Abstract
Depression is one of the most burdensome diseases worldwide, garnering significant attention. The uric acid to high-density lipoprotein cholesterol ratio (UHR) is a novel and easily obtainable indicator used to assess the body’s inflammatory and metabolic status. It has attracted interest due to its potential role in the prevention and treatment of depression. This study aims to explore the potential correlation between UHR and depression. A cross-sectional study was conducted using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Depression occurrence was defined as the dependent variable, and UHR was defined as the independent variable. Multivariable logistic regression was performed to assess the relationship between the independent and dependent variables. Smooth curve fitting and threshold effect analyses were used to evaluate the nonlinear relationship and effect size between UHR and depression. Subgroup and sensitivity analyses were conducted to determine the stability of the results. This study included 24,272 adults based on NHANES data. Multivariable logistic regression analysis showed that, in the fully adjusted model, individuals with the highest UHR had a 42% increased likelihood of depression compared to those with the lowest UHR (OR = 1.42; 95% CI, 1.23–1.64; P < 0.001). Subgroup analyses indicated no significant interaction between UHR and specific subgroups (all interaction P > 0.05). Moreover, there is a nonlinear association between UHR and depression. When the UHR level was>10.21, the correlation between UHR and depression increased by 3% (OR = 1.03; 95% CI, 1.01–1.04; P < 0.01). The study found that UHR is significantly associated with a higher risk of depression among American adults. However, further prospective studies are needed to accurately elucidate the causal relationship between elevated UHR levels and depression risk. Therefore, larger cohort studies are required to support these findings.
Keywords: Depression, Cross-sectional study, UHR index, NHANES database
Subject terms: Psychology, Risk factors
Introduction
Depression is currently one of the most burdensome diseases worldwide1. As a mood disorder, depression can lead to various functional impairments and a loss of interest in daily activities, thereby reducing quality of life2. The occurrence and development of depression are mainly caused by the interaction of multiple factors such as stress, behavioral patterns, and sociodemographic factors3–5. The incidence of depression is increasing annually. As of 2021, more than 280 million people, or approximately 3.8% of the global population, are affected by depression6. Epidemiological studies have shown that among the top 25 leading causes of the total disease burden, depression ranks thirteenth in disability-adjusted life years and second in years lived with disability7. Depression not only has a profound impact on patients themselves but also imposes a significant burden on families and society.
The relationship between metabolic markers and depression has become a research hotspot in the field of psychiatry in recent years8–12. Uric acid (UA) is the end product of purine metabolism in the human body and acts as a plasma free radical scavenger, accounting for more than half of the plasma antioxidant capacity. Therefore, it has neuroprotective effects. Additionally, UA stabilizes ascorbic acid, which is abundant in neurons. Thus, UA has particular significance in neurological diseases such as depression13. High-density lipoprotein cholesterol (HDL-C) is the most important lipoprotein in the human brain. It promotes dietary cholesterol efflux through the reverse cholesterol transport pathway and exerts anti-inflammatory and antioxidant effects, regulates neurodegeneration, and affects brain function14. Interestingly, the relationship between HDL-C and depression has been reported as positive, negative, or non-existent15–17. These contradictory findings imply a more complex dynamic regulatory mechanism, emphasizing the value of further study. Recent research has found that the ratio of uric acid to high-density lipoprotein cholesterol (UHR), as a new biomarker, can reflect the body’s inflammatory burden and oxidative stress level18,19. Currently, there are few studies domestically and internationally on the association between UHR and depression. Based on this, we hypothesize that the UHR ratio may be a valuable new predictive marker for depression.
To provide clinical application evidence of UHR for depression, we utilized data from the National Health and Nutrition Examination Survey (NHANES) to conduct a cross-sectional study. NHANES provides valuable resources for understanding the epidemiology of depression in the U.S. population. Based on the NHANES 2005–2018 dataset, we explored the association between UHR and depression risk among American adults, aiming to provide guidance for future public health strategies and personalized medicine, especially in offering new strategies and directions for preventing the occurrence and development of depression.
