Abstract
Background Psychological distress is common in individuals with type 1 diabetes mellitus (T1DM) and can impair adherence to self-care and glycemic control. Limited studies in the Middle East, particularly post-COVID, have comprehensively examined the prevalence and associated factors of depression and anxiety in this population. Materials and methods We conducted a cross-sectional study of 122 Saudi adults with T1DM. Depression and anxiety were assessed using the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7). Diabetes self-care activities were evaluated using the Summary of Diabetes Self-Care Activities (SDSCA). Descriptive statistics, chi-square tests, t-tests, ANOVA, Pearson/Spearman correlations, and logistic/linear regression models were applied to examine associations. Results The mean PHQ-9 and GAD-7 scores were 5.8 ± 4.5 and 5.1 ± 4.9, respectively. Overall, 54.9% of participants had depression (37.7% mild, 11.5% moderate, 4.9% moderately severe, 0.8% severe), and 45.1% had anxiety (27.9% mild, 10.7% moderate, 6.6% severe). Females reported significantly higher scores than males for both depression (p = 0.030) and anxiety (p = 0.002). Logistic regression identified high-density lipoprotein (HDL) as an independent predictor of depression (OR 6.60, 95% CI 1.81–24.02, p = 0.004), while female gender (OR 0.36 for males, p = 0.018) and lower self-care adherence (OR 0.63, p = 0.025) independently predicted anxiety. Self-care adherence was inversely correlated with PHQ-9 (r=–0.180, p = 0.047) and GAD-7 scores (r=–0.207, p = 0.022). Conclusion Depression and anxiety were highly prevalent among Saudi adults with T1DM, with female gender, lipid profile, and poor self-care adherence emerging as significant associated factors. Therefore, we recommend integrating systematic mental health screening into diabetes clinic protocols. Collaborative care models that are tailored to the patient involving mental health professionals may enhance outcomes by addressing both medical and psychological needs, particularly in the post-COVID era where psychosocial stressors remain heightened.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-025-28865-y.
Keywords: Type 1 diabetes mellitus, Depression, Anxiety, Self-care adherence, HDL cholesterol, Post-COVID, Saudi Arabia
Subject terms: Psychology, Endocrinology
Background
Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition characterized by the destruction of insulin-producing pancreatic β-cells, resulting in absolute insulin deficiency and lifelong dependence on exogenous insulin therapy1,2. Globally, T1DM accounts for approximately 5–10% of all diabetes cases, but its prevalence is steadily rising, particularly in countries within the Middle East3. Saudi Arabia, in particular, ranks among the top countries worldwide in terms of incidence, with reports indicating nearly 33.5 new cases per 100,000 individuals annually, reflecting a concerning trend toward an expanding epidemic3. This rising incidence poses not only a metabolic and clinical challenge but also a considerable psychosocial burden for individuals and healthcare systems alike.
The psychological consequences of living with T1DM are increasingly recognized. Depression and anxiety are consistently reported at two to three times the rate observed in the general population4–6. Depression is associated with poor adherence to diabetes self-care, suboptimal glycemic control, higher rates of complications, reduced quality of life, and increased healthcare utilization7–9. Anxiety is likewise linked to impaired glycemic outcomes, fear of hypoglycemia, and greater diabetes distress10–12. A bidirectional relationship between psychological health and diabetes outcomes has been established, wherein poor glycemic control and the challenges of complex self-management exacerbate psychological distress, while depression and anxiety impair motivation and adherence to therapeutic regimens13–15.
The COVID-19 pandemic further amplified this concern. Disruptions to healthcare services, social isolation, and lifestyle changes during the pandemic worsened mental health outcomes across populations, with people with chronic illnesses being especially vulnerable16–18. International studies have reported a surge in depression and anxiety among individuals with diabetes during and after the pandemic, with prevalence estimates exceeding pre-COVID levels13,19. Regional studies continue to document high baseline prevalence in diabetes populations20,21. These findings are consistent with the “post-COVID psychological burden” model, which highlights long-term mental health sequelae due to persistent uncertainty, health anxieties, and disrupted support systems22. However, evidence from the Middle East remains limited, particularly in adult populations with T1DM, as most available studies in Saudi Arabia have concentrated on type 2 diabetes mellitus23,24.
Self-care is a cornerstone of diabetes management, encompassing adherence to diet, physical activity, blood glucose monitoring, medication use, and foot care. Poor self-care behaviors have been shown to mediate the relationship between psychological distress and diabetes outcomes25. Conversely, adequate self-care has protective effects against both metabolic complications and psychological distress. Despite this, research addressing the interrelationship between depression, anxiety, and self-care behaviors in Saudi adults with T1DM is sparse. Furthermore, limited data exist on how biological markers—such as lipid profiles, thyroid function, and hemoglobin—relate to mental health outcomes in this population. Recent findings suggest that lipid metabolism and thyroid function may play roles in the pathophysiology of depression and anxiety26–28, raising important questions for clinical integration.
