The relationship between the ratio of gamma-glutamyltransferase to high-density lipoprotein cholesterol and the risk of diabetes mellitus using publicly available data: A secondary analysis based on a longitudinal study in Japan

doi:10.21203/rs.3.rs-1764346/v1

Download PDF

Research Article

The relationship between the ratio of gamma-glutamyltransferase to high-density lipoprotein cholesterol and the risk of diabetes mellitus using publicly available data: A secondary analysis based on a longitudinal study in Japan

https://doi.org/10.21203/rs.3.rs-1764346/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 17 Jan, 2023

Read the published version in Lipids in Health and Disease →

You are reading this latest preprint version

Background:

The ratio of gamma-glutamyltransferase to high-density lipoprotein cholesterol (GGT/HDL-C) has been highlighted in nonalcoholic fatty liver disease (NAFLD) by previous studies. However, there have been fewer investigations into the correlation between the GGT/HDL-C ratio and the type 2 diabetes mellitus (T2DM) incidence. Our secondary analysis used published data from a Japanese population and aimed to investigate the role of the GGT/HDL-C ratio in the incidence of T2DM.

Methods:

The research was a longitudinal cohort study completed by Okamura, Takuro et al. We obtained the data from the DATADRYAD website and used it for secondary analysis only. The participants recruited from a medical program called the NAGALA database received regular medical examinations and standardized questionnaires to obtain the baseline variables. Abdominal ultrasound was used to diagnose fatty liver disease. The participants were followed up, and the duration and the occurrence of T2DM were documented. The GGT/HDL-C ratio evaluated at baseline served as the independent variable, while the occurrence of diabetes served as the dependent variable.

Results:

A total of 15,453 cases (8,419 men and 7,034 women) were included in our study. After adjusting for age, gender, BMI, DBP, SBP, ALT, AST, TG, T-CHO, HbA1C, FPG, drinking status, smoking status, exercise status, and fatty liver, we observed that GGT/HDL-C ratio was positively associated with the incidence of T2DM: (Hazard ratio = 1.005, 95% confidence interval:1.000 to 1.010, P = 0.0667). The results were consistent when the quartile GGT/HDL-C was used as a categorical variable (P for trend < 0.00396). A curvilinear relationship with a threshold effect was identified between GGT/HDL-C ratio and the risk of incident T2DM. On the left of the point, one unit increase in the GGT/HDL-C ratio was associated with a 1.5 times increase in the risk of incident T2DM (Hazard ratio 2.57, 95% confidence interval 1.20 to 5.49). On the right of the point, when GGT/HDL-C was greater than 6.53, their relationship turned to be saturated.

Conclusion:

The GGT/HDL-C ratio correlated with the incidence of T2DM in a curvilinear form with a threshold effect. Their positive relationship could be observed when the GGT/HDL-C was less than 6.53.

ratio of gamma-glutamyltransferase to high-density lipoprotein cholesterol

incidence of T2DM

non-linear relationship

threshold effect

Type 2 diabetes mellitus (T2DM) is an epidemic metabolic disorder with a rapidly increasing incidence worldwide. The disease can lead to subsequent metabolic disorders and other complications that are hazardous to health and potentially lethal, increasing mortality.

Abnormal glucose and lipid metabolism often accompany the onset and development of T2DM [1]. The liver is the leading site in charge of lipids metabolism, glucose metabolism, and homeostasis. In clinical practice, we often use lipids indicators such as triglyceride (TG), total cholesterol (T-CHO), HDL-C, and files of liver enzyme indicators such as ALT (alanine aminotransferase), AST (aspartate aminotransferase) and GGT to reflect lipids metabolism and liver function. Single and complex liver enzymes, as well as the lipid index, have been explored in the research of risk factors for diabetes[2–5]. Previous works of literature have reported that TG/HDL-C, ALT/AST, and GGT all have associations with the incidence of T2DM, and some of them have predictive value to the incidence of T2DM [6]. Recently, the role of the GGT/ HDL-C ratio in NAFLD patients has been discussed. The authors suggested that this GGT/HDL-C ratio might predict the risk of NAFLD and metabolic diseases such as diabetes [7]. So far, very little attention has been paid to the role of the GGT/HDL-C in patients with T2DM. To determine whether there is a link between the GGT/HDL-C and the incidence of T2DM or whether the GGT/HDL-C is a more comprehensive indicator for assessing the risk of T2DM, we used published data on the Japanese population to conduct secondary data analysis. This secondary analysis used the GGT/HDL-C ratio as the independent variable and the incidence of T2DM as the dependent variable to probe into the independent association.

Data source

We downloaded the data for this study from www.Datadryad.org, the ‘DATADRYAD’ website. According to the website's request for data usage, the Dryad data package ([8]Dryad data package: Okamura, Takuro et al. (2019), Data from Ectopic fat obesity presents the greatest risk for incident T2DM: a population-based longitudinal study, Dryad, Dataset, https://doi.org/10.5061/ dryad. 8q0p192) need to be cited when using these data.

