Abstract
Purpose:
We assessed whether resilience is associated with hemoglobin A1c (HbA1c) in youth and young adults (YYA) with youth-onset type 1 (T1D) and type 2 diabetes (T2D).
Methods:
A cross-sectional analysis of data from the multicenter SEARCH Food Security Cohort study (2019–2022) was conducted including 574 and 82 YYA with youth-onset T1D and T2D, respectively. Resilience, assessed with the 10-item Connor-Davidson Resilience scale, was analyzed in categories determined through tertiles (low ≤25, moderate 26–32, and high ≥33). Multivariable logistic regression models adjusted for sociodemographic and clinical factors, perceived social support, and symptoms of depression, anxiety, and eating problems.
Results:
Regardless of diabetes type, compared to participants with high resilience, those with low resilience had higher odds of HbA1c >9% (OR 1.90, 95% CI 1.06–3.41); the OR was 1.62 (95% CI 0.97–2.70) for those with intermediate resilience. Among YYA with T1D, those with low and intermediate resilience had elevated odds of HbA1c >9% (OR 1.96, 95% CI 1.02–3.79; OR 2.01, 95% CI 1.13–3.59, respectively) compared to those with high resilience.
Conclusions:
Findings were consistent with the hypothesis that resilience was protective of elevated HbA1c in YYA with diabetes. Strategies to enhance resilience in these populations might be beneficial.
Keywords: resilience, hemoglobin A1c, youth and young adults, diabetes
Graphical Abstract
1. Introduction
Resilience is “the ability to bounce back from negative emotional experiences and flexibly adapt to the changing demands of stressful experiences” [1]. It incorporates attitudes such as optimism, beliefs such as self-efficacy and self-esteem, and abilities like self-mastery [2, 3]. Resilience acts in three ways: first, it protects against stressors; second, it facilitates a positive adaptation to stressful events; third, it restrains the development of pathological conditions in response to stress [4]. Resilience is particularly important for individuals with diabetes because they experience continuous stressors due to the complexity of diabetes management, worries of hypoglycemia and diabetes complications, and stigma related to diabetes [5, 6]. In qualitative interviews, adults and adolescents with diabetes express themes pertaining to resilience when asked about dealing with diabetes; for example, they mention that a positive attitude leads to better health, state that they control their diabetes, not vice versa, and advise keeping a forward-looking perspective [5, 6]. A review of the psychosocial context related to diabetes in studies published from 1995 to 2020 reveals the need for more studies on resilience, whether at the time of diabetes diagnosis or afterwards, which could provide further information for interventions aimed at empowering people to improve diabetes-related outcomes [7].
In adolescents/young adults [3, 8] and adults [2, 9, 10] with diabetes, a low resilience is associated with higher distress [3, 8, 9], more maladaptive coping strategies including social withdrawal and blame of others [2, 3], and worse mental health composite scores [9, 10] in the few studies that have examined resilience and mental health. In addition, among adults with diabetes, resilience has been positively associated with perceived social support [10, 11] and quality of life [9, 10]. Among adolescents with diabetes, resilience is also positively associated with quality of life [12]. These studies, however, did not control for mental health symptoms that are common among people with diabetes, such as symptoms of depression, anxiety, and disordered eating behaviors [13, 14]. Mental health symptoms can significantly impact diabetes self-management [9, 15–18], which is the day-to-day management of diabetes through appropriate blood glucose monitoring (with or without continuous glucose monitor [CGM]), medication intake (with or without insulin pump), diet, physical activity, and problem-solving [19]. Diabetes self-management is essential for achieving optimal hemoglobin A1c (HbA1c) levels [18, 20], which lowers the risk of diabetes complications [21].
Even less is known about the relation between resilience and HbA1c among individuals with diabetes. Three studies assessing the association in adults [2, 9, 22] and three in adolescents and/or young adults [3, 8, 23] with diabetes have reported inconsistent results, some detecting no association [2, 9] and others finding an inverse association between resilience and HbA1c [3, 8, 22, 23]. The inconsistencies might be due to a lack of statistical power given small samples included in four of these six studies (n = 34, 50, 68, and 111). Considering that resilience incorporates self-efficacy and self-mastery and facilitates positive adaptation [2–4], resilience may affect HbA1c through the quality of diabetes self-management [24], which is also influenced by social support and mental health symptoms [18]. In addition, resilience’s bolstering action against stressors [4] may influence HbA1c levels via the reduction of chronic exposure to stress [25], which is important because stress induces abnormal levels of cortisol [26], a hormone that increases blood glucose level [26].
