The Association between Normal Weight Obesity and Metabolic Syndrome in Older Korean Adults

doi:10.21203/rs.3.rs-3089453/v1

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The Association between Normal Weight Obesity and Metabolic Syndrome in Older Korean Adults

https://doi.org/10.21203/rs.3.rs-3089453/v1

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Background: Body mass index (BMI) has its drawback in that it is unable to distinguish between the distribution and quantity of fat mass and lean mass. Normal weight obesity (NWO) refers to a special phenotype of having excessive body fat despite a normal BMI. This cross-sectional study examined the association between NWO and metabolic syndrome in older Korean adults aged ³65 years.

Methods: Data for the current study (612 men and 479 women) was obtained from the 2008-2011 Korea National Health and Nutrition Examination Survey. NWO was defined as a BMI between 18.5 and 24.9 kg/m²and body fat of ³26.0% in men and body fat of ³36.0% in women. Metabolic syndrome was defined according to the revised National Cholesterol Education Program definition, with a modified waist circumference threshold of >90 cm for Korean men or >85 cm for Korean women.

Results: People with NWO had an increased risk for metabolic syndrome (odds ratio, OR=2.531, 95% confidence interval, CI=1.287~4.980, p=0.007 in men; OR=1.970, 95% CI=1.020~3.850, p=0.043 in women) compared to people with NWNO (OR=1). When all covariates, including age, sex, education, income, smoking, heavy alcohol intake, serum vitamin D, physical activity, and nutrient intake of carbohydrates, fats, and proteins, and fat-to-lean mass ratio, were controlled, however, the risk of NWO for metabolic syndrome was no longer significant in both genders.

Conclusions: The current findings suggest that older adults with NWO should be monitored in clinical settings for early intervention.

Normal-weight obesity

metabolic syndrome

lifestyle risk factors

older adults

Obesity, defined as excessive fat accumulation, is a multifactorial health condition that is associated with increased morbidity and mortality [1]. Cutoff points of body mass index (BMI) tailored to age, gender, and ethnicity are developed and used to determine the presence of obesity [2, 3]. However, regardless of the cutoff point(s) selected, BMI has a serious weakness in that it cannot differentiate between the distribution and amount of fat mass and lean mass, which are strongly associated with the metabolic complications of obesity [4, 5].

The term "normal weight obesity (NWO)" refers to a special phenotype of having excessive body fat despite a normal BMI [6]. Given the drawback of BMI, recent research has concentrated on NWO as a novel biomarker of cardiometabolic complications [7]. Increasing evidence suggests that NWO is associated with unfavorable cardiometabolic risk profiles, including elevated inflammatory cytokines, insulin resistance, oxidative stress, unfavorable lipid profiles, increased visceral fat, and low lean mass [7, 8]. The clinical importance of NWO has been reviewed and succinctly outlined in a recent meta-analysis [9].

Obesity can manifest in various phenotypes, ranging from seemingly lean-looking people to those with morbid obesity. Despite a lower proportion of BMI-based obesity, Asians have a higher prevalence of metabolic disorders than Caucasians [10, 11]. This disparity could be explained by the presence of excess body fat in otherwise thin Asian phenotypes with a normal BMI [8, 12]. Additionally, aging is associated with an increase in fat mass and a decrease in muscle mass [9] as well as an increase in physical functional impairment [10], all of which may contribute to the development of NWO. Yet, NWO is a relatively underrecognized health condition in geriatric populations. Here, we propose that NWO may play a novel risk factor for metabolic syndrome among older adults. The purpose of this study was to examine the relationship between NWO and metabolic syndrome in older Korean adults.