Study population

The longitudinal study was based on the medical examination program NAGALA (NAfld in the Gifu Area, Longitudinal Analysis). The database comes from the medical examination program at Murakami Memorial Hospital (Gifu, Japan), founded in 1994. The object of this project is to investigate risk factors for chronic diseases. Takuro Okamura et al. [8] extracted the cases from 2004 to 2015 who participated in the medical examination program at Murakami Memorial Hospital in their analysis.

The study initially enrolled 20,944 participants (12,498 male and 8,446 female), of which 863 participants (504 male and 359 female) were excluded because of missing data. Another 416 participants (278 male and 138 female) were excluded due to known liver disease; 739 participants (635 male and 104 female) were excluded due to ethanol consumption over the level regarded as toxic to the liver (420 g/w for male, 280 g/w for female) [9]; And 2,321 participants (1,709 male and 612 female) were also excluded due to any medication usage at baseline; 322 patients (265 male and 58 female) and 128 patients (677 male and 131 female) were excluded due to diabetes at the baseline examination or FPG ≥ 6.1 mmol/L [10], respectively. Another 11 participants (11 male) were excluded due to having an invalid GGT/HDL-C ratio. Thus, 15,453 subjects (7,034 female and 8,419 male) remained to be part of this study.

The ethics committee of Murakami Memorial Hospital approved this study. Takuro Okamura et al. [8] obtained written informed consent for data usage from each participant.

The procedure of data collection and measurements

All the baseline variables included in the database were collected at the baseline by a regular medical exam, including basic information like age, gender, BMI (body mass index), DBP (diastolic blood pressure), and SBP (systolic blood pressure); biochemical indicators ALT, AST, GGT, TG, T-CHO, HDL-C, glycosylated hemoglobin (HbA1C), fasting plasma glucose (FPG). Other covariates related to lifestyle habits such as smoking and drinking status, exercise status, and ethanol consumption was obtained by questionnaire. B-ultrasonography was used to diagnose fatty liver disease by trained technicians. And a follow-up study was performed on participants’ incidence of T2DM [11].

BMI is obtained by dividing weight (in kg) by the square of height (in meters). Okamura et al. [8] applied a standardized self-administered questionnaire to ascertain the participants’ information on medication usage, drinking, smoking, and exercise habits. We estimated average ethanol intake per week by the type and amount of alcohol consumed weekly during the prior month, and we divided ethanol consumption into four groups: 1) no or minimal, 2) light, 3) moderate, 4) heavy; the corresponding ethanol consumption was <40 g/week, 40 to 140 g/week, 140 to 280 g/week, >280 g/week [12]. The smoking status was divided into three categories: never, ex-smoker, or current smoker. Regular exercise habits were defined as participating in any sport regularly, more than once a week [13] [14]. Trained technicians diagnosed fatty liver disease by the criteria of the scoring system of abdominal ultrasonography [15]. The technicians were not informed of other personal information of the specific participants. Finally, T2DM was diagnosed by FPG ≥ 7 mmol/L, or HbA1c ≥ 6.5% [16] or by self-reported, and the date of diagnosis was recorded.

Statistical analysis

Data management and analysis were performed using the statistical software package R (http://www. R-project.org, The R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). P < 0.05 (two-sided) were considered statistically significant.

We carried out the data analysis according to the following procedures: For the data of baseline characteristics of participants (the GGT/HDL-C was grouped in quartiles), data were presented as the means ± standard deviations for normal distribution, medians (quartiles) for skewed distribution, frequency or percentage for categorical variables. The statistical differences among quartiles of GGT/HDL-C were tested with the one-way ANOVA for continuous variables of normal distribution, the Kruskal-Wallis H test for continuous variables of skewed distribution), and the chi-square test for categorical variables. We used Cox proportional hazard regression models to explore the predictive effect of GGT/HDL-C on the risk of T2DM. Hazard ratio (HR) and 95% confidence interval (CI) were calculated. To satisfy the recommendations comprising the STROBE statement [17], if the covariance added to the model altered the matched effect size by at least ten percent, they were adjusted [18], and we presented the results of unadjusted model and models with different covariates were adjusted simultaneously. For trend testing, we converted the continuous variables GGT/HDL-C into categorical, and the P for trend was calculated. Next, considering possible non-linear relationships that might exist between dependent and independent variables, a generalized additive model (GAM) was applied. In terms of the smoothing plot, we used a two-piecewise linear regression model to calculate the inflection point of the GGT/HDL-C on the incidence of T2DM. And the recursive method automatically calculated the inflection point by using the maximum model likelihood when the inflection point appeared in the curve. In the next step, subgroup analysis was also explored by stratified Cox regression models, and we used the likelihood ratio test tested for interactions across subgroups. We used the Kaplan–Meier method to compare cumulative diabetes event rates within specific time intervals. The log-rank test compared HR and corresponding 95% CI for the events of T2DM in each GGT/HDL-C quartile.

Baseline characteristics of participants

The baseline characteristics of the patients divided by GGT/HDL-C quartiles show in table 1. The average age of the participants was 43.71 ± 8.90 years. The average GGT/HDL-C was 10.78 (1.57-342.82). The factors of age, gender, BMI, ALT, AST, T-CHO, TG, SBP, DBP, FPG, HbA1C, and ethanol consumption were compared across the GGT/HDL-C quartiles. In the higher GGT/HDL-C group (Q3, Q4), males accounted for the majority, and relatively more participants in Q3 and Q4 were diagnosed as having fatty liver disease and being current smokers compared with the lower GGT/HDL-C groups (Q1, Q2). The participants of the Q3 and Q4 group also had a relatively higher ethanol consumption compared with the lower GGT/HDL-C group (Q1 and Q2).

