Leukocyte glucose index discriminates metabolic dysfunction-associated steatotic liver disease from type 2 diabetes: a finding independent of oral antidiabetic therapy

Abstract

Aim:

This study aimed to evaluate the leukocyte glucose index (LGI) as a discriminator between patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and those with type 2 diabetes mellitus (T2DM), and to examine its association with the triglyceride-glucose index (TyGI) in both conditions.

Background:

LGI is derived from fasting plasma glucose (FPG) and leukocyte count, both of which may fluctuate under pathological conditions. LGI has been proposed as a predictor of disease severity and mortality.

Methods:

This cross-sectional study was conducted from 1 January 2024 to 1 September 2024 at the College of Pharmacy, University of Sulaimani, Sulaimaniyah, Iraq. Patients with MASLD were randomly selected. The primary outcome was LGI, calculated from FPG and total leukocyte count.. The secondary outcome was TyGI.

Results:

A total of 126 patients were included and divided into three groups. Group I included patients with risk factors related to MASLD (n = 25), Group II included patients with MASLD (n = 51), and Group III included patients with T2DM (n = 50). Baseline characteristics did not differ significantly between Groups I and II, whereas Group III differed significantly from both Groups I and II. The median LGI value was significantly higher in Group III (1.53) than in Group I (0.793) and Group II (0.744) (p < 0.001). Significant positive correlations were observed between LGI and TyGI in Group II (r = 0.489, p < 0.001) and Group III (r = 0.705, p < 0.001). An LGI cutoff value of 1.167 significantly discriminated T2DM from MASLD, with a sensitivity of 86.1% and a specificity of 77.6%.

Conclusion:

LGI may help distinguish MASLD from T2DM and is significantly associated with TyGI in both conditions.

Introduction

Leukocyte glucose index (LGI) is a biomarker derived from fasting plasma glucose (FPG) and leukocyte count, both of which may fluctuate under pathological conditions. Previous studies have shown that LGI is a useful biomarker for assessing disease severity and mortality. With a sensitivity of 83.3% and specificity of 93.1% (area under the curve [AUC] = 0.915), LGI has been identified as a significant predictor of severe COVID19 infection., especially in individuals with diabetes mellitus (1). Another study conducted in patients with coronary artery disease found that LGI values were significantly higher in patients with critical coronary artery stenosis, and there is a significant relationship with disease severity (2). Furthermore, it has been reported that a better prognosis in acute hemorrhagic stroke has been observed in patients who had lower FPG and total leukocyte counts at admission (3). LGI is a significant predictor of all‑cause mortality following acute myocardial infarction in non‑diabetic patients, and such a significant prediction is missed in diabetes patients (4). Also, it is a significant predictor of mortality in patients with chronic coronary total occlusion (5). Higher total leukocyte counts have been reported in patients with type 2 diabetes mellitus (T2DM) compared with healthy subjects or prediabetes, with an odds ratio of 1.287; it showed a significant relationship with glycosylated hemoglobin (6). Moreover, increased neutrophil counts and higher triglyceride‑glucose index (TyGI) values are risk factors for metabolic syndrome in people with T2DM (7). On the other hand, in terms of metabolic reasons, such as obesity, dyslipidemia, insulin resistance, etc., MASLD is similar to T2DM. MASLD, formerly known as non‑alcoholic fatty liver disease, affects more than 30% of the global population, resulting from complex interactions with several risk factors (8,9).

The risk of developing MASLD increases with higher levels of systemic immune‑inflammatory biomarkers related to neutrophils, lymphocytes, and blood platelets (10). In addition, patients with MASLD and T2DM have a higher risk of all‑cause mortality (hazard ratio of 4.71) and cardiac-specific deaths (hazard ratio of 20.01) (11). Also, both T2DM and MASLD are associated with significantly higher TyGI values which served as a predictor for the severity or complications of these diseases (12-15). Patients with T2DM, including those with concomitant MASLD, are commonly treated with oral antidiabetic medications to lower their FPG to less than 110 mg/dL, otherwise they may be unable to control their blood glucose levels. Therefore, we hypothesized that LGI would be higher in patients with uncontrolled FPG in both pathological conditions. There is a need to address this research gap by looking for the LGI as a discriminator biomarker in two pathological conditions that share metabolic dysfunctions and are associated with fluctuations in the hematological indices and taking into consideration the effect of oral antidiabetics on the FPG. This cross-sectional study aimed to evaluate LGI levels in patients with MASLD and T2DM and to examine its association with TyGI in both conditions.

