaud F, Mooser V et al. (2010) Normal weight obesity: Relationship with lipids, glycaemic status, liver enzymes and inflammation. Nutrition, Metabolism and Cardiovascular Diseases 20(9): 669-675.
23. Kouvari M, Panagiotakos DB, Yannakoulia M, Chrysohoou C, Georgousopoulou E, et al. (2019) P4410 Normal weight central obesity and 10-year cardiovascular disease onset in apparently healthy males and females: The interacting effect of sex. European Heart Journal 40(1): ehz745-0814.
24. Shirasawa T, Ochiai H, Yoshimoto T, Nagahama S, Kobayashi M, et al. (2019) Associations between normal weight central obesity and cardiovascular disease risk factors in Japanese middle-aged adults: A cross-sectional study. Journal of Health Population and Nutrition 38(1): 46
25. Andersson DP, Arner E, Hogling DE, Ryden M, Arner P (2017) Abdominal subcutaneous adipose tissue cellularity in men and women. International journal of Obesity 41(10): 1564-1569.
26. Toth MJ, Tchernof A, Sites CK, Poehlman ET, (2000) Effect of menopausal status on body composition and abdominal fat distribution. International Journal of Obesity 24(2): 226-231.
27. Lovejoy JC, Champagne CM, De Jonge L, Xie H, Smith SR (2008) Increased visceral fat and decreased energy expenditure during the menopausal transition. International Journal of Obesity 32(6): 949-958.
28. Goossens GH, Jocken JW, Blaak EE (2021) Sexual dimorphism in cardiometabolic health: the role of adipose tissue, muscle and liver. Nature Reviews Endocrinology 17(1): 47-66.
29. Romero-Corral A, Somers VK, Sierra-Johnson J, Korenfeld Y, Boarin S et al. (2010) Normal weight obesity: A risk factor for cardiometabolic dysregulation and cardiovascular mortality. European Heart Journal 31(6): 737-746.
30. García Casilimas GA, Martin DA, Martínez MA, Merchán CR, Mayorga CA, et al. (2017) Pathophysiology of hypertension secondary to obesity. Mexico Cardiology Archives 87(4): 336-344.
31. Kosmala W, Jedrzejuk D, Derzhko R, Przewlocka-Kosmala M, Mysiak A, et al. (2012) Left ventricular function impairment in patients with normal-weight obesity: Contribution of abdominal fat deposition, profibrotic state, reduced insulin sensitivity, and proinflammatory activation. Circulation: Cardiovascular Imaging 5(3): 349-356.
32. Assaf I, Brieteh F, Tfaily M, El-Baida M, Kadry S, et al. (2019) Students’ university healthy lifestyle practice: quantitative analysis. Health Information Science and Systems 7(1): 7.
33. Edwardson CL, Gorely T, Davies MJ, Gray LJ, Khunti K, et al. (2012) Association of sedentary behaviour with metabolic syndrome: A meta-analysis. PloS one 7(4): e34916.
34. Kang S, Kyung C, Park JS, Kim S, Lee SP, et al. (2014) Subclinical vascular inflammation in subjects with normal weight obesity and its association with body fat: an 18 f-FDG-PET/CT study. Cardiovascular Diabetology 13: 70.
35. Kim S, Kyung C, Park JS, Lee SP, Kim HK et al. (2015) Normal-weight obesity is associated with increased risk of subclinical atherosclerosis. Cardiovascular Diabetology 14: 58.
36. Despres JP (2006) Intra-abdominal obesity: An untreated risk factor for type 2 diabetes and cardiovascular disease. Journal of Endocrinological Investigation 29(3): 77-82.
37. Sahakyan KR, Somers VK, Rodriguez-Escudero JP, Hodge DO, Carter Re, et al. (2015). Normal-weight central obesity: Implications for total and cardiovascular mortality. Annals of Internal Medicine 163(11): 827-835.