1. Data source and study participants

As shown in Fig. 1, we included 6,370 participants aged 65 years and older (2,647 males and 3,723 females) who took part in the Fourth and Fifth Korea National Health and Nutrition Examination Surveys (KNHNES) from 2008 to 2011 in South Korea. We then excluded the following participants: those with no dual x-ray absorptiometry (DXA) (n = 360) or no vitamin D (n = 176); who were underweight (n = 550); and those with stroke (n = 51), myocardial infarction/ischemia (n = 101), arthritis (n = 627), liver cirrhosis (n = 8), chronic renal disease (n = 12), hypertension (n = 2,931), diabetes (n = 342), lipid disorders (n = 46), thyroid disease (n = 28), or any type of cancer (n = 47). A total of 1,091 participants (612 males and 479 females) were included in the final analyses. Detailed information regarding the 2008–2011 KNHNES is available through the national public database (https://www.cdc.gov/nchs/nhanes/about_nhanes.htm; accessed on December 10, 2022).

2. Variables

2.1. Measurement of body composition

Body composition was measured with whole-body DXA (Hologic, Bedford, MA, USA). Trained technologists carried out standardized daily quality control of the DXA instrument using a spine phantom provided by the manufacturer. Total and trunk LM (g) and total and trunk FM (g) were quantified. Total body fat (%) was calculated, and the fat-to-lean mass ratio (FMR) was calculated as the whole-body FM divided by the whole-body LM.

2.2. Definition of normal-weight obesity

The presence of NWO was determined using the previously suggested cutoffs of BMI and body fat for Korean populations [7]: a BMI between 18.5 and 24.9 kg/m² and high body fat (≥26.0% in men and ≥36.0% in women). Normal-weight non-obesity (NWNO) was defined as a BMI between 18.5 and 24.9 kg/m² and normal body fat (< 26.0% in men and > 36.0% in women). Obesity (OB) was defined as a BMI of ≥25 kg/m².

2.3 Definition of metabolic syndrome

The National Cholesterol Education Program definition [13], with the adoption of a Korean-specific waist circumference (WC) threshold [14], was used to determine the presence of metabolic syndrome: 1) WC of ≥ 90 cm in men or ≥ 85 cm in women, 2) fasting blood glucose (FBG) of ≥ 100 mg/dL or use of anti-diabetic medication, 3) triglycerides (TG) of ≥ 150 mg/dL or medication use, 4) low high-density lipoprotein cholesterol (HDLC) of < 40 mg/dL in men and < 50 mg/dL in women, and/or 5) high resting systolic blood pressure (SBP) of ≥ 130 mmHg and diastolic blood pressure (DBP) of ≥ 85 mmHg or use of antihypertensive agents.

3. Covariates

The covariates included in the study were age (years); sex (male vs. female); education (elementary/less, middle/high school, or college/better); monthly income (in Korean won); smoking (past/current smoker vs. non-smoker); heavy alcohol consumption (≥7 drinks per week for males or ≥5 drinks per week for females); and nutrient intake of carbohydrates (g/day), fats (g/day), and proteins (g/day). Additionally, serum vitamin D level (25-hydroxyvitamin D, 25(OH)D, ng/mL) was assessed using a gamma counter (1470 Wizard, Perkin-Elmer, Turku, Finland) with a radioimmunoassay kit (DiaSorin, Stillwater, MN, USA). The Korean version of the International Physical Activity Questionnaire (https://sites.google.com/view/ipaq) was used to monitor moderate to vigorous physical activity in terms of duration (min per session) and frequency (days per week), and activity was quantified in units of min per week and classified as inactive/insufficiently active or sufficiently active (or 150 min per week). Detailed information regarding the covariates is available elsewhere [15].

4. Statistical analyses

Quantile-quantile plots were used to verify the normality of the data distribution before statistical analyses. Analysis of variance (ANOVA), followed by Tukey’s post hoc test if necessary, and Chi-square test were used to test mean group differences of continuous (in mean and standard deviation) and discontinuous (in number and percentage) variables, respectively. One-way ANOVA with a post-hoc contrast was used to test incremental linear trends in the prevalence of each metabolic syndrome component according to body composition phenotype (from NWNO to NWO and OB). Binary logistic regression was used to calculate the odds ratio (OR) and 95% confidence interval (CI) of NWO and obesity for metabolic syndrome with NWNO as a reference. Statistical significance was tested at p = 0.05 using SPSS-PC software (version 27, SPSS, Chicago, IL, USA).

