Predictive markers of depression in hypertension

Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review

OBJECTIVES

This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions.

METHODS

This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type.

RESULTS

The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management.

LIMITATIONS

This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population.

CONCLUSIONS

To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.

Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research

Objective

Depression is a common comorbidity in hypertensive older adults, yet depression is more difficult to diagnose correctly. Our goal is to find predictive models of depression in hypertensive patients using a combination of various machine learning (ML) methods and metabolomics.

Methods

Methods We recruited 379 elderly people aged ≥65 years from the Chinese community. Plasma samples were collected and assayed by gas chromatography/liquid chromatography-mass spectrometry (GC/LC-MS). Orthogonal partial least squares discriminant analysis (OPLS-DA), volcano diagrams and thermograms were used to distinguish metabolites. The attribute discriminators CfsSubsetEval combined with search method BestFirst in WEKA software was used to find the best predicted metabolite combinations, and then 24 classification methods with 10-fold cross-validation were used for prediction.

Results

34 individuals were considered hypertensive combined with depression according to our criteria, and 34 subjects with hypertension only were matched according to age and sex. 19 metabolites by GC-MS and 65 metabolites by LC-MS contributed significantly to the differentiation between the depressed and non-depressed cohorts, with a VIP value of more than 1 and a P value of less than 0.05. There were multiple metabolic pathway alterations. The metabolite combinations screened with WEKA for optimal diagnostic value included 12 metabolites. The machine learning methods with AUC values greater than 0.9 were bayesNet and random forests, and their other evaluation measures are also better.

Conclusion

Altered metabolites and metabolic pathways are present in older adults with hypertension combined with depression. Methods using metabolomics and machine learning performed quite well in predicting depression in hypertensive older adults, contributing to further clinical research.

Feasibility and acceptability of an integrated mind-body intervention for depression: whole-body hyperthermia (WBH) and cognitive behavioral therapy (CBT)

OBJECTIVE

To examine the feasibility of an integrated mind-body MDD treatment combining cognitive behavioral therapy (CBT) and whole-body hyperthermia (WBH).

METHODS

In this single-arm trial, 16 adults with MDD initially received 8 weekly CBT sessions and 8 weekly WBH sessions. Outcomes included WBH sessions completed (primary), self-report depression assessments completed (secondary), and pre-post intervention changes in depression symptoms (secondary). We also explored changes in mood and cognitive processes and assessed changes in mood as predictors of overall treatment response.

RESULTS

Thirteen participants (81.3%) completed ≥ 4 WBH sessions (primary outcome); midway through the trial, we reduced from 8 weekly to 4 bi-weekly WBH sessions to increase feasibility. The n = 12 participants who attended the final assessment visit completed 100% of administered self-report depression assessments; all enrolled participants (n = 16) completed 89% of these assessments. Among the n = 12 who attended the final assessment visit, the average pre-post-intervention BDI-II reduction was 15.8 points (95% CI: -22.0, -9.70), p = 0.0001, with 11 no longer meeting MDD criteria (secondary outcomes). Pre-post intervention improvements in negative automatic thinking, but not cognitive flexibility, achieved statistical significance. Improved mood from pre-post the initial WBH session predicted pre-post treatment BDI-II change (36.2%; rho = 0.60, p = 0.038); mood changes pre-post the first CBT session did not.

LIMITATIONS

Small sample size and single-arm design limit generalizability.

CONCLUSION

An integrated mind-body intervention comprising weekly CBT sessions and bi-weekly WBH sessions was feasible. Results warrant future larger controlled clinical trials.Clinivaltrials.gov Registration: NCT05708976.

Elevated body temperature is associated with depressive symptoms: results from the TemPredict Study

Correlations between altered body temperature and depression have been reported in small samples; greater confidence in these associations would provide a rationale for further examining potential mechanisms of depression related to body temperature regulation. We sought to test the hypotheses that greater depression symptom severity is associated with (1) higher body temperature, (2) smaller differences between body temperature when awake versus asleep, and (3) lower diurnal body temperature amplitude. Data collected included both self-reported body temperature (using standard thermometers), wearable sensor-assessed distal body temperature (using an off-the-shelf wearable sensor that collected minute-level physiological data), and self-reported depressive symptoms from > 20,000 participants over the course of ~ 7 months as part of the TemPredict Study. Higher self-reported and wearable sensor-assessed body temperatures when awake were associated with greater depression symptom severity. Lower diurnal body temperature amplitude, computed using wearable sensor-assessed distal body temperature data, tended to be associated with greater depression symptom severity, though this association did not achieve statistical significance. These findings, drawn from a large sample, replicate and expand upon prior data pointing to body temperature alterations as potentially relevant factors in depression etiology and may hold implications for development of novel approaches to the treatment of major depressive disorder.

