Keywords

Older adults, Obesity, Nutritional status, Body composition

Introduction

Obesity is an abnormal or excessive accumulation of body fat and it is currently a significant public health issue. The incidence of obesity has reached epidemic proportions worldwide and it is a risk factor for chronic diseases [1,2]. The most commonly used criteria for diagnosing obesity are the Body Mass Index (BMI) ≥ 30 kg/m ² [1]. However, although BMI is important for population-based studies, it has limitations. BMI cannot distinguish between body compartments and does not fully reflect the percentage of body fat and its distribution [3]. To overcome the BMI limitations, it is essential to use more accurate and reliable assessment methods, such as Bioelectrical Impedance (BIA) to assess body fat and distinguish between different body tissues [4].

Current evidence shows that individuals with a normal BMI (18.5-25 kg/m ² ) [1] may have an excessive amount of body fat that is masked by their normal weight, which is referred to as normal weight obesity (NWO) [5]. In addition, normal weight central obesity (NWCO) is another obesity phenotype, which is defined by the coexistence of a normal BMI and abdominal obesity, as measured by waist circumference (WC) [6]. Several studies have reported different prevalence ratios of NWO and NWCO [6-8], and have suggested that the ageing process increases the prevalence of both NWO and NWCO due to the changes in body composition, including decrease of lean body mass and rearrange of adipose tissue [2,4,6,9]. Among older adults, NWO has been reported in 27.9% of the population [10], while NWCO has been reported in 12.7% [11].

Despite the growing body of evidence on the presence of NWO and NWCO, particularly in individuals of advanced age, there is still a lack of research that explores the frequency of these conditions in older populations. As a result, our study sought to address this gap by examining the prevalence of NWO and NWCO in hospitalized older adults, with a particular focus on geriatric syndromes, including sarcopenia and frailty, as well as muscle abnormalities and function. Through our investigation, we aimed to shed further light on the factors that contribute to these obesity phenotypes in older adults and provide valuable insights for future research and clinical practice.

Methods

Design, local, population and eligibility criteria

This is a cross-sectional study, involving older adults who attended to a University Hospital in Northeast of Brazil, from March to September 2021. Clinical and surgical patients of both sexes, who aged ≥ 60 years and had BMI > 18.5 kg/m ² were included.

We excluded the patients who were unable to undergo anthropometric assessment, clinically serious conditioned and those unable to answer the questionnaires, such as hemiparesis, restricted to bed, presence of ascites and/or edema.

Sample size

Sample size was calculated using the Epi Info ^® software, version 6.04, in the STATCALC module. We considered the number of hospitalizations of older patients in the same study period of the previous year (total hospitalizations in 7 months = 300), a prevalence of 34.1% NWO [2], a confidence level of 95% and a standard error of 5%. The minimum n = 161 patients was obtained. To cover any losses, 10% was added to the minimum sample quantity, totaling a sample size of 172 individuals.

NWO and NWCO evaluation

NWO was determined by the coexistence of eutrophic BMI (18.5-25 kg/m² [1] and high body fat percentage, determined by the highest tercile for sex (> 33.5% for men and > 42.8% for women) [9,10]. NWCO was determined by the coexistence of eutrophic BMI and a very increased waist circumference (≥ 102 cm for men and ≥ 88 cm for women) measured by the midpoint between the last rib and the iliac crest [1]. Body fat was obtained by the estimated body fat percentage, using the Biodynamics ^® tetrapolar Bioelectrical Impedance Device (BIA) Model 310e. Body fat percentage was estimated through equation: impedance = √R²+X², where R is Resistance and X is Reactance [12-15].

Demographical and clinical data

We registered demographic data such as age, sex, per capita family income (stratified into < 2 minimum wages and ≥ 2 minimum wages) and scholarly level (obtained in years of study and dichotomized into ≤ 9 years and > 9 years) [16]. Regarding to clinical data, we considered the presence of comorbidities, such as systemic arterial hypertension (SAH), type 2 diabetes mellitus (DM), and the diagnosis of the moment of hospitalization.

Clinical diagnoses at the moment of hospitalization were categorized into three groups 1); Malignancies (all types of cancers); 2) Non-malignant organic disorders (digestive disorders, endocrine disorders, infectious diseases, nervous system diseases, respiratory diseases, systemic autoimmune diseases, kidney and ureteral diseases); 3) Psychiatric disorders (depressive episode, somatoform disorders, anxiety disorders, obsessive-compulsive disorder, bipolar affective disorder and schizophrenia) [17].

Functionality measurement

Functional capacity was assessed using the Barthel Index. Those with the maximum score (100 points) were considered totally independent; mild dependence (99 to 76 points); moderate dependence (75 to 51 points); severe dependence (50 to 26 points); total dependence (25 or less points) [18]. For statistical analysis, we classified the functional capacity into functional independence (independence and mild dependence) and functional dependence (moderate to severe dependence).

