Sex-Specific Differences in Severity of Depressive Symptoms, Heart Rate Variability, and Neurocognitive Profiles of Depressed Young Adults: Exploring Characteristics for Mild Depression

Lim, Jong-Deock; Yun, Je Yeon; Choi, Yoobin; Choi, Seong Hee; Kwon, Young-Sang; Lee, Hwa Young; Jang, Joon Hwan

doi:10.3389/fpsyt.2020.00217

Cited by 8 publications

(9 citation statements)

References 61 publications

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“…26 ( 15 – 20 , 22 – 37 , 42 , 44 , 45 , 47 , 55 ) studies reported the outcome indicator SDNN. 28 ( 15 – 26 , 28 , 29 , 31 , 33 – 35 , 38 , 40 – 42 , 44 , 45 , 47 , 49 , 50 , 52 , 55 ) studies reported the outcome indicator RMSSD. 13 ( 14 , 15 , 22 , 25 – 27 , 29 , 31 , 33 – 35 , 42 , 48 , 50 ) studies reported the outcome indicator PNN50.…”

Section: Resultsmentioning

confidence: 99%

Heart rate variability status at rest in adult depressed patients: a systematic review and meta-analysis

Wu,

Miao,

Cao

et al. 2023

Front. Public Health

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PurposesA meta-analysis was conducted to examine the differences in heart rate variability (HRV) between depressed patients and healthy individuals, with the purpose of providing a theoretical basis for the diagnosis of depression and the prevention of cardiovascular diseases.MethodsTo search China National Knowledge Infrastructure (CNKI), WanFang, VIP, PubMed, Web of Science, Science Direct, and Cochrane Library databases to collect case–control studies on HRV in depressed patients, the retrieval date is from the establishment of the database to December 2022. Effective Public Health Practice Project (EPHPP) scale was used to evaluate literature quality, and Stata14.0 software was used for meta-analysis.ResultsThis study comprised of 43 papers, 22 written in Chinese and 21 in English, that included 2,359 subjects in the depression group and 3,547 in the healthy control group. Meta-analysis results showed that compared with the healthy control group, patients with depression had lower SDNN [Hedges’ g = −0.87, 95% CI (−1.14, −0.60), Z = −6.254, p < 0.01], RMSSD [Hedges’ g = −0.51, 95% CI (−0.69,-0.33), Z = −5.525, p < 0.01], PNN50 [Hedges’ g = −0.43, 95% CI (−0.59, −0.27), Z = −5.245, p < 0.01], LF [Hedges’ g = −0.34, 95% CI (−0.55, − 0.13), Z = −3.104, p < 0.01], and HF [Hedges’ g = −0.51, 95% CI (−0.69, −0.33), Z = −5.669 p < 0.01], and LF/HF [Hedges’ g = −0.05, 95% CI (−0.27, 0.18), Z = −0.410, p = 0.682] showed no significant difference.ConclusionThis research revealed that HRV measures of depressed individuals were lower than those of the healthy population, except for LF/HF, suggesting that people with depression may be more at risk of cardiovascular diseases than the healthy population.

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Section: Resultsmentioning

confidence: 99%

Heart rate variability status at rest in adult depressed patients: a systematic review and meta-analysis

Wu,

Miao,

Cao

et al. 2023

Front. Public Health

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“…Diagnoses of major depressive disorder (MDD) or mild depressive symptoms (MDS) were made by licensed psychiatrists based on semi-structured MINI-International Neuropsychiatric Interviews. The classification criteria of the MDD and MDS participants have been reported as in previously published manuscripts ( 24 , 25 ). The MDD and MDS participants met at least one of the following criteria: Patient Health Questionnaire-9 ≥ 10 points; General Anxiety Disease-7 ≥ 10 points; State-Trait Anxiety Inventory-State ≥ 61 points (for males) or ≥65 points (for females); a history of at least 1 suicidal thought/attempt/plan within the past 6 months.…”

Section: Methodsmentioning

confidence: 99%

“…This is our topic here. Although young adulthood is the peak period of new-onset major depression ( 2 ), young adults rarely seek treatment because most are unaware of their symptoms ( 24 ). Young adults are thus the optimal population for an intensive longitudinal study of the association between daily sleep variability and depression.…”