As shown in Table 1, men were younger (p = 0.013), more educated (p < 0.001), more likely to smoke (p < 0.001) and drink heavily (p < 0.001), had higher LM (p < 0.001) and WC (p < 0.001), but lower FM (p < 0.001) and FMR (p < 0.001) than women. Men also consumed more carbohydrates (p < 0.001), fats (p < 0.001), and proteins (p < 0.001), and had higher ALT (p = 0.001), AST (p < 0.001), and vitamin D (p < 0.001), but lower TC (p < 0.001) than women.

Table 1. Descriptive statistics of study participants.

		Men (n=612)	Women (n=479)	Total (n=1,091)
Age (years)		71.5±4.5	72.2±4.9	71.8±4.7	0.013
Education, n (%)					<0.001
	Elementary or lower	324 (55.3)	386 (86.9)	710 (68.9)
	Middle/high school	207 (35.3)	47 (10.6)	254 (24.7)
	College or higher	55 (9.4)	11 (2.5)	66 (6.4)
Current/past smoker		502 (84.4)	49 (10.8)	551 (52.5)	<0.001
Heavy drinking, n (%)		61 (10.3)	0 (0)	61 (5.8)	<0.001
Physical activity status, n (%)					0.100
	Inactive/insufficient	469 (78.8)	339 (74.5)	808 (77.0)
	Sufficient	120 (21.2)	116 (25.5)	242 (23.0)
Body composition
	Body mass index (kg/m²)	22.8±2.5	22.8±2.5	22.8±2.5	0.995
	Body fat (%)	21.8±5.1	32.2±	26.3±7.5	<0.001
	Waist circumference (cm)	83.6±7.8	79.9±8.0	82.0±8.1	<0.001
	Fat mass (g)	13,630±4,516	16,681±4,623	14,970±4,806	<0.001
	Lean mass (g)	47,920±5,390	34,356±3,744	41,964±8,233	<0.001
	Fat-to-lean mass ratio	0.28±0.08	0.49±0.12	0.37±0.14	<0.001
Nutrient intake
	Carbohydrates (g/day)	64.3±33.6	46.3±30.6	56.5±33.5	<0.001
	Fats (g/day)	28.0±28.1	18.8±19.0	24.0±25.0	<0.001
	Proteins (g/day)	336.8±116.0	274.1±102.0	310.0±114.4	<0.001
Metabolic risk factors
	Systolic BP (mmHg)	129.3±17.8	130.0±18.0	129.6±17.9	0.517
	Diastolic BP (mmHg)	77.2±9.9	76.7±10.0	77.0±9.9	0.431
	FBG (mg/dL)	98.2±19.5	95.9±14.6	97.2±17.7	0.051
	TC (mg/dL)	186.3±34.0	204.6±34.5	194.0±35.4	<0.001
	TG (mg/dL)	132.9±86.4	133.2±75.7	133.1±82.0	0.952
	HDLC (mg/dL)	46.0±11.7	47.4±10.0	46.6±11.0	0.061
	ALT (IU/L)	25.1±13.4	22.6±8.3	24.0±11.6	0.001
	AST (IU/L)	20.5±14.0	17.1±8.2	19.0±12.0	<0.001
	Serum vitamin D (ng/mL)	22.1±7.7	19.4±7.7	21.0±7.8	<0.001
NWNO, normal-weight non-obesity; NWO, normal-weight obesity; OB, obesity; FMR, fat-to-lean mass ratio; BP, blood pressure; FBG, fasting blood glucose; TC, total cholesterol; TG, triglycerides; HDLC, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate transaminase. Heavy drinking was defined as seven drinks per day for men and five drinks per day for women.

Table 2 compares body fatness, health behaviors, nutrient intake, and metabolic risk factors according to body composition phenotypes. People with NWO smoked less (p = 0.025), had higher BMI (p < 0.001), percent body fat (PBF) (p < 0.001), WC (p < 0.001), and FMR (p < 0.001), and had higher FBG (p = 0.029), TC (p < 0.001), TG (p = 0.018), and AST (p = 0.006), but lower vitamin D (p = 0.002) than people with NWNO. People with OB consumed more carbohydrates (p = 0.034), and had higher BMI (p < 0.001), PBF (p < 0.001), WC (p < 0.001), and FMR (p < 0.001), FBG (p < 0.001), TC (p = 0.001), and AST (p < 0.001), but lower HDLC (p < 0.001) and vitamin D (p = 0.050) than people with NWNO.