38. Peterson LR, Herrero P, Schechtman KB, Racette SB, Waggoner AD, et al. (2004) Effect of obesity and insulin resistance on myocardial substrate metabolism and efficiency in young women. Circulation 109(18): 2191-2196.
39. Kosmala W, Plaksej R, Przewlocka-Kosmala M, Kuliczkowska-Plaksej J, Bednarek-Tupikowska G, et al. (2008) Matrix metalloproteinases 2 and 9 and their tissue inhibitors 1 and 2 in premenopausal obese women: Relationship to cardiac function. International Journal of Obesity 32(5): 763-771.
40. Martins I (2018) Sirtuin 1, a diagnostic protein marker and its relevance to chronic disease and therapeutic drug intervention. Ec Pharmacology and Toxicology 6(4): 209-215.
41. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD et al. (2019) 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: A report of the American college of cardiology/American heart association task force on clinical practice guidelines. Circulation 140(11): e596-e646.
42. Bray GA (1989) classification and evaluation of obesities. Medical Clinics of North America 73(1): 161-184.
43. Cota BC, Suhett LG, Leite NN, Pereira PF, Ribeiro SAV, et al. (2021) Cardiometabolic risk and health behaviours in adolescents with normal-weight obesity: A systematic review. Public Health Nutrition 24(5): 870-881.
44. Curtis AB, Karki R, Hattoum A, Sharma UC (2018) Arrhythmias in patients≥ 80 years of age: Pathophysiology, management, and outcomes. Journal of the American College of Cardiology 71(18): 2041-2057.
45. Han JH, Kim SM (2006) Relationship between Percent body fat and cardiovascular risk factors for normal weight adults. Journal of the Korean Academy of Family Medicine 27(5): 352-357.
46. Kim JY, Han SH, Yang BM (2013) Implication of high‐body‐fat percentage on cardiometabolic risk in middle‐aged, healthy, normal‐weight adults. Obesity 21(8): 1571-1577.
47. López-Alvarenga JC, Montesinos-Cabrera RA, Velázquez-Alva C, González-Barranco J (2003) Short stature is related to high body fat composition despite body mass index in a Mexican population. Archives of Medical Research 34(2): 137-140.
48. Madeira FB, Silva AA, Veloso HF, Goldani MZ, Kac G, et al. (2013) Normal weight obesity is associated with metabolic syndrome and insulin resistance in young adults from a middle-income country. PloS One 8(3): e60673.
49. Männistö S, Harald K, Kontto J, Lahti-Koski M, Kaartinen Neet al. (2014) Dietary and lifestyle characteristics associated with normal-weight obesity: The National FINRISK 2007 Study. British Journal of Nutrition 111(5): 887-894.
50. Oliveros E, Somers VK, Sochor O, Goel K, Lopez-Jimenez F (2014) The concept of normal weight obesity. Progress In Cardiovascular Diseases 56(4): 426-433.
51. Sahakyan K, Somers V, Rodriguez-Escudero J, Thomas R, Sochor O, et al. (2013) Normal-weight central obesity and cardiovascular mortality risk in hypertensive patients. Journal of the American College of Cardiology 61(10S): E1399.
52. Shea JL, King MTC, Yi Y, Gulliver W, Sun G, (2012) Body fat percentage is associated with cardiometabolic dysregulation in BIM-defined normal weight subjects. Nutrition Metabolism and Cardiovascular Diseases 22(9): 741-747.
53. Tayefi M, Tayefi B, Darroudi S, Mohammadi-Bajgiran M, Mouhebati M, et al. (2019) There is an association between body fat percentage and metabolic abnormality in normal weight subjects: Iranian large population. Translational Metabolic Syndrome Research 2(1): 11-16.
54. The Asia-Pacific perspective: Redefining obesity and its treatment (2000) World Health Organization, p. 56.
55. World Health Organization (2013) A global brief on hypertension: Silent killer, global public health crisis: World health day 2013.
56. World Health Organization (2022) WHO European regional obesity report 2022. World Health Organization. Regional Office for Europe.