Adverse Childhood Experiences, Family Support, and Depression: Evidence from Internal Migrants in China

Previous studies have linked poor family support and adverse childhood experiences (ACEs) to increased risk of depression; however, little is known about the interplay between the two when it comes to their effects on depression. Therefore, the current study examined if family support moderated the cumulative effect of ACEs on depression. Based on data from a migrant survey in Shiyan, Hubei Province, in 2019 (N = 1,326), this study used the ordinary least squares method to analyze the effect of ACEs on depression and evaluate whether family support moderated this effect. Higher exposure to ACEs and lower scores of family support were associated with higher depression levels in adulthood. The moderation model indicated that family support significantly moderated the relationship between ACEs and depression. Appropriate interventions to reduce depression should target internal migrants with history of ACEs. Community nurses should consider ACEs as an integral part of psychosocial assessment. Negative effects of ACEs can be reduced through teaching skills that increase effective family interaction and maintain supportive family networks. [Journal of Psychosocial Nursing and Mental Health Services, 61(3), 19-25.].

Hypertension, sleep quality, depression, and cognitive function in elderly: A cross-sectional study

Background

Hypertension, sleep disorders, and depression are highly prevalent in the elderly population and are all associated with cognitive impairment, but the role that sleep quality and depression play in the association between hypertension and cognitive impairment is unclear. The aim of this study was to investigate whether sleep quality and depression have a mediating role in the association between hypertension and cognitive impairment.

Methods

A cross-sectional study was conducted to collect data from the Tongji Hospital Comprehensive Geriatric Assessment Database. Sleep quality, depression and cognitive function were measured by the Pittsburgh Sleep Quality Index (PSQI), the Geriatric Depression Scale (GDS-15) and the Mini-Mental State Examination (MMSE), respectively. Correlation analysis, regression analysis and Bootstrap analysis were used to examine correlations between key variables and mediating effects of sleep quality and depression. Adjustments for multiple comparisons were performed using Benjamini-Hochberg adjustment for multiple testing.

Results

A total of 827 participants were included, hypertension was present in 68.3% of the sample. After correcting for covariates, hypertensive patients aged 65 years or older had worse cognitive function, poorer-sleep quality and higher levels of depression. Sleep quality was significantly negatively associated with depression and cognitive function, while depression was negatively associated with cognitive function. Mediation analysis revealed that hypertension can affect cognitive function in older adults through a single mediating effect of sleep quality and depression and a chain mediating effect of sleep quality and depression.

Conclusion

This study found that sleep quality and depression can mediate the relationship between hypertension and cognitive function in elderly. Enhanced supervision of sleep quality and depression in elderly patients with hypertension may be beneficial in maintaining cognitive function.

Relationship between depressive disorders and biochemical indicators in adult men and women

BACKGROUND

Depression is a psychiatric disorder with global public health concerns. Although a number of risk factors have been identified for depression, there is no clear relationship between biochemistry and depression. In this study, we assessed whether depressive disorders are significantly associated with biochemical indicators.

METHODS

Our study included 17,561 adults (age ≥ 18 years) participating in the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The relationship between depression and biochemical and obesity indicators was analyzed by logistic regression.

RESULTS

As compared to the control group, men with depression showed significantly higher levels of gamma-glutamyl transferase, glucose, and triglycerides, and lower levels of albumin and total bilirubin. The depressed group had higher levels of alkaline phosphatase, bicarbonate, and sodium than the control group.

CONCLUSION

Several biochemical and anthropometric indices were associated with depression in this study. It would be interesting to further analyze their cause-effect relationship.

LIMITATIONS

This study is a cross-sectional study. The population is less restricted and does not exclude people with diabetes, pregnancy, etc., so it is less significant for a specific population. Dietary information was not included, as diet plays an important role in many indicators.

An insight into diagnosis of depression using machine learning techniques: a systematic review

BACKGROUND

In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis.

METHODS

This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis.

RESULTS

A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test.

CONCLUSION

The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.

Interaction between trouble sleeping and depression on hypertension in the NHANES 2005-2018

BACKGROUND

Hypertension, trouble sleeping and depression, as three major public health problems, were closely related. This study evaluated the independent association of trouble sleeping and depression with hypertension and interaction effect between trouble sleeping and depression on hypertension in Americans.

