Peripheral and Neural Correlates of Self-Harm in Children and Adolescents: A Scoping Review

doi:10.21203/rs.3.rs-955587/v1

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Peripheral and Neural Correlates of Self-Harm in Children and Adolescents: A Scoping Review

https://doi.org/10.21203/rs.3.rs-955587/v1

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Background

Self-harm in children and adolescents is difficult to treat. Peripheral and neural correlates of self-harm could lead to biomarkers to guide precision care. We therefore conducted a scoping review of research on peripheral and neural correlates of self-harm in this age group.

Methods

PubMed and Embase databases were searched from January 1980-May 2020, seeking English language peer-reviewed studies about peripheral and neural correlates of self-harm, defined as completed suicide, suicide attempts, suicidal ideation, or non-suicidal self-injury (NSSI) in subjects, birth to 19 years of age. Studies were excluded if only investigating self-harm in persons with intellectual or developmental disability syndromes. A blinded multi-stage assessment process by pairs of co-authors selected final studies for review. Risk of bias estimates were done on final studies.

Results

We screened 5537 unduplicated abstracts, leading to the identification of 79 eligible studies in 76 papers. Of these, 48 investigated peripheral correlates, and 31 examined neural correlates. Suicidality was the focus in 2/3 of the studies, with NSSI and any type of self-harm (subjects recruited with suicidality, NSSI, or both) studied in 1/3 of the remaining studies. All investigations used observational designs (primarily case-control), most used convenience samples of adolescent patients, predominately female, half of whom were recruited based on a disorder. Over a quarter of the specific correlates were investigated with only one study. Inter-study agreement on findings from specific correlates with more than one study was low for most correlates. Estimates of Good for risk of bias were assigned to 37% of the studies, but the majority of studies were rated as Fair.

Conclusions

Research on peripheral and neural correlates of self-harm is not sufficiently mature to identify potential biomarkers. Conflicting findings were reported for many of the correlates studied. Methodological problems may have produced biased findings and results are mainly generalizable to patients and girls. We provide recommendations to improve future peripheral and neural correlate research in children and adolescents, ages 3-19 years with self-harm.

Psychiatry

Suicide

suicide attempt

non-suicidal self-injury

peripheral correlates

neural correlates

children

adolescents

Self-harm, defined as completed suicide, attempted suicide, suicidal ideation, or non-suicidal self-injury (NSSI)), is a significant health problem in children and adolescents worldwide [1]. The leading cause of death in 10-19 year-olds in England is suicide [2] and the rate of suicide in 15-19-year-old British and Welsh girls increased by 13.2% from 2010 to 2017 and in boys by 5.9% [3]. Completed suicide was the second leading cause of death in the US in 2019 for 10-14-year-olds and 15-19-year-olds (https://webappa.cdc.gov/sasweb/ncipc/leadcause.html). Studies from several countries also show that completed suicides and suicide attempts or thoughts in children under 12 are no longer unusual [4–8].

Completed suicide is associated with previous suicide attempts, suicidal ideation [9, 10], and NSSI [11, 12]. The issue of NSSI is critical when discussing children and adolescents, as it has been reported to occur in up to 35% of the adolescent population worldwide [13, 14]and nearly 8% of the general population of 7-8 year-olds [15]. Moreover, in a large clinical sample of 3-6 year-olds with major depressive disorder, rates of NSSI, suicidal ideation, and suicide attempts were present in 21.3%, 19.1% and 3.5% of the children, respectively [16] Recent data demonstrate that the younger the age of onset, specifically if younger than 13 years of age, the more severe and protracted the course of NSSI and suicidality [17].

In addition to completed suicide, children and adolescents with self-harm have poorer adult outcomes with lower educational and occupational attainment and more mental and physical health problems [18–20]. Ideally, successful treatment would not only reduce the risk of completed suicide or other forms of self-harm, but facilitate healthy development.

Unfortunately, child and adolescent self-harm has proven difficult to treat with psychotherapy. A recent meta-analysis showed that the more commonly used treatments only reduced adolescent suicidal ideation and self-harm with moderate to small effect sizes, and no data on treatment of pre-adolescent children were reported separately [21]. Perhaps of more concern is that there are no randomized controlled trials (RCTs) of pharmacological interventions for self-harm in children and youth, and non-RCT studies adapting adult medication treatments have been disappointing, possibly another indication that this demographic may be different from adults [22–24].

Research about peripheral and neural correlates of self-harm in children and adolescents may advance the development of better treatments or the clinical capacity to administer care to individuals more precisely. Identifying valid and reliable correlates of clinical symptoms, prognosis, or treatment response comprises the first stage of biomarker development, which could substantially improve the current clinical approach of “trial and error” based on self-reported symptoms [25].

Research on peripheral and neural correlates of self-harm has expanded rapidly in the past fifteen years. Most reviews focus on adults, but eight have summarized research that included children and adolescents from birth to 19 years of age [26–33]. A total of thirty-one studies (fifteen of peripheral correlates and sixteen of neural correlates) were presented in these reviews. However, the reviews integrated findings on children and adolescents with the data from adults in the summaries.

Combining child and adolescent data with that from young adults in their twenties may mislead us in developing biomarkers for these younger age groups. Although neuroimaging research shows that brain development continues into the late twenties, risk factors and clinical profiles of self-harming children and adolescents differ from young adults in their twenties [17, 34, 35]. For example, compared to adults who attempted suicide, adolescents had a significantly higher number of previous attempts, were more likely to be responding to interpersonal problems, and were more likely to use medication for self-poisoning [36]. Several reviews indicate that imaging findings may be different in self-harming adolescents than reported in adults, e.g., for decision-making and impulsivity [31] and that the roles of hopelessness, loneliness, or trait impulsivity are less clearly related to self-harm than in adults [28]. Adolescents’ social risk factors are also different: parent-child conflict, school stressors, vicissitudes of early romantic relationships, victimization from bullying, and internet addiction and stress [30, 37]. These factors may affect associations between self-harm and biological correlates differently in children and adolescents than in young adults.

The discovery of clinical peripheral and neural biomarkers requires evidence of reliable and valid biological correlates studied in samples from the target population. Identifying potential diagnostic, treatment, or prognostic biomarkers often begins with a quantitative synthesis of such a body of research. To our knowledge, such a synthesis does not exist for peripheral and neural correlates of self-harm in children and adolescents. Thus, in preparation for a systematic review and meta-analysis, we conducted a scoping review of this corpus of work.

