Reviews in Endocrine and Metabolic Disorders (2023) 24:809–823

https://doi.org/10.1007/s11154-023-09796-3

Relevance of body composition in phenotyping the obesities

Laura Salmón-Gómez1,2 · Victoria Catalán1,2,3 · Gema Frühbeck1,2,3,4 · Javier Gómez-Ambrosi1,2,3

Accepted: 8 March 2023 / Published online: 17 March 2023

© The Author(s) 2023

Abstract
Obesity is the most extended metabolic alteration worldwide increasing the risk for the development of cardiometabolic
alterations such as type 2 diabetes, hypertension, and dyslipidemia. Body mass index (BMI) remains the most frequently
used tool for classifying patients with obesity, but it does not accurately reflect body adiposity. In this document we review
classical and new classification systems for phenotyping the obesities. Greater accuracy of and accessibility to body com-
position techniques at the same time as increased knowledge and use of cardiometabolic risk factors is leading to a more
refined phenotyping of patients with obesity. It is time to incorporate these advances into routine clinical practice to better
diagnose overweight and obesity, and to optimize the treatment of patients living with obesity.

Keywords Obesity · Body composition · Body fat percentage · Phenotyping · BMI · Waist circumference · Visceral
adipose tissue · Metabolic health · Cardiometabolic risk

1 Introduction morbidity and mortality due to its association with patholo-

gies such as type 2 diabetes (T2D), cardiovascular disease
Many of the health advancements gained over the past few (CVD), non-alcoholic fatty liver disease (NAFLD), dyslip-
decades are now at risk due to the unstoppable increase in idemia, stroke, osteoarticular disorders, obstructive sleep
the prevalence of obesity that has confirmed to be one of the apnea syndrome, and the development of certain types of
top causes of disease and death in this century [1]. Despite cancer [4, 5]. It has been suggested that the economic bur-
the alarm raised, the pandemic is expanding unabatedly. den of obesity represents between 8.2% and 8.7% of the
In 2015, a total of 108 million children and 604 million total health expenses in Western countries [6]. In 2015, high
adults had obesity. Since 1980, the prevalence of obesity body mass index (BMI) accounted for 4.0 million deaths
has doubled in more than 70 countries and has continu- globally, nearly 40% of which occurred in persons who
ously increased in most other countries [2]. Overnutrition were not considered as having obesity. More than two thirds
and a marked decline in physical activity, together with of deaths related to high BMI were due to CVD [2].
an expanding array of emerging obesogenic factors, are In recent decades we have witnessed spectacular prog-
the main causes for this increment [3]. Obesity is a major ress in understanding the etiology as well as the molecular
public health challenge, since it constitutes a risk factor for and cellular phenomena that underlie the pathophysiologi-
cal changes that lead to the development of obesity. How-
ever, it seems that these advances have not been translated
into clinical practice and nowadays patients with obesity are
Javier Gómez-Ambrosi
jagomez@unav.es generally diagnosed and therapeutically managed in a very
similar way, as if they all suffered from the same medical
1
Metabolic Research Laboratory, Clínica Universidad de condition and all were to respond to treatment in the same
Navarra, Irunlarrea 1, Pamplona 31008, Spain manner [7]. Greater accessibility to body composition tech-
2
CIBER Fisiopatología de la Obesidad y Nutrición niques together with increased knowledge and use of car-
(CIBEROBN), Instituto de Salud Carlos III, Pamplona, Spain diometabolic risk factors currently at our disposal, should
3
Obesity and Adipobiology Group, Instituto de Investigación allow better phenotyping of patients living with obesity. In
Sanitaria de Navarra (IdiSNA) Pamplona, Pamplona, Spain this work we review the existing knowledge in relation to
4
Department of Endocrinology & Nutrition, Clínica the phenotyping of obesity, with special emphasis on the
Universidad de Navarra, Pamplona, Spain