Table 1 Baseline characteristics of participants

GGT/HDL-C ratio quartiles	Q1 (1.57-7.20)	Q2 (7.20-10.77)	Q3 (10.78-17.95)	Q4 (17.96-342.82)	P value
N	3862	3861	3866	3864
Gender					<0.001
Male	441 (11.42%)	1576 (40.82%)	2881 (74.52%)	3521 (91.12%)
Female	3421 (88.58%)	2285 (59.18%)	985 (25.48%)	343 (8.88%)
Age (years)	42.31±8.36	43.12±9.05	44.31±9.25	45.09±8.65	<0.001
BMI (kg/m2)	20.36±2.31	21.26±2.64	22.56±2.95	24.28±3.08	<0.001
SBP (mmHg)	107.48±13.14	111.86±13.90	116.78±14.32	121.85±14.50	<0.001
DBP (mmHg)	66.40±9.16	69.54±9.52	73.23±10.02	77.15±10.09	<0.001
ALT (IU/L)	13.00 (10.00-15.00)	15.00 (12.00-18.00)	18.00 (15.00-23.00)	26.00 (20.00-36.00)	<0.001
AST (IU/L)	15.00 (13.00-18.00)	16.00 (13.00-19.00)	18.00 (15.00-21.00)	21.00 (17.00-26.00)	<0.001
T-CHO (mmol/L)	5.02 (4.47-5.61)	4.94 (4.40-5.51)	5.04 (4.47-5.66)	5.33 (4.76-5.87)	<0.001
TG (mmol/L)	0.51 (0.38-0.69)	0.61 (0.44-0.86)	0.84 (0.60-1.20)	1.23 (0.86-1.80)	<0.001
HbA1C (mmol/mol)	32.24 (30.06-35.52)	32.24 (30.52-35.52)	33.34 (31.15-35.52)	33.34 (31.15-36.09)	<0.001
FPG (mg/dL)	89.00 (85.00-94.00)	91.00 (87.00-96.00)	94.00 (90.00-99.00)	97.00 (92.00-102.00)	<0.001
Ethanol consumption (g/week)	1.00 (0.00-12.00)	1.00 (0.00-36.00)	10.27 (0.00-84.00)	36.00 (1.00-132.00)	<0.001
Fatty liver					<0.001
No	3777 (97.80%)	3600 (93.24%)	3123 (80.78%)	2216 (57.35%)
Yes	85 (2.20%)	261 (6.76%)	743 (19.22%)	1648 (42.65%)
Habit of exercise					<0.001
No	3206 (83.01%)	3139 (81.30%)	3123 (80.78%)	3279 (84.86%)
Yes	656 (16.99%)	722 (18.70%)	743 (19.22%)	585 (15.14%)
Smoking status					<0.001
Never	3204 (82.96%)	2645 (68.51%)	1811 (46.84%)	1367 (35.38%)
Past	374 (9.68%)	598 (15.49%)	929 (24.03%)	1048 (27.12%)
Current	284 (7.35%)	618 (16.01%)	1126 (29.13%)	1449 (37.50%)
Incidence of diabetes					<0.001
No	3842 (99.48%)	3819 (98.91%)	3791 (98.06%)	3628 (93.89%)
Yes	20 (0.52%)	42 (1.09%)	75 (1.94%)	236 (6.11%)

Values are mean±SD or median (interquartile range) or n(%)

BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; ALT, alanine aminotransferase; AST, aspartate aminotransferase; HbA1C, glycosylated hemoglobin; TG, triglyceride; T-CHO, total cholesterol; GGT, gamma-glutamyltransferase; HDL-C, high-density lipoprotein cholesterol; FPG, fasting plasma glucose.

Univariate analysis

Univariate analysis was performed, and the results are shown in Table 2. In summary, age, BMI, SBP, DBP, ALT, AST, T-CHO, TG, FPG, ethanol consumption, paste and current smoking status, and the GGT/HDL-C were positively associated with the incidence of T2DM. Habitual exercise had no relationship with the incidence of diabetes mellitus. The risk of developing T2DM in male was higher than in female, and smokers (current and past) also has a higher risk of developing T2DM compared to never smokers.

The Kaplan-Meier curves of the four GGT/HDL-C quartile groups showed that the diabetes incidence risk between each of them was significantly different (the P value of the log-rank test < 0.0001). The increase in cumulative diabetes event rates kept pace with the increased GGT/HDL-C quartiles (Figure 1).

