Triglyceride glucose-waist circumference, a novel and effective predictor of diabetes in first-degree relatives of type 2 diabetes patients: cross-sectional and prospective cohort study
Zheng et al. J Transl Med
Triglyceride glucose-waist circumference, a novel and effective predictor of diabetes in first-degree relatives of type 2 diabetes patients: cross-sectional and prospective cohort study
Shuang Zheng 0
Sheng Shi 2
Xingxing Ren 0
Tingting Han 0
Yangxue Li 0
Yawen Chen 0
Wei Liu 0
Peter C. Hou 1
Yaomin Hu 0
0 Department of Endocrinology, Renji Hospital, School of Medicine, Shanghai Jiaotong University , No.160 Pujian Road, Shanghai 200127 , China
1 Department of Emergency Medicine, Brigham and Women's Hospital and Harvard Medical School , Boston, MA , USA
2 Department of Orthopedic Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University , No.160 Pujian Road, Shanghai 200127 , China
Background: Body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI), triglyceride glucose index (TyG), TyG-BMI, and TyG-WC have been reported as markers of insulin resistance or type 2 diabetes mellitus (T2DM). However, little is known about the associations between the aforementioned markers and the risk of prediabetes and diabetes in first-degree relatives (FDRs) of T2DM patients. Methods: 1544 FDRs of T2DM patients (635 men and 909 women) were enrolled in the initial cross-sectional study and all of them finished corresponding examinations. Logistic regression analysis and receiver operating characteristic (ROC) curve were used to compare and identify the associations of the six parameters (BMI, WC, VAI, TyG, TyG-BMI and TyG-WC) with the prevalence of prediabetes and diabetes. Subsequently, 452 of them were followed-up for an average of 5 years. Cox proportional hazard regression model was applied to confirm the predictive value of the optimal marker. Results: Among the indices, TyG-WC was more strongly associated with the prevalence of prediabetes and diabetes. Compared with participants in the lowest quartile of TyG-WC, the adjusted odds ratio and 95 % CIs for prediabetes and diabetes was 11.19 (7.62-16.42) for those in the top quartile of TyG-WC. Moreover, the largest AUC was also observed in TyG-WC (0.765, 95 % CIs 0.741-0.789, P < 0.001). The robust predictive value of TyG-WC was further confirmed in the follow-up study (HR: 7.13, 95 % CIs 3.41-14.90, P < 0.001). Conclusions: TyG-WC is a novel and clinically effective marker for early identifying the risks of prediabetes and diabetes in FDRs of T2DM patients.
Type 2 diabetes mellitus; First-degree relative; Triglyceride glucose index; Visceral adiposity index; TyG-WC
The occurrence rate of type 2 diabetes mellitus (T2DM)
is quite astonishing worldwide, of which is a major risk
factor for cardiovascular disease and even premature
]. Thus, it is of utmost significance to early
identify and treat subjects at high risk of developing
T2DM, though the unclear etiologies and pathological
process of it. Familial clustering phenomenon of T2DM
may back the genetic susceptibility to T2DM. Ma et al.
] demonstrated that first-degree relatives (FDRs) of
patients with T2DM may have a higher prevalence of
diabetes than those without a family history of T2DM
(26.6 versus 9.2 %). Therefore, it is more important to
early determine the susceptible population vulnerable to
T2DM via simple and effective diagnostic tools,
considering the enormous population of FDRs.
Previous literature indicated that several effective and
inexpensive variables, ranging from simple
anthropometric measures to more complex models, are closely
related to insulin resistance (IR) or diabetes. Body mass
index (BMI) and waist circumference (WC), two clinical
indices for body fat assessment, are commonly used for
detecting prediabetes and diabetes risk [
visceral adiposity index (VAI), a mathematical model
based on BMI, WC, triglyceride (TG) and high-density
lipoprotein cholesterol (HDL-C), is a more effective tool
for prediabetes and diabetes prediction [
]. In addition,
triglyceride glucose index (TyG) as well as TyG-related
indicators (TyG-BMI and TyG-WC) have been reported
as excellent surrogate markers of IR, which is deemed to
be the vital pathological mechanism of T2DM [
our knowledge, little is known about the accuracy and
predictability of these indicators in suffering prediabetes
and diabetes in FDRs of T2DM patients.