Given this context, the present study aimed to (1) assess the prevalence of depression and anxiety in Saudi adults with T1DM using validated Arabic versions of the PHQ-9 and GAD-7, and (2) explore their associations with sociodemographic, clinical, laboratory, and self-care variables. We hypothesized that female gender, poor adherence to self-care behaviors, and specific metabolic parameters would be associated with higher risk of depression and anxiety. By integrating both psychosocial and biological domains, and by situating our findings within the post-COVID era, this study seeks to provide timely evidence to inform clinical practice and guide future mental health interventions in diabetes care within Saudi Arabia.
Methods
Study design and setting
This study employed a cross-sectional design and was conducted at the adult endocrinology and diabetes clinic of King Abdulaziz Medical City in Jeddah, Saudi Arabia, between January and December 2022. The center is a large tertiary referral hospital that provides comprehensive diabetes care for a diverse patient population across the western region of the country. The study was designed to evaluate the prevalence of depression and anxiety among adults with type 1 diabetes mellitus (T1DM) and to investigate their associations with sociodemographic, clinical, laboratory, and self-care variables.
Eligibility criteria
Eligible participants were Saudi adults aged 18 years and older with a confirmed diagnosis of T1DM for at least one year, based on medical records and endocrinologist assessment. Exclusion criteria were: diagnosis of type 2 diabetes, gestational diabetes, pregnancy at the time of study, history of major psychiatric disorders diagnosed before T1DM onset, intellectual disability, or inability to provide informed consent.
Sampling technique
A convenience sampling technique was used to recruit participants attending the diabetes clinic. This technique was employed due to logistical constraints, including limited clinic access, the requirement for in-person data collection as mandated by institutional permissions, and the inability to attend every clinic day due to scheduling conflicts and resource limitations. While this approach facilitated recruitment and allowed us to exceed the calculated sample size, it may limit the generalizability of findings to the broader T1DM population in Saudi Arabia, particularly given the cohort’s high education level and access to tertiary care resources. These limitations are further discussed in the Discussion section.
Sample size determination
Sample size was estimated using OpenEpi software, assuming a 25% prevalence of depression among adults with T1DM1. With a 95% confidence interval, 8% margin of error, and design effect of 1, the minimum required sample was 115. To account for potential non-response or incomplete data, we recruited 122 participants, which exceeded the calculated minimum and ensured adequate power for subgroup analyses.
Data collection instruments and variables
Data were collected via structured, direct patient interviews using a pre-designed and validated questionnaire. The questionnaire consisted of four sections:
Sociodemographic and clinical data: Collected using structured forms and included age, gender, marital status, educational attainment, employment status, family income, diabetes duration, insulin regimen (multiple daily injections [MDI] or insulin pump), use of continuous glucose monitoring (CGM), number of diabetic ketoacidosis (DKA) admissions, complications, and comorbidities.
Adherence to diabetes self-care activities: We assessed self-care practices, such as diet, physical activity, blood glucose monitoring, foot care, and medication adherence, using the validated Arabic version of the Summary of Diabetes Self-Care Activities (SDSCA)29. An extra item on medication adherence, translated by the research team, was added from the original SDSCA30. The tool measures how many days per week (0–7) patients follow self-care practices. A total adherence score was calculated and grouped into low (0–2.9), moderate (3–4.9), or high (5–7) adherence.
Depression: Assessed using the Arabic version of the Patient Health Questionnaire-9 (PHQ-9)31. Scores range from 0 to 27, with cut-offs of 5, 10, 15, and 20 indicating mild, moderate, moderately severe, and severe depression, respectively.
Anxiety: Measured with the Arabic version of the Generalized Anxiety Disorder-7 (GAD-7)31. Scores range from 0 to 21, with thresholds of 5, 10, and 15 indicating mild, moderate, and severe anxiety.
The questionnaire was reviewed by two diabetes specialists and one psychiatry consultant to ensure face validity. It was piloted on a sample of 30 participants to confirm reliability, yielding a Cronbach’s alpha score that validated its consistency. Minor modifications were made to enhance clarity. Upon completion of data collection, internal-consistency reliability was evaluated using Cronbach’s alpha: PHQ-9 = 0.88, GAD-7 = 0.87, and SDSCA = 0.82, indicating good-to-excellent reliability. (Supplemental Table S1).
Furthermore, the electronic medical report system in the hospital was used to collect anthropometric measurements, blood pressure readings, and recent laboratory results including fasting glucose, HbA1c, lipid profile, hemoglobin, thyroid stimulating hormone, vitamin D, and vitamin B12.
Handling of missing data
The questionnaire was administered via a Google form that required all fields to be completed for submission, ensuring no missing data for depression, anxiety, self-care, and sociodemographic variables among the included participants. Laboratory data, however, had some missing values due to reliance on existing medical records, though the majority of participants had complete blood investigation records.
Participant exclusions
Of the 156 patients screened for eligibility, 34 were excluded for the following reasons: one patient was excluded due to an incorrect medical record number (MRN), one patient refused participation, one pregnant patient was excluded, 15 patients were younger than 18 years old, and 16 patients were excluded as they reported a previous diagnosis of mental illness. The final sample comprised 122 participants (Fig. 1).