We conducted a scoping review because they are designed to 1) identify and characterize studies in a body of research; 2) summarize how the research is conducted; 3) identify factors that can affect findings; 4) delineate research gaps; and 5) present implications for researchers (instead of for clinicians, as with systematic reviews) [38]. We used the Joanna Briggs Institute (JBI) structure for scoping reviews [39] and our review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Reviews (PRISMA-ScR) guidelines, as summarized in the PRISMA-ScR checklist [40] (Supplement 1).

Search strategy and information sources

Our search strategy used terms mapping onto constructs of age, e.g. ‘child’, ‘youth’, ‘adolescent’; self-harm, e.g. ‘suicide’, ‘self-injury’, ‘suicidality’, ‘non-suicidal self-injury’; and the broad search term of ‘biological correlates’, in addition to specific correlates from reviews on adult or adolescent self-harm and categories of correlates, e.g., ‘nutrition’ and ‘neurotransmitters’. Medical Subject Headings (MeSH terms) and keywords were incorporated into the search strategy. The searches were conducted in the PubMed and EMBASE databases from 1980 onwards. Searching was initiated on June 17, 2018, updated on September 13, 2019, and again on May 6, 2020. No studies published after May 6, 2020, were reviewed. Reference lists from studies obtained or reviews were also examined for missed studies. Any found were processed in the same manner as those found with the searches. The gray literature was not searched. The search strategy details are in Supplement 2.

Eligibility criteria

Eligibility criteria were established a priori. A study was included if the following were investigated: 1) suicidality, defined as completed suicide, attempted suicide, suicide plans, or suicidal ideation; 2) NSSI, defined as self-harm of any type, e.g., cutting or burning, without intention to kill oneself; 3) any type of self-harm, i.e., not designated as only suicidality or NSSI; 4) participants birth -19 years of age; 5) peripheral or neural biological correlates, i.e., objective, biological data collected with non-invasive methods, including neural assessments. Studies with participants older than 19 years were included if data were reported separately on those within our age range. Only peer-reviewed studies written in English were included. There were no restrictions on the study location.

A study was excluded if it: 1) examined genetic correlates; 2) only investigated self-harm in patients with intellectual or developmental disability syndromes, e.g., Lesh-Nyhan syndrome or tuberous sclerosis complex; or 3) was a conference abstract, review or case report.

Screening process and data extraction

Covidence was used to assist in the processing of records[41]. Duplicates were removed from the abstracts, followed by a three-step process of blinded assessments by pairs of co-authors. Titles and abstracts were screened against inclusion and exclusion criteria, and those still eligible were then subjected to full-text screening. The remaining eligible studies were then subjected to full-text information extraction, using a spreadsheet adapted from the STROBE criteria.[42] Disagreements between raters were resolved through consensus.

Assessment of risk of bias

Although not required in a scoping review, we estimated the risk of bias of each study to characterize the research in this field better and formally assess factors that could affect findings. We used one of three tools, depending on study design: the Quality Assessment Tool for Case-Control Studies, the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, or the Quality Assessment Tool for Pre-Post Intervention Studies from the U.S. National Heart, Lung, and Blood Institute (https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools). These rating tools evaluate study methods and implementation as sources of bias, including sample characteristics (subject selection, participation, attrition), confounding, study power, and estimation of causality between exposures or interventions and outcomes. Ratings were done by multiple raters, blind to each other’s work. Disagreements were resolved by the senior author who reviewed each study in question, blind to ratings from the other raters.

Figure 1 displays the PRISMA diagram of article processing. We located 4025 abstracts in PubMed and 1953 in Embase, resulting in a total of 5978 records. Back-searching reference lists identified 30 more records meeting eligibility criteria, for a total of 6008 articles processed. Removal of 471 duplicates left 5537 records for title/abstract screening, which found that 5322 articles were ineligible for inclusion. The full-text screening was conducted with the remaining 215 studies, and 136 were excluded for the reasons listed in Figure 1. The most common reasons for exclusion were that studies were out of the age range or did not study a biological correlate.

The final number of studies located was 79. This includes three papers that collected data on two different specific correlates (see Table 1) [43–45]. Therefore, for any of our results concerning the total number of studies, we use 79, as this refers to the number of studies of specific correlates. However, since these three papers conducted their research on the same sample for both specific correlates, we only used each paper once when summarizing designs, sample characteristics, and risk of bias across our dataset.