The objectives of the present study were to investigate
the corresponding associations of the aforementioned
indicators with the prevalence of prediabetes and
diabetes in FDRs of T2DM patients and identify the excellent
one firstly. Subsequently, a follow-up study was
conducted to evaluate the incidence of diabetes in this
population and further assess the performance of the optimal
indicator in predicting the risk of T2DM.
Stratified random sampling was performed to select
T2DM patients from the database of Renji hospital
from January 1995 to 2005. The family of each randomly
selected subject were contacted by telephone or
doorto-door visit. Only one of the FDRs (including parents,
children and full siblings) of each T2DM patient was
randomly selected and invited to our study from
September 2005 to August 2009. A total of 2392 FDRs of these
T2DM patients were invited to the survey. After
excluding ineligible subjects, 2018 FDRs were recruited to the
study and finished structured questionnaires on their first
visit. Next, 474 subjects were further excluded
according to the exclusion criteria including self-reported
diabetes diagnosis and/or regular diabetic medication use,
less than 18 or more than 90 years old, pregnant, chronic
renal or hepatic failure, cancer, taking regular
medication for dyslipidemia and/or hypertension. Finally, 1544
subjects (635 men and 909 women) were enrolled in the
To further test whether the optimal marker identified
through cross-sectional study is useful for predicting
incident diabetes, we conducted a 5-year prospective
cohort study including FDRs of T2DM patients
diagnosed with NGT or prediabetes in the initial study. After
excluding ineligible participants, 452 of the 1544 FDRs
completed the annual examinations with the average
duration of 5 years (Fig. 1).
The study protocol was in compliance with the
declaration of Helsinki and approved by the Ethical Committee
of Renji Hospital, School of Medicine, Shanghai Jiaotong
University. Written informed consents were signed from
all participants included in the study.
Body height, weight, WC and blood pressure (BP) were
measured by trained survey personnel. Both height and
weight measurements were taken in light clothing
without shoes. The smallest abdominal circumference was
measured as WC, which was taken twice and the mean
value was recorded. Blood pressure was measured three
times in each subject on the right arm after 5 min resting
in a sitting position, and the mean value was recorded.
Each participant received a 75 g OGTT after at least 10 h
of overnight fasting. Blood samples were collected at 0,
30, 60, 120 and 180 min after the glucose load. Plasma
glucose levels were measured using the glucose oxidase
method. Serum insulin levels were obtained using a
bioantibody technique (Linco, St Louis, MO, USA). Serum
lipid profiles were tested with an automated
biochemical instrument by radioimmunoassay (RIA) based on
the double-antibody technique (DPC, Los Angeles, CA,
USA). HbA1c was measured by the high-performance
liquid chromatography (HPLC) method with a BIO-RAD
analyzer (Bio-Rad Variant II; Bio-Rad Laboratories,
Hercules, CA, USA).
Diagnostic criteria and definition
The 1999 World Health Organization (WHO) diagnostic
criteria for T2DM was adopted [
Normal glucose tolerance (NGT) was defined as
fasting plasma glucose <6.1 mmol/l and 2-h plasma
glucose <7.8 mmol/l. Prediabetes includes isolated impaired
fasting glucose (IFG), isolated impaired glucose
tolerance (IGT) and combined glucose intolerance (CGI).
IFG was defined as fasting plasma glucose between 6.1
and 7.0 mmol/l and 2-h plasma glucose <7.8 mmol/l;
IGT was defined as fasting plasma glucose <6.1 mmol/l
and 2-h plasma glucose between 7.8 and 11.1 mmol/l;
CGI was defined as fasting plasma glucose between 6.1
and 7.0 mmol/l and 2-h plasma glucose between 7.8
and 11.1 mmol/l. Diabetes mellitus (DM) was defined as
fasting plasma glucose ≥7.0 mmol/l and/or 2-h plasma
glucose ≥11.1 mmol/l.
