Association between initial mental health status and glycemic control in pediatric diabetes

Jeongho Han; Mi Yang; Hakyung Lee; Dong Jun Ha; Hwa Young Kim; Hee Jeong Yoo; Jae Hyun Han; Jaehyun Kim

doi:10.6065/apem.2550236.118

Ann Pediatr Endocrinol Metab > Volume 31(2); 2026 > Article

Han, Yang, Lee, Ha, Kim, Yoo, Han, and Kim: Association between initial mental health status and glycemic control in pediatric diabetes

Original Article

Annals of Pediatric Endocrinology & Metabolism 2026;31(2): 101-109.

Published online: October 11, 2025

DOI: https://doi.org/10.6065/apem.2550236.118

Association between initial mental health status and glycemic control in pediatric diabetes

Jeongho Han¹

, Mi Yang¹

, Hakyung Lee¹

, Dong Jun Ha¹

, Hwa Young Kim^1,²

, Hee Jeong Yoo^3,⁴

, Jae Hyun Han³

, Jaehyun Kim^1,²

¹Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea

²Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea

³Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea

⁴Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea

Address for correspondence: Jaehyun Kim Department of Pediatrics, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam 13620, Korea Email: jaehyun.kim@snu.ac.kr

Received July 21, 2025 Revised August 24, 2025 Accepted September 3, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

See commentary "A commentary on "Association between initial mental health status and glycemic control in pediatric diabetes"" in Volume 31 on page 87.

Abstract

Purpose

Psychiatric conditions are common in children and adolescents with diabetes and can hinder disease management. In this study, we examined whether mental health status at diagnosis predicts glycemic control at 1 year.

Methods

We included 57 patients aged 6–18 years diagnosed with type 1 or type 2 diabetes between 2019 and 2023 at Seoul National University Bundang Hospital. Mental health was assessed within 3 months of diagnosis using the Eating Disorder Inventory-2, Children’s Depression Inventory, and Child Behavior Checklist (CBCL) for ages 6–18. Poor glycemic control was defined as glycated hemoglobin >6.5% at 1 year. Associations between screening results and glycemic control were analyzed using Fisher exact test and multivariate logistic regression.

Results

Of the 57 patients, 32 (56.1%) had type 1 diabetes, and the mean age at diagnosis was 12.9±3.1 years; 31 (54.4%) were male. Poor glycemic control at 1 year was observed in 16 patients (28.1%). Although individual subscale positivity was not significantly associated with glycemic control, borderline somatic complaints on the CBCL were significantly associated with poor control (p=0.022). In multivariate analysis, having 2 or more positive CBCL subscales showed a trend toward association with poor glycemic control (adjusted odds ratio=21.47, p=0.054).

Conclusions

Early psychological screening, especially for somatic symptoms or multiple psychological problems, may help identify those at risk for poor glycemic control in pediatric diabetes. These findings underscore the importance of early detection and intervention in optimizing diabetes management.

Keywords: Type 1 diabetes mellitus, Type 2 diabetes mellitus, Mental health, Adolescent, Child, Korea

Highlights

· This study examined whether mental health screening at diagnosis is associated with 1-year glycemic control in 57 children and adolescents with type 1 or type 2 diabetes.

· Although individual screening tools were not significantly associated with glycemic control, borderline somatic complaints on the Child Behavior Checklist and cumulative psychological burden showed potential links to poor control.

· These findings suggest that early, multidimensional psychological assessment may help identify pediatric patients at risk for suboptimal glycemic control and guide targeted interventions.

Introduction

The global prevalence of type 1 (T1DM) and type 2 diabetes mellitus (T2DM) in children and adolescents is steadily increasing [1,2]. Similarly, South Korea has reported a rising incidence of newly diagnosed pediatric cases [3-5]. With the continued rise in pediatric diabetes, it becomes increasingly important to understand how age-related psychological vulnerabilities impact its clinical course.

