Clinical Significance of the Maximum Body Mass Index Before Onset of Type 2 Diabetes for Predicting Beta-Cell Function

Harutoshi Ozawa; Kenji Fukui; Sho Komukai; Yoshiya Hosokawa; Yukari Fujita; Takekazu Kimura; Ayumi Tokunaga; Junji Kozawa; Hiromi Iwahashi; Iichiro Shimomura

Disclosures

J Endo Soc. 2020;4(4) 

In This Article

Materials and Methods

Study Population

We retrospectively reviewed 1304 consecutive patients with type 2 diabetes who were admitted to Osaka University Hospital between August 1, 2010, and June 30, 2017, for treatment of poor glycemic control. Data for the present study were obtained from the medical records of Osaka University Hospital. The patient flow diagram is shown in Figure 1. A total of 58 subjects were excluded because their maximum BMI was not recorded; 298 subjects whose maximum BMI was reached after the diagnosis of type 2 diabetes mellitus were also excluded. We excluded these subjects because we could not identify their maximum BMI before onset and considered that in those patients, the maximum BMI after the development of type 2 diabetes mellitus was affected by the use of hypoglycemic agents and/or insulin and would not indicate the patients' potential beta-cell function. Furthermore, 185 patients with cancer, 51 patients with pancreatic diseases, 14 patients with liver cirrhosis, 82 patients taking diabetogenic medicines such as glucocorticoids, 126 patients with an additional secondary form of diabetes, 11 patients who had pregestational diabetes mellitus, and 36 patients with diabetes-related autoantibodies, including antibodies against glutamic acid decarboxylase, insulin, and insulinoma-associated protein 2, were excluded. In addition, 33 patients whose estimated glomerular filtration rate was less than 30 mL/min/1.73 m2 were excluded because the turnover of serum C-peptide immunoreactivity (CPR) is prolonged due to decreased renal function.[10] Finally, 410 patients were enrolled in this study.

Figure 1.

Patient flow diagram.

Study Protocol

After admission, most of the patients were treated by medical nutrition therapy plus bolus insulin therapy to improve preprandial plasma glucose levels, including fasting plasma glucose (FPG), the target level of which was below 8.4 mmol/L. We performed a blood evaluation before breakfast 12 hours after the last meal. Some of the patients were also treated with additional oral hypoglycemic agents or basal insulin. After glycemic control was almost maintained at the target levels, beta-cell function was evaluated. Beta-cell function was evaluated using the C-peptide index (CPI), which was calculated by using the following formula: F-CPR (ng/mL) × 100/FPG (mmol/l) × 18. We previously demonstrated significant positive correlations between the relative beta-cell area, indicating beta-cell mass, and various parameters of insulin secretory capacity, including CPI.[11] FPG was 7.4 ± 1.7 mmol/L at evaluation. The medications used as glucose-lowering agents before admission and at evaluation are described in Table 1. We defined the onset of type 2 diabetes mellitus as occurring when the patients had been diagnosed with type 2 diabetes mellitus based on the criteria of the American Diabetes Association[12] or started to take glucose-lowering agents. Based on their history of maximum BMI and the age at diabetes mellitus onset, we defined their MBBO.

This study was approved by the institutional ethics review board of Osaka University Hospital and was carried out in accordance with the principles of the Declaration of Helsinki. The study was announced to the public on the website of our department at Osaka University Hospital, and all patients were allowed to participate or refuse to participate in the study.

Statistical Analyses

We summarize the background variables as the mean +/– standard deviation (SD) for continuous variables and as the counts with proportions for categorical variables. We considered 3 groups based on the MBBO (low group: MBBO < 25 kg/m2, intermediate group: 25 kg/m2 ≤ MBBO < 30 kg/m2, high group: 30 kg/m2 ≤ MBBO), and the background variables are also presented as medians (interquartile range) for the continuous variables and as counts with proportions for the categorical variables according to MBBO group. The continuous and categorical variables were compared among the 3 MBBO groups using the Kruskal–Wallis test and chi-squared test, respectively.

Univariate and multivariate linear regression analyses were conducted to evaluate associations between CPI and duration of diabetes and between CPI and MBBO groups or BMI groups (low group: BMI < 25 kg/m2, high group: 25 kg/m2 ≤ BMI). In the multivariate analyses, we evaluated the relationship between CPI and the duration of diabetes adjusted by age, sex, HbA1c, and group (MBBO groups or BMI groups). To elucidate whether high MBBO or high BMI on admission was associated with high CPI, the impact of the MBBO groups or BMI groups on CPI was also assessed in the same multivariate analyses.

To investigate whether the rate of decline in CPI was different in MBBO subgroups or BMI groups, we conducted multivariate analyses with an interaction term between the duration of diabetes and the groups (MBBO groups or BMI groups). In these analyses, we report the effects of duration and groups and the magnitude of the interaction terms after adjusting for age, sex, and HbA1c. Multivariate analyses were performed for subcohorts stratified by both MBBO and BMI.

To investigate how a trait, characterized by MBBO in this study, might influence the relationship between CPI and the duration of diabetes, we conducted multiple linear regression analysis and estimated this relationship using an approximate equation: CPI = k0 + k1 × diabetes duration + k2 × MBBO, where k0, k1, and k2 are constants. If MBBO did not contribute significantly to the model, the regression lines might be almost identical (scenario 1) (Figure 2).[1] When MBBO significantly indicated that the 2 lines were at least different, multiple linear regression analysis including the interaction effect (product of duration of diabetes and MBBO) as a parameter was performed to examine the positional relations of these lines. If MBBO was significant but the interaction effect was not significant, these 2 slopes were not different (scenario 2) (Figure 2).[2] If MBBO was significant and the interaction effect was also significant, these slopes were different (scenario 3) (Figure 2).[3]

Figure 2.

Conceptual figures to show the potential contribution of a variable in a linear regression. Ellipses represent the scatter of data, and lines represent regression lines. (1) Two groups with the same intercept and slope but with different data ranges; (2) two groups with the same slope but different intercepts; and (3) two groups differing in both slope and intercept.

The significance level in all analyses was P < .05, and all statistical analyses were performed with JMP® Pro 13 (SAS Institute, Inc., Cary, NC, US).

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