Table 2

Physical and lifestyle characteristics of study participants according to body composition phenotype
			NWNO (n = 860)	NWO (n = 138)	OB (n = 200)	p-value
Health behaviors
	Past/current smokers, n (%)		401 (54.9)	57 (44.2)^a	93 (48.9)	0.046
	Heavy drinking, n (%)		46 (6.3)	5 (3.9)	10 (5.3)	0.523
	Physical activity status, n (%)
		Inactive/insufficient	567 (77.6)	97 (75.2)	144 (75.8)	0.769
		Sufficient	164 (22.4)	32 (24.8)	46 (24.2)
Body fatness
	Body fat (%)		23.7±6.3	33.7±5.7^a	31.1±6.9^ab	< 0.001
	Body mass index (kg/m²)		21.7±1.7	23.2±1.3^a	26.7±1.5^ab	< 0.001
	Waist circumference (cm)		79.1±6.7	84.2±6.2^a	91.4±6.5^ab	< 0.001
	FMR		0.25±0.06	0.39±0.04^a	0.36±0.09^ab	< 0.001
Nutrient Intake
	Proteins (g/day)		55.5±32.7	54.2±26.2	61.5±39.8	0.066
	Fats (g/day)		23.6±26.4	21.6±14.6	27.2±25.0	0.110
	Carbohydrates (g/day)		308.8±115.4	291.9±95.6	324.5±120.8^b	0.043
Metabolic risk factors
	Systolic BP (mmHg)		129.7±18.1	128.6±18.0	130.0±17.2	0.756
	Diastolic BP (mmHg)		76.6±10.0	77.1±9.6	78.3±9.7	0.083
	FBG (mg/dL)		95.9±16.5	99.5±18.0^a	100.1±20.7^ab	0.001
	TC (mg/dL)		190.6±34.7	204.4±32.5^a	200.3±37.9^a	< 0.001
	TG (mg/dL)		128.7±81.3	148.5±96.3^a	139.5±72.7^ab	0.026
	HDLC (mg/dL)		47.3±11.0	46.4±11.4	43.9±10.6^ab	< 0.001
	Serum vitamin D (ng/mL)		21.5±8.0	19.1±7.4^a	20.2±7.1^ab	0.003
	ALT (U/L)		24.1±12.3	23.2±7.3	24.5±11.3	0.638
	AST (U/L)		17.7±8.7	20.2±10.0	23.3±20.2^ab	< 0.001
NWNO: normal weight non-obesity; NWO: normal weight obesity; OB: obesity; FMR, fat-to-lean mass ratio; BP, blood pressure; FBG, fasting blood glucose; TC, total cholesterol; TG, triglycerides; HDLC, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate transaminase. Heavy drinking was defined as seven drinks per day for men and five drinks per day for women. The superscript (^a) represents a significant difference compared to NWNO. The superscript (^b) represents a significant difference compared to NWO.

Table 3 presents the prevalence of individual metabolic risk factors according to body composition phenotypes. In the total group, people with NWO had a higher prevalence of hyperglycemia (p = 0.040) and hypertriglyceridemia (p = 0.023) than people with NWNO. People with OB had a higher prevalence of hyperglycemia (p < 0.001) and low HDLC (p = 0.016) than people with NWNO. Specifically, men with NWO had a higher prevalence of hyperglycemia (p < 0.025), hypertriglyceridemia (p = 0.008), and low HDLC (p < 0.001) than men with NWNO, but a lower prevalence of decreased HDLC (p < 0.001) than men with OB. Women with NWO had a higher prevalence of hyperglycemia (p < 0.001) than women with NWNO, but a lower prevalence of hyperglycemia (p < 0.001) than women with OB. As shown in Fig. 2, there was a significant incremental trend (F = 53.391, df = 2, p < 0.001) in the number of individual metabolic risk factors according to body composition phenotype (from NWNO to NWO and OB).