Children and adolescents with diabetes mellitus are at heightened risk for psychiatric comorbidities, including depression, anxiety, and eating disorders [6,7]. Psychological distress at diagnosis may adversely affect selfmanagement, treatment adherence, and long-term metabolic outcomes [8-10]. In children and adolescents with T1DM, depressive symptoms have been associated with poor glycemic control [10,11]. Moreover, comorbid eating disorders have been linked to a higher incidence of diabetic ketoacidosis (DKA) [12]. Similarly, in T2DM, depression has been associated with poor glycemic control [13]. However, most studies assess mental health after the diagnosis and progression of the disease, limiting our understanding of early psychological risk factors.

Recognizing the impact of mental health on diabetes outcomes, the International Society for Pediatric and Adolescent Diabetes Clinical Practice Consensus Guidelines recommend conducting a psychosocial assessment at the time of diagnosis [14]. However, routine early mental health screening is not widely implemented in clinical practice, and its long-term prognostic value remains insufficiently studied. Therefore, this study aimed to evaluate whether early psychological screening at the time of diagnosis is associated with subsequent glycemic control in children and adolescents with T1DM or T2DM.

Methods

1. Study design and participants

This retrospective observational study included pediatric patients aged 6–18 years who were newly diagnosed with T1DM or T2DM at Seoul National University Bundang Hospital (SNUBH) between January 2019 and December 2023. The minimum age of 6 years was chosen to align with the eligibility criteria for the Child Behavior Checklist (CBCL) for Ages 6–18, a component of the standardized mental health screening protocol.

At diagnosis, most patients were hospitalized to receive structured inpatient diabetes education and achieve glycemic stabilization. During hospitalization, they were referred to the psychiatry department for mental health screening.

Inclusion criteria were completion of mental health screening within 3 months of diagnosis (87.7% completed within the first week) and availability of at least 1 year of follow-up data. Exclusion criteria included preexisting neurodevelopmental or psychiatric disorders (n=3), screening completed more than 1 year after diagnosis (n=3), insufficient follow-up data (n=4), or no psychiatric screening after diagnosis (n=147). After applying these criteria, 57 patients were included in the final analysis.

2. Data collection

Clinical data were retrospectively extracted from electronic medical records, including age at diagnosis, sex, pubertal status, diabetes type (T1DM or T2DM), presence of DKA at diagnosis, family history of diabetes, baseline glycated hemoglobin (HbA1c), and body mass index (BMI) z-scores. HbA1c values at 1 year were collected, and glycemic control was further dichotomized as good (HbA1c ≤ 6.5%) or poor (HbA1c > 6.5%) based on a relatively strict threshold in accordance with recent guideline revisions reflecting advancements in diabetes technology and the trend toward tighter glycemic targets in pediatric care [15-17]. Caregivers also completed a structured questionnaire on family socioeconomic status and parental education, which was dichotomized as “high school or less” versus “college or higher.” Based on caregiver reports, all families were classified as lowermiddle to upper-middle socioeconomic status; none fell into the lowest or highest income brackets.

This study was approved by the institutional review board (IRB) of SNUBH (IRB No. B-2407-912-110).

3. Mental health assessment

Mental health was assessed using validated tools appropriate for children and adolescents with diabetes, targeting depressive symptoms and disordered eating behaviors [18]. Self-report measures included the Eating Disorder Inventory-2 (EDI-2) and the Children’s Depression Inventory (CDI), both of which have been validated in the Korean population [19,20]. The EDI-2 is a selfreport questionnaire designed to evaluate cognitive and behavioral traits associated with eating disorders. For this study, only 3 subscales, drive for thinness, bulimia, and body dissatisfaction, were used, as they are most relevant to disordered eating in adolescents and helped minimize respondent burden [21,22]. Given that the EDI-2 lacks established clinical cutoffs, individuals scoring in the top 10th percentile based on the internal distribution of the study sample were classified as positive. The CDI is a self-report instrument that assesses depressive symptoms across emotional, cognitive, and behavioral domains in children and adolescents [23]. To complement self-reported measures and capture broader psychosocial functioning, the CBCL was used as a parentreport tool. The CBCL 6–18 assesses a wide range of emotional and behavioral problems as well as adaptive functioning in the areas of social competence, academic performance, and overall adjustment. Clinical positivity for each subscale was determined using standardized T-score cutoffs [24].