Table 2 The results of univariate analysis

	Statistics	HR (95% CI)	P value
Gender
Female	7034 (45.52%)	Ref
Male	8419 (54.48%)	2.53 (1.99, 3.21)	<0.0001
Age (years)	43.71 ± 8.90	1.06 (1.04, 1.07)	<0.0001
BMI (kg/m2)	22.12 ± 3.13	1.24 (1.22, 1.27)	<0.0001
SBP (mmHg)	114.49 ± 14.97	1.03 (1.03, 1.04)	<0.0001
DBP (mmHg)	71.58 ± 10.50	1.05 (1.04, 1.06)	<0.0001
ALT (IU/L)	19.99 ± 14.35	1.006 (1.005, 1.007)	<0.0001
AST (IU/L)	18.40 ± 8.64	1.008 (1.006, 1.010)	<0.0001
T-CHO (mmol/L)	198.22 ± 33.41	1.010 (1.008, 1.013)	<0.0001
TG (mmol/L)	80.78 ± 58.07	1.007 (1.006, 1.007)	<0.0001
HbA1C (mmol/mol)	33.03 ± 3.52	1.44 (1.40, 1.48)	<0.0001
FPG (mg/dl)	92.96 ± 7.44	1.20 (1.18, 1.22)	<0.0001
Ethanol consumption (g/week)	47.71 ± 82.31	1.002 (1.001, 1.003)	0.0011
Fatty liver
No	12716 (82.288%)	Ref
Yes	2737 (17.712%)	7.03 (5.71, 8.64)	<0.0001
Habit of exercise
No	12747 (82.49%)	Ref
Yes	2706 (17.51%)	0.76 (0.56, 1.02)	0.0652
Smoking status
Never	9027 (58.42%)	Ref
Past	2949 (19.08%)	1.66 (1.26, 2.19)	0.0003
Current	3477 (22.50%)	2.59 (2.06, 3.25)	<0.0001
GGT/HDL-C ratio	15.55 ± 15.73	1.02 (1.01, 1.02)	<0.0001

CI Confidence interval, Ref Reference

Relationship between GGT/HDL-C and the incidence of T2DM

In table 3, we used a cox proportional hazard regression model to evaluate the associations between the GGT/HDL-C and the incidence of T2DM. Non-adjusted model and models with different covariates adjusted were presented. In the crude model and model I (minimally adjusted model), the GGT/HDL-C ratio had a significant positive correlation with the incidence of T2DM: in the crude model, HR = 1.015, 95% CI: 1.013 to 1.017, P < 0.00001; in the adjusted model I (adjusted age, gender, and BMI), HR = 1.010, 95% CI:1.007 to 1.014, P < 0.00001. In the adjusted model II (fully adjusted model), HR = 1.005, 95% CI:1.000 to 1.010, P = 0.0670; the positive correlation between them was insignificant. The GGT/HDL-C ratio was then handled as a categorical variable (quartile) for the purpose of sensitivity analysis. There was an increased risk for developing T2DM as the quartiles of GGT/HDL-C increased in the crude model and the adjusted model I (both P for trend <0.00001), and we also observed significant P for trend (< 0.00396) in the fully adjusted model.

Table 3 Relationship between GGT/HDL-C and incident of DM2 in different models

Exposure	Crude model (HR, 95% CI, P)	Model I (HR, 95% CI, P)	Model II (HR, 95% CI, P)
GGT/HDL-C	1.015 (1.013, 1.017) <0.00001	1.010 (1.007, 1.014) <0.00001	1.005 (1.000, 1.010) 0.0670
GGT/HDL-C quartiles
Q1	Ref	Ref	Ref
Q2	2.22 (1.30, 3.78) 0.0033	1.87 (1.09, 3.22) 0.0228	1.28 (0.74, 2.20) 0.3790
Q3	4.04 (2.47, 6.62) <0.0001	2.75 (1.62, 4.68) 0.0002	1.25 (0.73, 2.16) 0.4171
Q4	11.59 (7.34, 18.30) <0.0001	6.08 (3.59, 10.29) <0.0001	1.84 (1.04, 3.24) 0.0361
P for trend	<0.0001	<0.0001	0.00396

Crude model did not adjust other covariant
Model I adjusted age, gender, BMI
Model II adjusted age, gender, BMI, SBP, DBP, ALT, AST, T-CHO, TG, HbA1C, FPG, fatty liver, smoking and exercise status and ethanol consumption

CI Confidence interval, Ref Reference

Non-linear relationship between GGT/HDL-C and T2DM

Next, we used GAM to explore whether there was a curvilinear relationship between the independent and dependent variables and showed the results in figure 2. We observed a curvilinear relationship after covariates (gender, age, BMI, ALT, AST, T-CHO, TG, HbA1C, FPG, SBP, DBP, smoking, exercise status, ethanol consumption, and fatty liver disease) were adjusted. Subsequent threshold effect analysis found an inflection point in their curvilinear relationship at GGT/HDL equaled to 6.35 (log-likelihood ratio test, P = 0.001). When the GGT/HDL was less than 6.53, the risk of incident T2DM increased with the increase of GGT/HDL-C (HR: 2.57, 95% CI: 1.20 to 5.49, P = 0.0151). When the GGT/HDL-C ratio was greater than 6.53, the incidence of T2DM no longer increased (HR: 1.00, 95% CI: 1.00 to 1.01, P = 0.0803), and their relationship tended to saturate (Table 4).