Given the challenges faced by youth and young adults (YYA) with diabetes, the possible mechanisms through which resilience acts, the dearth of studies, and the inconsistency of results, it is essential to explore further the association of resilience and HbA1c levels of YYA with diabetes, with careful consideration of mental health assets and mental health symptoms. Such findings could be useful for interventions targeting resilience to improve the health of YYA with diabetes in the presence or absence of mental health symptoms. We sought to assess whether resilience is associated with HbA1c in YYA with youth-onset type 1 (T1D) and type 2 diabetes (T2D). We hypothesized that YYA with diabetes who scored lower on resilience would have higher HbA1c than those with higher resilience.
2. Methods
2.1. Subjects
This study consists of a cross-sectional analysis of data from the multicenter SEARCH Food Security Cohort study (SFS) follow-up 2 conducted between 2019 and 2022 among YYA with youth-onset T1D and T2D. The SEARCH for Diabetes in Youth study (SEARCH) started as surveillance effort monitoring physician-diagnosed diabetes mellitus among youth under the age of 20 in five US states and was later transformed into a cohort study [27]. As an ancillary study to SEARCH, SFS followed participants from three of the five sites, namely South Carolina, Colorado and Washington, over two points, follow-up 1 and 2. Follow-up 1 occurred between 9 and 27 months after the last participation in SEARCH, and follow-up 2 occurred approximately 9 months after follow-up 1.
Overall, 1,011 YYA participated in SFS follow-up 2. Those missing resilience score (n=80), covariates (n=66), or HbA1c (n=210) were excluded, leaving 656 participants for the analysis. Compared to the analytical sample, the excluded group had a higher percentage of adolescents, males, individuals living in a household with an income of ≤ $25,000, and individuals with a high school diploma or less education (Supplemental Table S1). The excluded group had a lower percentage of individuals owning a CGM or reporting social support. The SFS study followed procedures in accordance with ethical standards and was approved by local institutional review boards. All participants aged 18 years or older provided written informed consent. Parents/caregivers of participants younger than 18 years gave written informed consent, and participants aged 10 to 17 years provided assent.
2.2. Resilience
Resilience over the last month was assessed with the 10-item Connor-Davidson Resilience scale (CD-RISC) which has been shown to be valid and reliable [28]. Each item of the CD-RISC is rated from 0 to 4, yielding a total score ranging from 0 to 40 with higher scores signifying higher resilience. Resilience was further categorized by tertiles into low (score ≤25), moderate (score 26–32), and high (score ≥33), which is a common practice in the literature [2, 3, 22].
2.3. Hemoglobin A1c
HbA1c percent was classified following the American Diabetes Association (ADA) and International Society for Pediatric and Adolescent Diabetes (ISPAD) recommended target of achieving a HbA1c <7.0% for patients with T1D and T2D [29] into optimal (<7.0%), suboptimal (7.0–9.0%), and high risk (>9.0%) [30, 31]. At the start of the study, HbA1c was initially determined from whole blood samples collected through venipuncture (n=101 participants), processed in the Northwest Lipid Metabolism and Diabetes Research Laboratories in Seattle, WA, and analyzed with an automated nonporous ion-exchange high-performance liquid chromatography system (model G-7; Tosoh Bioscience, Montgomeryville, Pennsylvania) [30]. With the onset of the COVID-19 pandemic, the HbA1c analysis protocol for the study pivoted to collecting a remote dried blood spot (DBS) (n=555 participants), processed in the Department of Laboratory Medicine of the University of Washington by performing punching and elution of punches, and analyzed in the DBS eluates. DBS methodology is well established and has been validated in numerous studies against HbA1c determined on a blood sample obtained via venipuncture (ρ = 0.86–0.97) [32, 33]. A variable indicating the type of HbA1c assessment was included in all analyses.
2.4. Covariates
Covariates identified as possible confounders based on the literature [14, 30, 34], organized by means of a directed acyclic graph (Supplemental Figure S1), were demographic (age in years [10–17,18–37], sex [male, female], race and ethnicity, and site location [South Carolina, Colorado, and Washington]), socioeconomic (participant education, parent education, household income [<$25,000, $25,000–49,999, $50,000–74,999, ≥$75,000], health insurance [state/federal, private/exchanges, other, none], and food insecurity), and clinical characteristics (insulin pump use [yes, no], CGM use [yes, no], diabetes duration in years, and diabetes type). Race and ethnicity was classified into Hispanic, non-Hispanic Black, non-Hispanic White, and other (Asian, Pacific Islander, Native American, and two or more races). Education was categorized into high school graduate or less, some college or associate degree, and bachelor's degree or more for both participants and parents. Food insecurity, assessed with the Household Food Security Survey Module [35], was classed as yes or no. Insulin regimen was categorized as insulin via insulin pump, insulin via injections, and no insulin; to correct sparse data, it was dichotomized as insulin pump use (yes or no), knowing that insulin pump use is associated with better HbA1c [36]. Type of diabetes (type 1 and 2) was based on the diagnosis made by the participant’s healthcare provider during the parent study, SEARCH [27].