Table 3

Prevalence of individual metabolic syndrome risk factors according to body composition phenotype
			NWNO (n = 773)	NOW (n = 117)	OB (n = 180)	p-value
Total (n = 1,070)
		Hyperglycemia	185 (23.9)	40 (34.2)^a	70 (38.9)^ab	< 0.001
		Hypertriglyceridemia	197 (25.4)	44 (37.3)^a	61 (33.9)^a	0.027
		Decreased HDLC	338 (43.6)	60 (50.8)^a	99 (55.0)^ab	0.011
		Hypertension	154 (17.9)	26 (18.8)	46 (23.0)	0.246
Men (n = 617)
	Hyperglycemia		121 (26.5)	22 (37.9)^a	39 (37.9)^a	0.022
	Hypertriglyceridemia		110 (24.0)	21 (36.2)^a	38 (36.9)^a	0.015
	Decreased HDLC		131 (28.8)	21 (36.2)^a	51 (50.5)^ab	< 0.001
	Hypertension		89 (17.9)	14 (21.5)	24 (21.6)	0.419
Women (n = 453)
	Hyperglycemia		64 (20.2)	18 (30.5)^a	31 (40.3)^ab	< 0.001
	Hypertriglyceridemia		87 (24.7)	23 (38.3)	23 (29.9)	0.586
	Decreased HDLC		206 (64.8)	39 (65.0)	47 (61.0)	0.457
	Hypertension		65 (17.9)	12 (16.4)	22 (24.7)	0.410
NWNO, normal-weight non-obesity; NWO, normal-weight obesity; OB, obesity. Hyperglycemia, fasting blood glucose > 100 mg/dL or drug treatment for impaired fasting glucose. Hypertriglyceridemia, triglycerides ≥150 mg/dL or drug treatment for high serum triglycerides. Hypertension, systolic blood pressure ≥130 mmHg and/or diastolic blood pressure ≥85 mmHg or drug treatment for hypertension. Decreased high-density lipoprotein cholesterol (HDLC) of < 40 mg/dL for men or < 50 mg/dL for women. The comparison of categorical variables among groups was performed using a Chi-square test. Superscript (a) indicates a significant difference between NWNO and NWO or between NWNO and OB at p < 0.050. Superscript (b) indicates a significant difference between NWO and OB at p < 0.050.

Table 4 represents the results of logistic regression analyses to estimate OR and 95% CI of body composition phenotypes for metabolic syndrome. People with NWO had a higher risk of metabolic syndrome (OR = 2.531, 95% CI = 1.287∼4.980, p = 0.007 in men and OR = 1.970, 95% CI = 1.020∼3.850, p = 0.043 in women) compared to their counterparts with NWNO (OR = 1). The increased OR of NWO men for metabolic syndrome remained statistically significant (OR = 2.233, 95% CI = 1.029∼4.845, p = 0.042) even after adjustments for demographics, health behaviors, and nutrient intake parameters, but it was no longer significant when additionally adjusted for FMR. The increased OR of NWO women for metabolic syndrome was no longer significant when adjusted for demographics, health behaviors, and nutrient intake parameters. Similarly, people with OB had a higher risk of metabolic syndrome (OR = 2.803, 95% CI = 2.803∼7.716, p < 0.001 in men and OR = 4.650, 95% CI = 2.697∼8.071, p < 0.001 in women) compared to people with NWNO (OR + 1). The increased OR of OB men for metabolic syndrome remained significant (OR = 2.402, 95% CI = 1.153∼5.005, p = 0.019) even after adjustments for all the covariates including FMR. The increased OR of OB women for metabolic syndrome remained statistically significant (OR = 5.003, 95% CI = 2.715∼7.716, p < 0.001) even after adjustments for demographics, health behaviors, and nutrient intake parameters, but it was no longer when additionally adjusted for FMR.