Table 4 The result of two-piecewise linear regression model

	Incident of diabetes (HR, 95%CI)	P value
Fitting model by standard linear regression	1.01 (1.00, 1.01)	0.0670
Fitting model by two-piecewise linear regression
Inflection point of GGT/HDL-C	6.53
≤ 6.53	2.57 (1.20, 5.49)	0.0151
≥ 6.53	1.00 (1.00, 1.01)	0.0803
P for log likelihood ratio test	0.001

Adjusted age, gender, BMI, SBP, DBP, FPG, ALT, AST, HbA1C, T-CHO, TG, fatty liver, smoking and drinking status, exercise status.

Subgroup analysis

We used age, gender, BMI, SBP, DBP, smoking, drinking, exercise status, and fatty liver disease as categorical variables to evaluate the effect sizes in different subgroups and to explore potential interactions in table 5. Tests for interactions were not statistically significant across all the subgroups listed above (all P-values for interactions were > 0.05). Considering the non-linear relationship between the independent variable and dependent variable might also exist in other subgroups, we supplemented the smoothing plots in stratified age, gender, BMI, smoking, drinking, exercise status, SBP, DBP, and fatty liver disease in the supplement figure 1 (Figure S1). We discovered that the curvilinear relationship existed in each of the above subgroups. Therefore, the test for interaction needed to consider the existence of the curvilinear relationship and required further in-depth research.

Table 5 Effect size of GGT/HDL-C on incident DM2 in prespecified and exploratory subgroups

Character	Number of participant	Effect size (95%CI)	P value	P for interaction
Age (years)				0.7182
<60	14741	1.00 (1.00, 1.01)	0.0903
>=60	712	1.00 (0.98, 1.02)	0.9106
Gender				0.9279
Male	8419	1.005 (0.983, 1.027)	0.6853
Female	7034	1.006 (1.000, 1.011)	0.0455*
BMI (kg/m2)				0.6066
<23	10059	1.004 (0.996, 1.012)	0.3575
>=23	5394	1.006 (1.000, 1.012)	0.0375*
SBP (mmHg)				0.4241
<140	14668	1.00 (1.00, 1.01)	0.1285
>=140	785	1.01 (1.00, 1.03)	0.1511
DBP (mmHg)				0.3525
<90	14690	1.00 (1.00, 1.01)	0.1642
>=90	763	1.01 (1.00, 1.02)	0.0866
Current smoker				0.7553
Never	9027	1.00 (0.99, 1.02)	0.3915
Past	2949	1.01 (1.00, 1.02)	0.1355
Current	3477	1.00 (0.99, 1.01)	0.4486
Drinking status				0.5413
Non	11802	1.00 (1.00, 1.01)	0.2545
Light	1754	1.00 (0.99, 1.02)	0.5992
Moderate	1357	1.00 (0.98, 1.01)	0.6772
Heavy	540	1.02 (1.00, 1.03)	0.0150*
Fatty liver				0.4586
No	12716	1.00 (1.00, 1.01)	0.4310
Yes	2737	1.007 (1.001, 1.014)	0.0290*
Exercise				0.3589
No	12747	1.004 (0.998, 1.010)	0.1552
Yes	2706	1.012 (0.997, 1.027)	0.1164

Note 1: Above model adjusted for age, gender, BMI, SBP, DBP, FPG, ALT, AST, HBA1C, T-CHO, TG, fatty liver, smoking and drinking status, exercise status

Note 2: In each case, the model is not adjusted for the stratification variable

The role of liver enzymes and lipids profile in the pathogenesis and progression of T2DM has long been discussed in the literature. As far as we know, this is the first study focusing on the independent relationship between the GGT/HDL-C ratio and the incidence of T2DM. We found that in the Japanese population, the GGT/HDL-C was positively associated with the incidence of diabetes, independent of age, gender, BMI, SBP, DBP, FPG, ALT, AST, HbA1C, T-CHO, TG, fatty liver, smoking status, drinking status, and exercise status. A further non-linear relationship was explored, and a threshold or a saturation effect was found. The relationship could be characterized as follows: When the GGT/HDL-C was less than 6.53, the risk of diabetes increased 1.56 times as GGT/HDL-C ratio increased by 1 unit; and the risk leveled off when the ratio was greater than 6.53. Further subgroup analysis was conducted, but no interaction was found in the subgroups investigated.

The enzyme GGT is a sensitive index of liver enzymes. Serum GGT can sensitively reflect the liver function, obvious and rapid elevation of the GGT level can be observed in fatty liver (both nonalcoholic and alcoholic), hepatic inflammation, and hepatitis [19]. Apart from this, elevated serum GGT level can also be related to various biochemical reactions and various systemic diseases such as systemic inflammation, oxidative stress, atherosclerosis, metabolic syndrome, and an increased risk of T2DM [20–22]. Since GGT activity is related to oxidative stress, aggravated inflammation, and potential NAFLD, which are major pathological processes involved in T2DM, we have reason to speculate that the GGT may be involved in the pathogenesis of T2DM [23, 24]. Previous research has already provided considerable evidence of GGT and the incidence of T2DM [6, 25–27].