Behavior variables

Information on smoking, alcohol intake and level of physical activity were collected. Smoking was categorized into: Smokers (individuals who maintained the habit of smoking), non-smokers (individuals who reported never having smoked) and ex-smokers (individuals who reported smoking for some time in their lives, but who did not use at the time of application of the questionnaire). The alcoholic consumption was considered by self-reporting the intake of alcoholic beverages in the 30 days prior to the application of the questionnaire. The level of habitual physical activity was assessed using the International Physical Activity Questionnaire (IPAQ), in its short version. Individuals who performed less than 150 minutes per week in activities were considered not sufficiently active [19].

Weight loss and nutritional status assessment

Weight loss was defined by self-report of unintentional weight loss by ≥ 5% of body weight in the last 12 months. The percentage of weight loss (%WL) was calculated from the equation: %WL = (Usual weight - Current weight) × 100/Usual weight [17]. For nutritional status assessment, we considered BMI and calf circumference (CC). BMI was obtained from the quotient between weight (kg) and height (m) ² [1]. The patients were classified based on the cutoff points established by the WHO [1]: BMI > 18.5 to 24.9 kg/m ² (eutrophic); ≥ 25 kg/m ² (overweight); and ≥ 30.0 kg/m ² (obesity). The CC was measured in the region with the largest volume of the calf. Values of ≤ 34 cm for men and ≤ 33 cm for women were considered low [20].

Sarcopenia and frailty measurement

Sarcopenia was determined by the presence of both reduced strength and muscle mass [21]. Muscle strength was measured from handgrip strength (HS), using the JAMAR® digital dynamometer [21,22]. Values of HS < 27 kg/F for men and HS < 16 kg/F for women was defined as low muscle strength [21]. Appendicular Skeletal Muscle Mass (ASM) was obtained from the equation by Sergi, et al. [23]: ASM = (0.227 × resistance index (RI)) + (0.064 × reactance (Xc)) + (0.095 × weight (W)) + (1.384 × sex) - 3.964. The resistance measurement was obtained by the BIA. From the results of Sergi’s equation, the Appendicular Skeletal Mass Index (ASMI) was calculated using the formula: ASM/Height ² [21]. We adopted the cut-off points validated for the Brazilian population. Values ≤ 7.7 kg/m ² in men and ≤ 5.62 kg/m ² are defined as low muscle mass [20].

Frailty was established based on Fried’s phenotype [24], which considers frailty when three or more of the following five criteria are identified: 1) Unintentional weight loss; 2) Exhaustion assessed by self-report of fatigue; 3) Decreased HS; 4) Low level of physical activity; and 5) Decreased walking speed. Fatigue was defined based on exhaustion assessed by self-reported fatigue, indicated by two questions from the Center for Epidemiological Studies - Depression (CES-D) [25]. Physical performance was measured using the Gait Speed (GS) test, according to the model proposed by the International Academy on Nutrition and Aging (IANA) [26]. A low GS was defined when the speed was < 0.8 meters/second [21,27]. Weight loss, HS and level physical activity was assessed using the aforementioned methods.

Ethical aspects

All procedures performed our study were in accordance to the ethical standards of the institutional and/or national research committee and to the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Data collection was performed after consideration and approval by the Research Ethics Committee (CAAE: 54023221.4.0000.8807). Informed consent was obtained from all participants included in the study. Patients consented to participate in the research and have their data published.

Statistical analysis

The SPSS ^® statistical program version 20.0 (SPSS Inc., Chicago, IL, USA) was used to data analysis. The normality of continuous variables was measured by the Kolmogorov-Smirnov test. Normal distributions were described as mean and standard deviation. Non-normal distributions were described as median and interquartile interval. The association of NWO and NWCO with co-variables was assessed using Pearson’s chi-square or Fisher’s Exact test. Significance was found when the results presented p < 0.05.

Results

A total of 172 eligible patients were recruited to our study but 9 of them were excluded due to lack of BIA data or data inconsistency. Our final sample consisted in 163 patients. The mean age was 69.9 ± 8.1 years and 55.8% of the patients were male. The prevalence of SAH and DM was 71.2% and 44.8%, respectively. 57.7% of our sample were overweight, while abdominal obesity was found in 55.2%. We found a high percentage of reduced CC and ASMI (59.5% and 57.8%, respectively). High percentages of sarcopenia and frailty were also observed (65.0% and 35.4%, respectively) (Table 1).

