Association between osteoarthritis and mental health in a Korean population: a nationwide study

Jung, Junhee; Seok, Hongdeok; Kim, Jae Hoon; Song, Gwan Gyu; Choi, Sung Jae

doi:10.1111/1756-185x.13098

Cited by 32 publications

(19 citation statements)

References 28 publications

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“…2 Knee OA can lead to restrictions in ADLs, [3][4][5] declines in quality of life, 6 and subsequent detriments to mental health. 7,8 The relationship between depression and knee OA has been gaining attention recently, with some recent studies showing associations between knee OA and development of depressive symptoms, 9,10 but most of these studies focused on radiographic changes rather than actual symptoms in their assessments of knee OA. Few have examined directly how knee pain and function relate to depressive symptom.…”

Section: Discussionmentioning

confidence: 99%

Association Between Knee Pain, Impaired Function, and Development of Depressive Symptoms

Sugai

Takeda-Imai

Michikawa

et al. 2018

J American Geriatrics Society

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Section: Discussionmentioning

confidence: 99%

Association Between Knee Pain, Impaired Function, and Development of Depressive Symptoms

Sugai

Takeda-Imai

Michikawa

et al. 2018

J American Geriatrics Society

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“…We found potential confounders of current osteoarthritis, hypercholesterolemia, and diabetes using the network ( Table 3 ). Osteoarthritis causes pain and physical disability, which can reduce QoL and lead to the development of depression [ 39 ]. This can be seen in our network based on the relationships among current osteoarthritis, pain, and mobility problems in the QoL cluster ( Figure 3 ).…”

Section: Discussionmentioning

confidence: 99%

Discovery of Depression-Associated Factors From a Nationwide Population-Based Survey: Epidemiological Study Using Machine Learning and Network Analysis

et al. 2021

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Background In epidemiological studies, finding the best subset of factors is challenging when the number of explanatory variables is large. Objective Our study had two aims. First, we aimed to identify essential depression-associated factors using the extreme gradient boosting (XGBoost) machine learning algorithm from big survey data (the Korea National Health and Nutrition Examination Survey, 2012-2016). Second, we aimed to achieve a comprehensive understanding of multifactorial features in depression using network analysis. Methods An XGBoost model was trained and tested to classify “current depression” and “no lifetime depression” for a data set of 120 variables for 12,596 cases. The optimal XGBoost hyperparameters were set by an automated machine learning tool (TPOT), and a high-performance sparse model was obtained by feature selection using the feature importance value of XGBoost. We performed statistical tests on the model and nonmodel factors using survey-weighted multiple logistic regression and drew a correlation network among factors. We also adopted statistical tests for the confounder or interaction effect of selected risk factors when it was suspected on the network. Results The XGBoost-derived depression model consisted of 18 factors with an area under the weighted receiver operating characteristic curve of 0.86. Two nonmodel factors could be found using the model factors, and the factors were classified into direct (P<.05) and indirect (P≥.05), according to the statistical significance of the association with depression. Perceived stress and asthma were the most remarkable risk factors, and urine specific gravity was a novel protective factor. The depression-factor network showed clusters of socioeconomic status and quality of life factors and suggested that educational level and sex might be predisposing factors. Indirect factors (eg, diabetes, hypercholesterolemia, and smoking) were involved in confounding or interaction effects of direct factors. Triglyceride level was a confounder of hypercholesterolemia and diabetes, smoking had a significant risk in females, and weight gain was associated with depression involving diabetes. Conclusions XGBoost and network analysis were useful to discover depression-related factors and their relationships and can be applied to epidemiological studies using big survey data.

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“…Moreover, a positive relationship has been observed between osteoarthritis and dementia 33 , and individuals with dementia are at an increased risk for fracture compared with those without dementia 23 . Finally, osteoarthritis may favor the occurrence of several psychiatric conditions such as depression 34 and anxiety 4 , and these mental health disorders have been previously found to have a deleterious impact on the odds of fracture 35,36 .…”

Section: Osteoarthritis Andcartilagementioning

confidence: 99%

Osteoarthritis and the incidence of fracture in the United Kingdom: a retrospective cohort study of 258,696 patients

Jacob

Kostev²

2021

Osteoarthritis and Cartilage

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Association between osteoarthritis and mental health in a Korean population: a nationwide study

Abstract: OA is associated with impaired mental health. OA patients had high stress perception, depression and suicidal ideation. More numbers of male with OA had much more mental disorders than female with OA.

Cited by 32 publications

References 28 publications

Association Between Knee Pain, Impaired Function, and Development of Depressive Symptoms

Association Between Knee Pain, Impaired Function, and Development of Depressive Symptoms

Discovery of Depression-Associated Factors From a Nationwide Population-Based Survey: Epidemiological Study Using Machine Learning and Network Analysis

Osteoarthritis and the incidence of fracture in the United Kingdom: a retrospective cohort study of 258,696 patients

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