Table 1

Studies reviewed, type of self-harm, designs, specific correlates studied
Citation, Country	Type of Self-harm	Study Design	Specific Correlate
Peripheral Correlates
Robbins, Alessi 1985 US [46]	Suicidality	Case-Control	Circadian rhythm, HPA axis, cortisol
Rosenthal et al. 1986 US [47]	Suicidality	Case-Control	Circadian rhythm, HPA axis, cortisol
Dahl et al. 1991a US [48]	Suicidality	Case-Control	Circadian rhythm, HPA axis, cortisol
Dahl et al. 1992a US [49]	Suicidality	Case-Control	Circadian rhythm, HPA axis, cortisol
Ghaziuddin et al. 2014 US [50]	Suicidality	Case-Control	Reactivity, HPA axis, cortisol
Young et al. 2010 Scotland [51]	Suicidality	Cross-Sectional	Circadian rhythm, HPA axis, cortisol
Pfeffer et al. 1991 US [52]	Suicidality	Cohort	Circadian rhythm, HPA axis, cortisol
Giletta, et al. 2015 US [53]	Suicidality	Cohort	Reactivity, HPA axis, cortisol
Eisenlohr-Moul et al. 2018 US [54]	Suicidality	Cohort	Reactivity, HPA axis, cortisol
Reichl et al., 2016 Germany [55]	NSSI	Case-Control	Circadian rhythm, HPA axis, cortisol
Klimes-Dougan et al. 2019 US[56]	NSSI	Case-Control	Reactivity, HPA axis, cortisol
Reichl, et al. 2019 Germany [55]	NSSI	Case-Control	Circadian rhythm, Reactivity, HPA axis, cortisol
Beauchaine et al. 2015 US [58]	Any self-harm	Case-Control	Circadian rhythm, HPA axis, cortisol
Plener et al. 2016 Germany [59]	Any self-harm	Cohort	Reactivity, HPA axis, cortisol,
Yang et al., 2019 Hungary [60]	Suicidality	Case-Control	Reactivity ANS, CV system
Giletta et al., 2017 US [61]	Suicidality	Cohort	Reactivity, ANS, CV system, predictions
Koenig et al. 2017a Germany [62]	NSSI	Case-Control	Resting, ANS, CV system
Crowell et al. 2005 US [43]	Any self-harm	Case-control	Resting & Reactivity, ANS, CV system, skin conductance
Crowell et al. 2012 US [63]	Any self-harm	Case-control	Resting & Reactivity, ANS, skin conductance
Wielgus et al. 2016 US [64]	Any self-harm	Cohort	Resting & Reactivity, ANS, CV system, predictions
Aldrich et al. 2018 US [65]	Any self-harm	Cohort	Reactivity, ANS, skin conductance, predictions
Kaess et al. 2012 Germany [66]	NSSI	Case-control	Reactivity, HPA axis & ANS, cortisol, CV system
Koenig et al. 2017b Germany [67]	NSSI	Case-control	Reactivity, HPA axis & ANS, cortisol, CV system
Modai et al. 1989 Israel [68]	Suicidality	Case-control	Platelet serotonin function
Ambrosini et al. 1992 US [69]	Suicidality	Case-control	Platelet serotonin function
Pfeffer et al. 1998 US [70]	Suicidality	Case-control	Serotonin, precursor levels, platelet function
Tyano et al. 2006 Israel [71]	Suicidality	Case-control	Serotonin levels
Pine et al. 1995 US [72]	Suicidality	Cohort	Platelet serotonin function
Clark et al. 2003 US [73]	Suicidality	Cohort	Precursor levels
Crowell et al. 2005 US [43]	Any self-harm	Case-control	Serotonin levels
Crowell et al. 2008 US [74]	Any self-harm	Case-control	Serotonin levels
Dahl et al., 1990 US [75]	Suicidality	Case-control	Sleep characteristics
Dahl et al. 1991b US [76]	Suicidality	Case-control	Sleep characteristics
Emslie et al. 1994 US [77]	Suicidality	Case-control	Sleep characteristics
McCracken et al. 1997 US [78]	Suicidality	Case-control	Sleep characteristics
Boafo et al. 2019 Canada [79]	Suicidality	Case-control	Sleep characteristics
Singareddy et al. 2013 US [80]	Suicidality	Cohort	Sleep characteristics
Bilgiç et al. 2020 Turkey [81]	Suicidality	Case-Control	Neurotrophin levels
Falcone et al. 2010 US [44]	Suicidality	Case-Control	S100B protein levels
Falcone et al. 2015 US [82]	Suicidality	Case-Control	S100B protein levels
Kavurma et al. 2017 Turkey [83]	Any self-harm	Case-Control	Neurotrophin levels
Gabbay et al. 2009 US [84]	Suicidality	Case-Control	Cytokine levels
Falcone et al. 2010 US (supplement data)[44]	Suicidality	Case-Control	Cytokine levels
Amitai et al. 2020 Israel [85]	Suicidality	Non-Controlled Pre-Post Intervention	Cytokine levels, predicting side effects
Glueck et al. 1994 US [86]	Suicidality	Case-Control	Lipid levels
Plana et al. 2010 Spain [87]	Suicidality	Case-Control	Lipid levels
Ryan et al. 1988 US [88]	Suicidality	Case-Control	Reactivity, growth hormone
Dahl et al.1992b US [89]	Suicidality	Case-Control	Circadian rhythm, growth hormone
Neural Correlates
Pan et al. 2011 US [90]	Suicidality	Case-Control	Brain activity, Response inhibition
Pan et al. 2013a US [45]	Suicidality	Case-Control	Brain activity, Facial emotion processing
Pan et al. 2013b US [91]	Suicidality	Case-Control	Brain activity, Decision-making
Quevedo et al. 2016a US [92]	Suicidality	Case-Control	Brain activity, Emotion self-identity
Harms et al. 2019 US [93]	Suicidality	Case-Control	Brain activity, Social interaction
Oppenheimer et al. 2020 US [94]	Suicidality	Case-Control	Brain activity, Social interaction
Plener et al. 2012 Germany [95]	NSSI	Case-Control	Brain activity, Emotion processing
Groschwitz et al. 2016 Germany [96]	NSSI	Case-Control	Brain activity, Social interaction
Quevdo et al. 2016b US [97]	NSSI	Case-Control	Brain activity, Emotion self-identity
Brown et al. 2017 Germany [98]	NSSI	Case-Control	Brain activity, Social interaction
Perini et al. 2019 Sweden [99]	NSSI	Case-Control	Brain activity, Social interaction
Poon et al. 2019 US [100]	NSSI	Cross-sectional	Brain activity, Reward processing
Sauder et al., 2016 US [101]	Any self-harm	Case-Control	Brain activity, Reward processing
Pan et al. 2013a US [45]	Suicidality	Case-Control	Functional connectivity, Facial emotion processing
Alarcon et al. 2019 US [102]	Suicidality	Case-Control	Functional connectivity, Emotion self-identity
Ordaz et al. 2018 US [103]	Suicidality	Cross-sectional	Functional connectivity, Resting state
Schreiner al. 2018 US [104]	Suicidality	Cross-sectional	Functional connectivity, Resting state
Schwartz et al. 2019 US [105]	Suicidality	Cohort	Functional connectivity, Resting state, predicting change
Santamarina-Perez et al. 2019 US [106]	NSSI	Cohort	Functional connectivity, Resting state, predicting treatment effect
Tavakoli et al. 2018 Canada [107]	Suicidality	Case-Control	Event-related potential, Attention capture
Tsypes, et al. 2019 US [108]	Suicidality	Case-Control	Event-related potential, Reward-loss
Pegg et al. 2020 US [109]	Suicidality	Case-Control	Event-related potential, Reward-loss
Tsypes et al. 2018 US [110]	NSSI	Case-Control	Event-related potential, Reward-loss
Graae et al. 1996 US [111]	Suicidality	Case-Control	Resting state, Brain wave asymmetry
Lewis et al. 2019 US [112]	Suicidality	Non-Controlled Pre-Post Intervention	Reactive intracortical inhibition, post-treatment
Ho et al. 2018 US [113]	Suicidality	Cohort	Gray matter volume, prediction suicidality
Ando et al. 2018 Germany [114]	NSSI	Case-Control	Gray matter volume
Beauchaine, et al. 2019 US [115]	Any self-harm	Case-Control	Gray matter volume
Pan et al., 2015 US [116]	Suicidality	Case-Control	Gray, white matter volumes
Goodman et al. 2011 US [117]	Any self-harm	Case-Control	Gray and white matter volumes
Jovev et al. 2008 US [118]	Any self-harm	Cross-sectional	Pituitary gland volume

