Kim et al.
Journal of Physiological Anthropology(2024) 43:8
https://doi.org/10.1186/s40101-024-00355-6

Journal of
Physiological Anthropology

ORIGINAL ARTICLE

Open Access

Triglyceride‑glucose index is capableof identifying metabolically obese,
normal‑weight older individuals
Bokun Kim1,2, Keisuke Taniguchi2,3, Tomonori Isobe4* and Sechang Oh2,3,4*   

Abstract
Background The concept of metabolically obese, normal weight (MONW) has emerged to describe individuals
with a normal body mass index (BMI) who are at a relatively high risk of chronic diseases. However, BMI itself is a suboptimal index for the assessment of the health risks associated with visceral fat. The triglyceride-glucose (TyG) index
is considered to be a reliable and cost-effective marker of insulin resistance. Therefore, in the present study, we aimed
to determine the TyG index cut-off values that could be used to define MONW in older people and to determine
the usefulness of these values for the prediction of chronic diseases.
Methods A total of 4,721 participants in the Korea National Health and Nutritional Examination Survey whowere ≥ 60 years of age and did not have underweight or obesity were included. MONW was defined using the criteria for metabolic syndrome (MS), and the TyG index was calculated on the basis of the fasting plasma triglycerideand glucose concentrations. Chronic diseases, including T2DM, hypertension, and non-alcoholic fatty liver disease(NAFLD), were diagnosed.
Results The prevalence of MS increased from the lowest to the highest TyG index tertile. The cut-off values of the TyG
index for MONW were calculated as 8.88 and 8.80 for males and females, respectively. MONW, defined using thesecut-off values, was associated with high odds ratios for NAFLD, T2DM, and hypertension in both males and females.
Conclusions The TyG index cut-off values calculated in the present study can be used to discriminate individuals
with MONW from other older individuals without obesity and to predict the risk of chronic diseases. These findings
show that the TyG index is an effective and cost-efficient method of assessing the risk of chronic diseases in people
with MONW.
Keywords Chronic disease, Geriatric population, Metabolically obese but normal weight, Obesity, Triglycerideglucose index*Correspondence:
Tomonori Isobe
tiso@md.tsukuba.ac.jp
Sechang Oh
o-sechan@u.a-ru.ac.jp
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Kim et al. Journal of Physiological Anthropology(2024) 43:8

Background
The rapid increase in the prevalence of obesity worldwide
is a major contributor to the rising prevalence of several
chronic non-communicable diseases, including type 2
diabetes mellitus (T2DM), hypertension, and non-alcoholic fatty liver disease (NAFLD), and therefore contributes to an enormous socioeconomic burden worldwide[1–3]. For decades, body mass index (BMI) has been
widely used to quantify the level of obesity [4–6]. However, the limitations of its use have become increasingly
apparent. BMI is a measure of body weight relative toheight, but it cannot discriminate visceral fat, which predisposes toward several chronic diseases, and therefore a
BMI-based assessment of obesity cannot fully explain theprevalence of such conditions [4–6].
Another group of people, “metabolically obese, normal
weight (MONW)” individuals, have been shown to be ofsignificant public health concern. MONW individuals arecharacterized by insulin resistance and are highly susceptible to chronic diseases, despite having BMIs < 25 kg/m2[7–9]. In particular, there are significant numbers of older
Asian people who do not have obesity and Asian MONW
adults with T2DM, cardiovascular disease (CVD), or
other conditions [9, 10]. The incidences of CVD and allcause mortality for these people are even higher thanthose for their metabolically healthy counterparts who
have obesity [9]. These findings highlight the need for an
accurate marker of MONW, especially for older Asianpeople who do not have obesity, in order to prevent ormanage the progression of diseases involving metabolic
abnormalities.
In recent years, the triglyceride-glucose index (TyG
index) has been shown to be a valid and reliable index
for the assessment of insulin resistance; it was found tobe significantly associated with hyperinsulinemic-euglycemic clamp data collected in Korea, Mexico, and Brazil[11–13] and it outperforms the homeostasis model ofassessment-insulin resistance (HOMA-IR) for the identification of several chronic diseases associated withinsulin resistance [14, 15]. In addition, the TyG index isa more cost-effective way of assessing insulin resistancethan the two other methods named above because theplasma triglyceride and glucose concentrations are typically measured during health check-ups in many countries, such as Korea and Japan. Therefore, the authors
hypothesized that the TyG index could be used to discriminate individuals with MONW from others in theolder Korean population.
To test this hypothesis, we conducted a populationbased cross-sectional study to determine the cut-off values of the TyG index that could be used to discriminate
individuals who are MONW among older Korean people. In addition, we aimed to determine the usefulness ofPage 2 of 9

these TyG index cut-off values for MONW for the prediction of the risks of T2DM, hypertension, and NAFLD
in this population.