Several human studies have provided evidence to support that plasma HDL-C level is reversely correlated with the risk of T2DM [28–30]. Furthermore, rising serum HDL-C level through extra interventions can be related to remarkable improvement of glycemic regulation in T2DM patients [31]. Dyslipidemia is a known risk factor for T2DM. As one of the major blood lipid components, HDL-C not only undertakes the function of cholesterol retro transportation but also modulates glycometabolism through the improvement of insulin sensitivity and elevation of insulin secretion[31]. Moreover, HDL-C could inhibit oxidative stress and enhance insulin secretion efficiently, which may exert protective effects on the beta cells of the pancreas [32]. In other tissues and organs involved in glucose metabolisms, such as skeletal muscle, adipose tissue, and liver tissue, HDL-C has also been found to have the ability to enhance glucose uptake in both insulin-dependent and non-insulin-dependent ways [33]. These findings suggest that abnormal HDL-C level and dysfunctionality of HDL-C in the human body may give rise to the risk of developing diabetes [34].

Both elevated GGT levels and decreased HDL-C levels can be related to an increased risk of developing T2DM. Therefore, the GGT/HDL-C ratio is posited to have a predictive value for T2DM. In a cross-section study conducted on Chinese populations, Guofang Feng et al. investigated the independent association between GGT/HDL-C ratio and NAFLD. They suggested that the function of the ratio was the combination of the two indicators and might exert a predictive value on the prevalence of NAFLD or other related metabolic disorders [7]. A recent cross-section study in a Chinese population of T2DM patients also found that in the overweight (BMI greater than 23 kg/m²) subgroup, the GGT/HDL-C has a positive relationship with NAFLD incidence [35]. Our study is the first to highlight the role of the GGT/HDL-C ratio in T2DM and further confirm a non-linear relationship. Further and more profound research is needed to reveal the intrinsic mechanism.

The following limitations have to be acknowledged. Firstly, the cohort was conducted by the medical examination program NAGALA in Japan. The data source was the Japanese population, so we could not extrapolate our findings to different ethnic groups or special groups such as adolescents. Also, we could only use available variables in published data and were unable to encompass the entire variables that may be related to both GGT/HDL-C and T2DM, such as low-density lipoprotein and inflammatory cytokines. Secondly, T2DM in this cohort study was not diagnosed with a 2-hour oral glucose tolerance test, which might lead to some missing cases, and the incidence of diabetes might be lower than they actually were. Thirdly, the GGT/HDL-C was only measured at baseline. Its changes over time could not be observed during follow-up days. Finally, residual confounders caused by measurement error in assessing confounders and unmeasured factors could not be completely ruled out. Further research in diverse populations, with more extensive data collection and meticulous research design, is required.

The following are strong points in our study. First, we use the GAM to clarify that the relationship between the independent and dependent variables is non-linear, which assists in better understanding the actual correlation between exposures and outcomes. Secondly, we used rigorous statistical strategies to reduce the impact of a set of confounders on the results, thus minimizing residual confounders. Thirdly, we obtained the positive finding that when GGT/HDL-C ratio was less than 6.53, the risk of diabetes increased 1.56 times as GGT/HDL-C ratio increased by 1 unit. The clinical value of this finding is that the positive correlation between GGT/HDL-C and the risk of T2DM could only be observed when GGT/HDL-C is under a certain threshold.

The GGT/HDL-C ratio correlated with the incidence of T2DM in a curvilinear form with a threshold effect. Their positive relationship could be observed when the GGT/HDL-C was less than 6.53.

T2DM: type 2 diabetes mellitus; NAFLD: nonalcoholic fatty liver disease; BMI: body mass index; DBP: diastolic blood pressure; SBP: systolic blood pressure; ALT: alanine aminotransferase; AST aspartate aminotransferase; GGT: gamma-glutamyltransferase; HDL: high-density lipoprotein cholesterol TG: triglyceride, T-CHO: total cholesterol; HbA1C: glycosylated hemoglobin; FPG: fasting plasma glucose; HR: hazard ratio; CI: confidence interval; GAM: general addictive model.

Ethics approval and consent to participate

The ethics committee of Murakami Memorial Hospital approved the study.

The committee’s reference number (not applicable).

Consent for publication

Our study is a secondary analysis based on public databases, all data involved are anonymous, and the data provider stated that written informed consent for data usage was obtained from each participant.

Availability of data and materials

The dataset used in this study can be downloaded from the ‘DATADRYAD’ database (www.Datadryad.org).

Competing interests

All the authors declare no competing interests.

Funding (not applicable)

Authors' contributions

Yue Zhao contribute to the manuscript’s data analysis and interpretation and drafting. Xing Xin contributed to data analysis and interpretation. Xiaoping Luo contributed to the manuscript’s conception and critical revision of the manuscript, analysis and interpretation of the data and approved the final version of the submitted manuscript.

All authors critically revised or reviewed the manuscript and approved the final version.

Acknowledgments

We greatly appreciate Takuro Okamura, Yoshitaka Hashimoto, Masahide Hamaguchi, Akihiro Obora, Takao Kojima, Michiaki Fukui. They provide the data that enable us to conduct a second analysis. We thank Chang-zhong Chen and Xin-lin Chen of Empower U for their assistance with data analysis.