Psychosocial covariates were assessed using scales that are validated, well-established, and appropriate to YYA with diabetes. Depressive symptoms in the past week were measured using the 20-item Center for Epidemiological Studies-Depression scale (CES-D) [37], with higher score denoting more severe and/or frequent depressive symptoms (ranges from 0 to 60); a score ≥ 24 in adolescents and ≥ 16 in adults indicates the presence of depressive symptoms. Anxiety over the last two weeks was assessed with the 7-item Generalized Anxiety Disorder screener (GAD-7) [38], generating a score from 0 to 21 where a higher score means more anxiety; the score was dichotomized using 10 as threshold. Disordered eating behaviors during the past month were ascertained using the 16-item Diabetes Eating Problem Survey-Revised (DEPS-R) [39], with higher scores signifying more severe and/or frequent disordered eating behaviors (range 0 to 80) and a score ≥ 20 was considered as having disordered eating behaviors. Perceived social support was evaluated with the 12-item Multidimensional Scale of Perceived Social Support (MSPSS) [40], of which total score varies from 12 to 84 (higher scores mean greater perceived social support).
2.5. Statistical analysis
Chi square test, Fisher’s exact test, ANOVA, and Kruskal-Wallis H test were performed for bivariate analysis. Multivariable linear and logistic regression models were performed to assess the association between resilience score and HbA1c, and the association between resilience tertiles and HbA1c > 9%, respectively, to consider variable- and person-focused representations of resilience [2, 3]. While the former representation emphasizes which variable is independently associated with the resilience raw score in the entire study population, the latter representation compares population groups based on the level of resilience with respect to a clinically meaningful outcome. In addition to improving clinical interpretation and helping target at-risk groups from a public health and policy perspective, categorizing resilience allows for the identification of threshold effects of resilience, as found in other studies [3, 41], in contrast to linear effects. Regression models were conducted for the entire sample and by diabetes type to enhance the usefulness of our results for diabetes healthcare providers who tend to see the diabetes types distinctly (because of differences in etiology, diabetes management, and distress experiences [42]), adjusting first for sociodemographic and clinical characteristics, then adding psychosocial factors. Spearman correlation and tolerance revealed no multicollinearity between resilience and the psychosocial factors, namely social support, depressive symptoms, anxiety symptoms, and disordered eating behaviors (Supplemental Table S2). Analyses pertaining to participants with T2D were exploratory because of the small sample size. To test the robustness of the results with respect to the type of blood sample from which HbA1c was measured, a sensitivity analysis was performed considering only the participants who provided DBS. Given that diabetes technology is known to improve diabetes self-management and HbA1c [43], another set of sensitivity analyses explored the use of an insulin pump or a CGM on the association of resilience and high risk glycemic control. Data analysis was carried out using SAS version 9.4 (SAS institute, Cary, NC).
3. Results
3.1. Characteristics of study participants
The mean age of participants was 25.3 years ±4.8; 62.2% were female; 71.0% identified as Non-Hispanic White; 32.8% had a bachelor’s degree or more; and 37.0% lived in a household with an income ≥ $75,000 (Table 1). The mean resilience score was 28.6 ±7.2; 210, 231, and 215 participants had low, intermediate, and high resilience (Supplemental Table S3). Respectively, 574 and 82 participants had T1D and T2D (Table 1). Among participants with T1D, compared to those with high resilience, the group with low resilience had a higher percentage of females and individuals with a high school diploma or less education, and a lower percentage of individuals who are food secure, who own an insulin pump or a CGM. Among participants with T2D, compared to those with high resilience, the group with low resilience had a lower percentage of individuals who self-identify as Non-Hispanic Black or who are food secure.
Table 1.