Materials and methods
Study design and participants

For this study, we used a database containing data regarding the general health and nutritional status and lifestyleof South Koreans participating in the Korea National
Health and Nutritional Examination Survey (KNHANES)
that were collected between 2014 and 2018. Establishedin 1984, KNHANES is an ongoing national surveillancesystem for the assessment of the health and nutritional
status of inhabitants of South Korea [16]. A total of
4,721 participants (1,787 males and 2,934 females) wereselected from among all the participants ≥ 60 years ofage who were not underweight and did not have obesity(BMI ≥ 18.5 and < 25.0 kg/m2) in KNHANES 2014–2018(Fig. 1). Each participant provided written informed consent, and the study was conducted in accordance with theprinciples of the Declaration of Helsinki and approved by
the Institutional Review Board of the University of Silla.
(approval number 1041449–202203-HR-001).
Measurements madeThe height of the participants was measured to thenearest 0.1 cm while they were standing and not wearing shoes, and their body weight was measured to thenearest 0.1 kg using digital electronic scales while they
were wearing light clothing. BMI was calculated as bodyweight divided by height squared (kg/m2). Waist circumference (WC) was measured to the nearest 0.1 cm using
a glass fiberoptic tape measure. The blood pressure of
the participants was measured manually three times ina mobile health monitoring vehicle and the mean values
were used in the analyses. Blood samples were taken in
the morning after an overnight fast of > 8 h. The circulating concentrations of glucose, triglycerides, and highdensity lipoprotein (HDL)-cholesterol were determinedusing enzymatic or homogeneous enzymatic colorimetricmethods, using a Hitachi 7600–210 automatic analyzer(Tokyo, Japan). HbA1c was determined by high-performance liquid chromatography using a Tosoh G8 (Tosoh,Tokyo, Japan).
Assessment of MONW, MHNW, TyG index, and chronic
diseases

MONW and metabolically healthy, normal-weight(MHNW) participants were defined as those withand without metabolic syndrome (MS), respectively,
because MS is a constellation of metabolic abnormalities used to define MONW in previous studies. MS was diagnosed using the modified National

Kim et al. Journal of Physiological Anthropology(2024) 43:8

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Fig. 1 Flow diagram describing the classification of the data for the participants

Cholesterol Education Project Adult Treatment Panel(NCEP ATP III) criteria [17], which require the presence of three or more of the following components: (1)WC ≥ 90 cm in males and ≥ 80 cm in females; (2) systolic blood pressure (SBP) ≥ 130 mmHg, diastolic bloodpressure (DBP) ≥ 85 mmHg, or use of anti-hypertensive
medication; (3) fasting plasma triglyceride concentration > 150 mg/dL or the use of lipid-lowering medication;(4) fasting HDL-cholesterol concentration < 40 mg/dL
in males and < 50 mg/dL in females, or the use of lipidlowering medication; and (5) fasting plasma glucose
concentration ≥ 100 mg/dL or the use of anti-hyperglycemic medication. The TyG index was calculated as ln[triglycerides concentration (mg/dL) × fasting plasma
glucose concentration (mg/dL)]/2 [12, 16]. NAFLD was
diagnosed using a hepatic steatosis index (HSI) > 36,
according to the previously validated prediction model,
and calculated as 8 × plasma alanine aminotransferase(ALT) activity [U/L]/ plasma aspartate aminotransferase(AST) activity [U/L] + BMI [+ 2 if diabetes mellitus ispresent; + 2 if female] [18, 19]. T2DM was diagnosed

using an HbA1c ≥ 6.5% or the use of anti-hyperglycemic
medication. The definition of hypertension used was that
described above.
Statistical analysis