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810 Reviews in Endocrine and Metabolic Disorders (2023) 24:809–823

clinical utility of body composition, which we believe could BMI≥30.0 kg/m2 (Table 1) [12], although lower cut-off
improve the clinical diagnosis and treatment of the “obesi- points have been proposed for Asian populations [13]. BMI
ties” [8]. is very useful in large epidemiological studies [14] but in
spite of its widespread use BMI does not accurately reflect
body composition and is simply a proxy for measuring body
2 Current obesity classification fatness [12, 15–17]. Some researchers believe that the infor-
mation that BMI provides is not lower or that it is even supe-
The idea of relating height to the square of weight as an rior to the body fat percentage (BF%) or other indicators of
anthropometric indicator was first proposed by Quetelet adiposity such as the fat mass index (fat mass in kg divided
almost 200 years ago [9]. It was not until 1972 that the con- by the height in m squared) or muscle mass phenotyping for
cept of body mass index was coined by Ancel Keys [10]. the prediction of CVD risk [18, 19]. Noteworthy, obesity is
There has been a longstanding debate among researchers defined as a “complication of too much adipose tissue”, and
for decades about whether BMI is an adequate tool for the the amount of this excess dysfunctional adiposity is essen-
diagnosis of obesity [11]. It is true that, after almost two tially what causes the majority of the health problems linked
centuries since its conceptualization, the BMI remains the with obesity [20]. Therefore, identifying the clinical utility
simplest, cheapest, and easiest to calculate tool for clas- of assessing BF% and its usefulness for obesity phenotyp-
sifying patients with obesity, taking into account their ing to calculate the cardiometabolic risk linked with obesity
weight and height. The World Health Organization (WHO) may be of outmost importance.
and other important international health organizations
define overweight as a BMI≥25.0 kg/m2 and obesity as a

Table 1 Cut-off points for obesity BMI (kg/m2)*

classification according to differ-    < 18.5 Underweight
ent criteria
   18.5 - <25.0 Normal weight
   25.0 - <30.0 Overweight
   ≥ 30.0 Obesity
Waist circumference (cm)*
   Females
   ≥ 80 Increased risk
   ≥ 88 High risk
   Males
   ≥ 94 Increased risk
   ≥ 102 High risk
WHR*
   Females
   < 0.85 Non-obesity
    ≥ 0.85 Obesity
   Males
   < 1.00 Non-obesity
    ≥ 1.00 Obesity
WHtR
   < 0.5 Normal
   ≥ 0.5 Increased risk
Body adiposity (%)
   Females
   < 20 Underweight
   20 - <30 Normal weight
   30 - <35 Overweight
   ≥ 35 Obesity
*Different cut-off points have    Males
been proposed for Asian popula- Underweight
   < 10
tions [13, 149]
   10 - <20 Normal weight
BMI, body mass index; WHR,
   20 - <25 Overweight
waist-to-hip ratio; WHtR, waist-
to-height ratio    ≥ 25 Obesity

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3 The need to go beyond BMI 5 Genetic phenotyping

