Xu H, He DJ, Luo C, Yu XM, Duan CZ, Sun D, Wu DJ, Mao XQ, Jiang WF. Association between uric acid to high-density lipoprotein ratio and mental health symptoms in people with type 2 diabetes. World J Diabetes 2025; 16(10): 110211 [PMID: 41113500 DOI: 10.4239/wjd.v16.i10.110211]
Corresponding Author of This Article
Wei-Feng Jiang, MD, Associate Chief Physician, Department of Gerontology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, No. 100 Minjiang Avenue, Kecheng District, Quzhou 324000, Zhejiang Province, China. weifengjiang@wmu.edu.cn
Research Domain of This Article
Endocrinology & Metabolism
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Observational Study
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Oct 15, 2025 (publication date) through Oct 22, 2025
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World Journal of Diabetes
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1948-9358
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Xu H, He DJ, Luo C, Yu XM, Duan CZ, Sun D, Wu DJ, Mao XQ, Jiang WF. Association between uric acid to high-density lipoprotein ratio and mental health symptoms in people with type 2 diabetes. World J Diabetes 2025; 16(10): 110211 [PMID: 41113500 DOI: 10.4239/wjd.v16.i10.110211]
Hui Xu, Department of Hospital Management, Quzhou Hospital of Traditional Chinese Medicine, Quzhou 324000, Zhejiang Province, China
Dong-Juan He, Xian-Mei Yu, Department of Endocrinology, The Second People’s Hospital of Quzhou, Quzhou 324002, Zhejiang Province, China
Cheng Luo, Cheng-Zheng Duan, Department of Endocrinology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou 324000, Zhejiang Province, China
Da Sun, Institute of Life Sciences & Biomedical Collaborative Innovation Center of Zhejiang Province, Wenzhou University, Wenzhou 325000, Zhejiang Province, China
De-Jun Wu, Xiao-Qiang Mao, Wei-Feng Jiang, Department of Gerontology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, Zhejiang Province, China
Author contributions: Xu H and He DJ contributed to writing - original draft and contributed equally as co-first authors; He DJ, Sun D, and Jiang WF contributed to writing - review & editing; Xu H, Luo C, Duan CZ, and Mao XQ contributed to investigation, methodology, software; Xu H, Duan CZ, Wu DJ, and Mao XQ contributed to data curation; Xu H, Luo C, Sun D, and Jiang WF contributed to formal analysis; Luo C, Yu XM, Duan CZ, and Mao XQ contributed to validation; Xu H, Yu XM, Duan CZ, and Sun D contributed to visualization; He DJ and Jiang WF contributed to funding acquisition, project administration, resources, supervision; All authors contributed to conceptualization.
Supported by Science and Technology Program of Quzhou, China, No. 2022K67; Zhejiang Medical Association Clinical Research Fund Project, No. 2024ZYC-A526; and the Research Project of Quzhou People’s Hospital, No. KYQD2024-006.
Institutional review board statement: This study was approved by the Ethics Committee of the Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital (Approval No. 2022-133). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and national research committees and with the 1964 Helsinki Declaration and its later amendments.
Informed consent statement: Written informed consent was obtained from all participants after they were fully informed of the study objectives, procedures, potential risks, and benefits. Participation was entirely voluntary.
Conflict-of-interest statement: All authors declare that they have no conflicts of interest related to this study.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised accordingly.
Data sharing statement: The raw data supporting the conclusions of this study will be made available by the corresponding author upon reasonable request, without undue reservation.
Open Access: This article is an open-access article that was selected by an in-house editor and was fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Wei-Feng Jiang, MD, Associate Chief Physician, Department of Gerontology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, No. 100 Minjiang Avenue, Kecheng District, Quzhou 324000, Zhejiang Province, China. weifengjiang@wmu.edu.cn
Received: June 3, 2025 Revised: July 2, 2025 Accepted: August 18, 2025 Published online: October 15, 2025 Processing time: 136 Days and 22.4 Hours
Abstract
BACKGROUND
The association between the uric acid-to-high-density lipoprotein cholesterol ratio (UHR) and mental health among individuals with type 2 diabetes mellitus (T2DM) has not been thoroughly investigated.
AIM
To examine the link between UHR and symptoms of depression and anxiety in patients with T2DM.