Section: Introductionmentioning

confidence: 99%

Greater variability in daily sleep efficiency predicts depression and anxiety in young adults: Estimation of depression severity using the two-week sleep quality records of wearable devices

et al. 2022

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ObjectivesSleep disturbances are associated with both the onset and progression of depressive disorders. It is important to capture day-to-day variability in sleep patterns; irregular sleep is associated with depressive symptoms. We used sleep efficiency, measured with wearable devices, as an objective indicator of daily sleep variability.Materials and methodsThe total sample consists of 100 undergraduate and graduate students, 60% of whom were female. All were divided into three groups (with major depressive disorder, mild depressive symptoms, and controls). Self-report questionnaires were completed at the beginning of the experiment, and sleep efficiency data were collected daily for 2 weeks using wearable devices. We explored whether the mean value of sleep efficiency, and its variability, predicted the severity of depression using dynamic structural equation modeling.ResultsMore marked daily variability in sleep efficiency significantly predicted levels of depression and anxiety, as did the average person-level covariates (longer time in bed, poorer quality of life, lower extraversion, and higher neuroticism).ConclusionLarge swings in day-to-day sleep efficiency and certain clinical characteristics might be associated with depression severity in young adults.

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“…Despite the availability of various treatment methods for depression, the treatment effects remain poor due to the lack of medical resources and difficulties in early detection of depression ( 11 ). As a result, a risk prediction model for depression is essential to facilitate early detection of this disease.…”

Section: Introductionmentioning

confidence: 99%

A web-based novel prediction model for predicting depression in elderly patients with coronary heart disease: A multicenter retrospective, propensity-score matched study

Tan

Xu²,

He³

et al. 2022

Front. Psychiatry

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BackgroundDepression is associated with an increased risk of death in patients with coronary heart disease (CHD). This study aimed to explore the factors influencing depression in elderly patients with CHD and to construct a prediction model for early identification of depression in this patient population.Materials and methodsWe used propensity-score matching to identify 1,065 CHD patients aged ≥65 years from four hospitals in Chongqing between January 2015 and December 2021. The patients were divided into a training set (n = 880) and an external validation set (n = 185). Univariate logistic regression, multivariate logistic regression, and least absolute shrinkage and selection operator regression were used to determine the factors influencing depression. A nomogram based on the multivariate logistic regression model was constructed using the selected influencing factors. The discrimination, calibration, and clinical utility of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) and clinical impact curve (CIC), respectively.ResultsThe predictive factors in the multivariate model included the lymphocyte percentage and the blood urea nitrogen and low-density lipoprotein cholesterol levels. The AUC values of the nomogram in the training and external validation sets were 0.762 (95% CI = 0.722–0.803) and 0.679 (95% CI = 0.572–0.786), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. For the convenience of clinicians, we used the nomogram to develop a web-based calculator tool (https://cytjt007.shinyapps.io/dynnomapp_depression/).ConclusionReductions in the lymphocyte percentage and blood urea nitrogen and low-density lipoprotein cholesterol levels were reliable predictors of depression in elderly patients with CHD. The nomogram that we developed can help clinicians assess the risk of depression in elderly patients with CHD.

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Sex-Specific Differences in Severity of Depressive Symptoms, Heart Rate Variability, and Neurocognitive Profiles of Depressed Young Adults: Exploring Characteristics for Mild Depression

Abstract: present results suggest that recognizing the early signs of MDS that account for sexspecific differences in both subjective and objective measures may improve the diagnosis and monitoring of young adults with MDS.

Cited by 8 publications

References 61 publications

Heart rate variability status at rest in adult depressed patients: a systematic review and meta-analysis

Heart rate variability status at rest in adult depressed patients: a systematic review and meta-analysis

Greater variability in daily sleep efficiency predicts depression and anxiety in young adults: Estimation of depression severity using the two-week sleep quality records of wearable devices

A web-based novel prediction model for predicting depression in elderly patients with coronary heart disease: A multicenter retrospective, propensity-score matched study

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