The study found that 8.0% of the participants had NWO, while 7.4% had NWCO (Table 2). NWO was associated with weight loss, reduced waist circumference, reduced appendicular skeletal muscle mass index (ASMI), and sarcopenia (p < 0.05) (Table 3). On the other hand, NWCO was more common in women, patients who reported weight loss in the past 12 months, individuals with functional dependence, those with reduced waist circumference, reduced ASMI, low muscle strength, sarcopenia, and frailty (p < 0.05) (Table 4). However, demographic, clinical, and behavioral factors were not associated with NWO and NWCO (p > 0.05).

Discussion

Our study stands out for addressing two frequently underdiagnosed nutritional conditions, especially in hospitalized older individuals: NWO and NWCO. In addition, we conducted an investigative analysis on how these conditions are associated with clinical conditions, abnormalities in body composition, and muscular function, due to their impact on adverse outcomes. Thus, our research contributes to a better understanding of the prevalence of these nutritional conditions and their associations with clinical conditions and abnormalities in body composition and muscular function in hospitalized older patients.

In the past few decades, attention has been directed to the prognostic value of obesity, especially defined by the body mass index. BMI has high specificity in detecting excessive body adiposity, however it has low sensitivity, proving to be a poor discriminator of fat mass and body fat-free mass [4,5]. On the other hand, NWO and NWCO studies are raising and are currently described as conditions related to higher risk for cardiometabolic diseases, mortality and lower survival [5-7,28].

The prevalence of NWO found in our study (8.0%) is in concordance to results found by Kim, et al. (8.3%) [29] but in contrast to results found by Ji, et al. [4] that found a higher prevalence (10.7%) of NWO in older adults. In American population, evidence showed a variation in frequency of NWO (4.5-22%) [5]. It is important to note that the frequency of NWO and NWCO may vary due to epidemiological scenario and population characteristics, making it essential to investigate these conditions in different contexts and populations. Beyond that, the many cutoff values to determine body fat percentage and the different techniques of measurement can explain the variations in prevalence of NWO afore-mentioned. It may be a bias of comparison. Our study considered the highest tercile for both sexes [9,10] and it may be underestimated results.

Contrasting the results on NWCO prevalence found in our study (7.4%), Mohamed, et al.’s [6] study found a prevalence of 21.2% in adults living in Kenya. Mohamed, et al. [6] described that the prevalence of NWCO can vary from 18.1% to 38.1%, considering the variations of techniques to assess central obesity. Similar results were found by Shirasawa, et al.’s [30] study. They found a prevalence of NWCO in 19.9% of the population and used the combination of BMI and Waist to Hip Ratio (WHR) to determine NWCO. These differences may be related to the different WC measurement protocols and the different parameters used to define central obesity (WC, WHR) [30], waist-to-height ratio (WHtR) [31].

Studies evaluating the NWO and NWCO phenotypes showed that individuals with NWO had higher risk to develop cardiovascular diseases and metabolic syndrome [5,6,32], while NWCO was associated with decreasing in muscle mass or sarcopenia, lower energy expenditure, metabolic disorders and low functional capacity [28,33]. These results show us the importance of diagnosing these conditions, considering the fact that obesity phenotypes are not often identified, despite its association with cardiometabolic events and muscle abnormalities [5].

Evidence have showed that the aging process leads to a gradual increase in body fat and a decrease in lean mass. Considering this, the isolated evaluation based on BMI alone cannot accurately correspond to the body composition of individuals and may mask the occurrence of NWO and NWCO phenotypes. The misdiagnosis of these conditions results in a lack of multimodal interventions that may prevent and treat the related cardiometabolic events [2,5]. These facts highlight the importance of investigating the different obesity phenotypes beyond BMI. Although BMI is an easy, quick, and conventional index [2,7,34], it has limitations due to its low sensitivity (around 50%) in detecting excessive body fat [5,33,35].

Our results showed that NWCO phenotype was more frequent in women. Similar results were found in previous studies [6,35,36]. There are reasons that may explain this finding, such as reduced postmenopausal estrogen levels increase body fat and women can engage in less physically strenuous activities compared to men, causing them to have less muscle mass and more body fat [4,5]. Contrasting to the results found in our study among the frequency of NWO and NWCO, Ji, et al.’s [4] study showed that NWO increased with age. There are several possible explanations for these findings. First, body fat content gradually increases, while muscle mass decreases with age [37]. Second, the risk of tooth loss increases with age, resulting in reduced chewing ability, which can restrict optimal nutrition. Also, older adults over the age of 80 may have insufficient protein intake, leading to muscle breakdown [4].

Our other results showed no association of NWO and NWCO with sociodemographic data. Similar results were found in Madeira, et al.’s [38] study. They also did not found relationship between the obesity phenotypes and scholarly level, income, and marital status. No association of NWO and NWCO with clinical variables was found in our study, in contrast with the results found by Rakhmat, et al.’ [39]. They found an association of NWO with SAH and DM. Other studies showed that NWO individuals had higher systolic and diastolic pressure when compared to people without this obesity phenotype [9,40]. It has also been reported that NWO and NWCO may influence in metabolic factors, leading to insulin resistance, dyslipidemia and inflammation [41,42].