BMI was calculated as the body weight (kg) divided by
the square of body height (m2). VAI and TyG were
calculated using the former formula [
]. VAI: Men: [WC/(39.68
+ 1.88 × BMI)] × (TG/1.03) × (1.31/HDL); Women:
[WC/(36.58 + 1.89 × BMI)] × (TG/0.81) × (1.52/HDL),
where both TG and HDL levels are expressed in mmol/L.
The TyG index: Ln [TG (mg/dl) × FPG (mg/dl)/2].
TyGBMI: TyG index × BMI. TyG-WC: TyG index × WC (cm).
Incidence was calculated as the number of T2DM cases
per 100 person years of follow-up starting from the date
of finishing the initial examination in 2005–2009 to the
occurrence of diabetes or the final follow-up visit in the
All data were analyzed using SPSS version 17.0 for
Windows (SPSS, Chicago, IL, USA). Continuous data were
shown as medians and interquartile ranges (IQR) by
virtue of the skewed distribution and compared
utilizing Kruskal–Wallis H test or Mann–Whitney U test.
Adjusted means were calculated and compared with
general linear models. Categorical variables were expressed
as percentages and compared with Chi square test.
Multinomial logistic regression was conducted to determine
the correlations between different factors and the risk of
prediabetes and diabetes after controlling potential
confounding factors. For each indicator, odds ratios and 95 %
CIs of quartiles 2–4 were calculated and compared using
quartile 1 as the reference. Receiver operating
characteristic (ROC) curves were applied to compare the
relative diagnostic strengths of these indicators for correctly
discriminating prediabetes and diabetes. The area under
the ROC curve (AUC) was utilized to quantify the
overall diagnostic accuracy. Indicator with the largest AUC
was considered as the best marker. The cutoff point of
the optimal indictor was calculated according to Youden
Index and the corresponding sensitivity, specificity,
positive and negative predictive values were further assessed
in the cohort study. Cox proportional hazard
regression was taken to evaluate the predictive power of the
optimal marker for incident diabetes after adjusting for
confounding factors. Probability value less than 0.05 was
considered statistically significant.
A total of 1544 participants were enrolled in the
crosssectional study, including 657 with NGT, 423 with
prediabetes and 464 with previously undiagnosed diabetes.
Baseline characteristics of participants, stratified by
glucose tolerance status, were presented in Table 1. The
median ages of subjects with NGT, prediabetes and
diabetes were 47.0, 52.0 and 59.0 years old, respectively
(P < 0.05). After adjusting for age, subjects with
prediabetes and diabetes had higher levels of blood pressure, VAI,
BMI, WC, TyG, TyG-BMI, TyG-WC, TG, LDL-C and
lower levels of HDL-C than those with NGT.
Associations of indicators with prediabetes and diabetes risk
The ORs and 95 % CIs for prediabetes and/or diabetes
were progressively increased across quartiles of each
index after adjusting for age, sex, SBP and DBP (Table 2).
After direct comparison, TyG-WC presented the highest
ORs and 95 % CIs for prediabetes and diabetes, reaching
11.19 (95 % CIs 7.62–16.42) for the top quartile as
compared with the bottom quartile (P < 0.001), followed by
TyG index (Q4 11.04, 95 % CIs 7.57–16.09) and WC (Q4
5.65, 95 % CIs 3.97–8.04).
The results of ROC analyses and AUCs with their
corresponding 95 % CIs for VAI, BMI, WC, TyG, TyG-BMI
and TyG-WC were shown in Fig. 2. For prediabetes,
the largest AUC was observed in VAI (AUC = 0.600,
95 % CIs 0.569–0.631, Grade: D), followed by TyG
(AUC = 0.557, 95 % CIs 0.526–0.587, Grade: F) and
TyGWC (AUC = 0.544, 95 % CIs 0.513–0.575, Grade: F).