Data are presented as mean ± standard deviation (SD).
The independent t-test or the Mann–Whitney U test wasused to compare datasets in males and females. One-way
ANOVA was used to compare the sex-specific characteristics of the three groups, and the Bonferroni post-hoc
test was used when ANOVA indicated an overall significant difference (P < 0.05). The Mann–Whitney U test wasused to analyze group differences in the presence of a
non-normal data distribution (P < 0.05). The JonckheereTerpstra test was used to detect trends in data across thethree groups (two-tailed, P < 0.05). The sex-specific distributions and trends of MS and its components, according to TyG Index tertile, were explored using chi-squareand linear-by-linear association. The optimal cut-off values of the TyG index for the identification of MONW in
males and females were derived from receiver operating

Kim et al. Journal of Physiological Anthropology(2024) 43:8

characteristic (ROC) curves. The area under the ROC
curve (AUC), sensitivity, and specificity of each were
calculated. This analysis was performed using MedCalc
for Windows ver. 9.1.0.1 (MedCalc® Corp, Mariakerke
Ostend, Belgium). P < 0.05 was accepted as indicating statistical significance. Logistic regression was used to evaluate the sex-specific relationships between MONW andchronic diseases. The fully adjusted model was adjusted
for the potential confounders of age, educational level,
household income, smoking, alcohol consumption, handstrength, moderate-to-vigorous physical activity, anddiet, which are known or suspected to affect the relationships with chronic diseases. Statistical analyses were
performed using SPSS software, ver. 26.0 (IBM, Inc.,
Armonk, NY, USA), with the exception of the ROC curve
analysis.

Results
Table 1 shows the characteristics of the participants. The
mean age and BMI of the participants as a whole were64.4 (SD 12.0) years and 22.7 (1.4) kg/m2, respectively,
and the mean age and BMI of the male participants weresignificantly higher than those of the females (P < 0.001).
The mean TyG index was 8.6 for both males and females,with standard deviations of 0.5 and 0.6, respectively,
and no significant difference between the two groups.

Page 4 of 9

Additional information regarding the participants can befound in the Supplementary material (Table S1).
Table 2 shows the sex-specific characteristics andtrends in the anthropometric and MS components,
according to tertile of TyG index. In both male andfemale participants, we observed significant increasing
trends in SBP and DBP from the lowest to the highest
tertile, in contrast to the decreasing trend observed in
the HDL-cholesterol concentration (P < 0.001 for all). In
addition, post-hoc testing showed significant differencesamong the three tertiles with respect to SBP, DBP, andHDL-cholesterol concentrations. Specifically, there wereincreases in SBP and DBP, and a decrease in HDL-cholesterol, across the lowest, middle, and highest tertiles.
Table S2 shows the results of the analyses of the otherparameters and several covariates in males and females.
The sex-specific prevalence of MS and its components, according to TyG Index tertile, is shown in
Fig. 2. In both males and females, the prevalence of MS
increased significantly from the lowest to the highest
tertile (P < 0.001 for both). The percentage of participants with high triglyceride and glucose concentrations,high blood pressure or anti-hypertensive medicationuse, and low HDL-cholesterol concentration or lipidlowering medication use showed significant increases
from the lowest to the highest tertile (P < 0.001 for all).

Table 1 Characteristics of the participants
Overall (n = 4721)

Male (n = 1787)

Female (n = 2934)

P Value

Age, ­yeara

64.4 ± 12.0

69.9 ± 6.3

61.1 ± 13.3

< 0.001

TyG Index

8.6 ± 0.6

8.6 ± 0.5

8.6 ± 0.6

= 0.201

Height, ­cma

159.3 ± 8.1

166.0 ± 5.8

155.2 ± 6.4< 0.001

Body weight, ­kga

57.7 ± 6.8

62.9 ± 5.8

54.5 ± 5.3< 0.001

Body mass index, kg/m2a22.7 ± 1.4

22.8 ± 1.3

22.6 ± 1.4< 0.001

Waist circumference, ­cma

80.6 ± 6.5

84.3 ± 5.6

78.3 ± 6.0< 0.001

SBP, mm Hg

119.7 ± 16.7

119.9 ± 16.6

119.6 ± 16.8

= 0.450

DBP, mm Hg

75.4 ± 10.0

75.6 ± 9.975.3 ± 10.0

= 0.329

FPG, mg/dL

99.9 ± 19.2

99.9 ± 19.0

99.9 ± 19.3

= 0.965

HDLC, mg/dL

51.1 ± 12.6

51.0 ± 12.3

51.1 ± 12.8

= 0.747

Triglyceride, mg/dL

124.3 ± 67.4

125.3 ± 66.1

123.8 ± 68.2

= 0.453

Medication
Antihypertensive medication, n (%)