METHODS
A cross-sectional analysis was carried out from March 2023 to April 2024, involving participants diagnosed with T2DM. Data on sociodemographic characteristics, clinical parameters, and UHR values were systematically gathered. The Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) were utilized to evaluate depressive and anxiety symptoms, respectively. To assess the relationships between UHR and SDS/SAS scores, linear regression models were employed, incorporating adjustments for potential confounding variables. Additionally, smooth curve fitting and threshold effect analyses were conducted to explore potential nonlinear relationships.
RESULTS
A total of 285 patients with T2DM were included. Initial univariate analysis demonstrated a significant positive correlation between elevated UHR levels and higher SDS and SAS scores. Multivariate regression analysis revealed that a one-unit rise in UHR was associated with a 1.13-point increase in SDS scores (95%CI: 0.69-1.58) and a 0.57-point increase in SAS scores (95%CI: 0.20-0.93). After controlling for confounders, UHR remained positively correlated with SDS (β = 1.55, 95%CI: 0.57-2.53) and SAS (β = 0.72, 95%CI: 0.35-1.09). Nonlinear analysis identified critical thresholds at UHR values of 5.02 for SDS and 4.00 for SAS, beyond which the relationships between UHR and psychological symptom scores became markedly stronger (P < 0.05).
CONCLUSION
Higher UHR levels are significantly linked to exacerbated depressive and anxiety symptoms in patients with T2DM. These results indicate that UHR may function as a promising biomarker to identify individuals at greater risk of mental health complications within this population.
Core Tip: This study highlights the significant association between the uric acid-to-high-density lipoprotein cholesterol ratio (UHR) and symptoms of depression and anxiety in individuals with type 2 diabetes mellitus (T2DM). Using multivariate and nonlinear analyses, the study identifies threshold effects, suggesting that elevated UHR levels may serve as a cost-effective biomarker for psychological distress in T2DM. These findings underscore the importance of integrating metabolic and mental health assessments in diabetic care, particularly in high-risk subgroups.
Citation: Xu H, He DJ, Luo C, Yu XM, Duan CZ, Sun D, Wu DJ, Mao XQ, Jiang WF. Association between uric acid to high-density lipoprotein ratio and mental health symptoms in people with type 2 diabetes. World J Diabetes 2025; 16(10): 110211
Diabetes mellitus (DM) represents a chronic metabolic condition marked by persistent hyperglycemia, stemming from impaired insulin secretion, reduced insulin sensitivity, or dysfunction of pancreatic beta cells[1,2]. Recognized as a leading global noncommunicable disease, DM presents a major public health burden, significantly contributing to early mortality and increased morbidity rates[3]. Epidemiological data from 2021 indicate that approximately 537 million adults worldwide were affected by DM, with estimates predicting an increase to 783 million by 2045[4].
Among the various forms of diabetes, type 2 DM (T2DM) is the most prevalent, closely linked to insulin resistance and metabolic syndrome. In contrast, type 1 DM is an autoimmune disorder typically identified in younger populations[5]. Chronic hyperglycemia initiates a series of pathophysiological mechanisms, such as oxidative stress, systemic inflammation, and endothelial impairment, which collectively drive the progression of both microvascular and macrovascular complications. These include cardiovascular disorders, diabetic nephropathy, retinopathy, and neuropathy[6,7]. These complications severely diminish quality of life and elevate the likelihood of disability and premature death, highlighting the critical importance of holistic management approaches.
Beyond its physical health implications, DM is linked to an increased prevalence of psychiatric comorbidities, notably depression and anxiety disorders[8,9]. Depression, characterized by persistent low mood, anhedonia, and cognitive impairments, is prevalent in approximately 30%-40% of individuals with diabetes, substantially higher than that observed in the general population[10,11]. Similarly, anxiety disorders, marked by excessive worry, restlessness, and somatic symptoms such as palpitations and fatigue, affect nearly 25% of patients with diabetes[12]. The interplay between these psychiatric conditions and DM creates a bidirectional relationship: Psychiatric symptoms can impair self-care behaviors and glycemic control, while diabetes-related stressors may exacerbate psychological distress[13]. Despite their high prevalence and clinical significance, depression and anxiety in patients with diabetes remain underdiagnosed and undertreated, often due to symptom overlap with diabetes-related complications and insufficient integration of mental health screening into routine diabetes care.