Fig. 1.
Flowchart of participant inclusion and exclusion (CONSORT-style diagram).
Statistical analysis
Statistical analyses were performed using SPSS version 27 (IBM Corp., Armonk, NY, USA). Descriptive statistics were used to summarize data: means and standard deviations (SD) for continuous variables, and counts with percentages for categorical variables. Normality of continuous variables was assessed using the Shapiro–Wilk test (Supplemental Table S2).
Binary classification of mental health outcomes
Depression was defined as present (Yes/No) if PHQ-9 score ≥ 5.
Anxiety was defined as present (Yes/No) if GAD-7 score ≥ 5.
These thresholds were selected to include mild symptom burden and to enable binary logistic regression analysis, consistent with epidemiological research aiming to estimate the full spectrum of clinically relevant mental health conditions and maximize sensitivity in population-based screening32–36.
For bivariate analyses
Categorical variables (e.g., depression/anxiety Yes/No) were compared using chi-square or Fisher’s exact test.
Continuous variables (e.g., PHQ-9, GAD-7 scores, lab values) were compared using independent-samples t-tests or one-way ANOVA, as appropriate. When normality assumptions were not met, nonparametric alternatives (Mann–Whitney U test, Kruskal–Wallis test) were applied.
For multivariable analyses
Logistic regression was conducted to identify independent factors associated with depression and anxiety (binary outcomes). Odds ratios (OR) with 95% confidence intervals (CI) were reported.
Linear regression was used with PHQ-9 and GAD-7 total scores as continuous dependent variables to identify predictors. Standardized beta coefficients (β) with p-values were reported.
Correlation analyses were performed using Pearson’s correlation for normally distributed variables and Spearman’s rank correlation for skewed variables. Bonferroni correction was applied to control for multiple testing. Statistical significance was set at p < 0.05.
Results
Demographics
A total of 122 adults with type 1 diabetes were included in the analysis. The mean age was 29.4 years (SD 9.6; range 18–63), and 55.7% were female. More than half of the participants were single (55.7%) and the majority had attained at least a bachelor’s degree (61.5%). Family income was ≤ 10,000 SAR in 59.8% of participants. Regarding clinical characteristics, 70.5% reported diabetes duration greater than 10 years, 68.0% were managed with multiple daily injections, and 76.2% used continuous glucose monitoring. Approximately 31.1% had experienced more than one diabetic ketoacidosis (DKA) admission, and 16.4% reported more than one comorbidity. Detailed demographic and clinical characteristics are shown in Tables 1 and 2.
Table 1.
Associations of demographics with depression and anxiety (binary Yes/No).
| Demographics | Total | Depression | Anxiety | |||
|---|---|---|---|---|---|---|
| No | Yes | No | Yes | |||
| Total | 122 | 55(45.1%) | 67(54.9%) | 67(54.9%) | 55(45.1%) | |
| Gender | Male | 54 | 29(53.7%) | 25(46.3%) | 36(66.7%) | 18(33.3%) |
| Female | 68 | 26(38.2%) | 42(61.8%) | 31(45.6%) | 37(54.4%) | |
| p-value | 0.102 | 0.028 a | ||||
| Age | <=25 years | 49 | 21(42.9%) | 28(57.1%) | 26(53.1%) | 23(46.9%) |
| 26–35 years | 49 | 23(46.9%) | 26(53.1%) | 27(55.1%) | 22(44.9%) | |
| > 35 years | 24 | 11(45.8%) | 13(54.2%) | 14(58.3%) | 10(41.7%) | |
| p-value | 0.971 | 0.918 | ||||
| Marital status | Single | 68 | 32(47.1%) | 36(52.9%) | 38(55.9%) | 30(44.1%) |
| Married | 50 | 22(44.0%) | 28(56.0%) | 26(52.0%) | 24(48.0%) | |
| Divorced | 4 | 1(25.0%) | 3(75.0%) | 3(75.0%) | 1(25.0%) | |
| p-value | 0.752 | 0.709 | ||||
| Level of education | Intermediate school and below | 25 | 14(56.0%) | 11(44.0%) | 15(60.0%) | 10(40.0%) |
| High school/diploma | 11 | 2(18.2%) | 9(81.8%) | 5(45.5%) | 6(54.5%) | |
| Bachelor | 75 | 31(41.3%) | 44(58.7%) | 37(49.3%) | 38(50.7%) | |
| Master/Ph.D | 11 | 8(72.7%) | 3(27.3%) | 10(90.9%) | 1(9.1%) | |
| p-value | 0.041 b | 0.053 | ||||
| Family income | Less than or equal to 5.000 Saudi Riyals | 41 | 17(41.5%) | 24(58.5%) | 20(48.8%) | 21(51.2%) |
| From 5.001 to 10.000 Saudi Riyals | 32 | 15(46.9%) | 17(53.1%) | 18(56.3%) | 14(43.8%) | |
| From 10.001–15.001,000 Saudi Riyals | 18 | 5(27.8%) | 13(72.2%) | 8(44.4%) | 10(55.6%) | |
| More than 15.000 Saudi Riyals | 31 | 18(58.1%) | 13(41.9%) | 21(67.7%) | 10(32.3%) | |
| p-value | 0.216 | 0.323 | ||||
| Occupation | Employed | 52 | 19(36.5%) | 33(63.5%) | 29(55.8%) | 23(44.2%) |
| Not employed | 24 | 11(45.8%) | 13(54.2%) | 10(41.7%) | 14(58.3%) | |
|
Student/retired/ Others |
46 | 25(54.3%) | 21(45.7%) | 28(60.9%) | 18(39.1%) | |
| p-value | 0.212 | 0.301 | ||||
a−significant using chi-square test at < 0.05 level
b-significant using fisher exact test at < 0.05 level
Table 2.