1611 (34.1)

725 (40.6)

886 (30.2)< 0.001

Lipid-lowering medication, n (%)

832 (17.6)

267 (14.9)

565 (19.3)< 0.001

Antihyperglycemic medication, n (%)

664 (14.1)

328 (18.4)

336 (11.5)< 0.001

Chronic disease prevalence
Non-alcoholic fatty liver disease, n (%)

415 (8.8)

98 (5.5)317 (10.8)< 0.001

T2DM, n (%)

1053 (22.3)

473 (26.5)

580 (19.8)< 0.001

Hypertension, n (%)

2578 (54.6)

1078 (60.3)

1500 (51.1)< 0.001

Values are mean ± SD
Abbreviations: DBP diastolic blood pressure, FPG fasting plasma glucose, HDLC high-density lipoprotein-cholesterol, SBP systolic blood pressure, TyG triglycerideglucose, T2DM type 2 diabetes mellitusa

The Mann–Whitney U-test was used to assess the difference between groups

Kim et al. Journal of Physiological Anthropology(2024) 43:8

Page 5 of 9

Table 2 Sex-specific characteristics and trends in anthropometric and metabolic syndrome components, according to TyG index
tertileL (7.86 ± 0.22)
Male (n = 1787)

M (8.77 ± 0.12)

H (9.32 ± 0.25)

Post-hoc

Trendbn = 604n = 590n = 593

Age, year

69.8 ± 6.2

69.8 ± 6.3

70.0 ± 6.3

ns= 0.564

Height, cm

166.0 ± 5.8

165.9 ± 5.8

165.9 ± 5.7

ns= 0.660= 0.756

Body weight, kg

63.1 ± 5.7

62.8 ± 5.9

62.9 ± 5.9

ns

Body mass index, kg/m2

22.9 ± 1.3

22.8 ± 1.3

22.8 ± 1.4

ns= 0.553

Waist circumference, cm

84.5 ± 5.6

84.2 ± 5.3

84.3 ± 5.8

ns= 0.625

Triglyceride, mg/dLa

67.0 ± 15.1

111.8 ± 20.9

198.0 ± 59.7

L<M<H

< 0.001

HDLC, mg/dLa

57.3 ± 12.6

51.1 ± 10.9

44.6 ± 9.6

L>M>H< 0.001

FPG, mg/dLa

92.0 ± 9.999.3 ± 15.8

108.6 ± 24.5

L<M<H

< 0.001

SBP, mm Hg

115.4 ± 16.1

119.9 ± 16.4

124.5 ± 15.9

L<M<H

< 0.001

L<M<H< 0.001

DBP, mm H­ ga

73.6 ± 9.0

75.3 ± 9.7

77.9 ± 10.6

Female (n = 2934)

n = 973n = 988n = 973

Age, ­yeara

60.4 ± 13.5

61.4 ± 12.9

61.5 ± 13.6

ns

= 0.138

Height, cm

155.3 ± 6.5

155.4 ± 6.5

155.0 ± 6.3

ns= 0.256= 0.399

Body weight, kg

54.6 ± 5.2

54.6 ± 5.3

54.4 ± 5.4

ns

Body mass index, kg/m2a

22.6 ± 1.4

22.6 ± 1.3

22.6 ± 1.4

ns= 0.802

Waist circumference, cm

78.2 ± 6.0

78.5 ± 6.0

78.1 ± 6.0

ns= 0.581

Triglyceride, mg/dLa

63.8 ± 15.0

110.2 ± 19.4

197.5 ± 64.7

L<M<H

< 0.001

HDLC, mg/dLa

58.0 ± 13.0

51.1 ± 11.4

44.4 ± 9.9

L>M>H< 0.001

FPG, mg/dLa

91.8 ± 8.9

98.5 ± 14.2

109.4 ± 26.1

L<M<H

< 0.001

SBP, mm Hg

113.6 ± 15.9

120.6 ± 16.6

124.6 ± 16.0

L<M<H

< 0.001

DBP, mm H­ ga

72.8 ± 9.3

75.1 ± 9.8

77.9 ± 10.2

L<M<H

< 0.001

Values are mean ± SD
Abbreviations: DBP diastolic blood pressure, FPG fasting plasma glucose, H highest tertile, HDLC high-density lipoprotein-cholesterol, L lowest tertile, M middle tertile,ns not significant, SBP systolic blood pressure, TyG triglyceride-glucosea