Growing evidence underscores the significance of metabolic dysregulation in the development of neuropsychiatric comorbidities[14]. Uric acid (UA), the final product of purine metabolism, plays a dual role in both physiological and pathological contexts[15,16]. Within the central nervous system, UA functions as an antioxidant; however, elevated systemic concentrations can induce oxidative stress, impair endothelial function, and exacerbate insulin resistance, thereby contributing to cardiovascular and metabolic disorders[15,16]. In contrast, high-density lipoprotein cholesterol (HDL-C) exhibits anti-inflammatory and antioxidant properties, with low levels being a hallmark of metabolic syndrome and a predictor of cardiovascular risk[17,18]. The ratio of UA to HDL-C (UHR) has recently gained attention as a promising biomarker reflecting the balance between oxidative stress and lipid metabolism. Higher UHR levels are linked to an elevated risk of T2DM, cardiovascular complications, and overall mortality[19-21]. Furthermore, a recent cross-sectional investigation revealed a positive association between UHR and the likelihood of depression in the general population[22]. Despite these findings, the potential connection between UHR and psychiatric comorbidities in individuals with diabetes has yet to be investigated.
Given the interplay between metabolic dysregulation and neuropsychiatric health, we hypothesized that UHR could function as a novel biomarker for identifying depression and anxiety in individuals with T2DM. This study aimed to examine the link between UHR and the severity of depressive and anxiety symptoms in individuals with T2DM, utilizing a cross-sectional design to explore potential threshold effects and confounding factors.
MATERIALS AND METHODS
Study design, setting, and participants
This cross-sectional investigation was carried out between March 2023 and April 2024 at Quzhou People’s Hospital, China. The study population included consecutively recruited patients diagnosed with T2DM who were admitted to the hospital during the study period. Prior to participation, all eligible individuals were provided with comprehensive details regarding the study aims, procedures, potential risks, and benefits, and subsequently provided written informed consent.
Inclusion criteria: (1) Age ≥ 18 years; (2) Diagnosis of T2DM according to the American Diabetes Association diagnostic criteria[23]; and (3) Capacity to provide informed consent and complete the required study evaluations.
Exclusion criteria: (1) Presence of acute diabetic complications, such as diabetic ketoacidosis or hyperosmolar hyperglycemic state; (2) Severe cognitive impairment or communication barriers preventing reliable psychometric evaluation; (3) Current use of medications known to significantly affect UA metabolism (e.g., allopurinol or febuxostat); (4) Severe hepatic or renal dysfunction; (5) A history of psychiatric disorders or ongoing treatment with antipsychotic medications; and (6) Pregnancy or lactation.
Sociodemographic and clinical characteristics
Sociodemographic and clinical data was gathered through a structured questionnaire administered by a trained researcher. The questionnaire collected data on key variables, including age, gender, height, weight, educational level, smoking status, and alcohol consumption. Body mass index (BMI) was calculated using the following formula: BMI = weight (kg)/height squared (m2).
Biochemical assessments
After a minimum fasting period of 10 hours, venous blood samples were drawn from participants. These samples were processed within 2 hours of collection and analyzed at the Hospital’s Central Laboratory. The laboratory tests included the measurement of glycated hemoglobin (HbA1c), liver enzymes such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST), fasting plasma glucose (FPG), and UA. Additionally, lipid profiles were assessed, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and HDL-C. All blood samples were analyzed at the Central Laboratory of Quzhou People’s Hospital with an automated analyzer.
Assessment scales for depression and anxiety
The Self-Rating Depression Scale (SDS) is a reliable, 20-item self-administered tool designed to measure the intensity of depressive symptoms, widely employed in both clinical practice and research studies[24]. The scale evaluates four primary domains of depression: (1) Emotional and psychological symptoms (e.g., depressed mood, hopelessness); (2) Somatic manifestations (e.g., sleep disturbances, appetite changes); (3) Psychomotor symptoms (e.g., agitation or retardation); and (4) Psychological impairments of depression (e.g., confused thinking, hopelessness). Responses are scored on a 4-point Likert scale, ranging from 1 (“a little of the time”) to 4 (“most of the time”), where higher total scores correspond to more severe depressive manifestations. The overall score spans 20 to 80. The SDS has been shown to exhibit strong reliability and validity across various populations, including those with chronic illnesses like diabetes[24].