Authors' information (not applicable)

Janghorbani M, Amini M: Utility of serum lipid ratios for predicting incident type 2 diabetes: the Isfahan Diabetes Prevention Study. Diabetes Metab Res Rev 2016; 32:572-580.doi:10.1002/dmrr.2770.
Teshome G, Ambachew S, Fasil A, Abebe M: Prevalence of Liver Function Test Abnormality and Associated Factors in Type 2 Diabetes Mellitus: A Comparative Cross-Sectional Study. EJIFCC 2019; 30:303-316.doi.
Giandalia A, Romeo E L, Ruffo M C, Muscianisi M, Giorgianni L, Forte F, et al.: Clinical correlates of persistently elevated liver enzymes in type 2 diabetic outpatients. Prim Care Diabetes 2017; 11:226-232.doi:10.1016/j.pcd.2016.12.001.
De Silva N M G, Borges M C, Hingorani A D, Engmann J, Shah T, Zhang X, et al.: Liver Function and Risk of Type 2 Diabetes: Bidirectional Mendelian Randomization Study. Diabetes 2019; 68:1681-1691.doi:10.2337/db18-1048.
Wang Y L, Koh W P, Yuan J M, Pan A: Association between liver enzymes and incident type 2 diabetes in Singapore Chinese men and women. BMJ Open Diabetes Res Care 2016; 4:e000296.doi:10.1136/bmjdrc-2016-000296.
Zhao W, Tong J, Liu J, Liu J, Li J, Cao Y: The Dose-Response Relationship between Gamma-Glutamyl Transferase and Risk of Diabetes Mellitus Using Publicly Available Data: A Longitudinal Study in Japan. Int J Endocrinol 2020; 2020:5356498.doi:10.1155/2020/5356498.
Feng G, Feng L, Zhao Y: Association between ratio of γ-glutamyl transpeptidase to high-density lipoprotein cholesterol and prevalence of nonalcoholic fatty liver disease and metabolic syndrome: a cross-sectional study. Ann Transl Med 2020; 8:634.doi:10.21037/atm-19-4516.
Okamura T, Hashimoto Y, Hamaguchi M, Obora A, Kojima T, Fukui M: Ectopic fat obesity presents the greatest risk for incident type 2 diabetes: a population-based longitudinal study. Int J Obes (Lond) 2019; 43:139-148.doi:10.1038/s41366-018-0076-3.
Chitturi S, Farrell G C, Hashimoto E, Saibara T, Lau G K, Sollano J D: Non-alcoholic fatty liver disease in the Asia-Pacific region: definitions and overview of proposed guidelines. J Gastroenterol Hepatol 2007; 22:778-787.doi:10.1111/j.1440-1746.2007.05001.x.
Genuth S, Alberti K G, Bennett P, Buse J, Defronzo R, Kahn R, et al.: Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003; 26:3160-3167.doi:10.2337/diacare.26.11.3160.
Mkojima H: The metabolic syndrome as a predictor of nonalcoholic fatty liver disease. Digest of the World Core Medical Journals(Gastroenterology) 2006.doi.
Hashimoto Y, Hamaguchi M, Kojima T, Ohshima Y, Ohbora A, Kato T, et al.: Modest alcohol consumption reduces the incidence of fatty liver in men: a population-based large-scale cohort study. J Gastroenterol Hepatol 2015; 30:546-552.doi:10.1111/jgh.12786.
Aaron D J, Kriska A M, Dearwater S R, Cauley J A, Metz K F, LaPorte R E: Reproducibility and validity of an epidemiologic questionnaire to assess past year physical activity in adolescents. Am J Epidemiol 1995; 142:191-201.doi:10.1093/oxfordjournals.aje.a117618.
Ryu S, Chang Y, Kim D I, Kim W S, Suh B S: gamma-Glutamyltransferase as a predictor of chronic kidney disease in nonhypertensive and nondiabetic Korean men. Clin Chem 2007; 53:71-77.doi:10.1373/clinchem.2006.078980.
Hamaguchi M, Kojima T, Itoh Y, Harano Y, Fujii K, Nakajima T, et al.: The severity of ultrasonographic findings in nonalcoholic fatty liver disease reflects the metabolic syndrome and visceral fat accumulation. Am J Gastroenterol 2007; 102:2708-2715.doi:10.1111/j.1572-0241.2007.01526.x.
American Diabetes A: Standards of medical care in diabetes--2011. Diabetes Care 2011; 34 Suppl 1:S11-61.doi:10.2337/dc11-S011.
Yokoyama M, Watanabe T, Otaki Y, Takahashi H, Arimoto T, Shishido T, et al.: Association of the Aspartate Aminotransferase to Alanine Aminotransferase Ratio with BNP Level and Cardiovascular Mortality in the General Population: The Yamagata Study 10-Year Follow-Up. Dis Markers 2016; 2016:4857917.doi:10.1155/2016/4857917.
Vandenbroucke J P, von Elm E, Altman D G, Gøtzsche P C, Mulrow C D, Pocock S J, et al.: Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med 2007; 4:e297.doi:10.1371/journal.pmed.0040297.
Wu L, Zhang M, Hu H, Wan Q: Elevated gamma-glutamyl transferase has a non-linear association with incident non-alcoholic fatty liver disease in the non-obese Chinese population: a secondary retrospective study. Lipids Health Dis 2021; 20:142.doi:10.1186/s12944-021-01577-8.
