Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin

Association between solar insolation and a history of suicide attempts in bipolar I disorder

In many international studies, rates of completed suicide and suicide attempts have a seasonal pattern that peaks in spring or summer. This exploratory study investigated the association between solar insolation and a history of suicide attempt in patients with bipolar I disorder. Solar insolation is the amount of electromagnetic energy from the Sun striking a surface area on Earth. Data were collected previously from 5536 patients with bipolar I disorder at 50 collection sites in 32 countries at a wide range of latitudes in both hemispheres. Suicide related data were available for 3365 patients from 310 onset locations in 51 countries. 1047 (31.1%) had a history of suicide attempt. There was a significant inverse association between a history of suicide attempt and the ratio of mean winter solar insolation/mean summer solar insolation. This ratio is smallest near the poles where the winter insolation is very small compared to the summer insolation. This ratio is largest near the equator where there is relatively little variation in the insolation over the year. Other variables in the model that were positively associated with suicide attempt were being female, a history of alcohol or substance abuse, and being in a younger birth cohort. Living in a country with a state-sponsored religion decreased the association. (All estimated coefficients p <0.01). In summary, living in locations with large changes in solar insolation between winter and summer may be associated with increased suicide attempts in patients with bipolar disorder. Further investigation of the impacts of solar insolation on the course of bipolar disorder is needed.Peer reviewe

Results of the COVID-19 mental health international for the general population (COMET-G) study.

INTRODUCTION: There are few published empirical data on the effects of COVID-19 on mental health, and until now, there is no large international study. MATERIAL AND METHODS: During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively. STATISTICAL ANALYSIS: Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables. RESULTS: Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed. CONCLUSIONS: The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them

The relationship between depression and cardiovascular disease

Evidence from epidemiological studies has established that depression is a risk factor for the development of cardiovascular disease (CVD) and that the comorbidity of depression with pre-existing CVD worsens the prognosis for sufferers of CVD. Depression has also been associated with other behaviours that impact on CVD, such as medication non-compliance, and an unwillingness to adopt an exercise program, that reduce the likelihood of successful rehabilitation from CVD. Published literature on the current knowledge of the association between depression and CVD is reviewed in this paper.<br /

Reliability of the mood disorder questionnaire : comparison with the structured clinical interview for the DSM-IV-TR in a population sample

Objective: The Mood Disorder Questionnaire (MDQ) is a widely used self-report screening instrument for the detection of bipolar disorder in clinical populations. The aim of the present study was therefore to investigate the reliability of this instrument. Methods: Screening results using the MDQ were compared with results obtained using the Structured Clinical Interview for DSM-IV-TR Research Version, Non-patient edition (SCID) in a community-based sample of 1066 women. Trained personnel, who were blind to the results of the MDQ screen, conducted clinical interviews. Results: Using the MDQ, 21 women screened positive for bipolar disorder, and using the SCID diagnoses, 24 women were confirmed with a diagnosis of bipolar disorder. Six women were detected on both instruments. Compared to the SCID, the sensitivity for the MDQ was 25%, specificity 99%, positive predictive value 28%, negative predictive value 98%, and a demonstrated kappa of 0.25. The MDQ failed to detect any of the 11 participants in the study with bipolar II disorder and missed seven of 13 participants with bipolar I disorder or bipolar not otherwise specified. Of the 21 women who screened positive using the MDQ, 19 had current or past psychopathologies other than bipolar disorder. Conclusion: The MDQ has substantial limitations for detection of bipolar disorder, in particular bipolar II disorder, in non-clinical populations