Methods
This study used U.S. population data extracted from the NHANES database. All datasets have been approved by an ethics committee, and no separate ethical review is required.
Study population and design
NHANES is a significant study supported by the U.S. National Center for Health Statistics (NCHS), aiming to assess the health and nutritional status of U.S. residents. The survey underwent rigorous review and institutional approval, involving participant interviews, physical examinations, and laboratory tests. The purpose of this study was to assess the impact of UHR on depression. For more detailed information, kindly refer to the official website at https://www.cdc.gov/nchs/nhanes/index.htm.
In this cross-sectional study, we obtained UHR and depression-related data from 70, 190 subjects in the 2005–2018 dataset. We excluded subjects younger than 18 years old (n = 42,143), those missing UA and HDL-C information (n = 1781), and those missing depression information (n = 5647). Additionally, we excluded subjects with missing data on covariates, those who were uncertain, or those who refused to answer (n = 10,443). Finally, 24,272 eligible subjects were included (Fig. 1).
Fig. 1.
Diagram of participant enrollment process.
Diagnosis of depression
The diagnosis of depression was based on questionnaire surveys conducted among the U.S. population from 2005 to 2018. According to DSM-IV criteria, depressive symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ-9). In the questionnaire, each item is scored from 0 (“not at all”) to 3 (“nearly every day”), with a total score ranging from 0 to 27. A PHQ-9 score of ≥ 10 was used to diagnose depression20.
UHR assessment
UHR-related indicators were collected from physical examinations of the U.S. population from 2005 to 2018. UA measurements were based on fasting blood samples using various multi-channel analyzers (Hitachi Model 704, Beckman Synchron LX20, Beckman UniCel DxC800 Synchron, and Roche Cobas 6000). Fasting serum HDL-C concentrations were measured using the ARCHITECT automatic analyzer and Abbott reagent kits. UHR (%) was calculated by dividing UA (mg/dL) by HDL-C (mg/dL) and multiplying by 100%. Participants were divided into four groups (Q1–Q4) based on UHR quartiles, with Q1 as the reference group. This approach ensured data accuracy and completeness, providing a solid foundation for subsequent analysis21.
Other covariates
Covariates were selected from three aspects. First, from questionnaire information, including age, race, education level, marital status, drinking status, family poverty-to-income ratio (PIR), and common diseases affecting UHR levels such as hypertension, diabetes, stroke, arthritis, congestive heart failure, coronary heart disease, angina, acute myocardial infarction, emphysema, asthma, liver disease, thyroid disease, and malignant tumors. Race was classified as Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, or Other Race. Education levels were divided into Less than 9th Grade, 9–11th Grade, High School Graduate/GED or Equivalent, Some College or AA Degree, and College Graduate or Above. Marital status was categorized as married or unmarried. Diabetes was classified as yes/no/borderline based on the question “Have you been told by a doctor that you have diabetes?”. Drinking status was assessed by the frequency of alcohol consumption in the past 12 months. Family income was evaluated using the PIR index. Second, from laboratory examination information, smoking status and blood lipid levels were selected, and from measurement information, Body mass index (BMI, kg/m2) and blood pressure levels were included. BMI was calculated as weight (kg) divided by height squared (m2). Hypertension was defined by the average of three consecutive blood pressure readings (systolic blood pressure ≥ 140 mm Hg and/or diastolic blood pressure ≥ 90 mm Hg). Smoking status was assessed based on serum cotinine levels. Blood lipid levels were evaluated using total cholesterol levels in the blood. Third, from dietary questionnaires, total fat intake, saturated fatty acid intake, monounsaturated fatty acid intake, polyunsaturated fatty acid intake, and cholesterol intake were selected.