4. Statistical analysis

Descriptive statistics were used to summarize baseline characteristics. Categorical variables were compared using the chi-square test or Fisher exact test, as appropriate, and continuous variables were analyzed using an independent t-test. Associations between glycemic control and baseline characteristics, including sex, diabetes type, DKA, family history, age, pubertal status, BMI z-score, socioeconomic status, and parental education, were examined. Mental health screening results from all subscales of each tool (EDI-2, CDI, and CBCL) were analyzed as separate categorical variables to assess their individual associations with poor glycemic control. For the CBCL, T-scores within the clinical range were initially used to define positive results.

In addition to dichotomized outcome analyses, we compared mean one-year HbA1c values between positive and negative groups for each screening measure. Given the small sample size and the nonnormal distribution of HbA1c values, comparisons were conducted using the Wilcoxon rank-sum test. Results were visualized using boxplots (Fig. 1), and detailed values are presented in Supplementary Table 1.

Subgroup analyses stratified by diabetes type were also performed. In addition, analyses were repeated in the subset of patients aged ≥10 years to evaluate associations between mental health screening results and glycemic control in older children. Patients in this age group were considered more capable of accurately conveying their own psychological state. As a sensitivity analysis, the CBCL subscale analysis was repeated in the total population using borderline thresholds to examine whether subclinical emotional or behavioral concerns were associated with glycemic control. Fisher exact test was used for all comparisons owing to the small cell size.

To assess the association between cumulative mental health risks and glycemic control, multivariate logistic regression analysis was conducted. Two separate models were constructed: one evaluating the cumulative number of positive CBCL syndrome scale subscales (0, 1, or ≥2), and the other assessing cumulative risk across 4 mental health domains: EDI-2 total score, CDI depression, CBCL syndrome scale total score, and CBCL adaptive functioning. Covariates were selected using a stepwise variable selection method based on the lowest Akaike Information Criterion, and the final model was adjusted for sex, age at diagnosis, presence of DKA at diagnosis, and paternal education level, with adjusted odds ratios (aORs), 95% confidence intervals (CIs), and P-values reported for each level of cumulative risk.

All analyses were performed using R ver. 4.5.0 (R Foundation for Statistical Computing, Austria). A P-value <0.05 was considered statistically significant.

Results

1. Clinical characteristics

The mean age at diagnosis was 12.92±3.14 years, and 54.4% of patients were males. Of the 57 patients, 32 (56.1%) had T1DM and 25 (43.9%) had T2DM. DKA was present at diagnosis in 20 patients (35.1%). At 1 year, 41 patients (71.9%) achieved good glycemic control, whereas 16 (28.1%) exhibited poor glycemic control. When stratified by glycemic control, female sex was significantly associated with poor glycemic control (P=0.028). In addition, higher paternal educational attainment was significantly more common in the good glycemic control group compared to the poor glycemic control group (P=0.019). No significant differences were observed between the groups with respect to diabetes type, DKA at diagnosis, family history of diabetes, socioeconomic status, or maternal education. Similarly, there were no significant differences in age at diagnosis, pubertal status, BMI z-scores, or baseline HbA1c levels (Table 1).

When stratified by diabetes type, T2DM patients were older, had more advanced pubertal development (predominantly late puberty), had higher BMI z-scores, were more often male, and had a family history of diabetes, whereas DKA was more common in T1DM. HbA1c at diagnosis was slightly higher in T1DM, but one-year levels were similar. Socioeconomic status and parental education did not differ between groups (Supplementary Table 2).

2. Associations between mental health screening results and 1-year glycemic control

Based on mental health screening results, 18 patients (31.6%) screened positive in at least one domain when the EDI-2 was excluded, as it uses population-based percentile cutoffs rather than clinical thresholds. When the EDI-2 was included, this proportion increased to 23 patients (40.4%).

Table 2 presents the associations between mental health screening results and glycemic control at 1 year. Positive findings on individual subscales from the EDI-2, CDI, and CBCL were not significantly associated with poor glycemic control. Within the EDI-2 domain, no significant differences in glycemic control were observed between the positive and negative groups. Clinically significant depressive symptoms were identified in 9 patients (15.8%) using the CDI, but also not significantly associated with glycemic control. Several CBCL syndrome subscales, including anxiety/depression, withdrawal, delinquent behavior, and aggression, demonstrated numerically higher rates of poor control among those with positive scores, but none reached statistical significance. Likewise, positivity on the internalizing and externalizing problem scales, as well as on the total score, was not associated with glycemic control. Among the 53 patients with available CBCL adaptive functioning data, no significant association was observed.