For diabetes, the largest AUC was showed in TyG-WC
(AUC = 0.767, 95 % CIs 0.743–0.791, Grade: C), followed
by TyG (AUC = 0.748, 95 % CIs 0.722–0.774, Grade: C)
and WC (AUC = 0.709, 95 % CIs: 0.682–0.735, Grade:
C). For mixed prediabetes and diabetes, the largest AUC
was also showed in TyG-WC (AUC = 0.765, 95 % CIs
0.741–0.789, Grade: C), followed by TyG (AUC = 0.759,
95 % CIs 0.735–0.783, Grade: C) and WC (AUC = 0.703,
95 % CIs 0.677–0.730, Grade: C). Taking the odds ratio
and the AUC value into consideration, TyG-WC may be
regarded as an optimal marker for predicting prediabetes
and diabetes in those participants.
Clinical outcomes at the final follow‑up
Data from 184 men and 268 women with a median
age at baseline of 51.0 and 48.0 years respectively were
observed for an average of 5 years (4.62 ± 0.99).
During the 2013 person-years of follow-up, 75 of the 452
participants were identified as newly occurred diabetes
patients and the total incidence of diabetes was 3.7 per
100 person-years. When stratified by quartiles of
TyGWC, the incidences of diabetes, from Quartile1 to 4,
were 1.2, 2.1, 5.1 and 9.6 per 100 person-years,
respectively. In addition, the cumulative rates of incident
diabetes from Q1 to Q4 were 5.9, 9.8, 21.9 and 35.9 %,
respectively (Table 3).
59.0 (50.0, 65.0)a, b
129.0 (127.0, 131.0)a, b
79.0 (78.0, 80.0)a
25.26 (24.81, 25.70)a
92.2 (91.1, 93.3)a, b
2.82 (2.62, 3.03)a, b
9.21 (9.15, 9.27)a, b
233.45 (228.99, 237.91)a, b
851.94 (839.66, 864.22)a, b
1.94 (1.82, 2.07)a, b
5.09 (4.98, 5.19)a
1.31 (1.27, 1.34)a
3.06 (2.98, 3.13)a
8.65 (8.50, 8.81)a, b
17.11 (16.83, 17.39)a, b
12.58 (11.73, 13.43)a, b
43.02 (39.80, 46.25)b
7.36 (7.27, 7.46)a, b
Data were expressed as median (Interquartile range 25–75 %)
Comparisons among NGT, Prediabetes and Diabetes groups were performed after adjusting for age
SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, WC waist circumference, VAI visceral adiposity index, TyG triglyceride glucose index,
TyG-BMI combined TyG and BMI, TyG-WC combined TyG and WC, TG triglyceride, TC total cholesterol, HDL-C high density lipoprotein cholesterol, LDL-C low density
lipoprotein cholesterol, FPG fasting plasma glucose, 2hPG 2 h postload plasma glucose, FINS fasting serum insulin, 2hINS 2 h postload serum insulin, HbA1c glycated
a P < 0.05 versus NGT group
b P < 0.05 versus Prediabetes group
The predictive value of TyGW‑C
As compared to individuals with the lowest TyG-WC
(Table 4), those who had the highest TyG-WC were at
7.13-fold risk of diabetes (95 % CIs 3.41–14.90). The
positive trend between TyG-WC level and diabetes risk was
attenuated but still remarkable after adjusting for age,
sex, SBP, DBP, TC and LDL-C (HR: 3.69, 95 % CIs 1.65–
8.28). Additionally, according to the results of ROC curve
and the Youden Index, the optimal cutoff point of
TyGWC was 760.06, with the sensitivity of 74.7 % and the
specificity of 63.1 %. Meanwhile, the positive and
negative predictive values at this point were 28.7 and 92.6 %,
In the cross-sectional study, we directly compared six
parameters (BMI, WC, VAI, TyG, TyG-BMI and
TyGWC) as predictors of prediabetes and diabetes in FDRs
of T2DM patients. Overall, we found that TyG-WC
outperformed other predictors with a higher OR and a larger
AUC. Moreover, in the prospective study, we observed
that subjects in the highest quartile of TyG-WC had
3.7fold risk of diabetes for those in the lowest quartile even
after the adjustment of potential compounders, which
indicated that TyG-WC was an independent predictor of
diabetes in FDRs of T2DM patients.