Statistical analysis
In all analyses, appropriate variance estimates and sampling weights were used to account for NHANES’s complex sample design, including oversampling, nonresponse, and stratification. NHANES designed a set of weighting systems to accommodate the survey’s complex structure, including oversampling for specific groups, handling nonresponse, and post-stratification corrections based on total population numbers from the Census Bureau. Categorical variables were expressed as frequencies (percentages), while continuous variables were expressed as means (SE). Weighted linear regression and weighted chi-square tests were used to compare differences between baseline continuous and categorical variables. Student’s t-test was used for continuous variables, and the Rao-Scott chi-square test was used for categorical variables. A multivariate logistic regression model was used to study the relationship between UHR and depression. Model 1 was established using binary logistic regression without any adjustments. Model 2 adjusted for age, gender, and race. Model 3 further adjusted for education level, marital status, smoking status, drinking status, PIR, and dietary lipid intake indicators. After adjusting for covariates, smooth curve fitting and threshold effect analyses were performed to test the relationship and inflection point between UHR and depression. Finally, subgroup analyses were conducted by dividing the population into different levels, and interaction terms were added to test heterogeneity between subgroups. Then, Bonferroni method was used to perform multiple comparison correction for indicators with significant differences in subgroup analysis. All statistical analyses were performed using R 4.2.3 and EmpowerStats 2.0. A P-value of < 0.05 was considered statistically significant.
Results
Characteristics of included subjects
After excluding subjects missing UHR and depression data, this study included 24,272 subjects. Depression was used as a stratification variable to divide the population into non-depression and depression groups. Significant differences were found between participants with and without depression in terms of gender, age, PIR, race, education level, marital status, BMI, HDL-C, UA, triglycerides, total fat intake, total saturated fatty acid intake, monounsaturated fatty acid intake, polyunsaturated fatty acid intake, cholesterol intake, drinking status, smoking status, and various health conditions including diabetes, hypertension, asthma, arthritis, congestive heart failure, angina, coronary heart disease, myocardial infarction, stroke, emphysema, liver disease, and thyroid disease.
Participants with depression had a higher proportion of females, lower PIR, lower education levels, higher BMI, were more likely to be unmarried and non-Hispanic white, had lower serum HDL-C levels, higher triglyceride levels, and exhibited worse unhealthy habits (Table 1).
Table 1.
Weighted comparison in basic characteristics.
| Characteristics | Overall | Depression | P-value | |
|---|---|---|---|---|
| Without | With | |||
| n = 24,272 | n = 22,189 | n = 2083 | ||