METHOD

The data of this cross-sectional study was from the 2005-2018 National Health and Nutritional Examination Survey (NHANES) with hypertension, depression, trouble sleeping and confounding factor information. Multivariate logistic regression model and subgroup analyses of depression severity were conducted to assess the relationship between trouble sleeping and depression on hypertension. Relative excess risk due to interaction (RERI), attributable proportion of interaction (AP) and synergy index (S) were utilized to assess the additive interaction.

RESULTS

A total of 30,434 participants (weighted n = 185,309,883) were examined with 16,304 (49.37%) known hypertensive subjects. Compared with participants without trouble sleeping, those with trouble sleeping had a higher risk of hypertension [OR = 1.359 (95% CI: 1.229-1.503)]. We also found the significant association of depression with an increased risk of hypertension [OR = 1.276 (95% CI: 1.114-1.462)], compared with those without depression. Moreover, there was a significant interaction between trouble sleeping and depression on hypertension risk [RERI = 0.528 (95% CI: 0.182-0.873), AP = 0.302 (95% CI: 0.140-0.465), S = 3.413 (95% CI: 1.301-8.951)].

CONCLUSION

There was a synergistic interaction between trouble sleeping and depression on hypertension, especially the significant synergistic effect between moderate depression and trouble sleeping on hypertension. The results suggested that improving the psychological status and trouble sleeping of patients may be beneficial to the prevention and treatment of hypertension.

Increase in Right Temporal Cortex Thickness Is Related to Decline of Overall Cognitive Function in Patients With Hypertension

Background: Hypertension is associated with poorer cognitive functions, but the mechanisms are unclear.

Objective: This research aims to explore the cognitive status of elderly patients with hypertension and the possible mechanisms of hypertension affecting cognitive function.

Methods: Data were obtained from the China Longitudinal Aging Study (CLAS), and a total of 128 residents, aged 60 years and above, were recruited in this study. Based on whether they had hypertension, these 128 people were divided into the hypertension (n = 64) and non-hypertension groups (n = 64). The Beijing version of the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to assess the overall cognitive function of the subjects, while digit span, language fluency, Wechsler mapping, and Wechsler wood block were used to assess their domain-specific cognitive function (both at baseline and follow-up stages). At the same time, we also examined baseline blood biochemical indicators (such as total protein, fasting plasma glucose (FPG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), cholesterol, and triglyceride) and baseline MRI data of hippocampus and amygdala volume and temporal polar cortex thickness.

Results: The total protein and thickness of temporal polar cortex in patients with hypertension were significantly higher than those in normal controls, but the scores on MMSE, MoCA, digit span, Wechsler mapping and Wechsler wood block at baseline were significantly lower than those in normal controls (p < 0.05). By linear regression analysis and correlation analysis (age and education were controlled), we found that baseline Wechsler mapping scores were negatively correlated with total protein (B = −0.243, t = −3,735, p < 0.001, 95% confidence interval (CI): −0.371 to −0.114); and both the follow-up MMSE score (B = 2.657, t = 2.002, p = 0.049, 95% CI: 0.009~5.306) and the change score of MMSE (r = −0.025, p = 0.047) were related to the thickness of the right temporal pole cortex. Then, by linear regression analysis (mediating model), we found that hypertension may influence follow-up MMSE scores by influencing the cortical thickness of the right temporal pole (B = 1.727, p = 0.022, 95% CI: 0.261–3.193).

Conclusions: Elderly patients with hypertension exhibit poorer overall cognitive function and executive function, and the mechanism may be related to the effect of hypertension on the cortical thickness of the right temporal pole.

Economic Burden of Depressive Symptoms Conditions among Middle-Aged and Elderly People with Hypertension in China

People with hypertension are more prone to incur depressive symptoms, while depressive symptoms have an obvious influence on the healthy functioning, treatment, and management of hypertensive patients. However, there have been limited studies on the association between depression and the economic burden of hypertension. We used data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) to estimate the additional annual direct and indirect economic burden of depressive symptoms among middle-aged and elderly hypertensive patients with a multivariable regression model. The depressive symptoms were associated with substantial additional direct and indirect economic burden. Compared with non-co-MHDS (non-co-morbid hypertension and depressive symptoms) patients, the direct economic burden of lower co-MHDS (co-morbid hypertension and depressive symptoms) patients and higher co-MHDS patients increased 1887.4 CNY and 5508.4 CNY, respectively. For indirect economic burden, the lower co-MHDS patients increased 331.2 CNY and the higher co-MHDS patients increased 636.8 CNY. Both direct and indirect economic burden were incremental with the aggravation of depressive symptoms. The results showed depressive symptoms increased total healthcare costs by increasing the utilization and expenditure of primary healthcare services. Depressive symptoms also led to economic loss of productivity, especially for agricultural workers. This study highlights the importance of mental healthcare for hypertensive patients.

