sj-docx-1-anp-10.1177_00048674221123481 – Supplemental material for Psychological distress in siblings of people with mental illness: A systematic review and meta-analysis

Supplemental material, sj-docx-1-anp-10.1177_00048674221123481 for Psychological distress in siblings of people with mental illness: A systematic review and meta-analysis by Anuradhi Jayasinghe, Anna Wrobel, Kate Filia, Linda K Byrne, Glenn Melvin, Lesley Berk, Michael Berk and Sue Cotton in Australian & New Zealand Journal of Psychiatry</p

Adjusted odds ratios for women with EDS for BMI groups.

<p>Error bars represent the 95% CI. Group 1 (ideal BMI) is the reference group, with a broken line indicating the threshold of significance.</p

Adjusted odds ratios for men with EDS for BMI groups.

<p>Error bars represent the 95% CI. Group 1 (ideal BMI) is the reference group, with a broken line indicating the threshold of significance.</p

Frequency of smoking reported at different age categories.

<p>Frequency of smoking reported at different age categories.</p

Association between childhood internalizing behavior scores at 18 months and early adult anxiety scores (18–19 years) by adolescent smoking status.

<p><b>Caption:</b> Adolescent active smokers who demonstrated higher internalizing behaviors during infancy (18months) displayed significantly elevated anxiety in early adulthood that was not present for non-adolescent active smokers.</p

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

<div><p>Background</p><p>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.</p><p>Methods</p><p>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.</p><p>Results</p><p>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).</p><p>Conclusion</p><p>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.</p></div

Characteristics of men and women, with and without EDS^*.

<p>Values are given as median (interquartile range), mean (±standard deviation) or n (%).</p><p>*EDS = ESS score ≥10.</p><p>BMI groups: Normal = BMI <25 kg/m², Overweight = BMI ≥25–<30 kg/m², Obese = BMI ≥30 kg/m².</p><p>Characteristics of men and women, with and without EDS^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112238#nt102" target="_blank">*</a>}.</p

Association between childhood shyness at 18 months and early adult anxiety scores (18–19 years) by adolescent smoking status.

<p><b>Caption:</b> No relationship, both for adolescent active smokers and non-active smokers, was discovered between reported infant shyness at 18 months and early adult anxiety scores.</p

Baseline smoking and anxiety scores and correlations.

<p>Baseline = 18 months of age,</p>*<p>p<0.05,</p>**<p>p<0.01.</p

Descriptive statistics of included independent and dependent variables.

<p>Descriptive statistics of included independent and dependent variables.</p