Previous studies have indicated that both genetic and
environmental factors contribute to the development
of diabetes [
]. Of note, the prevalence of the
multifactorial disease and potential population of FDRs of
T2DM patients are increasing obviously with the change
of lifestyle [
]. FDRs of T2DM patients are regarded as
high-risk diabetic populations, considering the genetic
predisposition and the similar lifestyle [
]. In the
current study, the crude prevalence of diabetes in FDRs
was 30.1 % and the age-standardized prevalence was
15.6 %, which was higher than the national prevalence
of diabetes in China (9.7 %) . Furthermore, Du et al.
] demonstrated that the prevalence of diabetes was
independently associated with an increasing family
history risk level. Hence, more attention to FDRs of T2DM
patients should be paid in the clinical diagnosis and
All indices were divided into quartiles and examined by multinomial logistic analysis. P value was adjusted for age, sex, systolic blood pressure and diastolic blood
VAI visceral adiposity index, BMI body mass index, WC waist circumference, TyG triglyceride glucose index, TyG-BMI combined TyG and BMI,
TyG-WC combined TyG and WC
treatment of diabetes in an early stage, though the
contribution of genetic factors to the pathological development
of diabetes remains obscure.
The strong relationship between obesity and diabetes
has been mentioned in many studies [
]. Oti et al.
] found that obesity is closely associated with high
blood glucose. Matsuda et al. [
] maintained that
adipose tissue is the main source of reactive oxygen species,
which may contribute to a variety of metabolic problems,
including obesity-associated IR and T2DM. As simple,
cheap and noninvasive anthropometric parameters, BMI
and WC are commonly adopted as useful indicators of
obesity and metabolic risk. However, recent studies
indicated that some populations show unexpected metabolic
profiles that deviate from the typical dose-response
relationship between BMI and metabolic disturbances
]. In the current study, we also found the
association between BMI and abnormal glucose metabolism was
weaker than that of WC when considering the lower odds
ratios and AUCs of BMI. These results may be explained
by the different roles of BMI and WC in the evaluation of
adiposity status [
]. BMI, a measure of body fat based on
weight and height, stands for general obesity, while WC, a
measure of abdominal fat, stands for central obesity. The
National Cholesterol Education Program-Adult
Treatment Panel-III suggested that central obesity is an
independent risk factor for T2DM, and measuring WC is an
inexpensive tool to screen risk of diabetes [
WC may be more effective than BMI. However, WC
cannot sufficiently discriminate between visceral and
subcutaneous fat. Accumulating evidence has demonstrated
that visceral adipose tissue plays more critical roles in
the development of insulin resistance and diabetes than
subcutaneous fat. Molecular mechanisms responsible
for the differences are still under discussion. It has been
suggested that visceral fat produces more free fatty acid
than subcutaneous fat, thus increases the risk of IR and
]. Moreover, visceral adipose secretes various
inflammatory cytokines and adipokines, which may also
promote the occurrence of IR and diabetes [
Besides obesity, increased FPG levels have also been
demonstrated as an independent risk factor for
developing T2DM [
]. Moreover, elevated TG levels over
time also enhance the risk of developing diabetes in
various populations [
]. Additionally, Guerrero-Romero
et al. suggested that, TyG index, the product of FPG and
TG, could be a surrogate index of insulin resistance due to
its high sensitivity similar to euglycemic-hyperinsulinemic
clamp test . Meanwhile, it is also proposed that TyG
index is a valuable marker for predicting the risk of future
diabetes in both men and women [
]. Given that insulin
resistance is the core pathological mechanism of T2DM
and always occurs before the diagnosis of T2DM [
surrogate indices of insulin resistance might aid in the
prediction of incident diabetes. In our study, we found
TyG-WC, the combination of adiposity status and TyG,
was a better marker for early predicting the risk of
prediabetes and diabetes. The superiority of TyG-WC might
be achieved as TG, FPG and obesity are well validated for
their roles in IR and the development of diabetes. These
results also support that both glucotoxicity and
lipotoxicity play crucial roles in the pathogenesis of diabetes.
