White Blood Cell Count as a Predictor of Incident Type 2 Diabetes Mellitus Among Non-Obese Adults: A Longitudinal 10-Year Analysis of the Korean Genome and Epidemiology Study

The role of peripheral white blood cell counts in the association between central adiposity and glycemic status

AIMS

Although central adiposity is a well-known risk factor for diabetes, the underlying mechanism remains unclear. The aim of this study was to explore the potential mediation role of circulating WBC counts in the association between central adiposity and the risk of diabetes.

MATERIALS AND METHODS

A cross-sectional study was conducted using data from the Fuqing cohort study, which included 6,613 participants aged 35-75 years. Logistic regression analysis and Spearman's rank correlation analysis were used to examine the relationships between waist-to-hip ratio, WBC counts and glycemic status. Both simple and parallel multiple mediation models were used to explore the potential mediation effects of WBCs on the association of waist-to-hip ratio with diabetes.

RESULTS

The study revealed a positive relationship between waist-to-hip ratio and risk of prediabetes (OR = 1.53; 95% CI, 1.35 to 1.74) and diabetes (OR = 2.89; 95% CI, 2.45 to 3.40). Moreover, elevated peripheral WBC counts were associated with both central adiposity and worsening glycemic status (P < 0.05). The mediation analysis with single mediators demonstrated that there is a significant indirect effect of central adiposity on prediabetes risk through total WBC count, neutrophil count, lymphocyte count, and monocyte count; the proportions mediated were 9.92%, 6.98%, 6.07%, and 3.84%, respectively. Additionally, total WBC count, neutrophil count, lymphocyte count, monocyte count and basophil count mediated 11.79%, 11.51%, 6.29%, 4.78%, and 1.76%, respectively, of the association between central adiposity and diabetes. In the parallel multiple mediation model using all five types of WBC as mediators simultaneously, a significant indirect effect (OR = 1.09; 95% CI, 1.06 to 1.14) were observed, with a mediated proportion of 12.77%.

CONCLUSIONS

Central adiposity was independently associated with an elevated risk of diabetes in a Chinese adult population; levels of circulating WBC may contribute to its underlying mechanisms.

Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics

Hyperglycemia is an outcome of dysregulated glucose homeostasis in the human body and may induce chronic elevation of blood glucose levels. Lifestyle factors such as overnutrition, physical inactivity, and psychosocials coupled with systemic low-grade inflammation have a strong negative impact on glucose homeostasis, in particular, insulin sensitivity. Together, these factors contribute to the pathophysiology of diabetes (DM) and expanding landscape of its prevalence regionally and globally. The rapid rise in the prevalence of type 2 diabetes, therefore, underscores the need for its early diagnosis and treatment. In this work, we have evaluated the discriminatory capacity of different diagnostic markers including inflammatory biomolecules and RBC (Red Blood Cell) indices in predicting the risk of hyperglycemia and borderline hyperglycemia. For that, 208,137 clinical diagnostic entries obtained over five years from Chugtai Labs, Pakistan, were retrospectively evaluated. The dataset included HbA1c (n = 142,011), complete blood count (CBC, n = 84,263), fasting blood glucose (FBG, n = 35,363), and C-reactive protein (CRP, n = 9035) tests. Our results provide four glycemic predictive models for two cohorts HbA1c and FBG) each having an overall predictive accuracy of more than 80% (p-value < 0.0001). Next, multivariate analysis (MANOVA) followed by univariate analysis (ANOVA) was employed to identify predictors with significant discriminatory capacity for different levels of glycemia. We show that the interplay between inflammation, hyperglycemic-induced derangements in RBC indices, and altered glucose homeostasis could be employed for prognosticating hyperglycemic outcomes. Our results then conclude a glycemic predictor with high sensitivity and specificity, employing inflammatory markers coupled with RBC indices, to predict glycemic outcomes (ROC p-value < 0.0001). Taken together, this study outlines a predictor of glycemic outcomes which could assist as a prophylactic intervention in predicting the early onset of hyperglycemia and borderline hyperglycemia.