The Self-Rating Anxiety Scale (SAS) is another tool designed to evaluate the intensity of anxiety symptoms experienced by individuals[25]. It also consists of 20 items, focusing on both physical and psychological symptoms associated with anxiety, including nervousness, tension, and restlessness. Similar to the SDS, this tool utilizes a 4-point Likert scale, with responses ranging from 1 (“a little of the time”) to 4 (“most of the time”). The total score is derived by summing individual item scores, with higher values indicating more severe anxiety. Recognized for its reliability and validity, the SAS is widely used to assess anxiety across diverse populations.
Statistical analysis
Continuous variables following a normal distribution are expressed as mean ± SD, while non-normally distributed data are reported as medians with interquartile ranges. Categorical variables are summarized using frequencies and percentages [n (%)]. Group comparisons were performed using the t-test for normally distributed continuous variables, the Mann-Whitney U test for nonparametric data, and the χ2 test for categorical variables.
To investigate associations between UHR and scores on the SDS and SAS, linear regression analyses were conducted, adjusting for potential confounders. Subgroup analyses were carried out using stratified regression models, with continuous variables categorized by clinical thresholds, tertiles, or quartiles as appropriate.
Potential nonlinear relationships between UHR and SDS/SAS scores were examined using smooth curve fitting. When nonlinearity was detected, a recursive algorithm identified the threshold point, and a weighted two-piecewise linear regression model was applied to each segment. The optimal model (linear vs two-piecewise) was selected based on log-likelihood ratio test results.
All analyses were carried out by Empower (R) (www.empowerstats.com, X&Y Solutions, Inc., Boston, MA, United States) and R (http://www.R-project.org). Statistical significance was defined as a two-sided P value < 0.05.
RESULTS
Demographic and psychological characteristics
Out of 330 initially recruited participants, 307 completed and returned the questionnaires, yielding a response rate of 93.03%. However, 22 cases were excluded from the analysis: 6 participants were unable to complete the questionnaire or test, 3 did not meet the inclusion criteria, 10 had abnormal liver function, and 3 had a history of mental illness. Consequently, 285 participants were enrolled in the final analysis. The cohort had a mean age of 58.79 ± 15.00 years, with 63.86% being male. The mean HDL-C level was 1.11 ± 0.29 mmol/L, while the mean UA concentration was 5.48 ± 1.71 mg/dL. The mean UHR was 5.33 ± 2.38. When UHR was categorized into tertiles, significant differences in SDS scores were observed across the UHR groups (P < 0.001). In contrast, no statistically significant differences were found in SAS scores across the UHR quartiles (P > 0.05). A summary of the participants’ sociodemographic information and clinical characteristics by UHR tertile is presented in Table 1.
Table 1 Characteristics of study participants divided by uric acid-to-high-density lipoprotein cholesterol ratio tertile, n (%).
Associations between sociodemographic and clinical characteristics with depression and anxiety
A univariate analysis was performed to evaluate the associations between various variables and SDS and SAS scores. As presented in Table 2, female gender, smoking status, age, higher education level, UA, UHR, and lower TC, HDL-C, and LDL-C were significantly positively associated with SDS scores. Similarly, female gender, education level, smoking status, age, higher education level, UA, UHR, lower TC, lower HDL-C, and LDL-C were significantly positively associated with SAS scores. Smoking and drinking were significantly negatively associated with SAS scores.
Table 2 The results of univariate analysis, n (%).
Association between UHR and depression/anxiety symptoms
Linear regression analyses were performed across various subgroups to investigate the relationship between UHR and depression/anxiety symptoms. The results revealed that UHR was significantly positively correlated with SDS scores, irrespective of gender, age, smoking status, drinking status, or HbA1c levels (Table 3). A significant negative link between UHR and SDS was detected in individuals with an education level of junior high school or below, a history of diabetes for 1-20 years, and a BMI greater than 24 kg/m2 (Table 3).
Table 3 Subgroup analysis of the correlation of uric acid to high-density lipoprotein cholesterol ratio with depression/anxiety.
Similarly, UHR was significantly positively correlated with SAS scores, regardless of gender, smoking status, alcohol consumption, or HbA1c levels (Table 3). Additionally, a significant negative association between UHR and SAS was identified in individuals aged over 60 years, with an education level of illiterate or junior high school, a history of diabetes for 1-10 years, and a BMI greater than 24 kg/m2 (Table 3).