Associations of clinical characteristics with depression and anxiety (binary Yes/No).
| Variables | Total | Depression | Anxiety | |||
|---|---|---|---|---|---|---|
| No | Yes | No | Yes | |||
| Total | 122 | 55(45.1%) | 67(54.9%) | 67(54.9%) | 55(45.1%) | |
| Since when were you diagnosed with type 1 diabetes? | 1–5 years | 19 | 11(57.9%) | 8(42.1%) | 11(57.9%) | 8(42.1%) |
| 6–10 years | 17 | 5(29.4%) | 12(70.6%) | 9(52.9%) | 8(47.1%) | |
| > 10 years | 86 | 39(45.3%) | 47(54.7%) | 47(54.7%) | 39(45.3%) | |
| p-value | 0.260 | 0.961 | ||||
| Type of treatment used for type 1 diabetes? | Multiple daily injections | 83 | 36(43.4%) | 47(56.6%) | 41(49.4%) | 42(50.6%) |
| Insulin pump | 38 | 18(47.4%) | 20(52.6%) | 25(65.8%) | 13(34.2%) | |
| Others | 1 | 1(100.0%) | 0(0.0%) | 1(100.0%) | 0(0.0%) | |
| p-value | 0.553 | 0.140 | ||||
| Monitoring of blood sugar levels method | Continuous glucose monitoring system/sensor | 93 | 39(41.9%) | 54(58.1%) | 50(53.8%) | 43(46.2%) |
| Glucometer | 29 | 16(55.2%) | 13(44.8%) | 17(58.6%) | 12(41.4%) | |
| p-value | 0.285 | 0.675 | ||||
| Number of diabetic ketoacidosis admissions | 0 | 52 | 24(46.2%) | 28(53.8%) | 29(55.8%) | 23(44.2%) |
| 1 | 32 | 13(40.6%) | 19(59.4%) | 18(56.3%) | 14(43.8%) | |
| > 1 | 38 | 18(47.4%) | 20(52.6%) | 20(52.6%) | 18(47.4%) | |
| p-value | 0.847 | 0.946 | ||||
| Do you have any complications related to diabetes? | None | 104 | 48(46.2%) | 56(53.8%) | 59(56.7%) | 45(43.3%) |
| One complication | 13 | 4(30.8%) | 9(69.2%) | 4(30.8%) | 9(69.2%) | |
| More than one complication | 5 | 3(60.0%) | 2(40.0%) | 4(80.0%) | 1(20.0%) | |
| p-value | 0.442 | 0.119 | ||||
| Do you have other comorbidities beside diabetes? | No comorbidity | 66 | 31(47.0%) | 35(53.0%) | 37(56.1%) | 29(43.9%) |
| One comorbidity | 36 | 14(38.9%) | 22(61.1%) | 20(55.6%) | 16(44.4%) | |
| More than one comorbidity | 20 | 10(50.0%) | 10(50.0%) | 10(50.0%) | 10(50.0%) | |
| p-value | 0.702 | 0.938 | ||||
| Smoking status | Non-smoker | 93 | 44(47.3%) | 49(52.7%) | 52(55.9%) | 41(44.1%) |
| Smoker | 15 | 5(33.3%) | 10(66.7%) | 6(40.0%) | 9(60.0%) | |
| Ex-smoker | 14 | 6(42.9%) | 8(57.1%) | 9(64.3%) | 5(35.7%) | |
| p-value | 0.553 | 0.391 | ||||
Diabetes self-care activities
Adherence to diabetes self-care activities varied across domains. The mean number of days per week spent on physical activity was 2.9, while blood glucose monitoring was performed on average 5.5 days per week. Foot care had the lowest adherence, with a mean of 2.6 days per week. Medication adherence was highest, with a mean of 6.8 days per week. Based on total SDSCA scores, 18.9% of participants demonstrated low adherence, 59.8% moderate adherence, and 21.3% high adherence. Full results are presented in Table 3.
Table 3.