It is clear that obesity research has progressed enormously Genetic analysis performed using genome-wide associa-
in the last decades using the BMI. However, it has been tion studies (GWAS) have revealed that around a hundred
evidenced that, although being highly useful for epide- loci account for 2.7% of the variation in BMI, and insinu-
miological studies, the use of BMI for the diagnosis and ated that as much as 21% of the variation in BMI can be
management of obesity has tremendous limitations for the explained by common genetic variation [28]. Other studies
implementation of personalized nutrition in the context of have focused on more refined adiposity-related phenotypes,
obesity [11, 21]. Because changes in skeletal muscle and such as BF%, fat-free mass (FFM) or measures of adipose
other components of lean body mass create significant vari- tissue distribution allowing the global identification of more
ances in total body mass, BMI is not a valid clinical tool than 500 genetic loci related with obesity [29], although
for determining an individual’s body adiposity [22]. Fur- more recent studies describe about a thousand loci related
thermore, BMI provides misleading information in differ- with BMI only [30]. Alleles conferring individual risk do
ent conditions such as childhood and adolescence, ageing, not act independently but rather the interaction among
intense physical activity, and weight loss including or not different alleles or between these alleles and the environ-
exercise [15]. ment is what results in an augmented risk of developing
In the era of precision medicine, if we want to refine obesity [22]. Genetic variants related with adiposity traits
the clinical management of patients, we need to go beyond are in general associated with cardiometabolic markers as
the use of BMI and incorporate new tools that allow better revealed by epidemiological studies. In this sense, several
classification and follow-up of people living with obesity. genetic loci also discovered by GWAS have been shown to
In order to improve the phenotyping of patients with obe- uncouple adiposity from its cardiometabolic complications
sity, different approaches can be used. The different obesity suggesting that therapeutic manipulation of these genes may
phenotyping systems that could be implemented include represent novel therapeutic tools for the reduction of excess
the incorporation of morbidity and functional limitations, adiposity-associated cardiometabolic risk [30]. Recently,
genetics, metabolic health, muscular mass, body adiposity the combination of genomics and phenomics has allowed to
and body fat distribution. systematically establish the associations between more than
900 loci related with BMI and more than 1,200 diseases from
phenotype codes [31] defined in previous PheWAS analyses
4 Functional staging systems for obesity [32]. This approach has confirmed the broad impact of obe-
sity on multiple interconnected chronic and acute diseases
The usefulness of the BMI in obesity classification may be and opens the door to the establishment of obesity pheno-
improved by clinical obesity staging systems that incorpo- types by interconnecting genetic variants of obesity with a
rate details on the existence and severity of weight-related well-defined extensive disease comorbidity network [31].
health problems. Several of them have been proposed, More recently, phenome-wide comparative genetic-driven
including the King’s Obesity Staging Criteria [23], which analyses have allowed distinguishing obesity phenotypes
was later modified [24] and the Edmonton Obesity Stag- with either diabetogenic or antidiabetogenic proclivities
ing System (EOSS) [25]. Since its introduction in 2009, the based on differences in adiposity distribution, blood pres-
EOSS has emerged as one of the obesity staging systems sure and cholesterol content in high-density lipoproteins
that has been the subject of more intense investigation. For (HDL) particles [33]. Genetic studies of larger cohorts of
instance, several studies have looked into the use of the common and rare variations and the development of more
EOSS to forecast postoperative problems after bariatric sur- refined computational tools will allow the identification of
gery and treatment outcomes after conventional dietary or additional genetic variants associated with adiposity likely
pharmacological obesity treatment. However, these systems contributing to a better definition of obesity phenotypes.
do not discriminate different stages in patients with BMI This will help to explain why not all individuals with obe-
lower than 35 kg/m2 or present great variability in their defi- sity develop metabolic alterations and to find possible ways
nition and outcomes [24, 26]. The morphofunctional assess- to prevent the development of comorbidities in those who
ment including body composition, functional tests, muscle are already living with obesity.
ultrasound, and laboratory determinations has reportedly
shown to provide useful clinical information about the
nutritional status of the patients yielding valuable data to
better define characteristic phenotypes [27].

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6 Obesity phenotyping based on BMI and death in comparison to those who are NW and metaboli-
metabolic health cally healthy [42, 45]. It is challenging to compare stud-
ies regarding MHO/MUO since there is no consensus on its
The degree of body adiposity and in particular the accu- definition, making it almost impossible to estimate the real
mulation of visceral adipose tissue (VAT) associates with prevalence of the MHO and MUO phenotypes. In this sense,
an increased risk of developing obesity-related comorbidi- the reported prevalence of MHO varies widely ranging from
ties [12, 34, 35]. This is based on the extraordinarily active 3 to 70% of patients with obesity depending on the method
secretion profile VAT which includes adipokines and fac- used to define this condition [42, 46–54]. The first studies
tors with a strong cardiometabolic effect [36–41]. However, considered that a patient had MHO if, in addition to having
a proportion of individuals with obesity might not be at a BMI ≥ 30 kg/m2, met less than two conditions related to
increased risk for the development of metabolic alterations fasting glycemia, triglycerides, HDL-cholesterol and blood
and their clinical state has been referred as metabolically pressure similar to those used to define the metabolic syn-
healthy obesity (MHO) [34, 42]. In contrast, patients with drome (MetS) [55]. In the following years there was a very
obesity that at the same time suffer from T2D, hyperten- sensible improvement introducing the concept of having
sion or dyslipidemia are considered as having metabolically none of the MetS components [49, 51]. In our opinion, this
unhealthy obesity (MUO or MUHO) [42–44]. Therefore, we is the most appropriate definition since to say that, for exam-
may distinguish between healthy and unhealthy individuals ple, a patient with obesity and hypertension or with obesity
with normal weight (NW), overweight or obesity (Fig. 1). and T2D is healthy is an oxymoron. In addition, transmit-
Subjects who are NW but metabolically unhealthy (around ting patients with obesity the message that they are healthy
20% of the adult population with NW) have a more than can convey a wrong feeling of healthiness conferring them
3-fold higher risk of cardiovascular events and/or all-cause a false sense of security.