Methods

Study design

This cross-sectional study was performed at the Department of Clinical Pharmacy, College of Pharmacy, University of Sulaimani, and Shar Hospital, Iraq from 1 January to 1 September 2024. This study was conducted in Sulaimaniyah governorate. It was approved by the Institutional Ethical Review Committee of the College of Pharmacy at the University of Sulaimani (PH134-24) in September 2024 and was conducted in accordance with the Declaration of Helsinki. All participants were informed about the study's objectives and design, and each individual who wished to participate provided informed consent.

Data collection

Participants were included if they had (i) a history of MASLD confirmed by liver ultrasonography and abnormal hepatocellular enzymes; (ii) clinical and laboratory evidence of MASLD risk factors, e.g., obesity, alterations in the hepatic enzymes; (iii) T2DM with negative ultrasound features of MASLD, within the normal range of liver enzyme levels, treated with oral antidiabetics, and regardless of the FPG level at study entry into the study. Participants were excluded if they had (i) a history of acute or chronic viral hepatitis; (ii) alcohol consumption; (iii) complications of T2DM or using injectable antidiabetics; (iv) pregnant or breast-feeding women; (v): significantly higher values of inflammatory biomarkers e.g., Creactive protein or erythrocyte sedimentation rate, and (vi) terminal illnesses e.g., malignancies. In total, 126 patients were recruited for analysis. The participants were grouped into:

Group I (n = 25): patients presented with risk factors for MASLD

Group II (n = 51): patients who had MASLD. Those patients fulfilled the abovementioned criteria for inclusion. Ten of the 51 patients (19.6%) had a history of T2DM.

Group III (n = 50): T2DM patients

Definition of pathological conditions

Known cases of T2DM were randomly recruited using random tables. All participants were treated with oral antidiabetic agents belonging to different pharmacological classes., and their FPG levels showed a wide variation, i.e., no specific FPG cutoff level was required for inclusion. for inclusion in the study. Patients with MASLD had a positive ultrasonography appearance characterized by fatty infiltration or vacuoles (which are hyper-echonic in the hepatocellular areas compared with the surrounding organs, renal cortex, and spleen), hepatomegaly, as well as abnormally higher levels of hepatocellular enzymes, particularly ALT. Patients presented with obesity, alterations of hepatocellular enzymes, or abnormally high levels of triglycerides were at risk for developing MASLD or associated T2DM. 

Evaluation of anthropometric measurements

The waist circumference was measured at the level of the umbilicus using a metallic tape measure. The body mass index (BMI) was calculated by dividing the body weight (kg) and height (m) as the denominator. The conicity index was calculated by using the following equation (16):

$\frac{\mathrm{Waist\; circumference}\left(\mathrm{m}\right)}{0.109\times \sqrt[2]{\frac{\mathrm{Weight}\left(\mathrm{kg}\right)}{\mathrm{Height}\left(\mathrm{m}\right)}}}$

Blood biochemistry examination

All biochemical variables were measured at the laboratory of Shar Hospital. Fasting blood samples were collected from participants after an overnight fast, and serum or plasma samples were analyzed on the same day. EDTA‑anticoagulated whole blood was collected for a complete blood count measurement. Biochemical measurements included fasting glucose and lipid profiles (cholesterol, high-density lipoprotein cholesterol, and triglycerides). Furthermore, serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were measured using a colorimetric method.