Fig. 1 illustrates 1-year HbA1c values stratified by screening results. HbA1c values did not significantly differ between groups for most measures, although higher levels were observed in the CBCL anxiety/depression and withdrawal subscales (P=0.028, Wilcoxon rank-sum test).

When stratified by diabetes type, no significant associations were identified (Supplementary Tables 3 and 4). In the subgroup of patients aged ≥10 years, 22 of 46 (47.8%) demonstrated at least one positive finding on mental health screening, indicating a higher prevalence of abnormal results in older children. However, these findings also did not reach statistical significance in relation to glycemic control (Supplementary Table 5).

In the sensitivity analysis using borderline thresholds, somatic complaints were the only CBCL subscale significantly associated with poor glycemic control (60.0% vs. 28.1%, P=0.022). Other syndrome subscales, including anxiety/depression, withdrawal, and social problems, were associated with numerically higher rates of poor control among those with positive scores; however, none reached statistical significance (Table 3).

3. Cumulative mental health risk and glycemic control

Table 4 presents the multivariate associations between cumulative mental health risk and 1-year glycemic control. Among the 6 patients with 2 or more positive CBCL syndrome subscales, 4 (66.7%) exhibited poor glycemic control. This subgroup showed an elevated aOR (21.47; 95% CI, 1.03–774.77; P=0.054), suggesting a potential threshold effect. In contrast, having only one positive CBCL subscale was not significantly associated with glycemic control (aOR, 1.18; 95% CI, 0.03–41.87; P=0.757). When cumulative risk was defined across the 4 mental health domains, EDI-2 total score, CDI, total CBCL syndrome score, and CBCL adaptive functioning, no significant associations were observed. The aORs were 1.28 (95% CI, 0.11–12.71) for one positive domain and 1.31 (95% CI, 0.07–20.99) for 2 or more positive domains.

Discussion

In this study of children and adolescents with newly diagnosed diabetes, approximately one-quarter of participants exhibited poor glycemic control at 1 year. Individual positive screens on standardized mental health measures, including the EDI-2, CDI, and CBCL, were not significantly associated with glycemic control when analyzed independently. Consistent with the primary analysis on dichotomized glycemic outcomes, most individual screening measures were not associated with 1-year HbA1c levels. While positive findings were noted in a few CBCL subscales, the small number of cases limits their interpretability and precludes definitive conclusions. However, a potential cumulative effect was observed, particularly within the CBCL syndrome scale domain.

Previous studies in T1DM populations have identified female sex as a risk factor for suboptimal glycemic outcomes, whereas higher parental education has been shown to favor better control; however, such trends have been less evident in T2DM [25-28]. Consistent with these reports, our study also demonstrated poorer glycemic outcomes among female patients and those with lower paternal education. Maternal education was not associated with glycemic outcomes in our study, possibly due to the limited sample size.

Most baseline differences between T1DM and T2DM in our cohort were consistent with other studies [29]. In our cohort, male predominance among T2DM patients was also observed, in line with prior findings from South Korea [4]. Subgroup analyses by diabetes type were limited by the small sample size.

In the present study, more than one-third of the cohort exhibited some degree of psychological vulnerability. When limited to patients aged ≥10 years, nearly half met the criteria, suggesting that the burden may be particularly pronounced among adolescents newly diagnosed with diabetes. Consistent with our findings, previous studies in adolescents with T1DM have reported high rates of psychological concerns [30-33]. Although relatively few studies have examined adolescents with T2DM, existing evidence suggests that psychiatric comorbidities may be more prevalent in this group than in their peers with T1DM [34]. Collectively, these results suggest that psychological challenges are widespread in the pediatric diabetes population, reinforcing the importance of routine mental health screening in clinical care.