The self-harm focus in the studies was defined in one of three ways with three types of samples: suicidality (subjects only with suicide attempts, plans, or ideation), NSSI (subjects only with self-harm without intention to die), or what we labeled ‘any self-harm’ (subjects who manifested suicidality, NSSI, or both, but the samples were mixed with regards to types of self-harm). Most studies, 65% (51/79), focused on subjects with suicidality, while 19% (15/79) studied NSSI, and 16% (13/79) investigated participants recruited with Any Self-Harm.

Publication dates ranged from January 1985 to May 2020. There was a relationship between time of publication and type of self-harm studied, as shown in Figure 2. The counts of papers on suicidality have more than doubled every five years since 2009. Research on studies of any self-harm first showed up in 2005, whereas NSSI papers did not appear until 2012. Any self-harm studies have increased in the past five years and so have NSSI studies in the past ten years. However, work on suicidality has continued to show the largest growth.

We found 48 studies of peripheral correlates and 31 of specific neural correlates, which we categorized into seven sub-types of peripheral correlates, and three sub-types of neural correlates (see Figure 3). The stress response system was the correlate sub-category studied the most in the peripheral studies category, and brain function with imaging was studied the most in the neural correlates. There were no replication studies.

Most of the studies were conducted in the US. Germany was the location for eight studies. Three or fewer were done in Israel, Canada, Turkey, Scotland, Spain, Sweden, and Hungary.

Study designs, samples, identifying self-harm, specific correlates studied bias/quality ratings

Table 1 displays the studies included organized by whether they were in the peripheral or neural category, the type of self-harm that was the focus of the study, the design, and the specific correlate studied [43–118]. Details about samples, methods, and findings, as well as risk of bias rating for each study can be found in Supplement 3 (peripheral correlates) and 4 (neural correlates).

Designs

The proportions of study designs were similar across the correlate categories. Case-control designs were most frequently used, in 74% (34/46) of the peripheral correlates studies and 70% (21/30) of the neural correlate studies. Cross-sectional or cohort designs were employed in similar proportions across correlate categories (24% or 11/46, peripheral and 23% or 7/30 of the neural correlate studies). Likewise, each category had one non-controlled pre-post intervention design.

Samples

Table 2 and Supplements 3 and 4 show that sample characteristics varied widely across studies. Samples in the peripheral correlates more frequently comprised subjects with suicidality: 70% (32/46), compared to 57% of the neural correlate papers (18/30). The neural correlate data more often came from samples of participants with NSSI 30% (9/30), compared to only 13% (6/46) of the studies of peripheral correlates. The proportions of papers in each category studying participants with any type of self-harm were more similar, 17% (8/46) in the peripheral correlates and 13% (4/30) in the neural correlates research.

Table 2

Sample characteristics
Characteristic	Peripheral Correlates n (%) N of studies = 46¹	Neural Correlates n (%) N of studies = 30¹
Self-Harm Type
Suicidality	32 (70%)	17 (57%)
NSSI	6 (13%)	9 (30%)
Any Self-Harm	8(17%)	4 (13%)
Sample Size
9-20	2 (5%)	3 (10%)
21-50	13 (28%)	15(50%)
51-100	13 (28%)	7 (23%)
101-200	12 (26%)	5 (17%)
>200	6 (13%)	0 (0%)
Age Group
Children (3-11 years old)	2 (4%)	2 (6%)
Adolescents (12-19 years old)	30 (65%)	20 (67%)
Both	14 (31%)	8 (27%)
Source of Subjects
Clinical Setting	9 (20%)	4 (13%)
Community	5 (11%)	4 (13%)
Clinical setting & community	29 (63%)	15 (50%)
Previous research, registry	3 (6%)	6 (20%)
Not given	0 (0%)	1 (4%)
Recruitment Diagnosis
Depression	18 (39%)	12 (40%)
Other	6 (13%)	3 (10%)
None	22 (48%)	15 (50%)
Types of Controls²
Healthy controls	21 (62%)	13 (57%)
Psychiatric controls	7 (20%)	1 (4%)
Both	6 (18%)	9 (39%)
≥ 85% Girls
Yes	14 (31%)	8 (27%)
No	31 (67%)	21 (70%)
Not given	1 (2%)	1 (3%)
¹Total number of studies adjusted for 2 studies in peripheral correlates and 1 study in neural correlates that used the same samples for two specific correlates.
² Denominators are studies using controls: n = 34, peripheral correlates; n = 23, neural correlates.

Sample sizes for the entire body of work ranged from 9 to 1268. The range for the peripheral correlates’ samples was 9-1258 subjects (median = 62.5), but the neural correlates’ range was 10-152 participants (median = 44). Adolescents were studied most often, with only two studies in each category exclusively examining children younger than 12. Nearly the same percentage of studies in each correlate category used combined samples of children and adolescents.

Many studies (63% (29/46) of the peripheral correlate studies and 50% (15/30) of the neural correlates) recruited convenience samples of clinical cases from inpatient units or outpatient clinics. A number of these studies also recruited from the community, but this process was always to obtain healthy controls, not self-harming children or adolescents who were not patients. Self-harming participants in cross-sectional and cohort studies were also often collected as convenience samples, but several studies used samples of the general population recruited with population sampling methods.

Half of the studies in both correlate categories recruited subjects based on a clinical diagnosis, almost always major depressive disorder. Other disorders targeted subjects with borderline personality disorder, anxiety disorders, psychosis mixed with all types of mood disorders. Several studies also recruited subjects with “MH concerns,” but no diagnosis was associated with this.

Of the studies that used control groups (34 peripheral and 23 neural studies), healthy control groups were the most common: 62% (21/34) for peripheral correlates and 57% (13/23) for neural correlates. Few studies used only psychiatric controls, but 39% (9/23) of neural correlate projects recruited healthy and psychiatric controls, and 18% (6/34) of the peripheral correlates used both.