The visceral adiposity index (VAI), a mathematical
model based on BMI, WC, TG and HDL-C, is another
predictor of prediabetes and diabetes demonstrated by
several research, although the relationship might differ
by ethnicity [
]. In our study, we found the
association between VAI and diabetes was weaker than that of
TyG related parameters. However, it was noteworthy that
VAI was better correlated with prediabetes than with
diabetes, which was commensurate with the study of Yang
et al. . Underlying mechanisms are still unclear.
Possible explanation might be that subjects with prediabetes
have better glucose regulation than those with diabetes,
so the effect of glucotoxicity was slight in this stage. Thus,
VAI, an index stands for the condition of obesity and
lipid levels, is closely related to prediabetes while TyG
and related parameters are well associated with diabetes.
In the follow-up study, we further evaluated the
clinical outcomes of participants and confirmed the
predictive value of TyG-WC. Notably, the incidences of diabetes
were significantly increased in sequence of quartiles of
TyG-WC. Furthermore, compared with participants in the
highest quartile of TyG-WC value, the hazard ratio of
incident diabetes was more than threefold for those in the
lowest quartile after adjusting for age, gender, blood pressure
and other potential compounders, which demonstrated
the close association between TyG-WC and diabetes risk.
Several limitations may exist in this study. First, the
results might have potential bias due to the single-center
design. Second, some potential bias from socio-economic
background and general diet intake were not well
controlled. Third, the number of participants in the
followup study was relative small. Fourth, the results were
obtained from FDRs of diabetes patients, and further
investigations were required in other populations.
TyG-WC is a valuable marker for predicting the risk of
prediabetes and diabetes in FDRs of T2DM patients.
Because it can be easily calculated from routine
laboratory data, we suggest the possibility of applying this index
in risk assessment in real clinical practice or
Additional file 1. Clinical dataset.
BMI: body mass index; CGI: combined glucose intolerance; HDL-C: high
density lipoprotein cholesterol; IFG: impaired fasting glucose; IGT: impaired
glucose tolerance; LDL-C: low density lipoprotein cholesterol; NGT: normal
glucose tolerance; OGTT: oral glucose tolerance test; TC: total cholesterol; TG:
triglyceride; TyG: triglyceride glucose index; VAI: visceral adiposity index; WC:
SZ, SS and XR attended the data collection, statistical analysis, data
interpretation, manuscript writing and revision. TH, YL and YC contributed to the
acquisition and interpretation of the data. WL and PH contributed to the
revision of the paper. YH contributed to acquisition of funding, study design, and
revision of the paper. All authors revised and approved the final manuscript.
All authors read and approved the final manuscript.
The authors thank the staff of the Endocrinology and Metabolism
Laboratory and the nursing staff for their dedicated assistance in patient sample
The authors declare that they have no competing interests.
Availability of data and materials
The datasets supporting the conclusions of this article are included within the
article and its Additional file 1.
Ethics approval and consent to participate
The study protocol was in agreement with Helsinki declaration and the study
was approved by the Ethical Committee of Renji Hospital, School of Medicine,
Shanghai Jiaotong University. Written informed consents were signed from all
participants included in the study.
This study was supported by the National Natural Science Foundation of
China (No. 81270946, 81170758, 30670988) and the Foundation from Renji
Hospital, School of Medicine, Shanghai Jiaotong University (RJZZ14-003).
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