Diabetes self-care activities (SDSCA) adherence (n = 122).
| Diabetes Self-care assessment | N | Min | Max | Mean | SD |
|---|---|---|---|---|---|
| Diet | 122 | 0.00 | 7.00 | 2.66 | 2.3 |
| Exercise | 122 | 0.00 | 7.00 | 2.90 | 2.2 |
| Blood sugar testing | 122 | 0.00 | 7.00 | 5.52 | 2.1 |
| Foot care | 122 | 0.00 | 7.00 | 2.59 | 2.6 |
| Medications | 122 | 2.00 | 7.00 | 6.82 | 0.7 |
| Diabetes self-care assessment | 122 | 1.30 | 7.00 | 4.10 | 1.1 |
| Count | % | ||||
| Total | 122 | 100.0 | |||
| Diabetes self-care assessment | Low adherence | 23 | 18.9 | ||
| Moderate adherence | 73 | 59.8 | |||
| High adherence | 26 | 21.3 | |||
Prevalence of depression and anxiety
The mean PHQ-9 score was 5.8 (SD 4.5), and 54.9% of participants met the threshold for depression (≥ 10). Among these, 37.7% had mild, 11.5% moderate, 4.9% moderately severe, and 0.8% severe depression. The mean GAD-7 score was 5.1 (SD 4.9), and 45.1% met the threshold for anxiety (≥ 10). Of those with anxiety, 27.9% were mild, 10.7% moderate, and 6.6% severe. Prevalence distributions are graphically illustrated with total n and 95% confidence intervals in Figs. 2 and 3. Detailed symptom-level responses are presented in Supplemental Tables S3 and S4.
Fig. 2.
Distribution of PHQ-9 depression severity categories among participants (n = 122), with 95% confidence intervals.
Fig. 3.
Distribution of GAD-7 anxiety severity categories among participants (n = 122), with 95% confidence intervals.
Associations between demographics and mental health
Chi-square analyses (Table 1) revealed a significant association between gender and anxiety, with higher prevalence among females (54.4% vs. 33.3% in males; p = 0.028). Educational attainment was significantly associated with depression, with higher prevalence among participants with a high school education compared with those holding postgraduate degrees (p = 0.041). No significant associations were found for marital status, age group, employment, or income in relation to anxiety. On the other hand, participants with family income between 10,001 and 15,000 SAR had higher PHQ-9 scores compared to those with higher incomes (> 15,000 SAR) (8.4 vs. 4.7, p = 0.045) (Supplemental Table S5).
Associations with clinical characteristics
No significant associations were observed between duration of diabetes, insulin regimen, CGM use, or number of DKA admissions and the presence of depression or anxiety (Table 2). Likewise, neither the presence of comorbidities nor diabetes complications demonstrated significant associations with depression or anxiety outcomes (Supplemental Table S6).
Laboratory variables and mental health outcomes
Significant associations were identified between several laboratory measures and mental health outcomes. Participants with depression had significantly higher HDL cholesterol levels compared with those without depression (mean 1.52 vs. 1.33 mmol/L, p = 0.004). Participants with anxiety had significantly lower mean hemoglobin (12.9 vs. 13.9 g/dL, p = 0.031). Thyroid function was also associated: mean TSH was lower among participants with depression (1.69 vs. 2.42 mIU/L, p = 0.014) and anxiety (1.65 vs. 2.32 mIU/L, p = 0.027). These results are summarized in Table 4.
Table 4.
Laboratory variables stratified by depression and anxiety categories.
| Lab | Total | Depression | Anxiety | |||||
|---|---|---|---|---|---|---|---|---|
| No | Yes | p-value | No | Yes | p-value | |||
| Systolic BP | 109 | 120.44 ± 11.2 | 119.51 ± 11.6 | 0.561 | 118.79 ± 11.1 | 121.40 ± 11.7 | 0.321 | |
| Diastolic BP | 109 | 71.36 ± 7.9 | 70.59 ± 9.3 | 0.648 | 70.59 ± 8.3 | 71.40 ± 9.2 | 0.633 | |
| Total cholesterol | 106 | 4.68 ± 1.0 | 4.96 ± 0.9 | 0.133 | 4.70 ± 0.8 | 5.01 ± 1.1 | 0.103 | |
| LDL | 106 | 2.91 ± 0.8 | 3.02 ± 0.8 | 0.486 | 2.91 ± 0.8 | 3.04 ± 0.9 | 0.428 | |
| HDL | 107 | 1.33 ± 0.3 | 1.52 ± 0.3 | 0.004a | 1.38 ± 0.4 | 1.50 ± 0.3 | 0.088 | |