Characteristics of youth and young adults with youth-onset diabetes in the SEARCH Food Security Cohort study (2019–2022), according to tertiles* of resilience and diabetes type
| Total | Type 1 diabetes | Type 2 diabetes | |||||
|---|---|---|---|---|---|---|---|
| (N = 656) | Low resilience (n = 183) |
Intermediate resilience (n = 202) |
High resilience (n = 189) |
Low resilience (n = 27) |
Intermediate resilience (n = 29) |
High resilience (n = 26) |
|
| n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | |
| Age in years, mean (SD) | 25.3 (4.8) | 24.4 (4.3) | 25.0 (4.7) | 25.0 (5.0) | 28.2 (4.1) | 28.2 (4.5) | 29.3 (3.9) |
| 10 – 17 years | 43 (6.6) | 13 (7.1) | 15 (7.4) | 15 (7.9) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| 18 – 37 years | 613(93.5) | 170 (92.9) | 187 (92.6) | 174 (92.1) | 27 (100) | 29 (100) | 26 (100) |
| Sex | |||||||
| Female | 408 (62.2) | 129 (70.5) | 110 (54.5) | 101 (53.4) | 21 (77.8) | 26 (89.7) | 21 (80.8) |
| Male | 248 (37.8) | 54 (29.5) | 92 (45.5) | 88 (46.6) | 6 (22.2) | 3 (10.3) | 5 (19.2) |
| Race/ethnicity | |||||||
| Hispanic | 64 (9.8) | 18 (9.8) | 19 (9.4) | 18 (9.5) | 6 (22.2) | 3 (10.3) | 0 (0.0) |
| Non-Hispanic Black | 111 (16.9) | 22 (12.0) | 20 (9.9) | 20 (10.6) | 10 (37.0) | 20 (69.0) | 19 (73.1) |
| Non-Hispanic White | 466 (71.0) | 136 (74.3) | 159 (78.7) | 150 (79.4) | 11 (40.7) | 5 (17.2) | 5 (19.2) |
| Other† | 15 (2.3) | 7 (3.8) | 4 (2.0) | 1 (0.5) | 0 (0.0) | 1 (3.5) | 2 (7.7) |
| Study site | |||||||
| South Carolina | 254 (38.7) | 63 (34.4) | 65 (32.2) | 63 (33.3) | 18 (66.7) | 23 (79.3) | 22 (84.6) |
| Colorado | 292 (44.5) | 82 (44.8) | 99 (49.0) | 97 (51.3) | 7 (25.9) | 5 (17.2) | 2 (7.7) |
| Washington | 110 (16.8) | 38 (20.8) | 38 (18.8) | 29 (15.3) | 2 (11.4) | 1 (5.1) | 2 (10.0) |
| Parent education‡ | |||||||
| ≤ High school graduate | 132 (20.1) | 37 (20.2) | 26 (12.9) | 31 (16.4) | 16 (59.3) | 9 (31.0) | 13 (50.0) |
| Some college-Associate degree | 178 (27.1) | 54 (29.5) | 54 (26.7) | 42 (22.2) | 6 (22.2) | 13 (44.8) | 9 (34.6) |
| ≥ Bachelor's degree | 346 (50.7) | 92 (50.3) | 122 (60.4) | 116 (61.4) | 5 (18.5) | 7 (24.1) | 4 (15.4) |
| Participant education | |||||||
| ≤ High school graduate | 206 (31.4) | 73 (39.9) | 56 (27.7) | 50 (26.5) | 14 (51.9) | 7 (24.1) | 6 (23.1) |
| Some college-Associate degree | 235 (35.8) | 60 (32.8) | 68 (33.7) | 70 (37.0) | 6 (22.2) | 16 (55.2) | 15 (57.7) |
| ≥ Bachelor's degree | 215 (32.8) | 50 (27.3) | 78 (38.6) | 69 (36.5) | 7 (25.9) | 6 (20.7) | 5 (19.2) |
| Household income | |||||||
| < $25,000 | 147 (22.4) | 39 (21.3) | 39 (19.3) | 27 (14.3) | 16 (59.3) | 13 (44.8) | 13 (50.0) |
| $25,000–49,999 | 139 (21.2) | 42 (23.0) | 40 (19.8) | 36 (19.1) | 8 (29.6) | 8 (27.6) | 5 (19.2) |
| $50,000–74,999 | 127 (19.4) | 40 (21.9) | 46 (22.8) | 32 (16.3) | 2 (7.4) | 4 (13.8) | 3 (11.5) |
| ≥ $75,000 | 243 (37.0) | 62 (33.9) | 77 (38.1) | 94 (49.7) | 1 (3.7) | 4 (13.8) | 5 (19.2) |
| Health insurance | |||||||
| State/federal | 148 (22.6) | 46 (25.1) | 38 (18.8) | 35 (18.5) | 14 (51.9) | 7 (24.1) | 8 (30.8) |
| Private/exchanges | 463 (70.6) | 121 (66.1) | 156 (77.2) | 142 (75.1) | 10 (37.0) | 18 (62.1) | 16 (61.5) |
| None/Other | 45 (6.9) | 16 (8.7) | 8 (4.0) | 12 (6.4) | 3 (11.1) | 4 (13.8) | 2 (7.7) |
| Food insecurity | |||||||
| Yes | 111 (16.9) | 40 (21.9) | 27 (13.4) | 18 (9.5) | 15 (55.6) | 9 (31.0) | 2 (10.0) |
| No | 545 (83.1) | 143 (78.1) | 175 (86.6) | 171 (90.5) | 12 (44.4) | 20 (69.0) | 24 (90.0) |
| Diabetes duration in years, mean (SD) | 14.8 (3.1) | 14.7 (3.2) | 15.0 (3.0) | 15.0 (2.9) | 14.3 (3.5) | 13.5 (4.0) | 14.2 (3.6) |
| Insulin regimen | |||||||
| Insulin via insulin pump | 387 (59.0) | 112 (61.2) | 133 (65.8) | 140 (74.1) | 1 (3.7) | 0 (0.0) | 1 (3.9) |
| Insulin via injections | 226 (34.5) | 68 (37.2) | 65 (32.2) | 49 (25.9) | 15 (55.6) | 16 (55.2) | 13 (50.0) |
| No insulin | 43 (6.6) | 3 (1.6) | 4 (2.0) | 0 (0.0) | 11 (40.7) | 13 (44.8) | 12 (46.2) |