Classifying depression using blood biomarkers: A large population study

BACKGROUND

Depression is a common mood disorder characterized by persistent low mood or lack of interest in activities. People with other chronic medical conditions such as obesity and diabetes are at greater risk of depression. Diagnosing depression can be a challenge for primary care providers and others who lack specialized training for these disorders and have insufficient time for in-depth clinical evaluation. We aimed to create a more objective low-cost diagnostic tool based on patients' characteristics and blood biomarkers.

METHODS

Blood biomarker results were obtained from the National Health and Nutrition Examination Survey (NHANES, 2007-2016). A prediction model utilizing random forest (RF) in NHANES (2007-2014) to identify depression was derived and validated internally using out-of-bag technique. Afterwards, the model was validated externally using a validation dataset (NHANES, 2015-2016). We performed four subgroup comparisons (full dataset, overweight and obesity dataset (BMI≥25), diabetes dataset, and metabolic syndrome dataset) then selected features using backward feature selection from RF.

RESULTS

Family income, Gamma-glutamyl transferase (GGT), glucose, Triglyceride, red cell distribution width (RDW), creatinine, Basophils count or percent, Eosinophils count or percent, and Bilirubin were the most important features from four models. In the training set, AUC from full, overweight and obesity, diabetes, and metabolic syndrome datasets were 0.83, 0.80, 0.82, and 0.82, respectively. In the validation set, AUC were 0.69, 0.63, 0.66, and 0.64, respectively.

CONCLUSION

Results of routine blood laboratory tests had good predictive value for distinguishing depression cases from control groups not only in the general population, but also individuals with metabolism-related chronic diseases.

Epidemiology of depressive disorders in people living with hypertension in Africa: a systematic review and meta-analysis

OBJECTIVES

Better knowledge of epidemiology of depressive disorders in people living with hypertension can help to implement pertinent strategies to address its burden. The objective was to estimate the prevalence of depressive disorders and symptoms in people living with hypertension in Africa.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

PubMed, EMBASE, African Index Medicus, African Journals OnLine were searched up to 31 January 2020, regardless of the language of publication.

ELIGIBILITY CRITERIA

We included studies conducted among adult patients with hypertension (≥18 years) living in Africa and reporting the prevalence of depressive disorders and symptoms.

DATA EXTRACTION AND SYNTHESIS

Two independent investigators selected studies, extracted data and assessed the methodological quality of included studies by using the tools developed by Joanna Briggs Institute. Multivariate random-effects meta-analysis served to pool data by considering the variability between diagnostic tools used to identify patients with depressive disorders or symptoms.

RESULTS

We included 11 studies with 5299 adults with hypertension. Data were collected between 2002 and 2017, from South Africa, Nigeria, Ghana, Ethiopia and Burkina Faso. The mean age varied between 50.3 years and 59.6 years. The proportion of men ranged from 28% to 54%. The adjusted prevalence of depressive disorders taking into account the variance between diagnostic tools was 17.9% (95% CI 13.0% to 23.4%). The prevalence of depressive symptoms and major depressive symptoms was 33.3% (95% CI 9.9% to 61.6%) and 7.8% (95% CI 3.0% to 14.5%), respectively. There was heterogeneity attributable to the diagnostic tools for depressive disorders and symptoms. There was no publication bias.

CONCLUSION

Notwithstanding the representativeness lack of some (sub) regions of Africa, weakening the generalisability of findings to the entire region; depressive disorders and symptoms are prevalent in people living with hypertension in Africa, indicating that strategies from clinicians, researchers and public health makers are needed to reduce its burden in the region.

Bidirectional association between blood pressure and depressive symptoms in young and middle-age adults: A cohort study

AIMS

To evaluate the bidirectional relationship between blood pressure (BP) and depressive symptoms using a large prospective cohort study.

METHODS

Prospective cohort study was performed in 276 244 adults who participated in a regular health check-up and were followed annually or biennially for up to 5.9 years. BP levels were categorised according to the 2017 American College of Cardiology and American Heart Association hypertension guidelines. Depressive symptoms were assessed using Centre for Epidemiologic Studies-Depression (CESD) questionnaire and a cut-off score of ≥25 was regarded as case-level depressive symptoms.