Fig. 1 Phenotyping system according to body mass index (BMI) MUOW, metabolically unhealthy OW; MUO, metabolically unhealthy
and metabolic health. MHNW, metabolically healthy normal weight obesity. NW: BMI 18.5 - <25.0 kg/m2; OW: BMI 25.0 - <30.0 kg/m2;
(NW); MHOW, metabolically healthy overweight (OW); MHO, obesity (OB): BMI ≥ 30.0 kg/m2. *The criteria for defining healthy vs.
metabolically healthy obesity; MUNW, metabolically unhealthy NW; unhealthy metabolism are commented in the text

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Several mechanisms have been proposed to account for MHO, therefore, should not be viewed as an obesity that
the apparently less harmful metabolic profile of subjects is safe and does not require treatment, but it may help in
with MHO. Among them, a lower inflammatory profile, guiding decision-making for a customized and risk-based
increased physical activity/higher cardiorespiratory fitness, treatment of obesity [42]. Since the distinction between the
better renal function, lower uric acid, better sleep pattern, various obesity phenotypes may have significant therapeu-
good nutritional status, higher concentrations of adiponec- tic implications, a proper definition for the stratification of
tin, reduced adipocyte size or adipose tissue fibrosis and people living with obesity and an accurate diagnosis are
inflammation, or reduced liver fat/liver function have been crucial for the individualized care of these patients. A better
put forward. Those factors might contribute to the observed definition of the obesity subphenotypes and a precise diag-
differences in the metabolic status among patients with nosis that more accurately identifies the actual metabolic
MHO and MUO [42, 51, 53, 56, 57]. Adipose tissue amount state together with the function and expansion capacity of
and distribution are also determinant factors for the MHO adipose tissue without incurring into the contradiction of
phenotype. It has been clearly established that a greater adi- applying the term healthy when actually metabolic derange-
pose tissue accumulation in the visceral region contributes ments are already present both at circulating and tissue level
to the appearance of the MUO phenotype, while, on the are needed to improve the management of patients with
contrary, subcutaneous adipose tissue (SAT) would have a obesity.
certain protective role against the appearance of this detri-
mental phenotype [57, 58]. In addition, body composition
studies have shown that the MHO is associated with a lower 7 Phenotyping of obesity based on
BF% besides a lower VAT [59]. The same is observed in anthropometric measurements different to
NW people, in whom a lower amount of body adiposity is BMI
associated with a healthy phenotype [60].
Many studies have questioned the apparently healthy Although the BMI has been shown to be a powerful tool to
metabolic profile of MHO suggesting that the risk of comor- classify patients according to their adiposity, with the limi-
bidity is lower but not absent [61]. A previous work from tations that have been commented above, and to somehow
our group showed that around 30% of patients considered as estimate their cardiometabolic risk, it seems clear that the
with MHO exhibited impaired glucose intolerance or even distribution of adiposity should also be taken into account
T2D and that circulating proinflammatory factors levels in the management of patients with obesity [75–78]. In this
were similar between individuals with MUO as compared sense, more than seven decades have passed since the intro-
to subjects with MHO, reinforcing the idea that the clinical duction in the forties of the notion that VAT has a much
concept of MHO should be used with caution [62]. Accord- greater pathogenic effect than SAT, that may even exert a
ingly, adults with MHO show a consistently increased risk certain protective effect [79]. In this sense, simple to obtain
of T2D compared to metabolically healthy normal weight measurements as waist circumference (WC) have shown to
(MHNW) subjects across different study populations [63]. relatively estimate the amount of VAT being good indicators
In addition, MHO is associated with increased risk of cor- of both morbidity and mortality [80–82].
onary artery calcification [64] and CVD as compared to Although there is no clear consensus on the anatomical
MHNW [50, 65], even when metabolic health is sustained point where to correctly measure the WC, the protocol used
over a lengthy period of time [66]. Furthermore, MHO does not seem to have a substantial influence on the estima-
has recently shown to confer a higher relative risk for any tion of the associated cardiometabolic risk [83]. Global cut-
obesity-related cancer, such as endometrial, liver, renal, and off points to define increased or high cardiometabolic risk
gallbladder cancer, albeit weaker compared to MUO [67]. have been established (Table 1; Fig. 2), although optimized
Some authors consider that the appearance of obesity- values for specific ethnicities have been proposed later [83].
associated comorbidities is only a question of temporal evo- WC thresholds within each BMI category for the estimation
lution of the disease as evidenced by studies showing that of CVD risk have been also proposed [84]. Several studies
subjects defined as with MHO show higher risk of devel- have analyzed the impact on the obesity-associated cardio-
oping T2D, atherosclerosis, hypertension or MetS in the metabolic risk by stratifying patients according to both BMI
long-term [42, 68–70]. In this sense, a growing number of and WC, showing that taking into account estimators of body
publications have questioned the seemingly healthy meta- adiposity and distribution produces a marked improvement
bolic condition of MHO evidencing that these patients with in the predictive capacity over those measures considered
obesity exhibit increased morbidity and mortality as com- separately [80, 81, 85–88]. However, well defined and con-
pared to MHNW [63, 65, 71–74]. sensus phenotypes according to the combined use of BMI
and WC have not been established so far.

