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

Results

Clinical Characteristics of the Study Subjects

The clinical characteristics of the subjects are shown in Table 1. The mean HbA1c was 9.0% (75 mmol/mol), and most of the subjects had poor glycemic control, reflected by the need to be admitted to our hospital. Age at entry and age at type 2 diabetes mellitus diagnosis in patients with MBBO ≥ 30 kg/m2 were lower than those in the other groups. In the 3 groups, the duration of diabetes was not different, but a higher MBBO value was associated with a higher CPI value. Before admission, 38.8% of subjects were treated with sulfonylurea, 2.7% with glinide, 24.9% with biguanide, 8.3% with thiazolidinedione, 17.3% with α-glucosidase inhibitor, 35.4% with dipeptidyl peptidase-4 inhibitor, 2.4% with sodium glucose cotransporter 2 inhibitor, 4.1% with glucagon-like peptide-1 receptor agonist, 26.3% with insulin, and 21.2% with no medication (Table 1). At the laboratory evaluation, 14.9% of subjects were treated with insulin secretagogues, including sulfonylurea, glinide, dipeptidyl peptidase-4 inhibitor, or glucagon-like peptide-1 receptor agonist, and 45.1% were treated with basal insulin (neutral protamine Hagedorn or long-acting insulin) (Table 1).

Multivariate Regression Analyses Between CPI and Various Clinical Parameters

To investigate the associations between CPI and other clinical variables, we first performed a univariate analysis between CPI and other variables. This analysis revealed significant associations between CPI and age, sex, age at diagnosis, duration of diabetes, BMI on admission and MBBO (Table 2, univariate analyses). This analysis also revealed an association between CPI and HbA1c at the margin of statistical significance (Table 2, univariate analyses). Baseline variables with P values < .20 in the univariate analysis were included in the multivariable models. Age was strongly correlated with age at diagnosis, and age and age at diagnosis had similar characteristics. We adopted only age in the multiple linear regressions. MBBO was strongly correlated with BMI on admission, so we entered both variables in each model separately to avoid multicollinearity. A multiple linear regression analysis was performed to test the independent association of CPI with MBBO groups and duration of diabetes. This analysis revealed that after adjustment for age, sex, and HbA1c, MBBO groups and duration of diabetes were correlated with CPI (Table 2, multivariate 1). CPI was independently associated with the duration of diabetes and MBBO.

Furthermore, a multiple linear regression analysis was performed to test the independent association of CPI with BMI on admission groups and duration of diabetes. This analysis revealed that after adjustment for age, sex, and HbA1c, BMI on admission groups and duration of diabetes were associated with CPI (Table 2, multivariate analysis 2). CPI was also independently associated with BMI on admission.

CPI Correlates With the Duration of Diabetes in Each MBBO Group and in Each BMI on Admission Group

Figure 3A shows a scattergram and a linear regression analysis of the CPI and duration of diabetes in all subjects. Scattergrams and linear regression analyses of CPI and duration of diabetes in each MBBO group are shown in Figure 3B (MBBO < 25), Figure 3C (25 ≤ MBBO < 30), and Figure 3D (30 ≤ MBBO). CPI was negatively correlated with the duration of diabetes in all 3 groups (MBBO < 25: n = 75, r = –0.40, P = .004; 25 ≤ MBBO < 30; n = 164, r = –0.40, P < .0001; 30 ≤ MBBO: n = 171, r = –0.43, P < .0001) (Figures. 3B, 3C, and 3D, respectively). In addition, scattergrams and linear regression analyses of CPI and duration of diabetes in each BMI on admission group are shown in Figure 3E (BMI on admission < 25) and Figure 3F (25 ≤ BMI on admission). CPI was negatively correlated with the duration of diabetes in the two groups (BMI on admission < 25: n = 206, r = –0.27, P < .0001; 25 ≤ BMI on admission: n = 204, r = –0.34, P < .0001).

Figure 3.

Scattergrams and linear regression analyses between CPI and the duration of diabetes. CPI was negatively correlated with the duration of diabetes in all single analyses. (A) All patients. (B) Patients whose MBBO was less than 25 kg/m2. (C) Patients whose MBBO was greater than 25 kg/m2 and less than 30 kg/m2. (D) Patients whose MBBO was greater than 30 kg/m2. (E) Patients whose BMI on admission was less than 25 kg/m2. (F) Patients whose BMI on admission was greater than 25 kg/m2. MBBO: maximum BMI before onset. Abbreviation: r, partial regression coefficient.

The Estimated CPIs at Diagnosis are Different, but the Rates of CPI Decline are not Different in Each MBBO Group

To determine the interactions between diabetes duration and MBBO, we performed a multiple linear regression analysis with variables including age, sex, HbA1c, diabetes duration, MBBO, and the product of diabetes duration and MBBO (Table 3, model 1). The P value of this interaction was greater than .05, indicating that the interaction between diabetes duration and MBBO had no significant effect (Table 3, model 1). This fact indicated that the rate of CPI decline was not different among MBBO subgroups after we adjusted CPI for age, sex and HbA1c (Table 3, subgroup analyses by MBBO group). In the analysis performed to test the independent association of CPI with MBBO groups and duration of diabetes, the categorical variables in the MBBO groups were significant (Table 2, multivariate analysis 1). These findings indicated that the estimated CPIs at diagnosis were different but that the rates of CPI decline were not different between the 3 MBBO groups, supporting scenario 2 in Figure 2.[2]

The Estimated CPIs at Diagnosis, and the Rates of CPI Decline are Different in Each BMI on Admission Group

To determine the interactions between diabetes duration and BMI on admission, we performed a multiple linear regression analysis with variables including age, sex, HbA1c, diabetes duration, BMI on admission, and the product of diabetes duration and BMI on admission (Table 3, model 2). The P value of this interaction was .0083, indicating that the interaction between diabetes duration and BMI on admission has a significant effect (Table 3, model 2). This result suggested that the rates of CPI decline were different in the BMI on admission subgroups after we adjusted CPI for age, sex, and HbA1c (Table 3, subgroup analyses by BMI group). In the analysis performed to test the independent association of CPI with BMI on admission groups and duration of diabetes, the categorical variables of the BMI on admission groups were significant (Table 2, multivariate analysis 2). These findings indicated that the estimated CPIs at diagnosis were different and that the rates of CPI decline were also different in the two BMI groups on admission, supporting scenario 3 in Figure 2.[3]

processing....