Calculating the biochemical indices

The triglyceride-glucose index (TyGI) was simply calculated by using the following equation:

$\mathrm{LN}\frac{\mathrm{Triglyceride}\left(\frac{\mathrm{mg}}{\mathrm{dL}}\right)\times \mathrm{Glucose}\left(\frac{\mathrm{mg}}{\mathrm{dL}}\right)}{2}$

TyGI-BMI is equal to TyGI × BMI

TyGI-WC is equal to TyGI x waist circumference (m)

TyGI-Conicity is equal to TyGI × conicity index

Leukocyte-glycemic index is calculated by using the following equation (1, 17):

$\frac{\mathit{Leukocyte\; count}\left(\frac{{10}^9}{L}\right)\times \mathit{glucose\; level}\left(\frac{\mathit{mg}}{\mathit{dL}}\right)}{1000}$

ALT-to AST ratio = $\frac{\mathit{ALT}\left(\frac{U}{L}\right)}{\mathit{AST}\left(\frac{U}{L}\right)}$

Fibrosis-4 (FiB-4) as a marker of liver fibrosis was calculated by using the following equation

FiB-4 = $\frac{\mathit{Age}\left(\mathit{year}\right)\times \mathit{AST}\left(\frac{U}{L}\right)}{\mathit{Platelet\; count}\left(\frac{{10}^9}{L}\right)\times \sqrt[2]{\mathit{ALT}\left(\frac{U}{L}\right)}}$

Statistical analysis

Statistical analyses were carried out using SPSS 25 (IBM Corp., Armonk, NY, USA). The sample size was calculated by using the margin of error (α error = 0.05, β = 0.2, the power is 0.8), two tails, and a 95% confidence interval. The Shapiro-Wilk test was used to evaluate the normality assumption of quantitative variables. Categorized and continuous data are presented as numbers (percentages) and mean ± standard errors, respectively. For comparing baseline characteristics between groups, the one-way analysis of variance (ANOVA) and Kruskal-Wallis tests were used for continuous data, while the categorical data was analyzed by the chi-squared test. Pearson’s (rho) simple correlation was performed to evaluate the associations between the dependent variable (leucocyte glucose index) and independent variables, including the BMI, conicity index, triglyceride-glucose index, ALT-to-AST ratio, and Fib-4 scan. To enable comparisons between groups in the LGI, cutoff values for fasting plasma glucose (110 mg/dL) and white blood cell count (8.0×109 cells/L) were applied, which are the upper normal levels. In addition, the area under the curve of a discriminating cutoff value with a 95% confidence interval, and a multivariate regression analysis were done to look for the predictors of LGI. A p-value of < 0.05 was considered statistically significant.

Results

Table 1 shows the characteristic features of the participants. There were no significant differences between Groups I and II in their characteristics. Group III patients had significantly higher median values in age, FPG, ALT, and leukocyte count than the corresponding values in Group I. The BMI, WC, ALT, and AST of Group III patients were significantly lower than those of Group II, while the median values for age, FPG, leukocyte count, and blood platelet count were significantly higher. Insignificant differences in the sex distribution, FSTG, and conicity index between Groups I, II, and III were observed. In Table 2, significantly higher median values in the TyGI, TyGI-WC, TyGI-conicity, LGI, and FiB-4 biomarkers were observed in Group III compared with Groups I and II. The LGI median value in Group III is approximately 1.9 and 2.1 folds of Groups I and II, respectively, while the FiB-4 value approximates 1.49 and 1.23 folds, respectively. The ALT-to-AST ratio as a biomarker of MASLD is significantly (p<0.001) declining in Group III compared with Groups I and II (1.0 versus 1.6 and 1.4, respectively). There are insignificant correlations between LGI (as a dependent variable) and age, BMI, WC, conicity index, ALT-to-AST ratio, and Fib-4 biomarkers (as independent predictors). Figure 1 shows significant correlations between LGI and TyGI in Group II (r = 0.489, p<0.001) and Group III (0.705, p<0.001). The LGI is significantly correlated with TyGI-conicity in Group III (r = 0.531, p<0.001) and Group II (r = 0.316, p = 0.024). The significant correlation coefficients declined after including the covariant factor of the conicity index. Adjusting the data according to the FPG into <110 mg/dL and >110 mg/dL showed that there were no significant differences in the mean values of the LGI between groups, while there was a significantly higher mean level of LGI in Group III (1.674± 0.085) compared with Group I (1.194±0.137, p=0.029) and Group II (1.097±0.103, p<0.001) when the FPG levels were >110 mg/dL (Figure 2). This observation indicates that oral antidiabetics that corrected the FPG to <110 mg/dL make the application of LGI as a discriminator biomarker of no significance. Conversely, the FPG level of > 110 mg/dL in patients treated with oral antidiabetics plays a role in the significantly higher LGI in Group III, indicating no specific oral antidiabetic ameliorates the LGI value. Furthermore, adjusting the data according to the leukocyte cutoff value of 8.0×109/L revealed that Group III patients who had a leukocyte count <8.0×109/L had a significantly higher mean value of LGI (1.38±0.141) compared with Group I (0.764±0.071, p<0.001) and Group II (0.741±0.03, p<0.001) (Figure 3).