Table 4. Odds ratios and 95% confidence intervals of metabolic syndrome according to body composition phenotype

		NWNO	NWO	p-value	OB	p-value
Total
	Model 1	1	2.290 (1.430 ~ 3.667)	<0.001	4.636 (3.198 ~ 6.720)	<0.001
	Model 2	1	2.009 (1.184 ~ 3.406)	0.010	5.143 (3.415 ~ 7.747)	<0.001
	Model 3	1	0.797 (0.409 ~ 1.552)	0.504	2.436 (1.444 ~ 4.109)	<0.001
Men
	Model 1	1	2.531 (1.287 ~ 4.980)	0.007	4.651 (2.803 ~ 7.716)	<0.001
	Model 2	1	2.233 (1.029 ~ 4.845)	0.042	5.518 (3.125 ~ 9.742)	<0.001
	Model 3	1	0.897 (0.357 ~ 2.256)	0.817	2.402 (1.153 ~ 5.005)	0.019
Women
	Model 1	1	1.970 (1.020 ~ 3.805)	0.043	4.650 (2.697 ~ 8.071)	<0.001
	Model 2	1	1.897 (0.905 ~ 3.980)	0.090	5.003 (2.715 ~ 7.716)	<0.001
	Model 3	1	0.621 (0.232 ~ 1.664)	0.344	2.036 (0.915 ~ 4.530)	0.082
NWNO: normal weight non-obesity; NWO: normal weight obesity; OB: obesity. Model 1: unadjusted. Model 2 adjusted for age, sex, education, income, smoking, heavy drinking, physical activity, serum vitamin D, and dietary intake of proteins, fats, and carbohydrates. Model 3: adjusted for model 2 plus total body fat-to-muscle ratio.

This cross-sectional study examined the association between NWO and metabolic syndrome in a representative sample of Korean older adults and showed that NWO is associated with a higher prevalence of several metabolic risk factors (hyperglycemia, hypertriglyceridemia, and low HDLC) in men and a higher prevalence of hyperglycemia in women, as well as an increased risk of metabolic syndrome in both genders. The current findings of the study suggest that the impact of NWO on metabolic syndrome risk is substantially influenced by its high FMR. Taken together, the current findings collectively suggest that NWO is a novel risk factor to be considered in determining the risk for obesity-related metabolic complications among geriatric populations.

In support of the current findings of the study, evidence is accumulating to support NWO as an independent risk factor of metabolic syndrome in different populations. By analyzing the data obtained from the National Health and Nutrition Examination Survey (NHANES) III (n = 6,171), Romero-Corral et al. [16] showed that NWO correlated with a higher prevalence of dyslipidemia, hypertension, and cardiovascular disease as well as an increased risk of metabolic syndrome in American adults. By conducting a secondary analysis of the data obtained from the NHANES III, Batsis et al. [10] also showed that NWO correlated with an increased risk of physical limitations in older American women, implying the importance of physical disability as a contributor to the development of obesity and associated metabolic complications. Similarly, NWO was significantly associated with an increased risk of cardiometabolic disorders and hyperglycemia in Iranian sedentary women aged 20–35 years [17] or an increased risk of hypertension, dyslipidemia, and diabetes in Japanese middle-aged adults who underwent health examinations [18]. By conducting a birth cohort study of 787 subjects born in 1978/1979 years, Coelho et al. [19] showed that individuals with NWO had a higher incidence rate of metabolic syndrome at the ages of 37 to 39 years. Mohammadian Khonsari et al. [8] conducted a meta-analysis involving177,792 participants aged 13–75 years drawn from 25 previously published articles and showed that NWO was significantly associated with an increased risk for dyslipidemia, hyperglycemia, diabetes, and hypertension, as well as metabolic syndrome. Furthermore, NWO correlates with poor clinical outcomes in non-diabetic patients with chronic kidney disease [20], poor cardiometabolic profiles in clinically stable older patients with chronic obstructive pulmonary disease [21], as well as increased risk of cancer morbidity [22] and coronary artery disease mortality [23]. Taken together, the findings from the current and previous studies indicate that NWO is a preclinical stage of metabolic disorders that should be monitored for early intervention.