Role of hematological parameters in pathogenesis of diabetes mellitus: A review of the literature

Diabetes mellitus (DM) is characterized by hyperglycemia and abnormalities in insulin secretion and activity. There are numerous hematological parameters; however, this review article only focuses on red blood cells, hemoglobin, hematocrit, red blood cell indices, platelet count, white blood cells, lymphocytes, neutrophils, monocytes, eosinophils, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio, which play an essential role in the pathogenesis of DM. Also, this review article aims to report the relationship between these hematological parameters and the development of DM. In con-clusion, this article shows that increased levels of platelets, red blood cells, hematocrit, lymphocytes, eosinophils, neutrophils, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio and decreased levels of hemoglobin are involved in the pathogenesis of DM. However, the role of basophils in DM is unknown yet.

White cell count trajectory and type 2 diabetes in health management population: a retrospective cohort study

OBJECTIVE

The purposes of current research were to investigate the time-dependent dynamic alteration of white cell count (WCC) in health management populations and to analyse its association with the type 2 diabetes mellitus (T2DM).

DESIGN

Retrospective cohort research.

SETTING

Shengjing Hospital of China Medical University in China's Shenyang.

PARTICIPANTS

The number of non-diabetic subjects (median age: 40 years) totaled 7924 in the present work.

PRIMARY AND SECONDARY OUTCOME MEASURES

Assessment and grouping of changes in WCC between 2016 and 2019 were accomplished through group-based trajectory modelling. Newly diagnosed incidence associated with T2DM was estimated based on varying trajectories of WCC by the Kaplan-Meier method. Statistical differences in the incidences of new-onset T2DM among various WCC trajectory groups were examined through log-rank test. The influence of WCC trajectory alterations on the new-onset T2DM was evaluated with a multiparameter Cox (proportional-hazards) model.

RESULTS

During a 3-year (2016-2019) period, the T2DM development was noted in 3.14% (249) of the subjects. The trajectory model of WCC change was divided into four groups. For participants with low stability (trajectory 1), medium stability (trajectory 2), medium stability (trajectory 3) and high stability (trajectory 4), the incidence of T2DM was separately 2.2%, 2.62%, 4.82% and 7.4%, respectively. After adjusting for the underlying confounders, new-onset T2DM HRs (95% CIs) for trajectories 3 and 4 were separately 1.94 (1.32 to 2.83) and 3.08 (1.82 to 5.21) compared with the trajectory 1, implying that the T2DM risks were statistically significantly high.

CONCLUSION

The 3-year T2DM incidence was impacted independently by the WCC trajectory fluctuations. The grouping trajectory can reflect the dynamic change of WCC over time, which is more representative than the measured WCC at a single time point.

A nomogram model for the risk prediction of type 2 diabetes in healthy eastern China residents: a 14-year retrospective cohort study from 15,166 participants

Background

Risk prediction models can help identify individuals at high risk for type 2 diabetes. However, no such model has been applied to clinical practice in eastern China.

Aims

This study aims to develop a simple model based on physical examination data that can identify high-risk groups for type 2 diabetes in eastern China for predictive, preventive, and personalized medicine.

Methods

A 14-year retrospective cohort study of 15,166 nondiabetic patients (12-94 years; 37% females) undergoing annual physical examinations was conducted. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) models were constructed for univariate analysis, factor selection, and predictive model building. Calibration curves and receiver operating characteristic (ROC) curves were used to assess the calibration and prediction accuracy of the nomogram, and decision curve analysis (DCA) was used to assess its clinical validity.

Results

The 14-year incidence of type 2 diabetes in this study was 4.1%. This study developed a nomogram that predicts the risk of type 2 diabetes. The calibration curve shows that the nomogram has good calibration ability, and in internal validation, the area under ROC curve (AUC) showed statistical accuracy (AUC = 0.865). Finally, DCA supports the clinical predictive value of this nomogram.

Conclusion

This nomogram can serve as a simple, economical, and widely scalable tool to predict individualized risk of type 2 diabetes in eastern China. Successful identification and intervention of high-risk individuals at an early stage can help to provide more effective treatment strategies from the perspectives of predictive, preventive, and personalized medicine.