| Age, years | 48.67 ± 17.39 | 48.75 ± 17.55 | 47.85 ± 15.56 | 0.03 |
| PIR | 2.63 ± 1.64 | 2.71 ± 1.63 | 1.78 ± 1.41 | < 0.001 |
| Gender | < 0.001 | |||
| Male | 12,713 (52.38%) | 11,914 (53.69%) | 799 (38.36%) | |
| Female | 11,559 (47.62%) | 10,275 (46.31%) | 1284 (61.64%) | |
| Race (%) | < 0.001 | |||
| Mexican American | 3604 (14.85%) | 3333 (15.02%) | 271 (13.01%) | |
| Other Hispanic | 2131 (8.78%) | 1901 (8.57%) | 230 (11.04%) | |
| Non-Hispanic White | 11,443 (47.14%) | 10,458 (47.13%) | 985 (47.29%) | |
| Non-Hispanic Black | 4910 (20.23%) | 4461 (20.10%) | 449 (21.56%) | |
| Other Race | 2184 (9.00%) | 2036 (9.18%) | 148 (7.11%) | |
| Education level (%) | < 0.001 | |||
| Less than 9th | 1901 (7.83%) | 1675 (7.55%) | 226 (10.85%) | |
| 9–11th | 3276 (13.50%) | 2853 (12.86%) | 423 (20.31%) | |
| High school | 5635 (23.22%) | 5112 (23.04%) | 523 (25.11%) | |
| Some college | 7536 (31.05%) | 6867 (30.95%) | 669 (32.12%) | |
| College graduate | 5924 (24.41%) | 5682 (25.61%) | 242 (11.62%) | |
| Marry (%) | < 0.001 | |||
| Unmarried | 11,639 (47.95%) | 10,283 (46.34%) | 1356 (65.10%) | |
| Married | 12,633 (52.05%) | 11,906 (53.66%) | 727 (34.90%) | |
| Drinking (%) | < 0.001 | |||
| Low | 20,160 (83.06%) | 18,709 (84.32%) | 1451 (69.66%) | |
| High | 4112 (16.94%) | 3480 (15.68%) | 632 (30.34%) | |
| Smoking (%) | < 0.001 | |||
| Low | 8059 (33.20%) | 7627 (34.37%) | 432 (20.74%) | |
| Med | 8122 (33.46%) | 7548 (34.02%) | 574 (27.56%) | |
| High | 8091 (33.33%) | 7014 (31.61%) | 1077 (51.70%) | |
| BMI (kg/m2) | < 0.001 | |||
| < 25 | 6816 (28.08%) | 6311 (28.44%) | 505 (24.24%) | |
| 25–30 | 8083 (33.30%) | 7537 (33.97%) | 546 (26.21%) | |
| ≥ 30 | 9373 (38.62%) | 8341 (37.59%) | 1032 (49.54%) | |
| Diabetes (%) | < 0.001 | |||
| Yes | 2887 (11.89%) | 2513 (11.33%) | 374 (17.95%) | |
| No | 20,836 (85.84%) | 19,192 (86.49%) | 1644 (78.92%) | |
| Pre-diabetes | 549 (2.26%) | 484 (2.18%) | 65 (3.12%) | |
| Hypertension (%) | < 0.001 | |||
| Yes | 8465 (34.88%) | 7519 (33.89%) | 946 (45.42%) | |
| No | 15,807 (65.12%) | 14,670 (66.11%) | 1137 (54.58%) | |
| Asthma (%) | < 0.001 | |||
| Yes | 3612 (14.88%) | 3092 (13.93%) | 520 (24.96%) | |
| No | 20,660 (85.12%) | 19,097 (86.07%) | 1563 (75.04%) | |
| Arthritis (%) | < 0.001 | |||
| Yes | 6506 (26.80%) | 5602 (25.25%) | 904 (43.40%) | |
| No | 17,766 (73.20%) | 16,587 (74.75%) | 1179 (56.60%) | |
| Congestive cardiac failure (%) | < 0.001 | |||
| Yes | 723 (2.98%) | 589 (2.65%) | 134 (6.43%) | |
| No | 23,549 (97.02%) | 21,600 (97.35%) | 1949 (93.57%) | |
| Coronary heart disease (%) | < 0.001 | |||
| Yes | 963 (3.97%) | 841 (3.79%) | 122 (5.86%) | |
| No | 23,309 (96.03%) | 21,348 (96.21%) | 1961 (94.14%) | |
| Angina pectoris (%) | < 0.001 | |||
| Yes | 603 (2.48%) | 486 (2.19%) | 117 (5.62%) | |
| No | 23,669 (97.52%) | 21,703 (97.81%) | 1966 (94.38%) | |
| Acute infarction (%) | < 0.001 | |||
| Yes | 995 (4.10%) | 839 (3.78%) | 156 (7.49%) | |
| No | 23,277 (95.90%) | 21,350 (96.22%) | 1927 (92.51%) | |
| Stroke (%) | < 0.001 | |||
| Yes | 834 (3.44%) | 675 (3.04%) | 159 (7.63%) | |
| No | 23,438 (96.56%) | 21,514 (96.96%) | 1924 (92.37%) | |
| Pulmonary emphysema (%) | < 0.001 | |||