Our study demonstrates that the clinical significance of psychological symptoms may become more apparent when subclinical thresholds are considered. To explore this, we conducted a sensitivity analysis using borderline CBCL thresholds, based on prior Korean studies reporting only modest CBCL score differences among well-controlled, poorly controlled, and healthy adolescents [7]. Given that clinically significant scores were infrequent in most CBCL subscales, using strict clinical cutoffs may have limited sensitivity in capturing meaningful associations with glycemic control. Indeed, previous reports have indicated that even subthreshold psychiatric symptoms can be associated with functional impairment in children and adolescents, underscoring their potential clinical relevance [35,36]. In the sensitivity analysis, somatic complaints emerged as the only subscale significantly associated with poor glycemic control. These findings suggest that somatic symptoms may serve as early indicators of adverse metabolic outcomes in pediatric patients with diabetes. Supporting this interpretation, greater somatic symptom burden in adults has been linked to impaired glucose metabolism and to challenges in diabetes self-management [37,38].

Cumulative psychological burdens that manifest as multiple emotional or behavioral difficulties may contribute to poor glycemic control through several mechanisms, such as lower adherence to treatment, impaired family functioning, and diminished motivation for selfcare under stress. These findings underscore the importance of multidimensional mental health screening to detect not only diagnosable psychiatric disorders but also subclinical psychological difficulties that may influence the course of disease. In addition to early identification, timely and appropriate psychological interventions are essential for improving outcomes. Children and adolescents with diabetes who receive psychological support have been shown to experience fewer episodes of severe hypoglycemia and DKA, as well as improved glycemic control [39,40]. As universal referral to mental health services may not be feasible, targeted screening can help prioritize those most at risk and guide the allocation of psychological care to improve long-term metabolic outcomes.

This study has several limitations. First, there is a potential for selection bias that may have influenced the observed associations. The study’s single-center design, relatively small sample size, and the exclusion of many eligible patients during the initial phase of the mental health screening program may have introduced systematic differences between those included and excluded from the analysis. Second, the small sample size limits our statistical power to detect modest or subtle associations. Third, while validated instruments were used, some constructs, such as disordered eating, were assessed using tools not specific to diabetes due to the lack of a culturally validated Diabetes Eating Problem Survey in Korean populations. Fourth, mental health screening was conducted only once, shortly after diabetes diagnosis. This is a period during which acute diabetes-related symptoms (e.g., weight loss or changes in appetite) may have influenced patient responses, particularly on eating-related items. Although parental psychological status plays a critical role in both the psychological wellbeing and glycemic control of pediatric patients, it was not assessed in this study, potentially overlooking an important determinant of patient outcomes [41,42]. Fifth, quality of life, which represents a key outcome and may influence both psychological well-being and glycemic control, was not evaluated in this study. Lastly, the study’s observational and cross-sectional design precludes both the evaluation of changes in psychological status over time and any causal inference regarding its relationship with long-term glycemic control.

Despite these limitations, our findings suggest that early psychological screening, particularly for somatic symptoms, may help identify patients with pediatric diabetes at risk for poor glycemic control. The observed cumulative effect reinforces the value of considering broader emotional and behavioral patterns, rather than focusing solely on isolated symptoms.

In conclusion, this study suggests that early psychological screening at the time of diabetes diagnosis may help identify pediatric patients at risk for suboptimal glycemic control. Somatic symptoms emerged as a potential early marker, and the presence of multiple psychological risk factors was associated with a greater likelihood of poor glycemic control, highlighting the need for further investigation. These findings support the integration of routine mental health screening into pediatric diabetes care to enable timely, individualized interventions and improve long-term disease management. Ongoing psychological assessments throughout the follow-up may be necessary to address evolving mental health needs. Given the key role of parents in pediatric diabetes management, it may also be important to evaluate their mental health. Further longitudinal research is warranted to clarify the dynamic relationship between psychological well-being and sustained glycemic control, and to evaluate the impact of parental mental health on pediatric diabetes outcomes.

Supplementary materials

Supplementary Tables 1-5 are available at https://doi.org/10.6065/apem.2550236.118.

Supplementary Table 1.

Associations between mental health screening results and 1-year HbA1c

apem-2550236-118-Supplementary-Table-1,2.pdf

Supplementary Table 2.

Baseline characteristics of participants stratified by diabetes type (T1DM vs. T2DM)

apem-2550236-118-Supplementary-Table-1,2.pdf

Supplementary Table 3.

Associations between mental health screening results and 1-year glycemic control in T1DM

apem-2550236-118-Supplementary-Table-3,4.pdf

Supplementary Table 4.