RESULTS

During 672 603.3 person-years of follow-up, 5222 participants developed case-level depressive symptoms. The multivariable-adjusted hazard ratios (HRs) [95% confidence interval (CI)] for incident case-level depressive symptoms comparing hypotension, elevated BP, hypertension stage 1 and hypertension stage 2 to normal BP were 1.07 (0.99-1.16), 0.93 (0.82-1.05), 0.89 (0.81-0.97) and 0.81 (0.62-1.06), respectively (p for trend <0.001). During 583 615.3 person-years of follow-up, 27 787 participants developed hypertension. The multivariable-adjusted HRs (95% CI) for incident hypertension comparing CESD 16-24 and ⩾25 to CESD < 16 were 1.05 (1.01-1.11) and 1.12 (1.03-1.20), respectively (p for trend <0.001) and in the time-dependent models, corresponding HRs (95% CI) were 1.12 (1.02-1.24) and 1.29 (1.10-1.50), respectively (p for trend <0.001).

CONCLUSIONS

In this large cohort study of young and middle-aged individuals, higher BP levels were independently associated with a decreased risk for developing case-level depressive symptoms and depressive symptoms were also associated with incident hypertension. Further studies are required to elucidate the mechanisms underlying the bidirectional association between BP levels and incident depression.

The effects of depression and age on sleep disturbances in patients with non-dialysis stage 3-5 chronic kidney disease: a single-center study

PURPOSE

Sleep disturbances have a negative impact on the prognosis of chronic kidney disease (CKD). However, information on the prevalence and predictors is limited. This study aimed to evaluate the prevalence and explore clinical factors affecting the quality of sleep in patients with non-dialysis CKD.

METHODS

Participants included 152 adult non-dialysis patients with stage 3-5 CKD. Demographic and clinical data were collected. Sleep quality and depression were assessed using the Pittsburgh Sleep Quality Index (PSQI) and Beck Depression Inventory (BDI), respectively. Sleep disturbances were defined as a PSQI score ≥ 5. Logistic regression was conducted to explore the independent factors of sleep disturbances. Clinical parameters were correlated with BDI scores using linear regression models.

RESULTS

The total prevalence of patients with sleep disturbances was 66.4%. Older age, higher BDI scores, lower estimated glomerular filtration rate (eGFR) changes per month (△eGFR/m) before the study, and lower serum magnesium levels were found in patients with sleep disturbances. BDI scores (odds ratio [OR] 1.224, 95% confidence interval [CI] 1.091-1.373, p = 0.001) and age (OR 1.041, 95% CI 1.013-1.069, p = 0.003) were independent predictors of sleep disturbances. Serum uric acid levels (β - 0.629, 95% CI - 1.244 to - 0.013, p = 0.046), △eGFR/m before the study (β - 0.454, 95% CI - 0.885 to - 0.024, p = 0.039), and daily protein intake (β - 0.052, 95% CI - 0.102 to - 0.002, p = 0.043) were negatively associated with BDI scores.

CONCLUSION

A high overall prevalence of sleep disturbances was found in patients with non-dialysis stage 3-5 CKD. Depression, as a manageable predictor, should be managed, especially in elderly patients.

Inverse association between serum albumin and depressive symptoms among drug-free individuals with a recent suicide attempt

Background and aim: Albumin is a protein with multifaceted functions in the human body. According to many studies, lower serum albumin may be associated with depression in various groups of psychiatric and non-psychiatric patients, as well as with attempted suicide. As more severe depressive symptoms have been identified as a reliable risk factor for suicide in patients with high suicide risk, it would be of interest to study whether, the inverse association between depressive symptoms and albumin may exist among patients with attempted suicide. Therefore, the aim of the study was to investigate the possible association between albumin and depressive symptoms among individuals who recently attempted suicide. Methods: One-hundred twenty-seven individuals with a recent suicide attempt were involved in the study between 1987 and 2001. Albumin was analyzed in serum. Patients were evaluated with the Comprehensive Psychopathological Rating Scale (CPRS) from which the Montgomery-Åsberg Depression Rating Scale (MADRS) and the item assessing Apparent sadness were derived. Results: Only among patients aged ≥45, serum albumin levels were significantly and negatively correlated with total scores of MADRS and the item Apparent sadness (all p values <.00625). Conclusions: Our findings indicate an inverse association between serum albumin and the severity of depressive symptoms in individuals who attempted suicide, older than 45 years.