Table 1.

Characteristics of the participants

Variables	Group I (n=25)	Group II (n=51)	Group III (n=50)	P1-value	P2-value	P3-value
Age, y	41 (37.0-50.5)	43 (36-50)	56.5 (51-64.3)	0.916	<0.001	<0.001
Sex, males: females	7:18	23:28	21:29	0.155	0.241	0.755
BMI, kg/m2	32.3 (28.0-37.8)	33.4 (30.2-36.5)	30.8 (27.7-34.3)	0.638	0.126	0.012
WC, cm	106 (100-115)	107 (103-114)	103 (99-108)	0.646	0.284	0.018
Conicity index	1.34 (1.3-1.38)	1.32 (1.28-1.39)	1.34 (1.32-1.38)	0.436	0.412	0.093
FPG, mg/Dl	104 (99.5-125.5)	107 (99-125)	190 (135-222)	0.825	<0.001	<0.001
FSTG, mg/Dl	160 (103.5-224)	156.5 (184-217)	155 (145.8-171.3)	0.715	0.809	0.978
ALT, U/L	36.0 (25.9-48.5)	39 (28-51)	21.6 (18.3-25.1)	0.569	<0.001	<0.001
AST, U/L	23 (16.2-36.6)	25 (18-38)	21.7 (17.7-27.3)	0.291	0.348	0.018
Leukocyte count,109/L	7.4(5.85-8.66)	6.8 (6.24-8.44)	8.4 (6.9-10.1)	0.868	0.027	0.003
Platelet count, 109/L	230 (189.5-281.5)	244 (200-264)	270 (220.5-309.75)	0.860	0.071	0.009

The results are presented as medians (25th–75th percentiles). The P-value was calculated using the Kruskal-Wallis test for continuous data and the Chi-squared test for categorical data. P1: comparison between Groups I and II; P2: comparison between Groups I and III; and P3: comparison between Groups II and III. Group I: risk factors; Group II: metabolic dysfunction-associated liver disease; Group III: type 2 diabetes mellitus; FPG: fasting plasma glucose; FSTG: fasting serum triglycerides; ALT: alanine aminotransferase; and AST: aspartate aminotransferase.

Table 2.

Results of biomarkers

Biomarkers	Group I (n=25)	Group II (n=51)	Group III (n=50)	P1-value	P2-value	P3-value
ALT-AST ratio	1.6(1.15-2.24)	1.4 (1.1-2.0)	1.0 (0.76-1.37)	0.322	<0.001	<0.001
TyGI	9.04 (8.60-9.65)	9.12 (8.73-9.36)	9.62 (9.31-9.8)	0.952	0.002	<0.001
TyGI-BMI	290.5 (262.9-343.3)	299.3 (271.5-339.3)	295.5 (251.4-331)	0.778	<0.001	<0.001
TyGI-WC	9.7 (8.63-10.73)	9.61 (9.19-10.25)	9.82 (9.35-10.49)	0.864	<0.001	<0.001
TyGI-Conicity	12.23 (11.35-12.9)	12.01 (11.39-12.39)	12.92 (12.49-13.23)	0.577	0.002	<0.001
LGI	0.793 (0.643-1.135)	0.744 (0.657-1.0)	1.53 (1.136-1.804)	0.821	<0.001	<0.001
FiB-4	0.670 (0.476-1.071)	0.814 (0.560-1.153)	0.999 (0.768-1.423)	0.537	0.011	0.018

The results are presented as medians (25th–75th percentiles). The P-value was calculated using the Kruskal-Wallis test for continuous data and the Chi-squared test for categorical data. P1: comparison between Groups I and II; P2: comparison between Groups I and III; and P3: comparison between Groups II and III. Group I: risk factors; Group II: metabolic dysfunction-associated liver disease; Group III: type 2 diabetes; ALT: alanine aminotransferase, AST: aspartate aminotransferase, TyGI: triglyceride glycemic index, BMI: body mass index, WC: waist circumference, LGI: leukocyte glycemic index, and FiB-4: fibrosis-4 index.