The Mann–Whitney U-test was used to assess the difference between groupsb

The Jonckheere-Terpstra test was used to assess the trend across the three groups

Fig. 2 Sex-specific prevalence of metabolic syndrome and its components, according to tertile of TyG index. The sex-specific trend of MS
prevalence and its components, according to TyG Index tertile, were analyzed using Jonckheere-Terpstra test. ***P < 0.001. Abbreviations: H,
highest tertile; H_BP, hypertension or the use of antihypertensive medication; H_BG, hyperglycemia or the use of antihyperglycemic medication;
H_WS, large waist circumference; H_TG, hypertriglyceridemia or the use of lipid-lowering medication; L, lowest tertile; L_HDL, low high-density
lipoprotein-cholesterol concentration or the use of lipid-lowering medication; M, middle tertile; MS, metabolic syndrome; ns, not significant

Kim et al. Journal of Physiological Anthropology(2024) 43:8

Figure 3 shows the ROC curves for the TyG index inmales and females with MS. The highest sensitivity andspecificity values were achieved with TyG indexes 8.88
(AUC: 0.727, sensitivity: 58.02%, and specificity: 81.93%)
and 8.80 (AUC: 0.740, sensitivity: 56.34%, and specificity
83.09%) for males and females, respectively (P < 0.001 forboth).
The sex-specific odds ratios for the relationships
between MONW and chronic diseases are shown in
Table 3. For males, in the unadjusted model, and compared with those who were MHNW, those who were
MONW had odds ratios of 3.439 (95% confidence interval (CI): 2.344–5.044), 1.810 (1.454–2.254), and 1.613(1.307–1.990) for NAFLD, T2DM, and hypertension,respectively. In the fully adjusted model, compared withparticipants who were MHNW, those who were MONW
had odds ratios of 3.260 (2.203–4.826), 1.887 (1.508–
2.361), and 1.591 (1.285–1.970) for NAFLD, T2DM, andhypertension, respectively. For females, in the unadjusted
model, compared with participants who were MHNW,
those who were MONW had odds ratios of 3.452(95% CI: 2.770–4.301), 2.203 (1.831–2.649), and 1.812(1.553–2.115) for NAFLD, T2DM, and hypertension,respectively. In the fully adjusted model, compared withparticipants who were MHNW, those who were MONW
had odds ratios of 3.184 (2.532–4.004), 2.271 (1.871–
2.758), and 1.845 (1.556–2.187) for NAFLD, T2DM, andhypertension, respectively.

Page 6 of 9

Table 3 Sex-specific odds ratios for the relationships between
the TyG index cut-off values and chronic diseases
Unadjusted model

Fully adjusted model  NAFLD

3.439 (2.344 - 5.044)***3.260 (2.203 - 4.826)***  T2DM***

1.887 (1.508 - 2.361)******1.613 (1.307 - 1.990)

1.591 (1.285 - 1.970)***referencereference

Male
MONW (n = 562)  Hypertension
MHNW (n = 1225)

1.810 (1.454 - 2.254)

FemaleMONW (n = 1025)  NAFLD

3.452 (2.770 - 4.301)***

3.184 (2.532 - 4.004)***  T2DM***

2.271 (1.871 - 2.758)******1.812 (1.553 - 2.115)

1.845 (1.556 - 2.187)***

referencereference  Hypertension
MHNW (n = 1909)

2.203 (1.831 - 2.649)

Values are odds ratio (95% confidence interval). The fully adjusted model
was adjusted for age, educational level, household income, smoking, alcohol
consumption, hand-grip strength, moderate-to-vigorous physical activity, andnutrition
Abbreviations: MHNW metabolically healthy, normal weight, MONW
metabolically obese, normal weight, NAFLD non-alcoholic fatty liver disease*P < 0.05**

P < 0.01***

P < 0.001

Fig. 3 ROC curves for the TyG index in males and females with metabolic syndrome. The optimal cut-off values of the TyG index
for the identification of MONW in males (A) and females (B) were derived from receiver operating characteristic (ROC) curves. Dotted line: reference;solid red line: AUC, indicating the accuracy of the TyG index for the identification of MS; cut-off value: the value of TyG index that predicts MS;
sensitivity: the probability of individuals who had MS to be predicted to have MS; specificity: the probability of individuals who did not have MS
to be predicted not to have MS. Abbreviations: AUC, area under the curve; MS, metabolic syndrome; TyG, triglyceride-glucose index