No relationship between NWO and NWCO and lifestyle data were found in our study. Contrasting to our results, Mannisto, et al. [43] observed that NWO was related to physical inactivity, smoking, alcoholic intake, in addition to inadequate dietary habits, such as low intake of roots, cereals and fish, in addition to a high sugar intake.

Regarding to physical activity, Wijayatunga and Dhurandhar [5] reported that individuals with NWO are less involved to physical activities when compared to individuals without this obesity phenotype. Other evidence showed that individuals with NWO had lower physical fitness [44]. It could be partially explained by the lower lean body mass content [45]. The higher frequency of NWO and NWCO in individuals with weight loss greater than 5% has not been sufficiently described in scientific literature. It is known that hospitalization is an important predictor of weight loss and this weight reduction during acute care can mainly affect lean body mass [46]. In this sense, the adipose tissue would be better preserved, justifying the high percentage of fat in normal weight.

Our results showed an association of NWO and NWCO with low muscle mass and sarcopenia. Similar results were found by Di Renzo, et al.’s [47] study. They described a lower muscle mass in women with NWO when compared to those without this obesity phenotype. In accordance to our results, Kim, et al. [34] showed that adult individuals with NWO had reduced appendicular skeletal muscle mass (p < 0.001), while Di Renzo, et al. [48] described an association of NWO and sarcopenia.

There are many factors that may explain these findings. The high percentage of body fat tend to have higher levels of tumor necrosis factor-alpha (TNF-a), which is a cytokine involved in chronic inflammation and may lead to anorexia and weight loss. In addition, TNF-a seems to be involved in the metabolic regulation and connection between muscle and adipose tissue, promoting abnormalities in body composition marked by tissue replacement. As a consequence of these mechanisms, body experiences a reduction in muscle mass and increase in fat deposits, despite normal BMI [3,34].

Other studies also demonstrated that patients with NWO had lower muscle mass when compared to groups of non-obese and obese individuals with a high fat percentage [49]. In addition, obesity characteristics in older adults are related to increased fat mass, fat infiltration into muscles, and decrease in muscle mass and muscle quality [4].

In our study, NWCO was associated with functionality assessed by the Barthel Index. Similar results were described by Batsis, et al. [11], who found that older adults with NWCO had greater risk of decline in function and physical activity. In addition, it was also described that older women with NWO had greater impairment in basic activities of daily living (ADLs). Corroborating to this, evidence shows that the aging process is related to decline in muscle quality and functional capacity [50,51]. In addition, the aging process leads to abnormalities in body composition, marked by greater deposition of body fat and lower lean mass among women. It may increase the risk of developing obesity and lower muscle strength. Additionally, the fat infiltration in muscle can also lead to a decrease in strength and muscle quality, which can decrease functionality in older adults [35].

Our results showed an association of frailty with NWCO. This relationship has not yet been fully explored in the scientific literature, but can be explained since abdominal obesity increases the chance of frailty [52]. So, this relationship may occur even in the presence of a normal BMI.

Our study has some limitations that need to be acknowledged. The study design was cross-sectional and lacks a control group, which may limit causal inferences. In addition, we included older patients from a single hospital unit, which limits the generalization of results. However, this study’s main strength is the investigation of associated factors with two important obesity phenotypes. It helps to summarize the risk profile of these conditions. Another strong point of our study is the evaluation of conditions that are often underdiagnosed and understudied.

Conclusion

NWO and NWCO were not uncommon in hospitalized elderly. It demonstrates that BMI may fail to represent excessive body adiposity and abnormal fat distribution in older adults. The risk profile for NWO development was: weight loss ≥ 5%, reduced muscle mass and sarcopenia, while for NWCO was being women, weight loss ≥ 5%, functional dependence, reduced muscle mass, reduced muscle strength, sarcopenia and frailty.

In health sciences there are still many gaps on NWO and NWCO exploration. In this sense, it is important that other studies may explore its etiology and define the profile of patients at greater risk for these conditions. The results found in our study suggest the need of combine the BMI with other anthropometric parameters, such as percentage of body fat and measurements of central adiposity, as a path to improve the screening of obesity in its all phenotypes and to properly treat these conditions.

Acknowledgements

None.

Statement of Authorship

All authors made substantial contributions to the conception and design of the study, acquisition, analysis and interpretation of data. They also contributed to the manuscript drafting and critical review of the intellectual content. 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.

Conflicts of Interest Statement

The authors declare that there are no conflicts of interest.

Funding Resources

None.

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