Girls were studied more often than boys. The percentage of girls, averaged across all studies, was 72% and the range was 11–100%. Nearly a third of the studies investigated chiefly or entirely female samples, which we defined as ≥ 85% girls: 30% (14/46) and 27% (8/30) in the peripheral and neural categories, respectively.

Methods to identify self-harm

Data assessing the type of self-harm were collected with six approaches: 1) self- or parent-report, 2) diagnostic interview, 3) clinician-rated scale/non-diagnostic interview, 4) combinations of the previous three approaches, 5) clinical records, or 6) non-standardized instruments created for the specific study. Data collection methods were similar in the peripheral and neural correlates research, but there was little inter-study consistency, as shown in Supplements 3 and 4.

The lack of consistency is illustrated by the finding that numerous different instruments were used within each of the first four approaches used above. Self-report data about self-harm were collected from one or more of 16 instruments, diagnostic interview data from one of five instruments, and information from clinician-rated scales/non-diagnostic interviews could have come from one or more of 14 instruments. Many of the instruments used were designed for adults and lack psychometric data for use in the pediatric age group. Moreover, several of the instruments in all categories were not designed to assess self-harm and categorized subjects based on answers to just a few questions (sometimes only one), e.g., the Youth Self-Report[119] or older versions of the Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADS)[120, 121].

The most frequently used approach was clinician rating scales/non-diagnostic interviews. Studies of peripheral correlates most often used the Self-Injurious Thoughts and Behaviors Interview (SITBI)[122], while the neural correlates research most frequently used the Columbia Suicide Severity Rating Scale (C-SSRS)[123]. The next most common data collection method was diagnostic interviewing, usually with a version of the K-SADS. Self-report studies were the third most common, with the neural correlate studies using this strategy nearly twice as often as peripheral correlates. Neural correlate research used the Suicidal Ideation Questionnaire (SIQ)[124] most frequently, in contrast to the YSR in peripheral correlate studies. Over a third of the neural correlate studies used a combination of methods compared to only 15% (7/48) of the peripheral. Clinical records used to categorize subjects on self-harm were used in 10% (5/48) of the peripheral correlate work, although only 3% (1/48) of the neural correlate studies did this. Only one study used a non-standardized instrument. An unusual study used a cognitive task to categorize participants as having implicit self-harm (vs. explicit from self-report data), a strategy that may yield more reliable classification than self-report, especially in younger children. Decreased gray matter volume at baseline in areas related to learning, reward, and motor control predicted 24-month implicit suicidality, but not explicit [113].

Specific Correlates

Table 3 presents the 28 specific correlates measured in these studies and the number of methods and outcomes for each (see Supplements 3 and 4 for further details). Over a quarter (29% (8/28)) of the specific correlates were investigated in only one study. The remainder were examined with two to eleven studies, but even in clusters of studies about one specific correlate, there was heterogeneity in the methods used to measure the correlate. Research on event-related potentials (ERPs) in reward processing had the most consistent methods. In contrast, five methods were used to investigate the reactive function of the autonomic nervous system (ANS). Outcomes of interest also varied considerably, with studies of some specific correlates all focusing on the same outcome, e.g., the reactivity of the HPA axis measuring changes in cortisol levels, while groups of other studies examined diverse outcomes, e.g., functional connectivity studies investigated six different outcomes.

Table 3

**Specific correlates: number of studies, measurement methods, and outcomes**
Specific Correlates, Number of Studies	Number of Methods	Number of Outcomes
Stress Response System
Cortisol levels, circadian (n = 11)	3	1
Cortisol levels, reactive (n = 8)	4	1
ANS, resting (n = 2)	1	5
ANS, reactive (n = 8)	4	5
Serotonin System
Platelet serotonin uptake or imipramine binding (n = 3)	2	2
Blood levels serotonin (n = 4)	2	1
Blood levels serotonin precursors (n = 2)	2	1
Sleep
Total duration, stage duration, latency to onset, latency to stages, REM density (n = 6)	1	2
Neuromodulators
Serum levels S100B protein (n = 2)	1	1
Serum levels neurotrophins (n = 2)	1	1
Immune System
Serum/plasma levels cytokines (n = 3)	1	2
Lipid Levels
Serum cholesterol, triglycerides (n = 2)	1	2
Pituitary Hormones
Circadian rhythm growth hormone (n = 1)	N/A
Reactivity growth hormone (n = 1)	N/A
Brain Function, Imaging
Response inhibition (n = 1)	N/A
Emotion processing (n = 3)	2	2
Decision-making (n = 1)	N/A
Social interaction (n = 5)	3	1
Reward processing (n = 2)	2	1
Self-identity paradigm (n = 3)	2	2
Functional connectivity (n = 6)	1	3
Brain Function, Non-Imaging
Event-related potentials, attention capture (n = 1)	N/A
Event-related potentials, reward processing (n = 3)	1	3
Alpha waves, resting (n = 1)	N/A
Intracortical inhibition (n =1)	N/A
Brain Structure
Gray matter volume (n = 3)	1	3
Gray and white matter volume (n = 2)	1	2
Pituitary gland volume (n = 1)	N/A

Risk of bias ratings

Table 4 summarizes the risk of bias assessment rating results, organized by correlate sub-categories. An estimate of Good (i.e., low risk of bias) was given to 37% (29/79) studies; Fair to 57% (45/79), and Poor to 5% (4/79). Neuromodulator and lipid metabolism had the highest percentage of Good studies with 100% in each. However, there were only four and two studies in each, respectively. And the two studies of S100B proteins appeared to be overlapping samples, although this was not stated. Slightly over half of the twenty-three studies of the stress response system were rated as Good, and likewise half of the sleep studies. The remaining studies were judged to be Fair. The high percentage (50%) of studies rated as Poor in the pituitary hormones sub-category is due to only having two papers for this correlate and one of them rated as Poor.