Associations between mental health screening results and 1-year glycemic control in T2DM

apem-2550236-118-Supplementary-Table-3,4.pdf

Supplementary Table 5.

Associations between mental health screening results and 1-year glycemic control in participants aged ≥10 years

apem-2550236-118-Supplementary-Table-5.pdf

Notes

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Funding

This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The data that support the findings of this study can be provided by the corresponding author upon reasonable request.

Author contribution

Conceptualization: JH, JK, HJY, JHH; Data curation: JH, HL, DJH, JHH; Formal analysis: JH, MY; Methodology: JH, JK, HJY, JHH; Project administration: JK, HJY; Visualization: JH, MY, HYK; Writing - original draft: JH; Writing - review & editing: JH, JK, HJY, JHH

Fig. 1.

One-year HbA1c according to psychological screening results. Boxes represent the interquartile range with the median line, whiskers extend to 1.5× IQR, and outliers are shown as points. No significant differences were observed between positive and negative groups for most measures, except for higher HbA1c in the CBCL anxiety/depression and withdrawal subscales (P=0.028, Wilcoxon rank-sum test). HbA1c, glycated hemoglobin; EDI-2, Eating Disorder Inventory-2; CDI, Children’s Depression Inventory; CBCL, Child Behavior Checklist for ages 6–18; IQR, interquartile range.

Table 1.

Baseline characteristics of participants stratified by one-year glycemic control status

Variable	Total (n=57)^†	HbA1c ≤6.5%^‡ (n=41)	HbA1c >6.5%^‡ (n=16)	P-value
Sex				0.028
Male	31 (54.4)	26 (83.8)	5 (16.1)
Female	26 (45.6)	15 (57.7)	11 (42.3)
Family history of DM				0.517
No	15 (26.3)	12 (80.0)	3 (20.0)
Yes	42 (73.7)	29 (69.0)	13 (31.0)
Diabetes type				0.992
T1DM	32 (56.1)	23 (71.9)	9 (28.1)
T2DM	25 (43.9)	18 (72.0)	7 (28.0)
DKA at diagnosis				0.392
No	37 (64.9)	28 (75.7)	9 (24.3)
Yes	20 (35.1)	13 (65.0)	7 (35.0)
Economic level				0.131
Low-middle	7 (12.3)	4 (57.1)	3 (42.9)
Middle	42 (73.7)	29 (69.0)	13 (31.0)
High-middle	8 (14.0)	8 (100)	0 (0)
Father’s education				0.019
High school	15 (26.3)	7 (46.7)	8 (53.3)
College or higher	42 (73.7)	34 (81.0)	8 (19.0)
Mother’s education				0.201
High school	17 (29.8)	10 (58.8)	7 (41.2)
College or higher	40 (70.2)	31 (77.5)	9 (22.5)
Age at diagnosis (yr)	12.92±3.14	12.70±3.22	13.50±2.94	0.370
Puberty (n=52)				0.680
Prepubertal	11 (21.2)	9 (81.8)	2 (18.1)
Early pubertal	7 (13.5)	5 (71.4)	2 (28.6)
Late pubertal	34 (65.4)	22 (64.7)	12 (35.3)
BMI z-score	0.50±1.74	0.61±1.56	0.22±2.18	0.446
HbA1c at diagnosis (%)	12.47±1.92	12.30±2.04	13.00±1.52	0.211

Values are presented as number (%) or mean±standard deviation.

HbA1c, glycated hemoglobin; DM, diabetes mellitus; T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; DKA, diabetic ketoacidosis; BMI, body mass index.

P-values were calculated using Fisher exact or chi-square tests for categorical variables, and Student t-test for continuous variables, as appropriate.

^† Percentages in the total column are based on the total sample.

^‡ Percentages in the glycemic control columns were calculated for each group.

Table 2.