| Continuous glucose monitor use | |||||||
| Yes | 402 (61.3) | 108 (59.0) | 131 (64.9) | 148 (78.3) | 6 (22.2) | 6 (20.7) | 3 (11.5) |
| No | 254 (38.7) | 75 (41.0) | 71 (35.1) | 41 (21.7) | 21 (77.8) | 23 (79.3) | 23 (88.5) |
| Depressive symptoms | |||||||
| Yes | 248 (37.8) | 127 (69.4) | 54 (26.7) | 29 (15.3) | 18 (66.7) | 14 (48.3) | 6 (23.1) |
| No | 408 (62.2) | 56 (30.6) | 148 (73.3) | 160 (84.7) | 9 (33.3) | 15 (51.7) | 20 (76.9) |
| Anxiety symptoms | |||||||
| Yes | 197 (30.0) | 104 (56.8) | 38 (18.8) | 25 (13.2) | 13 (48.2) | 11 (37.9) | 6 (23.1) |
| No | 459 (70.0) | 79 (43.2) | 164 (81.2) | 164 (86.8) | 14 (51.8) | 18 (62.1) | 20 (76.9) |
| Disordered eating behaviors | |||||||
| Yes | 128 (19.5) | 65 (35.5) | 26 (12.9) | 11 (5.8) | 8 (29.6) | 11 (37.9) | 7 (26.9) |
| No | 528 (80.5) | 118 (64.5) | 176 (87.1) | 178 (94.2) | 19 (70.4) | 18 (62.1) | 19 (73.1) |
| Perceived social support, mean (SD) | 67.7 (14.1) | 60.7 (15.5) | 68.0 (12.4) | 74.3 (10.4) | 60.7 (15.9) | 68.3 (16.5) | 74.4 (9.4) |
SD, Standard Deviation
Tertiles: low (score ≤25), moderate (score 26–32), and high (score ≥33)
Other race includes Native American, Asian, Pacific Islander, and two or more races.
Highest level of education of either parent
Percentages may not add up to exactly 100% due to rounding.
The mean scores for depressive symptoms, anxiety symptoms, and disordered eating behaviors were respectively 14.7 ±11.5, 7.1 ±5.8, and 13.4 ±9.9; percentages of YYA with depressive symptoms, anxiety symptoms, and disordered eating behaviors were 37.8%, 30.0%, and 19.5%, respectively (Table 1). Among participants with T1D, compared to those with high resilience, the group with low resilience had a higher percentage of individuals who reported depressive symptoms, anxiety symptoms, and disordered eating behaviors. Among participants with T2D, the group with low resilience had a higher percentage of individuals with depressive symptoms than their counterparts with high resilience. The mean social support score was 67.7±14.1, lower in YYA with low resilience than those with high resilience among participants with T1D and T2D.
3.2. Association between resilience and HbA1c
The mean HbA1c was 8.5% ±1.8 overall, 8.4% ±1.7 among YYA with T1D, and 9.1% ±2.5 among those with T2D. In all participants and in the group with T1D, a higher resilience score was associated with a lower HbA1c after adjusting for sociodemographic and clinical characteristics (Table 2). Betas and 95% CI for a ten-unit difference in resilience score were: −0.22 (−0.40 to −0.04) for all and −0.24 (−0.41 to −0.03) for those with T1D. Additional adjustment for perceived social support did not affect the associations, whereas additional adjustment for depressive symptoms, anxiety symptoms, and disordered eating behaviors attenuated them. In participants with T2D, for whom analyses were exploratory due to the small sample size, betas were in the expected direction, according to the same adjustments.
Table 2.
Association between resilience score and hemoglobin A1c among youth and young adults with youth-onset diabetes in the SEARCH Food Security Cohort study (2019–2022), according to diabetes type
| Model 1* | Model 2† | Model 3‡ | Model 4§ | Model 5|| | |
|---|---|---|---|---|---|
|
All participants
(N = 656) |
|||||
| β (p-value) | −0.35 (0.0002) | −0.22 (0.02) | −0.23 (0.03) | −0.06 (0.60) | −0.08 (0.50) |
| CI | −0.54 to −0.17 | −0.40 to −0.04 | −0.43 to −0.03 | −0.27 to 0.16 | −0.29 to 0.14 |
|
Type 1 diabetes (n = 574) |
|||||
| β (p-value) | −0.42 (<0.0001) | −0.24 (0.008) | −0.22 (0.03) | −0.06 (0.56) | −0.07 (0.54) |
| CI | −0.60 to −0.23 | −0.41 to −0.06 | −0.41 to −0.03 | −0.27 to 0.15 | −0.28 to 0.15 |
|
Type 2 diabetes (n =82) |
|||||
| β (p-value) | 0.07 (0.87) | −0.14 (0.78) | −0.35 (0.55) | −0.11 (0.85) | −0.32 (0.59) |
| CI | −0.73 to 0.86 | −1.16 to 0.87 | −1.50 to 0.80 | −1.19 to 0.98 | −1.51 to 0.86 |
Abbreviations: β, Beta; CI, Confidence interval.