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Fig. 2 Threshold values to estimate cardiometabolic risk according to waist circumference for females and males (left) and waist-to-height ratio
(WHtR, right)

Another frequently used anthropometric estimator of consistent conclusion about their usefulness with the pro-
abdominal adiposity is the waist-to-hip ratio (WHR), which posed phenotyping thresholds. Several studies have also
is calculated dividing the WC by the hip circumference both used the combined influence of BMI and WHR to predict
in cm. According to the WHO, the WHR cut-off points to CVD and mortality risk [80, 87].
detect obesity are ≥ 0.85 and ≥ 1.0 in females and males, In the last years, another estimator of adiposity that has
respectively (Table 1). WHR has been shown to have a simi- been gaining interest is the waist-to-height ratio (WHtR)
lar capacity than BMI or WC in predicting incident T2D in calculated dividing the WC by the height both expressed
prospective studies [89, 90], although WC and WHR seems in cm [95]. A boundary value of 0.5 for WHtR has been
to discriminate better than BMI the presence of T2D in suggested by Ashwell et al. being now frequently used [96].
cross-sectional studies [90]. Several meta-analyses reported This is equivalent to the straightforward screening instruc-
that WC and WHR similarly predict cardiovascular events tion “keep your waist to less than half your height” (Table 1
and mortality better than the BMI [91, 92], although more and Fig. 2). This message not only seems appropriate for all
recent studies suggest than WC is a better predictor of heart racial and ethnic groups, but it is also suitable for being used
failure than WHR [93]. Moreover, WC has been shown to with children [96]. The use of WHtR for the screening of
be a better estimator of VAT than the WHR [94]. However, adults at increased cardiometabolic risk has been shown to
these epidemiological studies analyze more the effect of the perform better than BMI by meta-analyses [97] and has been
WC and the WHR measured as continuous variables than suggested to be a more useful clinical screening tool than
the stratification according to the cut-off points defined WC [98–100] or a combination of BMI and WC [101]. A
to phenotype obesity. Therefore, it is difficult to draw a modification of this index has been proposed: the WC index

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(WC/height0.5) having stronger association with adiposity, 8 Body composition and obesity
but its clinical usefulness needs to be further explored [102]. phenotyping
Despite the fact that the WC is easy to obtain and inex-
pensive, it is worth mentioning that while being a good Obesity is defined as an excess of adiposity, with the amount
indicator of abdominal adiposity its correlation with VAT of this excess correlating with comorbidity development [8].
determined through imaging techniques such as computed Although the BMI shows a good correlation with adiposity
tomography (CT) or magnetic resonance imaging (MRI) is in large population studies, it presents a very high error rate
low, since it does not discriminate between SAT and VAT, when we study patients at the individual level; this fact is
abdominal adipose depots with very different pathophysi- very remarkable in the era of personalized medicine. In this
ological implications. However, these imaging techniques sense, we found that almost a third of subjects classified as
are expensive, require a well-trained observer, and are not having NW according to BMI and around 80% of subjects
always available in the clinical practice [77]. considered as with overweight according to BMI exhibited
a BF% that would make them to be considered as having
obesity, as can be observed in Fig. 3 with data obtained
using air displacement plethysmography. Moreover, these
incorrectly classified patients exhibited numerous risk fac-
tors above the thresholds established for predicting cardio-
metabolic risk. However, only a few of the subjects with