| Yes | 515 (2.12%) | 395 (1.78%) | 120 (5.76%) | |
| No | 23,757 (97.88%) | 21,794 (98.22%) | 1963 (94.24%) | |
| Liver disease (%) | < 0.001 | |||
| Yes | 955 (3.93%) | 778 (3.51%) | 177 (8.50%) | |
| No | 23,317 (96.07%) | 21,411 (96.49%) | 1906 (91.50%) | |
| Malignant tumor (%) | 0.02 | |||
| Yes | 2306 (9.50%) | 2078 (9.37%) | 228 (10.95%) | |
| No | 21,966 (90.50%) | 20,111 (90.63%) | 1855 (89.05%) | |
| Thyroid disease (%) | < 0.001 | |||
| Yes | 2382 (9.81%) | 2065 (9.31%) | 317 (15.22%) | |
| No | 21,890 (90.19%) | 20,124 (90.69%) | 1766 (84.78%) | |
| HDL-C (mg/dl) | 53.13 ± 16.35 | 53.31 ± 16.37 | 51.16 ± 15.96 | < 0.001 |
| Cholesterol (mg/dl) | 194.19 ± 42.52 | 194.09 ± 42.25 | 195.24 ± 45.35 | 0.52 |
| Aspartate transaminase (u/l) | 25.59 ± 16.41 | 25.50 ± 15.68 | 26.46 ± 22.76 | 0.01 |
| Creatinine (mg/dl) | 0.91 ± 0.42 | 0.91 ± 0.39 | 0.91 ± 0.65 | 0.95 |
| Triglyceride (mg/dl) | 154.64 ± 127.18 | 153.13 ± 124.37 | 170.71 ± 153.11 | < 0.001 |
| UA (mg/dl) | 5.49 ± 1.44 | 5.50 ± 1.44 | 5.37 ± 1.48 | < 0.001 |
| Total fat intake (gm) | 83.37 ± 47.69 | 83.87 ± 47.65 | 78.06 ± 47.76 | < 0.001 |
| Saturated fatty acid intake (gm) | 27.03 ± 17.12 | 27.13 ± 17.11 | 25.91 ± 17.14 | < 0.005 |
| Monounsaturated fatty acid intake (gm) | 29.90 ± 18.09 | 30.11 ± 18.10 | 27.71 ± 17.84 | < 0.001 |
| Polyunsaturated fatty acid intake (gm) | 18.93 ± 12.70 | 19.09 ± 12.70 | 17.30 ± 12.65 | < 0.001 |
| Cholesterol intake (gm) | 305.69 ± 247.50 | 308.81 ± 248.65 | 272.41 ± 232.29 | < 0.001 |
Relationship between UHR and depression
We observed that an increase in UHR was associated with an increased likelihood of depression (Table 2). In Model 1, without adjusting for any covariates, the odds ratio (OR) was 1.01 (95% CI, 1.00–1.02; P = 0.05). Model 2 adjusted for age, sex, and race (OR = 1.04; 95% CI, 1.03–1.05; P < 0.001). Model 3 further controlled for additional covariates, revealing a significant correlation (OR = 1.03; 95% CI, 1.02–1.04; P < 0.001). For every unit increase in UHR, the risk of depression increased by 3%. In the sensitivity analysis using UHR as a categorical variable (quartiles), individuals in the highest UHR category showed a 42% increased association with depression compared to those with the lowest UHR (OR = 1.42; 95% CI, 1.23–1.64; P < 0.001). Therefore, elevated UHR levels may be a risk factor for depression.
Table 2.
Association between UHR and depression.
| Continuous | P | Q1 | Q2 | P | Q3 | P | Q4 | P | |
|---|---|---|---|---|---|---|---|---|---|
| Model 1 OR (95% CI) | 1.01 (1.00, 1.02) | 0.05 | 1 | 1.04 (0.91, 1.18) | 0.55 | 1.04 (0.92, 1.19) | 0.52 | 1.06 (0.94, 1.21) | 0.35 |
| Model 2 OR (95% CI) | 1.04 (1.03, 1.05) | < 0.001 | 1 | 1.26 (1.10, 1.43) | < 0.001 | 1.50 (1.31, 1.72) | < 0.001 | 1.77 (1.54, 2.04) | < 0.001 |
| Model 3 OR (95% CI) | 1.03 (1.02, 1.04) | < 0.001 | 1 | 1.11 (0.97, 1.27) | 0.13 | 1.30 (1.13, 1.49) | < 0.001 | 1.42 (1.23, 1.64) | < 0.001 |
Model 1: no covariates were adjusted. Model 2: age, gender, and race were adjusted. Model 3: age, gender, race, education level, marital status, smoking status, drinking status, PIR, and dietary lipid intake indicators.