Associations between mental health screening results and one-year glycemic control

Measure	Total positive^†	HbA1c ≤6.5%^‡	HbA1c >6.5%^‡	P-value
EDI-2 (top 10%) subscales
Drive for thinness	7 (12.3)	5 (71.4)	2 (28.6)	>0.999
Body dissatisfaction	6 (10.5)	5 (83.3)	1 (16.7)	0.665
Bulimia	6 (10.5)	5 (83.3)	1 (16.7)	0.665
Total score	6 (10.5)	4 (66.7)	2 (33.3)	>.999
CDI depression	9 (15.8)	5 (55.6)	4 (44.4)	0.250
CBCL syndrome scales
Anxiety/depression	2 (3.5)	0 (0)	2 (100)	0.075
Withdrawal	2 (3.5)	0 (0)	2 (100)	0.075
Somatic complaints	5 (8.8)	2 (40.0)	3 (60.0)	0.129
Social problems	3 (5.3)	1 (33.3)	2 (66.7)	0.187
Schizoid/compulsive behavior	1 (1.8)	0 (0)	1 (100.0)	0.281
Attention problems	1 (1.8)	1 (100)	0 (0)	>0.999
Delinquent behavior	2 (3.5)	0 (0)	2 (100)	0.075
Aggressive behavior	2 (3.5)	0 (0)	2 (100)	0.075
Internalizing problems	8 (14.0)	4 (50.0)	4 (50.0)	0.202
Externalizing problems	4 (7.0)	2 (50.0)	2 (50.0)	0.312
Total score	6 (10.5)	3 (50.0)	3 (50.0)	0.335
CBCL adaptive functioning (n=53)	6 (13.2)	4 (66.7)	2 (33.3)	0.333

Values are presented as number (%).

HbA1c, glycated hemoglobin; EDI-2, Eating Disorder Inventory-2; CDI, Children’s Depression Inventory; CBCL, Child Behavior Checklist for ages 6–18.

All comparisons were performed using Fisher exact test because of the small sample size.

^† Percentages in the total positive column are based on the total sample.

^‡ Percentages in the glycemic control columns were calculated for each group.

Table 3.

Associations between Child Behavior Checklist subscale positivity based on borderline thresholds and 1-year glycemic control

Measure	Total positive^†	HbA1c ≤6.5%^‡	HbA1c >6.5%^‡	P-value
Anxiety/depression	3 (5.3)	1 (33.3)	2 (66.7)	0.187
Withdrawal	6 (10.5)	3 (50.0)	3 (50.0)	0.335
Somatic complaints	10 (17.5)	4 (40.0)	6 (60.0)	0.022
Social problems	8 (14.0)	4 (50.0)	4 (50.0)	0.202
Schizoid/compulsive behavior	10 (17.5)	5 (50.0)	5 (50.0)	0.124
Attention problems	5 (8.8)	3 (60.0)	2 (40.0)	0.613
Delinquent behavior	2 (3.5)	0 (0)	2 (100)	0.075
Aggressive behavior	5 (8.8)	3 (60.0)	2 (40.0)	0.613
Internalizing problems	15 (26.3)	9 (60.0)	6 (40.0)	0.317
Externalizing problems	10 (17.5)	7 (70.0)	3 (30.0)	>0.999
Total score	12 (21.1)	8 (66.7)	4 (33.3)	0.723

Values are presented as number (%).

HbA1c, glycated hemoglobin.

All comparisons were performed using Fisher exact test because of the small sample size.

^† Percentages in the total positive column are based on the total sample.

^‡ Percentages in the glycemic control columns were calculated for each group.

Table 4.

Multivariate associations between cumulative risk and glycemic control

Mental health risk indicator	Adjusted OR (95% CI)	P-value
Cumulative risk in CBCL syndrome scales
1 Positive screen	1.18 (0.03–41.87)	0.757
≥2 Positive screens	21.47 (1.03–774.77)	0.054
Cumulative risk across mental health domains^†
1 Positive domain	1.28 (0.11–12.71)	0.834
≥2 Positive domains	1.31 (0.07–20.99)	0.845

Adjusted for sex, age at diagnosis, presence of DKA at diagnosis, and paternal educational level.

OR, odds ratio; CI, confidence interval; EDI-2, Eating Disorder Inventory-2; CDI, Children’s Depression Inventory; CBCL, Child Behavior Checklist for ages 6-18.

^† Cumulative risk was based on the number of positive results across 4 mental health domains: total EDI-2 score, CDI depression, total CBCL syndrome scale score, and CBCL adaptive functioning.

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