Figure 1.

Correlations between leucocyte glycemic index and triglyceride glycemic index (A) or triglyceride glycemic index-conicity (B) in Group II (metabolic dysfunction associated steatotic liver disease) and Group III (type 2 diabetes mellitus).

Figure 2.

Leucocyte glycemic index in different groups according to FPG cutoff (110 mg/dL). The mean ± SE of leukocyte glycemic index in the participants according to the cutoff value of FPG at 100mg/dL p-value was calculated by a two-tailed one-way ANOVA test. *Compared with Group I, and **compared with Group II. Group I: risk factors, Group II: metabolic dysfunction associated steatotic liver disease, and Group III: type 2 diabetes mellitus.

Figure 3.

Leucocyte glycemic index in different groups according to leucocyte count cutoff. The mean ± SE of leukocyte glycemic index in the participants according to the cutoff value of leukocyte count at 8.0×109/L. p-value was calculated by a two-tailed one-way ANOVA test. *Compared with Group I, and **compared with Group II. Group I: risk factors, Group II: metabolic dysfunction associated steatotic liver disease, and Group III: type 2 diabetes mellitus

The mean value of LGI in Group III (1.625±0.101) is significantly higher than in Group I (1.148±0.138, p=0.036) at a leukocyte count of >8.0×109/L. There is no significant difference in the LGI between Groups II and III at a leukocyte count of more than 8.0×109/L (1.265±0.123 versus 1.652±0.101, p=0.068) (Figure 3). This indicates that none of the oral antidiabetics had positive or negative effects on the total leukocyte count. In addition, patients with a leukocyte count of <8.0 ×109, also have variable levels of FPG in both groups, which eliminates the effect of oral antidiabetics. Figure 4 shows the area under the curve of LGI that discriminates T2DM from MAFLD at the cutoff value of 1.167 with a sensitivity of 86.1%, a specificity of 77.6%, a positive predictive value of 74%, and a negative predictive value of 88.2%. The Youden’s index is 0.627. Table 3 shows that the age, BMI, conicity index, and ALT-AST ratio are non-significant predictors for LGI in Groups II and III, while the TyGI is a significant predictor of LGI in both groups. The results of these predictors are applicable for 22.5% (R²=0.225) and 56.3% (R²=0.563) of Groups II and III, respectively. This finding indicates that LGI is significantly and positively correlated with TyGI, and the variation in the prediction values indicates a discrimination between T2DM and MASLD.

Figure 4.

The area under the curve (0.859, C.I. 95%: 0.782–0.935, p<0.001) of the leukocyte glucose index that discriminates type 2 diabetes from metabolic dysfunction associated steatotic liver disease at a cutoff of 1.167.

Table 3.

Multivariate regression analysis of the Predictors of leucocyte glucose index in metabolic dysfunction associated steatotic liver disease (MASLD) and type 2 diabetes mellitus (T2DM)

Model	R2	B (C.I.)	F-value	P-value	Tolerance	VIF
MASLD Age Body mass index Conicity index ALT-AST ratio Triglyceride-glucose index	0.225	0.007 (-0.005, 0.018) 0.005 (-0.017, 0.027) -0.102 (-1.474, 1.270) -0.077 (-0.263, 0.108) 0.288 (0.103, 0.473)	2.618	0.037 0.258 0.654 0.882 0.406 0.003	0.932 0.968 0.965 0.903 0.991	1.073 1.034 1.036 1.108 1.009
T2DM Age Body mass index Conicity index ALT-AST ratio Triglyceride-glucose index	0.563	-0.018 (-0.033, -0.003) -0.039 (-0.101, 0.024) -4.781 (-11.88, 2.32) -0.073 (-0.449, 0.303) 1.003 (0.711, 1.294)	11.34	<0.001 0.020 0.218 0.182 0.696 <0.001	0847 0.174 0.178 0.924 0.926	1.181 5.734 5.616 1.082 1.080

ALT-AST: alanine-aspartate aminotransferase ratio, VIF: variance influence factor