| Triglyceride | 106 | 1.02 ± 0.8 | 0.95 ± 0.6 | 0.939 | 0.95 ± 0.7 | 1.02 ± 0.8 | 0.777 | |
| Hgb | 57 | 13.76 ± 1.9 | 13.14 ± 1.8 | 0.217 | 13.92 ± 2.0 | 12.86 ± 1.6 | 0.031a | |
| EGFR | 113 | 116.78 ± 15.5 | 119.15 ± 13.9 | 0.342 | 115.65 ± 16.6 | 121.04 ± 11.2 | 0.146 | |
| Fasting glucose | 48 | 9.63 ± 5.9 | 9.93 ± 4.6 | 0.846 | 9.24 ± 5.4 | 10.28 ± 5.0 | 0.492 | |
| Vitamin D | 101 | 62.80 ± 48.4 | 60.73 ± 25.2 | 0.370 | 66.33 ± 45.8 | 56.09 ± 23.3 | 0.352 | |
| Vitamin B12 | 27 | 378.33 ± 174.1 | 379.22 ± 188.1 | 0.991 | 357.27 ± 170.6 | 406.00 ± 195.6 | 0.496 | |
| TSH | 106 | 2.42 ± 1.6 | 1.69 ± 1.0 | 0.014b | 2.32 ± 1.6 | 1.65 ± 0.9 | 0.027b | |
| BMI | Underweight | 6 | 2(33.3%) | 4(66.7%) | 0.548 | 3(50.0%) | 3(50.0%) | 0.115 |
| Healthy range | 39 | 20(51.3%) | 19(48.7%) | 26(66.7%) | 13(33.3%) | |||
| Overweight | 40 | 15(37.5%) | 25(62.5%) | 17(42.5%) | 23(57.5%) | |||
| Obesity | 22 | 12(54.5%) | 10(45.5%) | 11(50.0%) | 11(50.0%) | |||
| Severe obesity | 3 | 2(66.7%) | 1(33.3%) | 3(100.0%) | 0(0.0%) | |||
| HbA1c | 7 or below | 38 | 17(44.7%) | 21(55.3%) | 0.277 | 20(52.6%) | 18(47.4%) | 0.855 |
| Between 7 and 9 | 51 | 27(52.9%) | 24(47.1%) | 30(58.8%) | 21(41.2%) | |||
| 9 and above | 24 | 8(33.3%) | 16(66.7%) | 14(58.3%) | 10(41.7%) | |||
a-significant using independent t-test at < 0.05 level
b-significant using Mann-Whitney Test at < 0.05 level
Correlation analyses (Supplemental Table S7) further confirmed these relationships. Anxiety scores correlated positively with total cholesterol (r = 0.218, p = 0.025), HDL (r = 0.215, p = 0.026), and eGFR (r = 0.207, p = 0.028), while correlating negatively with hemoglobin (r = − 0.313, p = 0.018) and TSH (r = − 0.259, p = 0.007). Depression scores correlated negatively with TSH (r = − 0.277, p = 0.004).
Multivariable analyses
Logistic regression (Table 5) identified HDL as an independent factor associated with depression (OR 6.60, 95% CI 1.81–24.02, p = 0.004). For anxiety, logistic regression revealed that male gender was protective (OR 0.36, 95% CI 0.16–0.84, p = 0.018), while higher diabetes self-care adherence decreased the risk by 37% (OR 0.63, 95% CI 0.42–0.94, p = 0.025) (Table 6).
Table 5.
Logistic regression model for depression (binary outcome).
| Variables in the equation | B | S.E. | Exp(B) | 95% C.I.for EXP(B) | p-value | ||
|---|---|---|---|---|---|---|---|
| Dependent variable: depression | Lower | Upper | |||||
| First stepa | Gender(male) | −0.236 | 0.505 | 0.790 | 0.294 | 2.126 | 0.641 |
| Age | 0.840 | ||||||
| <=25 years | 0.160 | 0.748 | 1.173 | 0.271 | 5.085 | 0.831 | |
| 26–35 years | −0.140 | 0.738 | 0.870 | 0.205 | 3.692 | 0.850 | |
| Level of education | 0.287 | ||||||
| Intermediate school and below | 0.577 | 0.937 | 1.780 | 0.284 | 11.173 | 0.538 | |
| High school/diploma | 2.438 | 1.367 | 11.455 | 0.786 | 166.908 | 0.074 | |
| Bachelor | 0.999 | 0.813 | 2.716 | 0.552 | 13.357 | 0.219 | |
| Do you have other comorbidities beside diabetes? | 0.443 | ||||||
| No comorbidity | −0.593 | 0.776 | 0.552 | 0.121 | 2.530 | 0.445 | |
| One comorbidity | 0.095 | 0.779 | 1.100 | 0.239 | 5.067 | 0.903 | |
| HDL | 1.469 | 0.766 | 4.346 | 0.968 | 19.519 | 0.055 | |
| HbA1c | 0.475 | ||||||
| 7 or below | −0.395 | 0.638 | 0.674 | 0.193 | 2.351 | 0.535 | |
| Between 7 and 9 | −0.718 | 0.602 | 0.488 | 0.150 | 1.589 | 0.234 | |
| Diabetes self care assessment | −0.352 | 0.224 | 0.703 | 0.453 | 1.091 | 0.116 | |
| Constant | −0.459 | 1.765 | 0.632 | 0.795 | |||
| Last stepa | HDL | 1.886 | 0.660 | 6.595 | 1.811 | 24.021 | 0.004b |
| Diabetes self care assessment | −0.363 | 0.201 | 0.696 | 0.469 | 1.031 | 0.071 | |
| Constant | −0.970 | 1.138 | 0.379 | 0.394 | |||
a-Variable(s) entered on step 1: Gender, Age, Level of education, Do you have other comorbidities beside diabetes?, HDL, HbA1c, Diabetes Self care assessment
b-Significant using Binary Logistic Regression Model, with Backward Conditional Elimination with Enter Criteria = 0.05, Elimination = 0.10
Table 6.
