Empirically determined severity levels for binge-eating disorder outperform existing severity classification schemes

BACKGROUND

Eating-disorder severity indicators should theoretically index symptom intensity, impairment, and level of needed treatment. Two severity indicators for binge-eating disorder (BED) have been proposed (categories of binge-eating frequency and shape/weight overvaluation) but have mixed empirical support including modest clinical utility. This project uses structural equation model (SEM) trees - a form of exploratory data mining - to empirically determine the precise levels of binge-eating frequency and/or shape/weight overvaluation that most significantly differentiate BED severities.

METHODS

Participants were 788 adults with BED enrolled in BED treatment studies. Participants completed interviews and self-report measures assessing eating-disorder and comorbid symptoms. SEM Tree analyses were performed by specifying an outcome model of BED severity and then recursively partitioning the outcome model into subgroups. Subgroups were split based on empirically determined values of binge-eating frequency and/or shape/weight overvaluation. SEM Forests also quantified which variable contributed more improvement in model fit.

RESULTS

SEM Tree analyses yielded five subgroups, presented in ascending order of severity: overvaluation <1.25, overvaluation = 1.25-2.74, overvaluation = 2.75-4.24, overvaluation ⩾4.25 with weekly binge-eating frequency <4.875, and overvaluation ⩾4.25 with weekly binge-eating frequency ⩾4.875. SEM Forest analyses revealed that splits that occurred on shape/weight overvaluation resulted in much more improvement in model fit than splits that occurred on binge-eating frequency.

CONCLUSIONS

Shape/weight overvaluation differentiated BED severity more strongly than binge-eating frequency. Findings indicate a nuanced potential BED severity indicator scheme, based on a combination of cognitive and behavioral eating-disorder symptoms. These results inform BED classification and may allow for the provision of more specific and need-matched treatment formulations.

KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.

SIMIT: Subjectively Interesting Motifs in Time Series

Numerical time series data are pervasive, originating from sources as diverse as wearable devices, medical equipment, to sensors in industrial plants. In many cases, time series contain interesting information in terms of subsequences that recur in approximate form, so-called motifs. Major open challenges in this area include how one can formalize the interestingness of such motifs and how the most interesting ones can be found. We introduce a novel approach that tackles these issues. We formalize the notion of such subsequence patterns in an intuitive manner and present an information-theoretic approach for quantifying their interestingness with respect to any prior expectation a user may have about the time series. The resulting interestingness measure is thus a subjective measure, enabling a user to find motifs that are truly interesting to them. Although finding the best motif appears computationally intractable, we develop relaxations and a branch-and-bound approach implemented in a constraint programming solver. As shown in experiments on synthetic data and two real-world datasets, this enables us to mine interesting patterns in small or mid-sized time series.

Reconsidering important outcomes of the nonsuicidal self-injury disorder diagnostic criterion A

OBJECTIVE

The nonsuicidal self-injury (NSSI) disorder diagnostic criteria have been the focus of empirical study. However, Criterion A (i.e., required frequency and timeframe) has received relatively limited attention. The current study aimed to examine the relationship between past 12-month NSSI frequency and eight NSSI behavior features among individuals with past 12-month and 1-month NSSI.

METHOD

Participants were 723 undergraduate students reporting at least 1 past 12-month NSSI act and completed online questionnaires. Decision trees and structural equation model trees were utilized to examine the relationship between NSSI frequency and behavior features.

RESULTS

Results highlight several potential subgroups: high (i.e., greater than 49 acts), moderate-to-high (i.e., 19-48 acts), low-to-moderate (i.e., 7-18 acts), and low (i.e., fewer than 6 acts) frequency subgroups.

CONCLUSIONS

Findings suggest that increasing the NSSI disorder criterion A frequency cutoff or requiring at least one past month NSSI act may better demarcate individuals with more severe NSSI behavior.

Using Exploratory Data Mining to Identify Important Correlates of Nonsuicidal Self-Injury Frequency

OBJECTIVE

Non-suicidal self-injury (NSSI) has been linked to many adverse outcomes, with more frequent NSSI increasing the likelihood of impairment, severity, and more serious self-harming behavior (e.g., suicidality; Andover & Gibb, 2010; Darke et al., 2010). Despite the determined importance of NSSI frequency in understanding the severity of one's behavior, there is still a need to identify which constructs may be influential in predicting frequency. The current study aimed to fill this gap by identifying which correlates are most important in relation to NSSI frequency through two exploratory data mining methods.

METHOD

Seven hundred twelve undergraduate students with a history of NSSI completed self-report measures of NSSI behavior, suicidality, cognitive-affective deficits, and psychopathology symptomology.

RESULTS

Both exploratory data mining methods, lasso regression and random forests, demonstrated number of NSSI methods to be the factor with the most importance in relation to lifetime NSSI frequency. Once this variable was removed, suicide plan and depressive symptomology were significant correlates across methods.

CONCLUSIONS

The current findings support the literature between NSSI frequency and NSSI methods, but also implicate suicide plans, an often-overlooked factor, and depression in NSSI severity.