Betas are estimated for a ten-unit difference in resilience
Unadjusted
Adjusted for age, sex, race and ethnicity, study site, parent education, participant education, household income, health insurance, food insecurity, diabetes duration, insulin pump use, continuous glucose monitor use, type of HbA1c assessment, and diabetes type (diabetes type is omitted from the stratified analysis).
Model 2 + additional adjustment for perceived social support
Model 2 + additional adjustment for depressive symptoms, anxiety symptoms, and disordered eating behaviors
Model 2 + additional adjustment for perceived social support, depressive symptoms, anxiety symptoms, and disordered eating behaviors
Among all participants and in the group with T1D, those with low and intermediate resilience had higher odds of HbA1c >9% (for all OR 2.21, 95% CI 1.33–3.67 and OR 1.80, 95% CI 1.10–2.95, and for those with T1D OR 2.46, 95% CI 1.40–4.33 and OR 2.25, 95% CI 1.29–3.93, respectively) compared to those with high resilience, controlling for sociodemographic and clinical characteristics (Table 3). Additional adjustments for psychosocial factors attenuated but did not explain the associations. Among participants with T2D, odds ratios of elevated HbA1c were in the expected direction when comparing participants with low resilience to those with high resilience, with additional adjustments for psychosocial factors.
Table 3.
Association between resilience tertiles and hemoglobin A1c >9% among youth and young adults with youth-onset diabetes in the SEARCH Food Security Cohort study (2019–2022), according to diabetes type
| Model 1* | Model 2† | Model 3‡ | Model 4§ | Model 5|| | |
|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| All participants (n = 656) | |||||
| Low vs. high resilience | 2.52 (1.64–3.87) | 2.21 (1.33–3.67) | 2.25 (1.31–3.85) | 1.86 (1.05–3.31) | 1.90 (1.06–3.41) |
| Intermediate vs. high resilience | 1.78 (1.16–2.74) | 1.80 (1.10–2.95) | 1.82 (1.10–2.99) | 159 (0.96–2.65) | 1.62 (0.97–2.70) |
| Type 1 diabetes (n = 574) | |||||
| Low vs. high resilience | 3.09 (1.90–5.02) | 2.46 (1.40–4.33) | 2.44 (1.34–4.43) | 1.95 (1.02–3.74) | 1.96 (1.02–3.79) |
| Intermediate vs. high resilience | 2.15 (1.32–3.51) | 2.25 (1.29–3.93) | 2.24 (1.27–3.93) | 2.00 (1.13–3.55) | 2.01 (1.13–3.59) |
| Type 2 diabetes (n = 82) | |||||
| Low vs. high resilience | 1.07 (0.36–3.16) | 1.03 (0.20–5.18) | 1.29 (0.22–7.75) | 1.37 (0.22–8.54) | 2.32 (0.31–17.32) |
| Intermediate vs. high resilience | 0.80 (0.28–2.31) | 0.59 (0.15–2.29) | 0.63 (0.16–2.46) | 0.57 (0.13–2.53) | 0.65 (0.14–3.02) |
Abbreviations: OR, odds ratio; CI, confidence interval.
Unadjusted
Adjusted for age, sex, race and ethnicity, study site, parent education, participant education, household income, health insurance, food insecurity, diabetes duration, insulin pump use, continuous glucose monitor use, type of HbA1c assessment, and diabetes type (diabetes type is omitted from the stratified analysis).
Model 2 + additional adjustment for perceived social support
Model 2 + additional adjustment for depressive symptoms, anxiety symptoms, and disordered eating behaviors
Model 2 + additional adjustment for perceived social support, depressive symptoms, anxiety symptoms, and disordered eating behaviors
Results of the sensitivity analysis restricting the sample to participants whose HbA1c was collected via DBS were similar to the main results. The only exception was the T1D model adjusted for psychosocial factors, where the confidence interval widened to include the null value. In the groups using insulin pump, not using insulin pump, or using CGM, YYA with low resilience had twice the odds of HbA1c >9% compared to those with high resilience, controlling for sociodemographic and clinical characteristics; in the group not using CGM, YYA with intermediate resilience had twice the odds (Table 4).
Table 4.