Association of the MACROD2 rs6110695 A>G polymorphism with an increasing WBC count in a Korean population

Introduction

We aimed to find a novel candidate gene related to the white blood cell (WBC) count in a Korean population. Since WBC count has been reported to have a relation to the risk of chronic diseases according to previous literature, WBC level prediction can be helpful for managing future risk of chronic disease development. In this aspect, a gene newly found in the present study is expected to be utilized as a tool for judging an individual's WBC level.

Methods

Based on the 153 study participants' genotype data produced by the Korean Chip. The mono‐adenosine diphosphate ribosylhydrolase 2 (MACROD2) rs6110695 A>G polymorphism had a significant strong association with WBC count, thus, the MACROD2 gene emerged as a novel candidate gene for WBC count. To verify the effects of the single‐nucleotide polymorphisms on WBC count, the participants were grouped according to the rs6110695 AA and AG genotypes.

Results

WBC to apolipoprotein A‐I ratio, WBC count, granulocyte to lymphocyte ratio, monocyte to platelet ratio, and interferon‐γ level were significantly higher in the AG genotype group than in the AA genotype group. Through the receiver operating characteristic curve analysis, the rs6110695 AA and AG genotypes were discriminated by the optimal WBC count cutoff value of 5.450. As expected, the results in the participants having a WBC count over 5.450 were similar to the AG genotype group.

Conclusions

We revealed that the MACROD2 rs6110695 AG genotype has an association with increasing WBC count. Since, as previous literature described, WBC count is one of the main risk factors for chronic diseases, WBC count measurement in individuals with the rs6110695 AG genotype that was found in the present study may help manage future chronic disease risk.

Peripheral Cytopenia and Its Associated Factors in Type 2 Diabetes Mellitus Patients, Northwest Ethiopia

Background

Hematological abnormalities are linked with diabetes mellitus (DM) and play a major role in diabetes-related micro- and macro-vascular complications. Therefore, this study aimed to investigate the magnitude of peripheral cytopenia and associated factors in type 2 diabetes (T2DM) patients.

Methods

A cross-sectional study was conducted from March to May 2021 at the University of Gondar Comprehensive Specialized Hospital. A total of 357 T2DM participants were selected using a simple random sampling technique. A total of 3 mL of venous blood samples were collected using the vacutainer method for the complete blood count (CBC). A univariate and multivariate regression analysis were used to investigate the association between dependent and independent variables. P-value ˂0.05 was considered statistically significant.

Results

The magnitude of cytopenia, bicytopenia, and pancytopenia were 21% (95% CI: 17.1, 25.53), 1.1% (95% CI: 0.44, 2.85), and 0.56% (95% CI: 0.01, 1.12), respectively. Furthermore, the magnitudes of anemia, leucopenia, and thrombocytopenia were 8.7% (95% CI: 6.18, 12.06), 10.9% (95% CI: 8.09, 14.59), and 5.3% (95% CI: 3.43, 8.16), respectively. Being male (AOR: 3.23; 95% CI: 1.43, 7.56), lack of exercise (AOR: 2.70; 95% CI: 1.137, 6.43), and never married (AOR: 3.90; 95% CI: 1.248, 12.18) were all associated with anemia.

Conclusion

This study showed that T2DM causes disturbances in the hematological parameters and leads to a mild level of cytopenia. It is, therefore, suggested that hematological abnormalities, especially cytopenia, should be monitored and controlled on a regular basis in T2DM patients for better prognosis and quality of life.

Hematological indices and their correlation with glucose control parameters in a prediabetic rat model

Background and Aim:

Chronic hyperglycemia in prediabetic individuals would progress to diabetes and lead to several systemic disruptions, including hematological parameters. This study aimed to investigate the correlation between prediabetes and hematological indices in a prediabetic rat model.

Materials and Methods:

Eighteen male rats were randomly divided into two groups of nine. Prediabetes was induced in nine rats by a 3-week high-fat and high-glucose diet, followed by low-dose streptozotocin (STZ) injection (30 mg/kg body weight). The oral glucose tolerance test was performed, and the fasting blood glucose (FBG) and insulin levels were measured 72 h after STZ administration. The control group of nine rats was given standard diets. At the end of the 3^rd week, the animals fasted overnight before blood collection. Blood samples were drawn and used for the analysis of the FBG and fasting insulin levels and glycated albumin to define prediabetes criteria before hematology analysis.