Ndrepepa G, Colleran R, Kastrati A: Gamma-glutamyl transferase and the risk of atherosclerosis and coronary heart disease. Clin Chim Acta 2018; 476:130-138.doi:10.1016/j.cca.2017.11.026.
Tao L, Li X, Zhu H, Gao Y, Luo Y, Wang W, et al.: Association between gamma-glutamyl transferase and metabolic syndrome: a cross-sectional study of an adult population in Beijing. Int J Environ Res Public Health 2013; 10:5523-5540.doi:10.3390/ijerph10115523.
Lee D H, Blomhoff R, Jacobs D R, Jr.: Is serum gamma glutamyltransferase a marker of oxidative stress? Free Radic Res 2004; 38:535-539.doi:10.1080/10715760410001694026.
Kunutsor S K: Gamma-glutamyltransferase-friend or foe within? Liver Int 2016; 36:1723-1734.doi:10.1111/liv.13221.
Ballestri S, Zona S, Targher G, Romagnoli D, Baldelli E, Nascimbeni F, et al.: Nonalcoholic fatty liver disease is associated with an almost twofold increased risk of incident type 2 diabetes and metabolic syndrome. Evidence from a systematic review and meta-analysis. J Gastroenterol Hepatol 2016; 31:936-944.doi:10.1111/jgh.13264.
Kim C H, Park J Y, Lee K U, Kim J H, Kim H K: Association of serum gamma-glutamyltransferase and alanine aminotransferase activities with risk of type 2 diabetes mellitus independent of fatty liver. Diabetes Metab Res Rev 2009; 25:64-69.doi:10.1002/dmrr.890.
Fujita M, Ueno K, Hata A: Association of gamma-glutamyltransferase with incidence of type 2 diabetes in Japan. Exp Biol Med (Maywood) 2010; 235:335-341.doi:10.1258/ebm.2009.009232.
Kaneko K, Yatsuya H, Li Y, Uemura M, Chiang C, Hirakawa Y, et al.: Association of gamma-glutamyl transferase and alanine aminotransferase with type 2 diabetes mellitus incidence in middle-aged Japanese men: 12-year follow up. J Diabetes Investig 2019; 10:837-845.doi:10.1111/jdi.12930.
Hwang Y C, Ahn H Y, Park S W, Park C Y: Association of HDL-C and apolipoprotein A-I with the risk of type 2 diabetes in subjects with impaired fasting glucose. Eur J Endocrinol 2014; 171:137-142.doi:10.1530/EJE-14-0195.
Tabara Y, Arai H, Hirao Y, Takahashi Y, Setoh K, Kawaguchi T, et al.: Different inverse association of large high-density lipoprotein subclasses with exacerbation of insulin resistance and incidence of type 2 diabetes: The Nagahama study. Diabetes Res Clin Pract 2017; 127:123-131.doi:10.1016/j.diabres.2017.03.018.
Waldman B, Jenkins A J, Davis T M, Taskinen M R, Scott R, O'Connell R L, et al.: HDL-C and HDL-C/ApoA-I predict long-term progression of glycemia in established type 2 diabetes. Diabetes Care 2014; 37:2351-2358.doi:10.2337/dc13-2738.
Drew B G, Duffy S J, Formosa M F, Natoli A K, Henstridge D C, Penfold S A, et al.: High-density lipoprotein modulates glucose metabolism in patients with type 2 diabetes mellitus. Circulation 2009; 119:2103-2111.doi:10.1161/CIRCULATIONAHA.108.843219.
Drew B G, Rye K A, Duffy S J, Barter P, Kingwell B A: The emerging role of HDL in glucose metabolism. Nat Rev Endocrinol 2012; 8:237-245.doi:10.1038/nrendo.2011.235.
de Haan W, Bhattacharjee A, Ruddle P, Kang M H, Hayden M R: ABCA1 in adipocytes regulates adipose tissue lipid content, glucose tolerance, and insulin sensitivity. J Lipid Res 2014; 55:516-523.doi:10.1194/jlr.M045294.
Arnold von Eckardstein C W: HDL, beta cells and diabetes. Cardiovascular research 2014; 103:384-394.doi:10.1093/cvr/cvu143.
Xing Y, Chen J, Liu J, Ma H: Associations Between GGT/HDL and MAFLD: A Cross-Sectional Study. Diabetes Metab Syndr Obes 2022; 15:383-394.doi:10.2147/DMSO.S342505.

No competing interests reported.

Supplementalfigure1.docx

Download PDF

Journal Publication

published 17 Jan, 2023

Read the published version in Lipids in Health and Disease →

Editorial decision: Major revision
08 Aug, 2022
Reviews received at journal
03 Jul, 2022
Reviewers agreed at journal
17 Jun, 2022
Reviewers invited by journal
17 Jun, 2022
Submission checks completed at journal
17 Jun, 2022
Editor assigned by journal
17 Jun, 2022
First submitted to journal
16 Jun, 2022

You are reading this latest preprint version

The relationship between the ratio of gamma-glutamyltransferase to high-density lipoprotein cholesterol and the risk of diabetes mellitus using publicly available data: A secondary analysis based on a longitudinal study in Japan

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1