Kim et al. Journal of Physiological Anthropology(2024) 43:8

Discussion
The present population-based cross-sectional study was
the first to determine sex- and age-specific TyG index
cut-off values that are capable of discriminating individuals who are MONW. Moreover, the usefulness ofthese values for predicting the risk of chronic diseases inolder Korean adults was explored. The following results
were obtained. First, the cut-off values of the TyG index
for the discrimination of individuals who are MONW
were calculated as 8.88 and 8.80 for males and females,
respectively. Second, males who were MONW were3.260, 1.887, and 1.591 times more likely to have NAFLD,
T2DM, and hypertension; and females who were MONW
were 3.184, 2.271, and 1.845 times more likely to havethese chronic diseases. These results suggest that the
TyG index cut-off values calculated in the present study
are capable of discriminating individuals with MONW inolder people without underweight or obesity and predicting the risk of chronic diseases.
The concept of MONW was first proposed by Ruderman et al. in 1981 [20]. Subsequently, its negative consequences, such as carotid atherosclerosis and arterial
stiffness, high levels of subclinical vascular inflammation and oxidative stress, and an undesirable adipokine
profile, have been reported numerous times [21–24].
However, a standardized definition of MONW has yet
to be established. In previous studies, MONW has been
defined as follows: having MS, being in the highest quartile of or within a specific range of HOMA-IR values,
having excess visceral or total adiposity, having a low
glucose disposal rate, or having several cardiovascularrisk factors [25]. The conditions used in previous studies
are all underpinned by insulin resistance; therefore, weinvestigated the utility of the TyG index as an indicatorof MONW.
In the present study, trend testing showed significant
increasing trends in the prevalence of MS, high triglyceride concentration, high glucose concentration, and bloodpressure; and a significant decreasing trend in HDL-cholesterol from the lowest to the highest tertile (P < 0.001
for all) in both males and females. However, no trend
in WC was identified (Fig. 2). Similarly, trend testing
showed significant increasing or decreasing trends in triglyceride, glucose, SBP, DBP, and HDL-cholesterol fromthe lowest to the highest tertile in both sexes (P < 0.001
for all), but not for WC (Table 2). These results demonstrate the close relationship between the TyG index and
MONW in older people.
Analysis of the ROC curves showed that the optimal
TyG index cut-off values for the prediction of MONW
are 8.88 (AUC: 0.727, sensitivity: 58.02%, specificity: 81.93%) and 8.80 (AUC: 0.740, sensitivity: 56.34%,
specificity: 83.09%) for males and females, respectively.

Page 7 of 9

Prior to the present study, Shin et al., who studied 4,415
Korean males and females aged ≥ 20 years, calculateda TyG index cut-off value of 8.81 (AUC: 0.894, sensitivity: 86.7%, specificity: 80.1%) for discriminating bothmales and females with insulin resistance [26]. In addition, Lee et al. showed that middle-aged Korean males
and females with a TyG index ≥ 8.8 (AUC: 0.751) are athigher risk of T2DM than those with values below thiscut-off, suggesting that it may be relevant to the development of insulin resistance [27]. Because these previous
studies did not provide sex- or age-specific cut-off values
for the TyG index, direct comparison of the present andprevious studies is not possible. However, considering
the cut-off values obtained in the present and previous
studies, a TyG index of approximately 8.80–8.90 appears
suitable for identifying insulin resistance in the Korean
population.
Males who were MONW, according to the TyG index
cut-off value of 8.88, were 3.260, 1.887, and 1.591 times
more likely to have NAFLD, T2DM, and hypertension,respectively; and females who were MONW, according to the TyG index cut-off value of 8.80, were 3.184,
2.271, and 1.845 times more likely to have these chronic
diseases, respectively, than healthy individuals who were
not obese. These findings can be explained using those ofprevious studies. Regarding NAFLD, Feng et al. and Stefan et al. showed that individuals who are MONW store
more fat in their liver and are therefore more likely tohave NAFLD [7, 8]. Regarding T2DM, Conus et al. andLacobini et al. showed that individuals who are MONW
are more likely to develop T2DM, because insulin resistance and low insulin sensitivity are features of MONW
[28, 29]. Regarding hypertension, Kim et al. and Oliveros
et al. reported that an abnormal lipid profile, and especially high triglyceride and low HDL-cholesterol concentrations, are common in individuals who are MONW [30,31]. Because an abnormal lipid profile negatively affects
blood pressure, individuals who are MONW are more
susceptible to hypertension. Taking all of these findings
together, individuals who are MONW according to the
TyG index cut-off value generated in the present study
are more susceptible to chronic diseases. This indicatesthe usefulness of the TyG index cut-off value generated in
the present study.
The present study had both strengths and limitations.
Important potential covariates were adjusted for, including demographic parameters and lifestyle factors that
may influence the relationship between MONW andchronic diseases. However, because of the cross-sectional
nature of the study design, the conclusions drawn regarding the predictive ability of MONW discriminated using
the TyG index cut-off for chronic disease remain tentative. Therefore, longitudinal studies should be performed