Logistic regression model for anxiety (binary outcome).
| Variables in the equation | B | S.E. | Exp(B) | 95% C.I.for EXP(B) | p-value | ||
|---|---|---|---|---|---|---|---|
| Dependent variable: anxiety | Lower | Upper | |||||
| First stepa | Gender(male) | −1.029 | 0.525 | 0.357 | 0.128 | 1.000 | 0.050 |
| Age | 0.638 | ||||||
| <=25 years | 0.685 | 0.810 | 1.985 | 0.405 | 9.717 | 0.398 | |
| 26–35 years | 0.759 | 0.809 | 2.135 | 0.437 | 10.433 | 0.349 | |
| Level of education | 0.182 | ||||||
| Intermediate school and below | 2.295 | 1.267 | 9.923 | 0.828 | 118.860 | 0.070 | |
| High school/diploma | 1.935 | 1.421 | 6.922 | 0.427 | 112.217 | 0.173 | |
| Bachelor | 2.498 | 1.178 | 12.153 | 1.208 | 122.212 | 0.034b | |
| Do you have other comorbidities beside diabetes? | 0.124 | ||||||
| No comorbidity | −1.699 | 0.857 | 0.183 | 0.034 | 0.980 | 0.047b | |
| One comorbidity | −1.606 | 0.855 | 0.201 | 0.038 | 1.072 | 0.060 | |
| HDL | 0.419 | 0.752 | 1.520 | 0.348 | 6.638 | 0.578 | |
| HbA1c | 0.679 | ||||||
| 7 or below | 0.291 | 0.618 | 1.338 | 0.398 | 4.496 | 0.638 | |
| Between 7 and 9 | −0.175 | 0.578 | 0.840 | 0.270 | 2.606 | 0.762 | |
| Diabetes self care assessment | −0.511 | 0.235 | 0.600 | 0.378 | 0.951 | 0.030b | |
| Constant | 0.201 | 1.981 | 1.223 | 0.919 | |||
| Last stepa | Gender(male) | −1.016 | 0.429 | 0.362 | 0.156 | 0.839 | 0.018b |
| Diabetes self care assessment | −0.462 | 0.207 | 0.630 | 0.420 | 0.944 | 0.025b | |
| Constant | 2.046 | 0.928 | 7.739 | 0.027b | |||
a-Variable(s) entered on step 1: Gender, Age, Level of education, Do you have other comorbidities beside diabetes?, HDL, HbA1c, Diabetes Self care assessment
b-Significant using Binary Logistic Regression Model, with Backward Conditional Elimination with Enter Criteria = 0.05, Elimination = 0.10
Linear regression confirmed that female gender was significantly associated with higher PHQ-9 (β = 2.03, p = 0.022) and GAD-7 scores (β = 2.75, p = 0.002). Poorer self-care adherence remained an independent predictor of higher GAD-7 scores (β = − 0.80, p = 0.049) (Supplemental Tables S8 and S9).
Self-Care and mental health
Correlation analysis (Table 7) revealed that overall self-care adherence was inversely correlated with both PHQ-9 (r = − 0.180, p = 0.047) and GAD-7 scores (r = − 0.207, p = 0.022). Independent t-tests showed that participants with anxiety had significantly lower overall self-care scores (3.79 vs. 4.35, p = 0.004) and poorer adherence to foot care (2.22 vs. 2.90, p = 0.036) compared with those without anxiety (Supplemental Table S10).
Table 7.
Correlations between self-care domains and PHQ-9/GAD-7 scores.
| Correlations | PHQ-9 | GAD-7 | |
|---|---|---|---|
| Diet | r | −0.161 | −0.211a |
| p-value | 0.076 | 0.020 | |
| Exercise | r | −0.106 | −0.052 |
| p-value | 0.243 | 0.569 | |
| Blood sugar testing | r | −0.028 | −0.081 |
| p-value | 0.758 | 0.374 | |
| Foot care | r | −0.103 | −0.170 |
| p-value | 0.257 | 0.061 | |
| Medications | r | −0.022 | 0.001 |
| p-value | 0.808 | 0.987 | |
| Diabetes Self care assessment | r | −0.180a | −0.207a |
| p-value | 0.047 | 0.022 | |
a-significant using Pearson Correlation Test at < 0.05 level.
ANOVA analysis demonstrated that GAD-7 scores were significantly higher among participants with low self-care adherence compared with those with high adherence (7.3 vs. 4.2, p = 0.045). Depression scores did not differ significantly across self-care categories (Supplemental Table S11).
Discussion
This study revealed a high prevalence of depression (54.9%) and anxiety (45.1%) among Saudi adults with type 1 diabetes mellitus (T1DM), highlighting a substantial psychological burden in this population. These prevalence rates are higher than pre-pandemic estimates from regional studies, which typically reported depression in 30–40% of T1DM patients20,21,24, and they align with post-COVID literature documenting increased rates of mental health problems among individuals with diabetes13,36. Our findings emphasize that psychological comorbidity in T1DM has not only persisted but has likely been exacerbated by the long-term psychosocial and healthcare disruptions of the COVID-19 era.