Fig. 3 Body mass index (BMI) misclassifies a high number of patients Men (n = 5,180). Right: Women (n = 9,570). Vertical dashed lines indi-
with overweight or obesity defined by body fat percentage (BF%). (A) cate cut-offs for defining overweight (OW) and obesity (OB) accord-
Air displacement plethysmography equipment used to estimate BF% ing to BMI (25.0 and 30.0 kg/m2, respectively) while horizontal lines
in people with a BMI ≥ 16.5 kg/m2 attending the Department of Endo- indicate cut-offs for defining OW and OB according to BF% (20.0 and
crinology and Nutrition at the Clínica Universidad de Navarra in Pam- 25.0% in males and 30.0 and 35.0% in females, respectively). The
plona, Spain. (B) Cut-off points used to define overweight and obesity number of subjects in each quadrant is indicated. Colors denote normal
according to BF% in men and women. (C) Correlation between BMI weight/underweight (NW/UW), OW or OB according to BF%
and BF% of a sample of 14,750 individuals stratified by gender. Left:

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a BF% within the NW or overweight range was misclassi- the evaluation of metabolic alterations in the routine clinical
fied as having obesity according to the BMI value [12]. Our practice both for the diagnosis and the instauration of the
data, together with other studies [16, 17] evidence that there most adequate management of obesity should be pursued
is a substantial degree of misclassification in the diagnosis [8].
of obesity in clinical practice using the BMI, in particular
in those considered as having overweight, and that we are
missing opportunities to treat patients with this life-threat- 9 Phenotyping visceral obesity with imagen
ening condition. techniques
The study of body composition for determining BF%
can be approached from very varied techniques including An elevated VAT is a hallmark sign of increased cardiomet-
skin-fold measurement, bioelectrical impedance analysis abolic risk, even among NW subjects [126–128]. Imaging
(BIA), dual-energy X-ray absorptiometry, air displacement techniques using first CT and MRI thereafter revealed that
plethysmography, MRI, CT, isotopic dilution or underwater the amount of VAT is a major determinant of the cardiomet-
weighing [103–105]. The availability of devices to deter- abolic risk [129, 130]. There is no consensus for VAT area
mine body composition has been increasing in recent years cut-off points to define increased metabolic risk, although it
and nowadays it is very common to have them (for example has been proposed that in both females and males a value of
BIA devices, whose accuracy has increased over the years) 100 cm2 was linked to significant changes in the risk profile
available in consultations with nutritionists, endocrinolo- for CVD, and when values of more than 130 cm2 of VAT
gists or even in primary care offices. were attained, a further elevation of the cardiometabolic
Excess adiposity measured as BF% correlates very risk was seen [131]. In addition, these techniques have been
well with the increase in the risk of CVD, T2D and other improved and volumetric data obtained from multislice
obesity-associated comorbidities [106–109]. Most of the imaging has confirmed that even after taking into consider-
studies aimed to determine the influence of adiposity on ation the usual anthropometric measures VAT is still more
cardiometabolic alterations have focused more on estima- strongly linked to a harmful cardiometabolic risk profile
tors of body fat distribution than on the amount of body fat [132]. Therefore, VAT determination may provide a more
per se. However, a growing number of studies indicate that detailed picture of the obesity-associated cardiometabolic
the amount of body fat is also exerting a fundamental role in risk. Furthermore, imaging techniques allowed the identi-
the increased cardiometabolic risk [12, 36, 110–115]. Body fication of a new subphenotype of subjects with NW cor-
composition provides a scientific explanation that may help responding to individuals with a BMI < 25 kg/m2 with an
to understand the observed increased cardiovascular risk in intra-abdominal adipose tissue/abdominal subcutaneous
metabolically unhealthy normal weight (MUNW) subjects adipose tissue ratio above 0.45 in women and 1.0 in men,
with high adiposity [43, 116, 117]. In this sense, the use that would have and increased cardiometabolic risk [133].
of BF% thresholds for the diagnosis of obesity (Table 1) This phenotype was named TOFI (standing for “thin-on-
allows to detect more subjects with an increased cardio- the-outside fat-on-the-inside”) representing people with
metabolic risk than the simple application of the BMI or NW but an abnormally high amount of “hidden” VAT [133],
the WC classification criteria. Those cut-off points for BF% that could contribute to explain the MUNW phenotype.
used for females (30 - <35% defining overweight and ≥ 35
defining obesity) and males (20 - <25% defining overweight
and ≥ 25 defining obesity) are frequently used in the litera- 10 Skeletal muscle mass and obesity:
ture [12, 16, 17, 112, 118–122], even in children and adoles- sarcopenic obesity
cents [123–125], although an international consensus does
not exist. The information obtained thanks to body composition tech-
This is of particular relevance due to the pathophysi- niques has allowed to detect the presence of a condition
ological implications that increased adiposity may have in with important functional implications that consists of the
the context of NW or overweight. Although BMI is widely simultaneous presence of excess adiposity and a deficit in
used as a proxy indicator of body adiposity, it does not pro- skeletal muscle mass and function (sarcopenia), which has
vide an actual measure of body composition as previously been defined as sarcopenic obesity [134–136]. The matrix
evidenced [12]. A high number of patients with obesity are resulting from the combination of low and high body adi-
being underdiagnosed, and, therefore, opportunities for posity and low and high skeletal muscle mass results in the
cardiometabolic risk assessment and instauration of appro- establishment of another classification system for obesity-
priate treatment measures are being lost. In this sense, the related phenotypes (Fig. 4). The diagnosis is based on skel-
inclusion of body composition determination together with etal muscle functional parameters (for example hand-grip