Association between resilience tertiles and hemoglobin A1c >9% among youth and young adults with youth-onset diabetes in the SEARCH Food Security Cohort study (2019–2022), according to insulin pump and continuous glucose monitor use
| Model 1* | Model 2† | Model 3‡ | Model 4§ | Model 5|| | |
|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| Insulin pump Yes (n = 387) | |||||
| Low vs. high resilience | 3.09 (1.59–5.98) | 2.37 (1.11–5.04) | 2.49 (1.12–5.51) | 1.70 (0.69–4.19) | 1.78 (0.72–4.44) |
| Intermediate vs. high resilience | 1.99 (1.02–3.89) | 1.52 (0.72–3.23) | 1.56 (0.73–3.32) | 1.37 (0.63–2.96) | 1.42 (0.65–3.09) |
| Insulin pump No (n =269) | |||||
| Low vs. high resilience | 1.79 (0.97–3.31) | 2.09 (0.99–4.40) | 2.09 (0.94–4.64) | 1.80 (0.78–4.18) | 1.84 (0.79–4.32) |
| Intermediate vs. high resilience | 1.43 (0.78–2.64) | 1.74 (0.85–3.55) | 1.74 (0.85–3.59) | 1.52 (0.73–3.20) | 1.54 (0.73–3.25) |
| CGM Yes (n = 402) | |||||
| Low vs. high resilience | 3.01 (1.67–5.41) | 2.84 (1.41–5.72) | 3.10 (1.47–6.55) | 2.01 (0.88–4.57) | 2.13 (0.93–4.89) |
| Intermediate vs. high resilience | 1.63 (0.90–3.00) | 1.32 (0.66–2.66) | 1.36 (0.67–2.76) | 1.07 (0.51–2.21) | 1.12 (0.54–2.32) |
| CGM No (n = 254) | |||||
| Low vs. high resilience | 1.62 (0.84–3.11) | 1.79 (0.80–4.00) | 1.72 (0.74–4.01) | 1.77 (0.72–4.36) | 1.73 (0.70–4.29) |
| Intermediate vs. high resilience | 1.61 (0.84–3.10) | 2.20 (1.01–4.80) | 2.16 (0.98–4.75) | 2.09 (0.95–4.64) | 2.06 (0.92–4.58) |
Abbreviations: OR, odds ratio; CI, Confidence interval; CGM, continuous glucose monitor.
Unadjusted
Adjusted for age, sex, race and ethnicity, study site, parent education, participant education, household income, insurance, food insecurity, diabetes duration, insulin pump use, continuous glucose monitor use, type of HbA1c assessment, and diabetes type (insulin pump and CGM use were omitted from respective stratified analysis).
Model 2 + additional adjustment for perceived social support
Model 2 + additional adjustment for depressive symptoms, anxiety symptoms, and disordered eating behaviors
Model 2 + additional adjustment for perceived social support, depressive symptoms, anxiety symptoms, and disordered eating behaviors
4. Discussion
Resilience was inversely associated with HbA1c in YYA with youth-onset diabetes, particularly T1D, controlling sociodemographic and clinical characteristics. As a biomarker of glycemia over a few months, HbA1c is an indicator of diabetes management and a predictor of diabetes cardiovascular complications [21]. A ten-unit increase in the 40-point resilience scale was associated with a lower HbA1c of about a quarter of a percentage point, which is important given the high average HbA1c in this population. These findings are consistent with four other studies, two conducted among adolescents/young adults and two among adults with diabetes, which found an inverse association between resilience and HbA1c [3, 8, 22, 23]. There are at least two possible mechanisms by which resilience affects HbA1c. The first is behavioral, in which higher resilience leads to more adaptive coping strategies [2, 3], a higher likelihood of healthy lifestyle adoption, and better diabetes self-management [24]. The second is biological, in which higher resilience decreases the allostatic load from chronic stress [25], drives better-regulated cortisol levels, and consequently better blood glucose and HbA1c levels [26].
Resilience was related to HbA1c controlling for perceived social support and symptoms of depression, anxiety, and disordered eating behaviors. Participants with diabetes, particularly T1D, who had low or intermediate resilience were twice as likely to have elevated HbA1c as those with high resilience, regardless of social support and mental health symptoms. This is an important finding as previous studies did not adjust for any of these mental health assets and symptoms [2, 3, 8, 9, 22, 23].
Moreover, because the relation between resilience and HbA1c remains with adjustments for social support and symptoms of depression, anxiety, and disordered eating behaviors, it is essential to consider increasing resilience, irrespective of the treatment of mental health symptoms, as a way to reduce HbA1c. Consistent with this, the American Diabetes Association recommends resilience-promoting interventions, even in the absence of mental health symptoms, particularly for diabetes distress prevention and adjustment to diabetes technology and treatment [44]. Resilience can be enhanced by several interventions such as coaching, mindfulness-based stress reduction, and multi-component positive psychology interventions [45–47]. In diabetes adaptation counseling, the emphasis is often deficit-focused, avoiding negative elements, such as illness, dysfunction, complications, and problems, even though it is equally important to reinforce strengths, virtues, and utilization of resources in managing the disease [7]. The present study supports focusing on positive, strength-based elements sometimes designated as personal determinants of health [48, 49], such as resilience and social support, which can improve diabetes self-management and glycemic levels [18, 20].