Kim et al. Journal of Physiological Anthropology(2024) 43:8

to validate the conclusion of the present study. In addition, because all the participants were older Korean
adults, whether the results of the present study can beextrapolated to individuals of other ethnicities or in othernations is unclear. Therefore, further research should be
conducted in participants of different ethnicities to assessthe utility of the TyG index cut-off value and the relationship between MONW and chronic diseases.

Conclusions
The TyG index cut-off values generated in the present
study are capable of discriminating individuals who are
MONW from other older individuals without underweight or obesity, and predicting the risk of chronic diseases. The findings of this study confirm that the TyG
index is a useful and cost-effective method of assessing the risk of chronic diseases in individuals who are
MONW.
Abbreviations
ALT	Alanine aminotransferase
AST	Aspartate aminotransferase
AUC​	Area under the ROC curve
BMI	Body mass index
CVD	Cardiovascular disease
DBP	Diastolic blood pressure
HDL	High-density lipoprotein
HOMA-IR	Homeostasis model of assessment-insulin resistance
KNHANES	Korea National Health and Nutritional Examination Survey
MHNW	Metabolically healthy, normal-weight
MONW	Metabolically obese, normal weightMS	Metabolic syndrome
NAFLD	Non-alcoholic fatty liver disease
ROC	Receiver operating characteristic
SBP	Systolic blood pressure
SD	Standard deviation
TyG	Index triglyceride-glucose index
WC	Waist circumference

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​024-​00355-6.
Additional file 1: Table S1. Characteristics of study subjects. Table S2.
Sex-specific characteristics of biochemical parameters and lifestyle andtrends by TyG Index tertiles.
Acknowledgements
In the present study, data provided by the Korean National Health and Nutrition Examination Survey 2014–2018, conducted by the Korea Centers for
Disease Control and Prevention, were analyzed. We thank Mark Cleasby, PhD
for editing a draft of this manuscript.
Authors’ contributions
Kim B and Oh S conceived and designed the experimalests; Kim B, Oh S, andTaniguchi K performed the experimalests; Kim B, Isobe T, and Taniguchi K analyzed and interpreted the data; Kim B, Oh S, Taniguchi K, and Tanaka K wrote
the paper. All authors read and approved the final version of the manuscript.

Page 8 of 9

Funding
This work was supported in part by a National Research Foundation of Korea
grant, funded by the Ministry of Education (No. NRF-2022R1l1A1A01063664),
and a Grant-in-Aid for Scientific Research, Japan (No. 23H03273).

Declarations
Ethics approval and consent to participate
This work was performed in accordance with the Code of Ethics of the World
Medical Association (Declaration of Helsinki) for experiments involving
humans, and informed consent was obtained from all the participants. The
study protocol was approved by the Institutional Review Board of Silla University (approval number 1041449-202203-HR-001).
Consent for publication
This is not applicable because the article does not include data derived fromindividuals.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Future Convergence Research Institute, Changwon National University,
Changwon, Republic of Korea. 2 Human Community Renovation ResearchCenter, R Professional University of Rehabilitation, Tsuchiura, Ibaraki, Japan.
3
Faculty of Rehabilitation, R Professional University of Rehabilitation, 2‑10‑35
Kohoku, Tsuchiura, Ibaraki 300‑0032, Japan. 4 Faculty of Medicine, Universityof Tsukuba, 1‑1‑1 Tennodai, Tsukuba, Ibaraki 305‑8575, Japan.
Received: 2 August 2023 Accepted: 19 January 2024

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