Smooth curve fitting and threshold effect between UHR and depression
To deepen the understanding of the association between UHR and depression, restricted cubic spline (RCS) curve plotting and threshold effect analyses were performed (Table 3; Fig. 2). We found a nonlinear association between UHR and depressive symptoms, with an inflection point of 10.21. When the UHR level exceeded 10.21, the risk of depressive symptoms increased by 3% for each additional unit of UHR (OR = 1.03; 95% CI, 1.01–1.04; P < 0.01). These findings suggest that elevated UHR to a certain extent may contribute to the occurrence of depression.
Table 3.
Analysis of threshold effect.
| UHR | Adjusted OR (95% CI), P value |
|---|---|
| Model 1 | |
| A straight-line effect | 1.01 (1.00, 1.02) 0.05 |
| Model 2 | |
| Fold points (K) | 10.21 |
| UHR ≤ 10.21 | 1.00 (0.98, 1.01) 0.49 |
| UHR > 10.21 | 1.03 (1.01, 1.04) < 0.01 |
| Effect size difference of 2 versus 1 | 1.03 (1.00, 1.06) 0.02 |
| Equation predicted values at break points | – 2.41 (– 2.50, – 2.33) |
| Log likelihood ratio tests | 0.02 |
Fig. 2.
The association between UHR and depression.
Subgroup analysis
To assess the consistency of the relationship between UHR and depression in different subgroups, a subgroup analysis was conducted. Interaction tests showed that the correlation between UHR and depression did not differ statistically across different subgroups, indicating that variables such as gender, age, race, education level, marital status, smoking status, drinking status, PIR, BMI, and various health conditions did not significantly affect this positive correlation (all interaction P > 0.05). Therefore, the risk of increasing false-positive rate due to multiple comparisons is low. Based on this, we did not perform Bonferroni correction. This indicates that the association between UHR and depression remains consistent across subgroups, demonstrating high stability and reliability (Fig. 3).
Fig. 3.
Subgroup analysis for the association between UHR and depression.
Discussion
In this cross-sectional study involving 24,272 participants, we identified a novel correlation between UHR and depression for the first time. Multivariable logistic regression analysis showed that, in the fully adjusted model, individuals with the highest UHR had a 42% increased likelihood of depression compared to those with the lowest UHR. Further restrictive cubic spline analysis indicated a nonlinear relationship between UHR and depressive symptoms. Specifically, When UHR levels exceed 10.21, for every additional unit increase, the risk of depressive symptoms increases by 3%. Additionally, subgroup analyses and interaction tests demonstrated that the association between UHR and depression was consistent across different subgroups and was not affected by other covariates. This finding validates and enhances our original hypothesis, emphasizing the complex involvement of UHR in depression, and provides valuable insights into the role of lipid metabolic processes in the development of depression. Therefore, serum UHR levels have the potential to serve as biomarkers for assessing depression risk, providing strong support for the diagnostic process. Comprehensively understanding the association between UHR levels and depression risk is crucial for developing effective treatment strategies. Interventions that can reduce UHR levels, such as dietary adjustments or medications, may help reduce the risk of depression in individuals with higher UHR levels. These interventions not only help improve overall health but may also have a positive impact on the prevention and treatment of depression.