Multivariate linear regression analysis
To further investigate the link between UHR and scores on SDS and SAS, we conducted a multivariate linear regression analysis. In the crude model, each additional unit of UHR was associated with an increase in SDS by 1.13 (95%CI: 0.69-1.58) and a decrease in SAS by 0.57 (95%CI: 0.20-0.93), as presented in Table 4. After adjusting for gender, age, education level, diabetes duration, smoking, drinking, and BMI, UHR remained positively correlated with SDS (β = 1.35, 95%CI: 0.91-1.78) and SAS scores (β = 0.67, 95%CI: 0.33-1.00). In Model II, after further adjusting for HbA1c, FPG, AST, ALT, TG, TC, and LDL-C, the effect sizes were 1.27 (95%CI: 0.80-1.74) for SDS and 0.72 (95%CI: 0.35-1.09) for SAS (Table 4).
Table 4 Multivariate regression analysis of uric acid-to-high-density lipoprotein cholesterol ratio and depression/anxiety.
Nonlinear relationships between UHR and depression/anxiety symptoms
To further examine the association between UHR and symptoms of depression and anxiety, generalized additive mixed models and smoothing curve fitting were utilized. A curvilinear relationship between UHR and both SDS and SAS was identified, as shown in Figure 1. The inflection points for SDS and SAS were identified at UHR values of 5.02 and 4.00, respectively, as detailed in Table 5. Before the inflection points, the association was not significant, with β (95%CI) values of 0.07 (-1.00 to 1.14) for SDS and -0.22 (-1.35 to 0.91) for SAS. However, after the inflection points, the relationship became significant, with β (95%CI) values of 1.83 (1.13-2.53) for SDS and 0.91 (0.43-1.40) for SAS, indicating that higher UHR levels were strongly associated with increased depression and anxiety symptoms.
This study provides novel evidence linking the UHR with psychological distress in patients with T2DM, extending previous findings that focused on individual biomarkers like UA or HDL-C. Our findings indicate that elevated UHR levels are strongly associated with heightened symptoms of depression and anxiety, as assessed by the SDS and SAS scores. These associations persisted even after adjusting for potential confounders, including age, gender, education level, diabetes duration, BMI, smoking, drinking, HbA1c, and lipid profiles. Additionally, our nonlinear analysis identified threshold effects, with inflection points at UHR values of 5.02 for SDS and 4.00 for SAS, beyond which the associations became more pronounced. Subgroup analyses further revealed that the relationship between UHR and depressive/anxiety symptoms was particularly significant in specific individuals. While prior studies have indicated relationships between metabolic dysregulation and mental health in diabetes, our study uniquely identifies UHR as a potential marker of psychological distress in this population.
The relationship between UHR and psychological distress is particularly relevant given the known roles of its components, UA and HDL-C, in both metabolic and neuropsychiatric health[26]. UA functions as a major antioxidant in the human body, contributing significantly to plasma antioxidant capacity[27]. Studies have suggested that UA exhibits neuroprotective properties by neutralizing free radicals and reducing oxidative stress[27], which is a known contributor to the pathophysiology of depression and anxiety[28,29]. For instance, a study involving stroke patients revealed that lower levels of UA were linked to poststroke depression[30]. Similarly, a cross-sectional study in people with epilepsy suggested that reduced levels of UA were independently linked to depressive symptoms but not to anxiety[31]. Nevertheless, the association between UA and mental health remains a debate. Some studies indicated that excessive UA may contribute to oxidative DNA damage, leading to neuronal injury and increased susceptibility to psychiatric disorders[32]. Elevated UA activates the toll-like receptor 4/nuclear factor κB pathway, thereby promoting the release of proinflammatory cytokines, increase hippocampal gliosis, and induce oxidative stress through several molecular mechanisms[33,34]. In the context of diabetes, the exact relationship between UA and anxiety/depression remains unclear. In our study, higher UA levels were found to be associated with both depressive and anxiety symptoms. To confirm these findings, additional well-designed studies are necessary to establish a clear link between UA levels and depressive and anxiety symptoms in individuals with DM.
