Association of Total and Differential White Blood Cell Counts to Development of Type 2 Diabetes in Mexican Americans in Cameron County Hispanic Cohort

Abstract

Objective To evaluate the relationship between total and differential White Blood Cell (WBC) counts with time to transition to type 2 diabetes in Mexican Americans using prospective data from the Cameron County Hispanic Cohort (CCHC). Results Multivariable Cox proportional hazards regression models revealed that obese Mexican-American cohort participants whose total WBC or granulocyte count increased over time had 1.39 and 1.35 times higher risk respectively of transition to type 2 diabetes when compared to overweight participants. The granulocyte or total WBC count in participants with BMI≥35 were significant risk factors for transition to type 2 diabetes. Conclusions Increased total WBC and WBC differential counts, particularly lymphocytes and granulocytes, are associated with risk of transition to type 2 diabetes in obese Mexican Americans, after adjusting for other potential confounders. Screening and monitoring the WBC counts, including lymphocytes and granulocytes can help with monitoring potential transition to type 2 diabetes.

A novel U‐shaped relationship between BMI and risk of generalized aggressive periodontitis in Chinese: A cross‐sectional study

“…WBC count increased with increase of BMI in BMI from 22 to 28, it was consistent with the previous studies which suggested that BMI had a significantly positive correlation with WBC counts. [16][17][18] When BMI was in 18 to 22, however, WBC count increased with the decreased of BMI. There was little evidence supporting the negative relationship between low BMI and WBC.…”

“…15 The association between BMI and WBC count was investigated. [16][17][18] Nagasawa et al found that WBC count had a positive correlation with BMI in a cross-sectional study of 3,594 Japanese (p = 0.001). 16 And similar results were in type 2 diabetes 17 , coronary, cancer and all-cause mortality 18 .…”

The Effect of Ignoring Statistical Interactions in Regression Analyses Conducted in Epidemiologic Studies: An Example with Survival Analysis Using Cox Proportional Hazards Regression Model

“…To demonstrate the importance of identifying interactions in epidemiologic studies, in this section we referred to an earlier finding from analyses of CCHC data with Cox proportional hazards regression model [ 8 ]. Briefly, using data from 636 participants in the CCHC, this study assessed crude and adjusted effects of total and differential WBC counts (lymphocytes, monocytes, and granulocytes), C-reactive protein (CRP), and Body Mass Index (BMI) in relation to time to progression to type 2 diabetes in Mexican Americans.…”

“…Our result indicated that family diabetes history and pre-diabetes status did not satisfy the proportional hazards assumption; hence all models that included these two variables were stratified by these variables by including strata statement in SAS Proc Phreg [ 18 ]. Details regarding the model development are described elsewhere [ 8 ].…”

“…Specifically, we studied the effect of ignoring interactions in a Cox proportional hazards regression model. In addition, to demonstrate the importance of including interactions in regression models in real life epidemiologic studies, we compared results from additive and interactive models using data from the Cameron County Hispanic Cohort (CCHC) [ 8 ]. For this we specifically focused on investigating the role of peripheral white blood cells (WBC) counts, WBC differential counts and BMI in association with the time to incidence of type 2 diabetes while controlling for the effect of other known type 2 diabetes risk factors.…”