Although previous studies have not investigated the role of UHR in depression, the associations between UA, HDL-C, and depression have been widely discussed. A study involving 124 patients with depression evaluated levels of TC, HDL-C, LDL-C, and triglycerides. The study showed that, compared with healthy controls, long-term depressed patients may have lower HDL-C values and higher atherosclerosis indices22. Another study involving 36 MDD patients investigated changes in blood lipid components and their association with suicide, major depression, and immune-inflammatory responses. This study indicated that lower serum HDL-C levels are markers of severe depression and suicidal behavior in depressed men23. A Mendelian randomization study showed that the risk of depression was significantly positively correlated with factors such as triglycerides and negatively correlated with HDL-C (95% CI: 0.885–0.981; P = 0.007)24. These early investigations may indirectly support our results. It is worth noting that higher HDL-C may also be significantly associated with an increased risk of depressive symptoms25. The contradictory findings in these studies may be attributed to differences in participants, race, and depression assessment methods. Additionally, a significant negative correlation exists between serum UA and depressive symptoms. Specifically, a UA level inflection point of 319.72 µmol/L was found, which is significant for clinically controlling serum UA levels in the prevention and treatment of depression26. A study using logistic regression to determine susceptibility factors of bipolar depression showed that higher UA levels (OR = 1.016; P = 0.001) and lower triglycerides (OR = 0.457; P = 0.025) were significantly associated with bipolar depression. This study acknowledged the potential of UA and blood lipid levels as biomarkers for diagnosing bipolar disorder27. Clinical trial results exploring the correlation between UA and clinical characteristics of depression showed that UA levels in depressed patients (271.97 ± 77.50 µmol/L) were significantly lower than those in patients with other types of mental disorders and healthy controls. UA levels in depressed patients returned to normal after 5 weeks of antidepressant treatment27. In summary, with the new inflammatory metabolic parameter UHR, our study provides new evidence that enhances the understanding of the relationship between UHR and depression risk.
Currently, UHR has been confirmed to be associated with various metabolic inflammatory diseases, including hypertension, diabetes, and kidney disease28–30. UHR reflects the balance between the antioxidant capacity of UA and the anti-inflammatory effect of HDL-C. In patients with depression, the increase of this ratio may mean higher levels of oxidative stress and inflammation in the body, which is consistent with the pathological mechanism of depression. This is closely related to UA and HDL-C, the two main components of UHR. UA, as an effective endogenous antioxidant, possesses antioxidant properties. Given the close association between depression and oxidative stress, the imbalance between ROS production and antioxidant defense mechanisms can lead to cell damage and dysfunction31. Therefore, elevated UA levels can reduce oxidative stress. Interestingly, excessive UA can lead to paradoxical oxidative reactions, resulting in neuronal damage associated with depression32. UA exhibits neuroprotective effects by reducing neuroinflammation and preventing neuronal apoptosis, thereby maintaining nervous system integrity and function33. Additionally, UA can regulate neurotransmitter systems, enhance dopamine release and receptor function, and modulate glutamate neurotransmission, which is closely related to the pathophysiology of depression34. On the other hand, depressed patients often have impaired neural function and energy metabolism, and excessive accumulation of UA may exacerbate these metabolic abnormalities35. HDL-C, commonly referred to as “good cholesterol,” is composed of two components: structural HDL and functional HDL. The relationship between functional high-density lipoprotein cholesterol and depression has increasingly garnered the attention of researchers. Under normal circumstances, HDL-C can mitigate levels of pro-inflammatory factors, thereby alleviating the inflammatory burden on the nervous system through its interaction with vascular endothelial cells36. Interestingly, paraoxonase-1 (PON1), a crucial protein associated with HDL-C, functions as an antioxidant enzyme that inhibits excessive activation and inflammatory responses in immune cells by acting directly upon them. Additionally, due to its capacity to scavenge free radicals, PON1 not only reduces the accumulation of reactive oxygen species within the body but also enhances the antioxidant capacity of the nervous system37. Moreover, HDL-C promotes brain-derived neurotrophic factor via the scavenger receptor, class B type 1, which plays a vital role in enhancing nerve cell growth, survival, and synaptic plasticity; thus improving both brain structure and function. Through these mechanisms, HDL-C not only mitigates inflammation within the brain but also sustains stability and functionality in the nervous system38. It is noteworthy that there exists a bidirectional relationship between low levels of HDL-C and depression. The “cholesterol-serotonin” model suggests that a decrease in serum cholesterol levels may reduce cell membrane fluidity, preventing full exposure of serotonin receptors on the cell membrane, further reducing intracellular serotonin and increasing depression risk. The decrease in cholesterol increases serotonin reuptake efficiency, reducing available serotonin and leading to depression39. In addition to 5-HT, immune-inflammatory factors may also be involved in lipid changes in depressed patients40. Furthermore, depressed patients often experience decreased appetite, reduced calorie intake, and weight loss, which may affect lipid metabolism and lower serum cholesterol levels. Research on metabolic biomarkers has provided novel perspectives on the mechanisms and diagnosis of depression, while the underlying roles of inflammation and oxidative stress have illuminated the biological foundations of the disorder. As a biomarker reflecting the levels of inflammation and oxidative stress, UHR holds significant potential for contributing to the clinical diagnosis, treatment monitoring, and prognostic assessment of depression.