HDL-C is integral to cholesterol transport, anti-inflammatory processes, and neuroprotection[35]. It exerts protective effects by promoting cholesterol efflux, reducing oxidative stress, and modulating neuroinflammation, all of which are important factors implicated in the pathogenesis of depression and anxiety[36]. However, the link between HDL-C and mental health remains complex and not completely understood. Some research identified a positive relationship between HDL-C and depression, while others reported a negative or non-significant association[37,38]. Lower HDL-C levels have been linked to major depression and suicidal behavior, particularly in depressed men, likely due to immune and inflammatory responses[39,40]. Conversely, some studies suggested that higher HDL-C levels correlate with more severe depressive symptoms[41]. In our study, lower HDL-C levels were significantly linked to depressive symptoms; however, no significant relationship was found with anxiety symptoms. To better understand the role of HDL-C in anxiety symptoms among patients with DM, further prospective cohort studies with larger sample sizes, adjusting for various confounding factors, are needed.
Recent advances have revealed a complex interplay between insulin resistance, lipid metabolism, and brain function, underscoring the multifaceted role of HDL-C in neuropsychiatric disorders. Prospective cohort studies have demonstrated that dyslipidemia, especially increased TG and decreased HDL-C, can precede and promote the development of peripheral insulin resistance[18]. Metabolic dysfunctions such as impaired insulin signaling and adverse lipid profiles are believed to contribute to brain insulin resistance, defective glucose utilization, altered synaptic plasticity, and increased neuroinflammation, all of which threaten psychological well-being[42,43]. Notably, HDL-C is now recognized for neuroprotective effects that go beyond cholesterol transport; these include maintaining blood-brain barrier integrity, exerting antioxidant and anti-inflammatory actions, and the potential to mitigate neuroinflammatory cascades relevant to emotional and cognitive stability[17].
UHR is a simple, cost-effective measure that has recently gained attention for its role in metabolic diseases. A prospective cohort study involving 53697 individuals in China demonstrated that higher UHR values were linked to an increased risk of myocardial infarction[20]. Additionally, a cross-sectional study identified UHR as a risk factor of diabetic retinopathy (DR) in patients with T2DM, highlighting its potential as a readily available indicator that combines both metabolic and inflammatory status for early DR detection[44]. Another cross-sectional analysis of 8817 individuals in the United States revealed a strong correlation between elevated UHR and insulin resistance[45]. Although research on UHR and depression remains limited, a cross-sectional study using NHANES data found that individuals with higher UHR levels had a 42% greater likelihood of depression compared to those with lower levels[22]. A nonlinear association between UHR and depression was also revealed, with a 3% increase in depression risk for each unit increase in UHR above 10.21 (odds ratio = 1.03; 95%CI: 1.01-1.04)[22]. Despite these findings, the association between UHR and both depression and anxiety in T2DM patients has not been thoroughly explored. Our study identified a significant relationship between UHR and symptoms of both depression and anxiety in this population. Multivariable regression analysis demonstrated that each additional unit of UHR was linked to a 1.27 increase in SDS scores (95%CI: 0.80-1.74) in the fully adjusted model. Subgroup analysis revealed that the relationship was consistent across various groups, including gender, age, smoking status, drinking status, and HbA1c levels. Individuals with an education level of junior high school or below, a diabetes history of 1-20 years, and a BMI greater than 24 kg/m2 showed a significant positive association with SDS scores. In some subgroups with lower education, we observed an inverse association between UHR and mental health symptoms, a finding contrary to our overall results. This trend might reflect the influence of socioeconomic factors such as increased financial stress and limited healthcare access among people with lower education, potentially making psychological symptoms less dependent on metabolic status. We identified a nonlinear relationship between UHR and SDS in patients with T2DM, with an inflection point at a UHR of 5.02. Above this threshold, each additional unit of UHR was associated with a 1.83-point increase in the risk of depressive symptoms (95%CI: 1.13-2.53). The distribution of our sample (mean UHR = 5.33 ± 2.38) spanned this inflection point, providing robust support for the reliability of our results. Sensitivity analyses that excluded participants at the extreme ends of the UHR distribution (below the 1st percentile or above the 99th percentile) further confirmed the stability of our findings and threshold effects (data not shown). Taken together, these results indicate that elevated UHR levels beyond a specific threshold may contribute to the development of depression in patients with T2DM.