Some limitations are worth noting. Because of the COVID-19 pandemic, in the interest of participants’ wellbeing, this study’s HbA1c assessment protocol was modified from in-person to remote. This in turn necessitated a change in the assay type. At the completion of the study, 15% of participants had HbA1c determined from whole blood samples and the remaining 85% from DBS, which is a well-established methodology [32, 33]. Comparing findings including all participants to those limited to DBS-based HbA1c revealed similar results, particularly higher odds of HbA1c >9% in participants who have low resilience and intermediate resilience, suggesting absence of any assay-associated bias. Due to missing resilience, covariates, and HbA1c data, 35% of the sample were excluded from the analyses. Excluded participants were more likely to have lower socioeconomic status, which is associated with lower resilience in this study and higher HbA1c according to previous publications using this cohort [31]. An underrepresentation of those with lower resilience and higher HbA1c among the analytical sample could have led to an underestimation of the odds ratios and limit somewhat the generalizability of the results. Another limitation was that measured body mass index was not available given the self-report data. Given the cross-sectional nature of the study, causality cannot be concluded. However, additional analyses exploring the role of a marker of diet quality and of physical activity suggest that these behaviors, while essential for diabetes self-management, do not play a meaningful role in the relation between resilience and glycemic control. The sample size of participants with T2D was small, which limited statistical power. However, our study is among the largest of participants with T1D with n = 574, far exceeding previous studies in size (n ranging from 34 to 233) [2, 3, 8, 9, 22, 23]. Other strengths of the present study include the use of validated questionnaires, the adoption of the variable- and person-focused representations of resilience [3], the participation of multiple sites, and the adjustments for sociodemographic and clinical confounders as well as mental health assets and mental health symptoms.
5. Conclusion
In conclusion, this study shows that higher resilience was associated with a lower likelihood of elevated HbA1c in YYA with youth-onset diabetes, particularly T1D, after controlling for sociodemographic and clinical characteristics, perceived social support, and mental health symptoms. Given the potentially cross-cutting effect of resilience on mental health and diabetes self-management in YYA with youth-onset diabetes, and given the growing evidence for interventions improving resilience, evaluating tailored strategies to enhance resilience in these populations might be beneficial.
Supplementary Material
Article Highlights:
People with diabetes face numerous stressors related to diabetes self-management
Studies on resilience in youth and young adults (YYA) with diabetes are scarce
Lower resilience is associated with elevated hemoglobin A1c in YYA with diabetes
Strategies to enhance resilience in populations with diabetes might be beneficial
Funding
Research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health under Award Number R01DK117461 (MPI A. Liese and J. Mendoza). This publication was also supported partly by the Centers of Biomedical Research Excellence (COBRE) grant P20GM130420 (PI R. Prinz) from the National Institute of General Medical Sciences (NIGMS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health NIDDK or NIGMS. The National Institutes of Health had no role in the design, analysis or writing of this article.
List of abbreviations
- ADA
American Diabetes Association
- CD-RISC
Connor-Davidson Resilience scale
- CES-D
Center for Epidemiological Studies-Depression scale
- CGM
Continuous glucose monitor
- CI
Confidence interval
- DEPS-R
Diabetes Eating Problem Survey-Revised
- DBS
Dried blood spot
- GAD-7
Generalized Anxiety Disorder screener
- HbA1c
Hemoglobin A1c
- ISPAD
International Society for Pediatric and Adolescent Diabetes
- MSPSS
Multidimensional Scale of Perceived Social Support
- OR
Odds ratio
- SEARCH
SEARCH for Diabetes in Youth study
- SFS
SEARCH Food Security Cohort study
- T1D
Type 1 diabetes
- T2D
Type 2 diabetes
- YYA
Youth and young adults
Footnotes
Prior Presentation
Parts of the results were presented as an abstract and a poster at the 84th Scientific Sessions of the American Diabetes Association held in June 2024. The abstract appears in the June 2024 edition of the journal Diabetes.
Conflict of Interest
None of the authors have a potential conflict of interest in relation to this publication.
Data and Resource Availability
Data described in the manuscript, code book, and analytic code will be made available upon publication to investigators whose proposed use of the data has been approved by an independent review committee. Proposals should be directed to the corresponding author Angela D. Liese at liese@sc.edu; to gain access, data requesters will need to sign a data use agreement.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data described in the manuscript, code book, and analytic code will be made available upon publication to investigators whose proposed use of the data has been approved by an independent review committee. Proposals should be directed to the corresponding author Angela D. Liese at liese@sc.edu; to gain access, data requesters will need to sign a data use agreement.