Limitations and strengths
This study, while providing new insights into the relationship between UHR and depression, has several limitations. First, PHQ-9 is a self-report tool used to assess depressive symptoms, widely accepted in clinical and epidemiological studies, and its high sensitivity and specificity have been validated. However, reliance on subjective reports may introduce bias. Second, the study focuses on adults aged 18 and above; thus, caution should be exercised when interpreting the results for individuals under 18 years old. In addition, while subgroup analysis and interaction testing contribute to understanding the heterogeneity among different groups, it is important to note that subgroup analyses may lack sufficient statistical power to detect weak associations due to sample size limitations. Therefore, it is recommended that the findings from these subgroup analyses be further validated in future studies with larger sample sizes or enhanced statistical power. Simultaneously, the research outcomes require additional verification through further investigations, particularly regarding their applicability under varying physiological and pathological conditions, in order to enhance the robustness of the results. Finally, this study employs a cross-sectional design; thus, establishing a causal relationship between UHR and depression may not be feasible, and there exists a possibility of reverse causality. Consequently, cohort studies, randomized controlled trials, or Mendelian randomization studies are warranted. Such approaches will enable us to elucidate more clearly the mechanisms by which UHR influences depression and provide a theoretical foundation for early intervention and treatment strategies in the future.
Undoubtedly, this study has significant strengths. UHR, as an easily obtainable biomarker, provides a powerful tool for exploring its connection with depressive symptoms. Second, the study utilizes data from NHANES, ensuring high-quality research data and broad representativeness among the non-institutionalized U.S. population. NHANES data are obtained through meticulous multi-stage probability sampling design, further enhancing the study’s external validity. Moreover, the study strictly controlled for multiple confounding factors, such as sociodemographic characteristics, dietary energy intake, and various comorbidities, allowing a more accurate assessment of the association between serum UA levels and depressive symptoms. Finally, this study revealed for the first time a positive correlation between UHR and depression risk among American adults and identified specific inflection points, which has important public health significance for depression prevention.
Conclusion
In conclusion, UHR, as a new biomarker, often indicates higher levels of oxidative stress and inflammatory burden, factors closely related to the occurrence of depression. The impact of UHR levels on depression risk is complex. Further prospective studies are needed to accurately elucidate the causal relationship between elevated serum UHR levels and depression risk. Therefore, larger cohort studies are required to support these findings.
Author contributions
LJW: Conceptualization, Methodology, Formal Analysis, Writing Original Draft, Writing Review & Editing. ZXB: Formal Analysis, Validation, Investigation, Data curation. MTW: Methodology, Formal Analysis, Formal Analysis, Investigation. WXY: Writing Review& Editing, Funding Acquisition, Project Management. WL: Supervision, Writing Review& Editing.
Funding
National Natural Science Foundation of China Project (81303044); Natural Science Foundation of Heilongjiang Province Project (LH2022H082);Heilongjiang Province Postdoctoral Research Start-up Fund Project (LBH-Q19185); Heilongjiang Province Administration of Traditional Chinese Medicine Project (ZHY2020-120).
Data availability
Publicly available datasets were analyzed in this study. All the raw data used in this study are derived from thepublic NHANES data portal (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).
Competing interests
The authors declare no competing interests.
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.
Data Availability Statement
Publicly available datasets were analyzed in this study. All the raw data used in this study are derived from thepublic NHANES data portal (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).