Table 4

**Summary of risk of bias ratings per correlate sub-category**
	Number of studies with each rating (%)
Peripheral Correlates Sub-Categories (Total numbers of studies)	Good	Fair	Poor
Stress Response System (n = 23)	12 (52%)	9 (39%)	2 (9%)
Serotonin System (n= 8)	2 (38%)	4 (50%)	1(13%)
Sleep (n = 6)	3 (50%)	3 (50%)	0 (0%)
Neuromodulators (n =4)	4 (100%)	0 (0%)	0 (0%)
Immune System (n = 3)	1 (33%)	2 (67%)	0 (0%)
Lipid Metabolism (n =2)	2 (100%)	0 (0%)	0 (0%)
Pituitary Hormones (n =2)	0 (0%)	1 (50%)	1 (50%)
Neural Correlates Sub-Categories (Total number of studies)
Brain Function, Imaging Studies (n = 19)	3 (16%)	16(84%)	0 (0%)
Brain Function, Non-Imaging Studies (n = 6)	1 (17%)	5 (83%)	0 (0%)
Brain Structure (n = 6)	1 (17%)	5 (83%)	0 (0%)

The most common reasons that studies were scored less than Good were: no sample size justification or power analysis, measurements of specific correlates were not blind to self-harm status (of particular importance in cohort studies), self-harm participants and control group members were not chosen at random from potential subjects (of particular importance in case-control studies), and analyses of the association between self-harm and specific correlates did not account for confounding variables, e.g. medication usage or gender. We did not find a relationship between ratings and publication date. As this was a scoping review, we did not exclude studies rated as Poor. Neither did we weight individual study findings by bias scores (next section).

Summary of study findings by type of self-harm

Table 5 presents a high-level summary of individual study findings, organized by the type of self-harm investigated. We could not calculate the number of unique participants studied for each correlate category because some studies appeared to have used the same sample in different studies, although that was rarely explicitly stated. At least one significant association with some type of self-harm was reported for all specific correlates, except three: neurotrophins[81, 83], neuroimaging and response inhibition [90]and neuroimaging and decision-making [91](see Supplements 3 and 4). In the last two studies, subjects with MDD plus suicidality and healthy controls showed no differences in neuroimaging findings, but subjects with MDD did have aberrant responses.

Table 5

Overview of findings by type of self-harm
Correlate Category	Type of Self-Harm Study
	Suicidality	NSSI	Any Type of Self-Harm
Peripheral	• HPA Axis, Circadian rhythm, cortisol: not associated (3/6). Associated with cortisol dysregulation (3/6). • HPA Axis, Reactivity, cortisol: associated with hyperreactivity (1/3), hyporeactivity (1/3), different pattern (1/3). • ANS, Reactivity, cardiovascular measures: Not associated (1/3). Decreased parasympathetic function (2/3), including symptom prediction. • Serotonin System, platelets: not associated imipramine binding sites (1/4). Not associated with serotonin uptake (1/4). Not associated with serotonin-induced aggregation (1/4). Associated decreased imipramine binding sites (1/4). • Serotonin System, serotonin levels: Not associated with levels (1/2). Associated higher levels (1/2). • Serotonin System, precursor levels: associated lower tryptophan levels (1/2). 5-year suicidality associated low ratio tryptophan to other amino acids, not baseline tryptophan levels (1/2). • Sleep Characteristics: not associated (2/6). Associated with longer sleep, shorter Stage 3, shorter delta sleep, more rapid eye movement (REM) after scopolamine (1/6). Associated with longer sleep latency (2/6), longer REM latency, higher percentage NREM1, higher REM density (1/6), higher percentage REM sleep (1/6). • Neuromodulators: Not associated BDNF (2/2), or GDNF, NGF, NTF3 (1/2). Associated increased levels of S100B protein (2/2). • Immune System, cytokine levels: associated with decreased TNFα, increased IFN-\(\gamma\) (1/3), increased IL-ß, IL-8 (1/3). Antidepressant suicidality associated with higher increase IL-6 (1/3). • Lipid Metabolism: not associated triglyceride levels (1). Associated lower cholesterol (2/2). • Growth Hormone: associated blunted reactivity (1). Associated with dysregulated circadian secretion (1).	• HPA Axis, Circadian rhythm, cortisol: associated with cortisol dysregulation (2/2). • HPA Axis, Reactivity, cortisol: associated with hyporeactivity (2/4), hyperreactivity (1/4), different pattern (1/4). • ANS, Resting, cardiovascular measures: not associated (1).	• HPA Axis, Circadian rhythm, cortisol: associated with cortisol dysregulation (1). • HPA Axis, Reactivity, cortisol: associated with hyporeactivity (1). • ANS, Resting, cardiovascular measures: not associated with parasympathetic tone (1/2). Associated with lower parasympathetic tone (1/2). • ANS, Reactivity, cardiovascular measures: not associated with parasympathetic function (1). • ANS, Resting, skin conductance: not associated with abnormal sympathetic system arousal (1/2). Associated with lower sympathetic arousal (1/2). • ANS, Reactivity, skin conductance: reactivity not associated prediction symptoms (1/2). Not associated with abnormal sympathetic system arousal (1/2). • Serotonin System, Peripheral Levels Serotonin: associated with lower blood levels (2/2).
Neural	• Activity, Response inhibition: not associated (1). • Activity, Emotion processing: angry faces – increased activation in ACG, bilateral sensory cortices, left dlPFC, right MTG, happy faces – decreased activation visual, sensory cortices, PFC, ACG.(1) • Activity, Decision-making: not associated (1). • Activity, Self-identity processing: associated decreased activation midline cortical, limbic structures self-happy vs. other-happy faces (1) • Activity, Social interaction: associated all settings with decreased activity insula, putamen, ACC, caudate, postcentral, precentral gyri (1). Associated greater activation insula only if more peer victimization or daily negative experiences (1). • Functional Connectivity, Emotion processing: angry faces - associated decreased connectivity ACG to bilateral insulae (1). • Functional Connectivity, Self-identity processing: associated greater connectivity between amygdala, dlPFC, dmPFC, precuneus (1). • Functional Connectivity, Intrinsic network coherence: associated lower resting state Executive Control Network coherence (1/2). Symptom improvement associated increased coherence Salience Network (1/2). • Functional Connectivity, Resting state: associated with increased connectivity between left precuneus and primary motor, somatosensory cortices, MFG, SFG; decreased connectivity between left PCC, left cerebellum, left OC, temporal-occipital fusiform gyrus (1). • Event-Related Potential, Attention capture: associated lower threshold involuntary attention switching (1). • Event-Related Potential, Reward-loss: associated more activation to reward and loss (2/2). • Brain Waves, Symmetry: associated with left > right posterior alpha asymmetry (1). • Cortical Inhibition, Post-treatment: associated with increase cortical inhibition (1). • Brain Structures, Gray matter volume: prediction of symptoms associated with decreased volume bilateral putamen, left caudate (1). • Brain Structures, Gray and matter volume: Not associated white matter differences; associated reduced thickness in rSTG (1). • Pituitary gland volume: associated with increased volume (1).	• Activity, Emotion Processing: associated greater activation amygdala, hippocampus, bilateral ACC, but MDD explained findings (1). • Activity, Social interaction: associated increased activation mPFC, vlPFC, parahippocampus (1/3). Associated increase activation putamen (1/3). dmPFC, PCC, sgACC function during social anticipation predicted NSSI group (1/3). • Activity, Self-identity processing: Associated greater activation limbic and cortical midline structures (1). • Activity, Reward processing: associated greater activation bilateral putamen (1). • Functional Connectivity, Resting state: associated with reduced connectivity amygdala-mPFC network, predicted better response to treatment (1). • Event-Related Potential, Reward-loss: associated more negative response to losses (1). • Brain Structures, Gray matter volume: associated decreased volume ACC, insula (1).	• Activity, Reward processing: Associated decreased activation putamen, OFC, bilateral amygdalae (1). • Brain Structures, Gray matter volume: associated with decreased volume bilateral insula cortices, rIFG (1). • Brain Structures, Gray and matter volume: associated with decreased gray and white matter volumes in BA24, higher white matter volume in BA23, but no difference gray matter volume (1). • Brain Structures, Pituitary gland: associated with greater volume (1).
ACC - Anterior Cingulate Cortex, ACG - anterior cingulate gyrus, BDNF - Brain-Derived Neurotrophin Factor, BA23, BA 24 - Brodmann Area 23 and 24 (ventral posterior cingulate area), dlPFC - Dorsolateral Prefrontal Cortex, dmPFC - Dorsomedial Prefrontal Cortex, GDNF - Glial-Derived Neurotrophin Factor, GMV – Gray Matter Volume, IFN-γ - Interferon-Gamma, IL-10 - Interleukin 10, IL-1α - Interleukin 1-Alpha, IL-1β - Interleukin 1-Beta, IL-2 - Interleukin 2, IL-4 - interleukin 4, IL-6 - Interleukin 6, IL-8 - Interleukin 8, OC - Occipital Cortex, mFC - medial frontal cortex, mOFC - Medial Orbitofrontal Cortex, mPFC - medial prefrontal cortex, NGF - Nerve Growth Factor, NTF3 - Neurotrophin-3 Factor, OFC – Orbitofrontal Cortex, PCC - Posterior Cingulate Cortex, PF – Prefrontal, rACC - Rostral Anterior Cingulate Cortex, rdACG - Right Dorsal Anterior Cingulate Gyrus, rIFG - Right Inferior Frontal Gyrus, rMTG - Right Medial Temporal Gyrus, rSTG – Right Superior Temporal Gyrus, S100B - S100 - calcium-binding protein B, sgACC - Subgenual Anterior Cingulate Cortex, SN - Salience Network, TNFα - Tumour Necrosing Factor-Alpha, vLPFC - Ventrolateral Prefrontal Cortex, WMV - White Matter Volume