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Fig. 4 Phenotyping system according to fat mass and skeletal muscle mass. The evaluation regarding skeletal muscle mass includes amount and
functionality. The diagnostic criteria are reported in a consensus statement [136]

strength adjusted by body mass) and, if a dysfunction is learning. With this approach it was shown that sarcopenic
detected, the process will continue with body composition obesity is associated with an increase in cardiometabolic
to identify potential increased fat mass and reduced skeletal risk [139, 140]. In the same line, other studies using this
muscle mass. When both situations concur the presence of phenotyping system have suggested that the maintenance of
sarcopenic obesity can be diagnosed [136]. This medical skeletal muscle mass with ageing reduced the development
condition, which has a global prevalence of around 11% in of T2D [141].
older adults [137], has been attributed to the consequences
of the ageing process, acute and chronic diseases, and the
lack of physical activity [136], and is associated with an 11 A new phenotyping classification
increase in mortality [138]. The lack of clear diagnostic combining body fat amount and distribution
criteria during the last years made it difficult to adequately
study the cardiometabolic risk associated with these phe- Both BMI and WC may have useful applications in rou-
notypes. To solve the lack of clear criteria, “alternative” tine clinical practice. However, they bear a high error rate,
diagnostic premises have been generated through machine as previously mentioned, being anthropometric-based