Results:

We found a significant increase in the FBG and insulin levels in the prediabetic versus the control group. There were decreases in red blood cells, hemoglobin, and hematocrit levels and red cell distribution in prediabetic rats versus the control. At the same time, a significant increase in the platelet count was observed in the prediabetic group. There was a positive correlation between FBG and lymphocytes and neutrophil-lymphocyte ratio in prediabetic rats. On the other hand, we found a negative correlation between white blood cell count and glycated albumin.

Conclusion:

Correlations were found in several hematological parameters in the prediabetic rat models. The changes in hematological indices in prediabetic rats may be further used as a valuable indicator of glycemic control.

The Association Between White Blood Cell Count and Insulin Resistance in Community-Dwelling Middle-Aged and Older Populations in Taiwan: A Community-Based Cross-Sectional Study

Background

Insulin resistance (IR) is a major pathophysiological factor in the development and progression of diabetes mellitus (DM). DM is highly prevalent in Taiwan and has become one of the most common health problems in family medicine and primary care. We aimed to use white blood cell count (WBC), a common physiological parameter, to develop a simple clinical prediction rule for IR in the middle-aged and old Taiwanese population.

Methods

In this cross-sectional community-based study, the participants completed a questionnaire comprising personal and medical history data and underwent anthropometric measurements and blood sampling. IR was defined as a HOMA-IR index ≥2. Independent t-test, Mann–Whitney U test, chi-square test, Pearson's correlation test, multivariate binary logistic regression, and receiver operating characteristic curves were used to evaluate the association between the WBC count and IR.

Results

A total of 398 community-dwelling middle-aged and older persons (34.9% men) with a mean age of 64.43 ± 8.45 years were enrolled for the analysis. A significant association was identified between the WBC counts and IR, with a Pearson's correlation coefficient of 0.37 (p-value &lt;0.001). Multivariate logistic regression revealed that WBC count (OR = 1.50; 95% CI = 1.25–1.81) was an independent risk factor for IR after adjusting for confounding variables. The area under the receiver operating characteristic curve for WBC count was 0.67, and the optimal threshold value was 5.65 1,000/uL.

Conclusion

A high WBC count is positively related to an increased risk of IR among middle-aged and older people in Taiwan.

Novel predictor of the occurrence of DKA in T1DM patients without infection: A combination of neutrophil/lymphocyte ratio and white blood cells

The role of inflammation has been identified in the pathogenesis of diabetic ketoacidosis (DKA). The neutrophil/lymphocyte ratio (NLR) and white blood cells (WBC) can be used to predict a systemic inflammatory response. Changes in NLR and WBC levels have never been explored in type 1 diabetes mellitus (T1DM) patients with DKA and an uninfected state. This retrospective study included a total of 644 participants. NLR and WBC were measured in the control group (n = 316) and in T1DM patients with mild-DKA (n = 92), severe-DKA (n = 52), and non-DKA (n = 184) in an uninfected state. Then, we assessed the independent predictors of DKA occurrence in T1DM patients in an uninfected state. The diagnostic performance of variables was determined by receiver operating characteristic curve analysis. Serum NLR of T1DM patients is significantly higher than that of normal controls, and if DKA occurs, NLR increases further and increases with the severity of DKA. In addition to diastolic blood pressure, blood urea nitrogen, glycated hemoglobin (HbA1c), and WBC, NLR was also independently associated with DKA in T1DM patients with an uninfected state (OR = 1.386, 95% CI: 1.127-1.705, p = 0.002). Furthermore, the diagnosis analysis showed that except for NLR and WBC, the area under the curve (AUC) of indicators with a statistical difference in patients with and without DKA were 0.747 for DKA diagnosis, and after the addition of NLR and WBC, the AUC was 0.806. The increased NLR level represents a low-cost and highly accessible predictor for DKA in T1DM patients with an uninfected state. The addition of inflammation indicators can play a statistically significant role in the prediction model of the DKA occurrence.