Table 5 shows that, first, there are substantially more data from children and adolescents with suicidality than on subjects with NSSI or those from studies of any type of self-harm. Second, the findings for the specific correlates are inconsistent, even for the correlates with larger numbers of studies and even within a single type of self-harm. For example, studies of the association between suicidality and HPA axis reactivity reported hyperreactivity, hyporeactivity, and an aberrant secretion pattern. In an example from the neural correlates, brain activity in response to social interaction was associated with suicidality, but one study reported decreased activity in the insula and the other reported increased activity. Heterogeneity in the methods used to identify self-harm, sample characteristics, and measurement of the specific correlates or outcomes is so prevalent that it is difficult to interpret the divergence in findings.

A third feature of these findings that can be seen in Table 5 is that there are some stronger signals from some of the studies of specific correlates. These appear to be from groups of studies more methodologically similar and with lower risks of bias. For example, two studies with larger sample sizes, similar age ranges, outpatients, and healthy controls, and similar proportions of girls investigated the association between neurotrophins and suicidality or any type of self-harm (for which data from subjects with suicidality were analyzed separately from the data on NSSI subjects). Correlate measurement and outcome methods were similar. Both were rated Good in risk of bias. No association was found with any neurotrophin in either study. Similarly, two studies of lipid metabolism and suicidality both used samples of inpatients, used clinical records to classify self-harm, used the same methods for measuring correlates, and compared the outcomes with normed levels in children and adolescents. Risk of bias ratings were Good for both studies. Both found that suicidality was associated with lower cholesterol.

Fourth, there are no clear patterns of findings, e.g., differences or similarities, for any of the specific correlates by self-harm sub-types. For most of the correlates, the issue is moot because there aren’t sufficient numbers of studies for comparison of findings by self-harm sub-type. But even with specific correlates in which there are studies in each type of self-harm, e.g. the stress response system or neuroimaging responses to social interaction, it is difficult to determine if self-harm sub-type makes any difference in the results because of such wide inter-study methodologic variability.

Fifth, most studies in this corpus of research could contribute to the development of diagnostic biomarkers. However, several are notable for searching for correlates of prognosis or treatment response.

This scoping review contributes uniquely to research on peripheral and neural correlates of self-harm by summarizing data on children and adolescents ages 3 to 19 years, a demographic with social, developmental, and psychological characteristics of self-harm that can differ from those found in young adults [17, 28, 30, 31, 34–37]. This contrasts with previous reviews, which combined data from this age group with adult information[26–33]. Our work also advances knowledge on this topic by reviewing 79 studies in 76 publications, significantly more studies than in earlier reviews and by covering 45 years from 1985 to 2020.

Publication timelines differed by the types of self-harm focused upon in the studies. Studies on suicidality in this age group have consistently been published since 1985, but those on any self-harm did not show up until 2005, and the first correlates-NSSI paper on children and adolescents did not come out until 2012. Publication of studies on correlates of all three sub-types of self-harm have been increasing since 2009. Our finding is similar to that reported in a review of MRI studies in adolescents and young adults, although publications in that dataset didn’t start until 2008 and the review did not separately report findings in adolescents [33].