Anxiety and depression are common mental health disorders that frequently co-occur in patients with chronic diseases such as T2DM. Comorbid anxiety and depression complicate disease management, often leading to poor adherence to treatment regimens, including medication, dietary guidelines, and exercise recommendations. In our research, we also analyzed the correlation between UHR and anxiety. Multivariable regression analysis revealed that each additional unit of UHR was related to a 0.57 increase in SAS scores (95%CI: 0.20-0.93), and 0.67 after adjusted confounders. After full adjustment, the relationship between UHR and anxiety remained statistically significant (β = 0.72, 95%CI: 0.35-1.09). When UHR levels were higher than 4.00, the risk of anxiety symptoms elevated by 0.91 for every additional unit of UHR (β = 0.91; 95%CI: 0.43-1.40). Stratified analysis revealed that the relationship between UHR and anxiety was robust across multiple groups, including gender, smoking status, drinking status, and HbA1c levels. The association was especially prominent among individuals aged over 60 years, with an education level of junior high school or lower, a diabetes history of 1-10 years, and a BMI above 24 kg/m2. These findings suggest that UHR may serve as a useful marker for psychological risk stratification in T2DM, particularly for high-risk populations. UHR is modifiable through both lifestyle changes (such as improved glycemic control, dietary adjustments, and regular physical activity) and commonly used diabetes medications, which can favorably alter UA and HDL levels. Lowering UHR by these interventions may help improve mental health outcomes, suggesting that incorporating UHR monitoring into diabetes care could enhance psychological risk assessment and support more holistic management, though prospective studies are still needed to confirm this pathway.
While our study presents valuable insights, some limitations should be considered. First, the cross-sectional design of the research limited the ability to establish causality between UHR and depression/anxiety. It is possible that elevated UHR may predispose individuals to psychological symptoms through pathways involving metabolic dysregulation or systemic inflammation. Conversely, psychiatric symptoms such as depression or anxiety could also influence metabolic status and inflammatory markers, potentially affecting the UHR. Future research should employ longitudinal or interventional study designs to clarify temporal dynamics and provide more robust evidence regarding the nature of the association between UHR and psychiatric symptoms. Second, although we accounted for several potential confounders, residual confounding from unmeasured factors such as genetic predisposition, long-term hyperglycemia, and diabetes complications cannot be ruled out. Individuals with diabetes complications, particularly microvascular complications, face a significantly higher risk of developing depression[46], and factors like lower quality of life, education, and socioeconomic status further compound this risk[47,48]. Future studies should incorporate these variables to provide a more comprehensive analysis. Third, we did not exclude participants on lipid-lowering medications, which may significantly alter HDL-C and subsequently affect UHR, potentially introducing confounding or measurement variability. Subsequent research may consider limiting analyses to those not receiving such therapies or performing stratified analyses to clarify medication effects. Fourth, the use of self-report measures (SDS, SAS) introduces risk of reporting bias due to individual interpretation, social desirability, or recall. Future studies should supplement these assessments with validated diagnostic interviews or objective clinical tools to enhance accuracy and reliability. Lastly, further research is necessary to determine whether the UHR threshold values identified here are generalizable to other populations and chronic diseases with similar metabolic profiles. Randomized controlled trials testing reduced UHR through pharmacological or lifestyle interventions leading to better mental health in T2DM patients would provide valuable clinical insights.
CONCLUSION
This study provides novel evidence that UHR is significantly associated with depression and anxiety in T2DM patients. As it is accessible and cost-effective, UHR may represent a potential biomarker for identifying individuals at higher risk of psychiatric comorbidities, although further validation in this population is needed. Our findings highlight the potential for integrating UHR into clinical practice to identify T2DM patients at higher risk for psychiatric comorbidities, thus enabling timely interventions. Future studies should be designed to validate these findings, explore causal mechanisms, and evaluate the therapeutic implications of modifying UHR to improve mental health outcomes in T2DM.
Footnotes
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Endocrinology and metabolism
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B
Novelty: Grade A, Grade B
Creativity or Innovation: Grade B, Grade B
Scientific Significance: Grade B, Grade B
P-Reviewer: You SH, PhD, China; Zhan YT, PhD, China S-Editor: Li L L-Editor: Filipodia P-Editor: Xu ZH
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