Gender differences were particularly evident in our cohort. Female participants reported significantly higher PHQ-9 and GAD-7 scores compared with males, and female gender remained an independent predictor of anxiety after adjusting for potential confounders. This finding is consistent with prior international studies and local research, which have shown that women are more vulnerable to psychological distress due to a combination of biological, hormonal, and sociocultural factors15,17,31. Such disparities underscore the importance of gender-sensitive approaches to screening and intervention in diabetes care.
Laboratory measures provided additional insights into the biological underpinnings of psychological distress in T1DM. HDL cholesterol emerged as an independent factor associated with depression, supporting prior research that links lipid metabolism with mood regulation through inflammatory and neuroendocrine pathways37. Similarly, lower hemoglobin and TSH levels were significantly associated with anxiety and depression, respectively, and these associations were confirmed through correlation analyses. Although causality cannot be inferred, these findings suggest potential biomarkers that may help identify patients at risk of mental health problems, warranting further mechanistic and longitudinal studies.
Diabetes self-care behaviors were strongly linked to mental health outcomes. Poor adherence to self-care was inversely correlated with both PHQ-9 and GAD-7 scores, and logistic regression confirmed that low self-care adherence independently predicted anxiety. Specific domains, such as foot care, were significantly poorer among participants with anxiety, while ANOVA showed that individuals with low overall self-care adherence reported higher GAD-7 scores compared with those with high adherence. These findings reinforce the bidirectional relationship between self-management and mental health described in prior studies38–40, where psychological distress compromises adherence and inadequate adherence further fuels distress through suboptimal glycemic control and complications.
This study has several strengths. It is among the few investigations in Saudi Arabia focusing specifically on T1DM adults in the post-COVID context. It employed validated Arabic versions of PHQ-9, GAD-7, and SDSCA, all of which demonstrated high internal reliability in this cohort. The analytic approach was comprehensive, including both binary and continuous outcomes, subgroup comparisons, correlation analyses, and multivariate models, which allowed for robust exploration of associations across sociodemographic, clinical, laboratory, and behavioral domains.
Nonetheless, limitations must be acknowledged. The cross-sectional design precludes causal inference. The use of convenience sampling at this single tertiary care center may limit the generalizability of the findings. The cohort’s high education level, with 61.5% holding a bachelor’s degree, and access to specialized diabetes care may not reflect the broader T1DM population in Saudi Arabia, where educational attainment and healthcare access vary widely. This bias may have underestimated mental health burdens and self-care challenges, as participants likely possess greater health literacy and resources than the average T1DM patient. Additionally, self-reported measures of depression, anxiety, and self-care are subject to recall and reporting biases. Furthermore, although multiple testing corrections were applied, residual confounding cannot be excluded.
Conclusion
This cross-sectional study demonstrated that more than half of Saudi adults with type 1 diabetes experienced clinically significant depression, while nearly half reported anxiety. Female gender and poor adherence to self-care behaviors were consistently associated with higher anxiety, and elevated HDL cholesterol was independently linked to depression. Additional associations with hemoglobin and thyroid function suggest that biological as well as behavioral factors contribute to the psychological health of this population.
These findings underscore the critical importance of integrating systematic mental health screening into diabetes clinic protocols using validated tools such as the PHQ-9 and GAD-7. Clinicians should give particular attention to women and patients with poor self-care adherence, as these groups are at heightened risk. Collaborative care models that are tailored to the patient involving mental health professionals may enhance outcomes by addressing both medical and psychological needs.
Given the cross-sectional design, causal inference is limited, and findings may not be generalizable beyond the single-center setting. Nevertheless, this study provides timely post-COVID evidence highlighting the intertwined relationship between mental health and diabetes self-care. Future multicenter and longitudinal research is needed to confirm causal pathways and to evaluate culturally tailored interventions aimed at reducing the psychological burden in adults living with type 1 diabetes.
Supplementary Information
Below is the link to the electronic supplementary material.
Author contributions
SM: Conceptualization, Supervision, Writing – Original Draft.AA: Supervision, Methodology, Formal Analysis.MS, MBB, MA: Data Collection, Validation.RK, GA, SB: Data Collection, Investigation, Resources.HT, BA: Visualization, Project Administration.AL, HZ: Writing – Review & Editing.
Data availability
The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.
Declarations
Competing interests
The authors declare no competing interests
Ethics approval and consent to participate
Ethical approval for the study was obtained from the King Abdullah International Medical Research Center Institutional Review Board (approval number: IRB/1626/23). All methods were performed in accordance with the Declaration of Helsinki. Participants provided written informed consent before data collection. To maintain confidentiality, participant data were anonymized, and all responses were securely stored. Patients scoring ≥ 10 on the PHQ-9 or GAD-7 were advised to seek further evaluation and care from mental health professionals. This study was not registered in a clinical trials registry, as it is an observational study.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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
The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.