Discussion

The results of this study showed that the LGI LGI could serve as a discriminatory biomarker between MASLD and T2DM at a cutoff value of 1.167.The LGI value is related to fasting plasma glucose and total leukocyte count; After adjusting for leukocyte count, there was no significant difference in the mean LGI values between MASLD and T2DM. In addition, the LGI is significantly correlated with TyGI in T2DM and MASLD, indicating that LGI may be a predictor of cardiovascular events (18). Furthermore, the role of LGI as a marker of inflammatory and immune activation cannot be excluded, as low-grade inflammation was reported in both T2DM and MASLD (19). Previous studies found that some hematological indices serve as discriminative markers that distinguish MASLD from other related pathological conditions, e.g., mean platelet volume, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio (20-23). There is evidence that the total leukocyte count is a significant predictor for the development of MASLD after adjusting for age, smoking, anthropometric profile, and glucose level (24). In contrast, in T2DM, the total leukocyte count is significantly correlated with glycosylated hemoglobin (HbA1c) (6). These observations explained why the LGI is significantly higher in T2DM compared with MASLD or risk factor groups, and this significant difference is absent whenever the total leukocyte count is adjusted. It is interesting to add a new observation that the median value (interquartile range) of LGI among Group II patients who had T2DM is 1.517 (0.849, 1.789, n=9), which is significantly (p < 0.001) higher than that of those who did not have T2DM in the same group, 0.716 (0.643, 0.895, n=42). This indicates that LGI is a discriminant biomarker. In addition, the role of antidiabetics in ameliorating the LGI value was missed when the leukocyte count was adjusted because there were no significant differences between Groups II and III in the LGI despite having wide variations in the FPG. The literature review does not show the assessment and the significance of LGI in T2DM. Recent evidence shows a significant correlation between HbA1c and leukocyte count as the leucocyte count is significantly elevated at an HbA1c of more than 7% (25). This indicates that the LGI is elevated in uncontrolled diabetes, and it could be related to low-grade inflammation.

Previous studies assessed LGI as a predictor of mortality in some pathological conditions e.g., septicemia. There is no evidence that LGI is a biomarker which, discriminates against diabetes with or without MASLD. It has been found that there is a non-significant association between the total leukocyte count and TyGI; in contrast, it was significantly associated with neutrophil count in healthy subjects and neutrophil-to-lymphocyte ratio in patients with coronary artery disease for assessment of disease severity (7, 26). The strength of this study is adding new information that LGI can serve as a predictor of cardio-metabolic events as well as a discriminator between MASLD and T2DM at a cutoff value of 1.167, which produces a good Youden’s index value of 0.637.

The non-significant correlations with biomarkers that are related to the severity of MASLD (using ALT-to-AST ratio) or to the existence of liver fibrosis (using FiB-4) indicate that LGI is out of benefit in the prediction of hepatic dysfunction in MASLD or T2DM (27). Furthermore, the non-significant differences between the risk factors Group and MASLD, while a non-significant difference between MASLD and T2DM in the LGI pointed out the specificity of the LGI for MASLD, which accounts for 77.6% at a cutoff value of 1.167. It is interesting to report that the cutoff value of LGI at 1.167 approximates the production of the normal cutoff value of 10.0 ×109/L of leukocytes and fasting plasma glucose of 110 mg/dL, which equals 1.100. One of the study’s limitations or drawbacks is the small sample size of Group I, which does not bias the results. An interesting observation is the significant correlation between LGI and TyGI in MASLD and T2DM, with a variable prediction percentage that is higher in T2DM compared with MASLD (56.3% versus 22.5%), which is not affected by the sample size, as the variance influence factor (VIF) is <5.

One of the limitations is that the study is retrospective, by which some of the data is missed or not specifically recorded, and this explains why the sample size was not large despite the VIF is < 5. This weakness in the study will not bias the results because the determination of LGI is derived from two laboratory investigations which routinely applied in hospitals and medical centers. The strength of this study is the determination of the LGI which is associated with TyGI, a known prognostic marker in many pathological conditions.

Conclusions

Leukocyte glucose index is a useful discriminator between metabolic dysfunction-associated liver disease and type-2 diabetes mellitus despite using oral antidiabetics, and it is significantly associated with the triglyceride-glucose index, which is usefully applied as a prognostic biomarker.

Conflict of interests

There is no conflict of interest for authors of this article.