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classification systems that are only useful for predicting grouped in five different risk phenotypes, following a traf-
health hazards and do not reveal accurate information about fic light-like classification system (green: no risk; yellow:
a patient’s health situation or clinical need [26, 103, 104, slightly increased risk; orange: increased risk; dark orange:
142]. As commented above, patient stratification according high risk and red: very high risk) according to the predicted
to BMI and WC simultaneously allows a better prediction risk. By using this approach, a very accurate stratification
of cardiovascular or death risk [80, 81, 85–87]. However of cardiometabolic risk factors is achieved (unpublished
those studies were not aimed to establish different pheno- results). Moreover, since information provided by body
types according to both amount and distribution of adipos- composition techniques is not always available we suggest
ity. Other studies have used the combination of BMI and to combine BF% directly measured or estimated by body fat
WC to define a kind of “matrix” to establish specific cardio- equations, such as the CUN-BAE (Clínica Universidad de
metabolic risk phenotypes [101, 143]. However, although Navarra-Body Adiposity Estimator), which provides a bet-
the cardiometabolic risk estimated with this approach is ter appraisal of actual BF% than BMI [144], with the mea-
more precise than the use of BMI or WC alone it still main- surement of WC.
tains the mentioned BMI limitations. We herein propose
that a combination of the actual adiposity expressed as BF%
and WC as a measure of distribution may represent a novel 12 Conclusions
and useful tool for the estimation of obesity-associated car-
diometabolic risk both for research, but also in the clinical It seems clear that the current obesity classification sys-
setting, getting a more precise insight and providing a bet- tems do not allow a good diagnosis and prediction of the
ter translation into increased risk. This phenotyping system comorbidity risk of the patients and, therefore, their clinical
(Fig. 5) establishes nine body phenotypes (3 BF% x 3 WC) management. More than a decade ago it was proposed that

Fig. 5 Proposed phenotyping system based on a combination of the ferent types (1a to 3c) clustered in five different phenotypes according
actual adiposity expressed as body fat percentage (BF%) and waist to the cardiometabolic risk. Green: no risk; yellow: slightly increased
circumference (WC) as a measure of adiposity distribution. The cutoff risk; orange: increased risk; dark orange: high risk and red: very high
points are those defined by the WHO for WC and the most frequently risk
used for BF% (see text). This phenotyping system establishes nine dif-

13
Reviews in Endocrine and Metabolic Disorders (2023) 24:809–823 819

obesity phenotyping should be more sophisticated including WHtR waist-to-height ratio

dynamic rather than static phenotypes taking into account WHO World Health Organization
the variability in body composition and its influence on
metabolism and the physiological responses to diet and Authors Contribution All co-authors participated in the redaction of
the manuscript, approved the final version to be submitted and consent
exercise [145]. Some advances have been achieved in the for publication.
phenotyping of obesities with the incorporation, for exam-
ple, of functionality or metabolic health. Moreover, the evo- Funding This study was funded by Plan Estatal I + D + I from the Span-
lution of software tools is allowing the development of new ish Instituto de Salud Carlos III–Subdirección General de Evaluación
approaches incorporating machine learning for establishing y Fomento de la investigación–FEDER (grants number PI20/00080,
PI20/00927 and PI22/00745) and CIBEROBN, ISCIII, Spain; Dpt.
new classification systems according to the combination of of Health 58/2021, Gobierno de Navarra-FEDER, Spain; and Dpt. of
genetics, adipocyte morphology and metabolic traits [146], University, Innovation and Digital Transformation PC098-099 MEP-
the calculation of adiposity by using smartphones [147] or ERTROBE, Gobierno de Navarra, Spain.
the development of whole-body three-dimensional opti- Open Access funding provided thanks to the CRUE-CSIC agreement
with Springer Nature.
cal scanning to predict the metabolic risk [148]. Improve-
ments in the resolution and availability of body composition
Declarations
equipments have also produced a greater implementation of
these techniques. Body composition has allowed the iden- Disclosure of potential conflict of interest No potential conflicts of in-
tification of sarcopenic obesity, to establish more refined terest were disclosed.
obesity phenotypes and to better define obesity-associated
cardiometabolic risk. It is time to implement these advances Ethical approval Not applicable.
in routine clinical practice in a more constant way to prevent
Informed consent Not applicable.
the development of overweight and obesity, as well as to
achieve a better management of people living with obesity. Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
Abbreviations adaptation, distribution and reproduction in any medium or format,
BIA bioelectrical impedance analysis 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
BF% body fat percentage if changes were made. The images or other third party material in this
BMI body mass index article are included in the article’s Creative Commons licence, unless
CT computed tomography indicated otherwise in a credit line to the material. If material is not
CUN-BAE Clínica Universidad de Navarra-Body Adi- included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted
posity Estimator use, you will need to obtain permission directly from the copyright
CVD cardiovascular disease holder. To view a copy of this licence, visit http://creativecommons.
EOSS Edmonton Obesity Staging System org/licenses/by/4.0/.
FFM fat-free mass
HDL high-density lipoproteins
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