Several explanations can be given for the link between NWO and metabolic syndrome. First, the NWO phenotype is characterized by high-fat accumulation along with low skeletal muscle mass, which may contribute to a clustering of metabolic risk factors, such as dyslipidemia, impaired glucose metabolism, insulin resistance, decreased serum vitamin D, increased inflammatory cytokines, and oxidative stress, as well as increased susceptibility of metabolic syndrome [24, 25]. Second, sedentary unhealthy lifestyles, such as physical inactivity, physical disability, poor physical fitness, poor diet, smoking, and heavy alcohol consumption, may contribute to NWO phenotype and associated metabolic complications [26–28]. Unlike previous studies, however, our findings showed that people with NWO smoked less with similar physical activity and nutrient intakes of proteins, fats, and carbohydrates compared to their counterparts with NWNO. The impact of unhealthy lifestyle factors on NWO phenotype among older adults remains to be confirmed in a well-designed cohort study. Third, circulating myokines and hormone abnormalities are linked to the NWO phenotype, and these factors work together to promote the onset of the metabolic syndrome [29–32].

This study has some limitations. First, the cross-sectional nature of the study involving older adults only limits us to provide any cause-and-effect explanation for the current findings. A cohort study will be necessary to examine any causal relationship between NWO and metabolic syndrome in a large sample size including both young- and middle-aged adults. Second, the direction of the relationship between lifestyle choices and NWO was unexpectedly the opposite of that found in previous studies. As a result, we cannot rule out the possibility that unexplored covariates may influence the relationship between NWO and metabolic syndrome.

Despite these limitations, this is the first study to investigate the impact of NWO on metabolic syndrome in older Korean adults with available DXA-based body composition data. Our participants consisted of older individuals who were healthy and free of chronic diseases. As a result, the study findings are clinically relevant to strategies for reducing the global increase in morbidity and mortality associated with obesity.

In conclusion, this cross-sectional study examined the association between NWO and metabolic syndrome in older Korean adults and showed that NWO was associated with the clustering of individual metabolic risk factors as well as an increased risk of metabolic syndrome. Given the limitation of BMI as a diagnostic tool for overweight and obese patients, the current findings of the study suggest that people with NWO should be monitored in clinical settings for early intervention.

BMI

body mass index

NWO

Normal weight obesity

NWNO

normal weight non-obesity

obesity

KNHNES

Korea National Health and Nutrition Examination Survey

DXA

dual x-ray absorptiometry

fat mass

lean mass

FMR

fat-to-lean mass

waist circumference

FBG

fasting blood glucose

triglycerides

total cholesterol

HDLC

high-density lipoprotein cholesterol

SBP

systolic blood pressure

DBP

diastolic blood pressure

odds ratio

confidence interval.

Ethics approval and consent to participate

The Institutional Review Board of the Korean Institute for Health and Social Affairs reviewed and approved this study (approval no. 2008-04EXP-01-C, 2009-01CON-03-2C, 2010-02CON-21-C, and 2011-02CON-06-C). Informed consent was obtained from all participants. This study protocol was approved by the ethics committee of the Institutional Review Board and was performed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.

Competing interests

The corresponding author (Hyunsik Kang) declares that he currently serves as an editorial board member of BMC Geriatrics. All the other authors (Jeonghyeon Kim and Seamon Kang) have no conflict of interest.

Funding

Not applicable.

Author’s contributions

Conceptualization, JK, SK, and HK; methodology, JK, SK, and HK; validation, JK, SK, and HK; investigation, JK, SK, and HK; data curation, JK, SK, and HK; writing-original draft preparation, JK, SK and HK; supervision, JK, SK, and HK; and project administration, JK, SK, and HK. All authors have read and approved the final manuscript.

Acknowledgments

I would like to thank the Korea Disease Control and Prevention Agency (KDCA) for approving my application to access the data.

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Competing interest reported. The corresponding author (Hyunsik Kang) declares that he currently serves as an editorial board member of BMC Geriatrics. All the other authors (Jeonghyeon Kim and Seamon Kang) have no conflict of interest.

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The Association between Normal Weight Obesity and Metabolic Syndrome in Older Korean Adults

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