The research was conducted in nine countries, with most of the studies done in the US. Two-thirds of the studies investigated suicidality, with the remaining third evenly divided between studies of subjects with NSSI or any type of self-harm. Of the 79 studies, 48 focused on peripheral and 31 on neural correlates. Studies were organized into seven correlate sub-categories under peripheral correlates (Stress Response System; Serotonin System; Sleep; Immune System; Lipid Metabolism; Neuromodulators; and Pituitary Hormones) and three within the neural correlates (Brain Function, Imaging; Brain Function, Non-Imaging; and Brain Structure).

A total of 28 specific correlates were investigated. Data came from studies which were all observational in design, mainly using convenience samples of patients who were adolescents, 12-19 years of age, about half of whom were recruited based on specific disorders (primarily MDD) and which were disproportionately female. From the limited information available, the samples may have been predominantly white participants.

Over a quarter of the specific correlates (29%, 8/28) were investigated with only one study: circadian rhythm of growth hormone, reactivity of growth hormone, functional imaging and response inhibition, function imaging and decision-making, ERPs and attention capture, brain wave asymmetry, and intracortical inhibition. For the remaining 21 specific correlates, inter-study agreement on findings was often low, even within one type of self-harm. We found no replication studies.

Resolution of inter-study divergence in findings is challenging because of heterogeneity in studies on multiple levels. For example, we found considerable variability in findings from HPA axis reactivity studies. But these studies defined self-harm in myriad ways, using a diagnostic instrument, or one of two different clinician-rated instruments, or one of two different self-reports. Moreover, these studies variously used subjects who were patients with MDD or patients not recruited on the basis of a diagnosis and they were compared to an assortment of healthy, psychiatric, or sibling controls. Finally, there were four different methods used to activate the HPA axis, only one of which has been demonstrated to be a reliable and valid paradigm for measuring HPA axis reactivity in children and adolescents [125]. Similar study heterogeneity characterizes the functional imaging studies of social interaction, which we also described in the results section as another example of low inter-study agreement in findings.

Most of the studies reviewed used case-control designs, which are vulnerable to selection and information biases, as well as confounding[126–128]. These problems all need to be mitigated by adequate sample size, minimization of classification error (including self-harm classification), and attention to the representativeness of participants. As can be seen in the examples described in the ‘Summary of study findings by type of self-harm’ sub-section of the Results section and in the previous paragraph, many of the studies fall short on one or more of these features. Consequently, only 37% of our studies were deemed at relatively low risk of bias.

Studies which did have similar methods and were rated as Good, did report similar findings, whether positive associations of the specific correlate with self-harm[86, 87] or no association with self-harm [81, 83]. It is also important to note that these were four independent samples from four different research groups.

Although the patient samples from earlier studies may have been representative of self-harming children and adolescents in the 1980s and early 1990s, current information suggests that is no longer the case. Up to 60% of adolescents with NSSI in the general population do not seek care [129] and half of the adolescents with suicidality or NSSI in a population study do not present for help.[130] Moreover, the ability to access care can be compromised by low socioeconomic status [131], rural geographic location[132], or minority race/ethnicity [133], thus reducing the generalizability of some of the findings for those who experience healthcare disparities. Similarly, recruiting participants based on a psychiatric disorder limits the applicability of results to the sub-population of self-harming children or adolescents with that disorder, despite evidence that self-harm can be transdiagnostic or exist independent of psychiatric disorders[134–136].

Numerous diagnostic interviews, clinician-rated instruments, or self-report instruments were used to identify self-harm, which may have produced information bias. The use of multiple instruments also is troubling because recent reviews of child and adolescent self-harm instruments have questioned their psychometric properties[137–139]. The reviews also point out possible threats to validity if an instrument is used in samples different from those used in development of the instrument or when used for purposes other than which they were initially designed. Unfortunately, these were common practices in the research we reviewed. Moreover, data from adults reporting self-harm shows that 40% of those responding affirmatively to a question about attempting suicide denied intention to die when asked a follow-up question [140], suggesting that single questions may be misleading and clarification of behavior definitions for respondents may be critical. Many of our studies classified self-harm based on one question taken from an instrument not even designed to measure self-harm.

Similar issues arose in the measurement of correlates or outcomes. Confounding was rarely handled by standard methods such as stratification or propensity scores [141] and researchers sometimes measured unique outcomes of specific correlates, making inter-study comparisons or interpretation of different findings challenging.

We identified four research gaps: 1) the absence of replication studies; 2) a paucity of studies on children younger than 11 years of age; 3) relatively few studies on non-patient children or adolescents, and 4) disproportionate representation of girls. A possible gap is the lack of data on non-white children and adolescents, but we could not confirm this.

If left unfilled, these gaps will significantly impede progress in this field. Replication studies can help verify that an association between self-harm and a specific correlate is not a spurious finding and they are a critical step in the development of all types of biomarkers [142]. However, replication studies are not always easy or fruitful, although several strategies have recently been proposed to determine which studies in a body of research should undergo replication[143–145]. Future research on correlates in child and adolescent self-harm should focus on replication attempts of some of the findings described in this review using these new methods to identify which ones.

More studies on correlates of self-harm in younger children are needed, as self-harm is increasing in younger age groups [146, 147]. For example, presentations to US emergency departments for suicidality increased substantially from 2007 to 2015 (the most recent data available) and 43% of those visits were from children 5-10 years of age. Moreover, suicide was the third leading cause of death in the US for younger children (10-12 years of age) (https://webappa.cdc.gov/sasweb/ncipc/leadcause.html) and the characteristics of younger children with self-harm are different from adolescents [4].

Gender proportions are essential to balance in the research landscape. Girls are more likely to engage in suicidality and NSSI, so samples comprised mostly or entirely of females can be appropriate, depending on the research goals. But results from such studies cannot be generalized to boys. Furthermore, given gender differences in help-seeking, it is unlikely that many boys with self-harm will be found in clinical settings.

New research studies must increase the number of non-patient children and adolescents under investigation. It is also essential that samples are not only more diverse with regards to gender, but also for race and ethnicity, as recent data show that from 1991 to 2017 suicide attempts among black adolescents in the US rose 73%, compared to a decrease of 7.5% in white adolescents [148].

There were several strengths in this body of research, including a larger number of studies and more specific correlates under investigation than we expected based on previous reviews. In addition, cohort studies using peripheral or neural correlates to predict changes in or new development of self-harm, and studies using correlates to better understand responses to treatments both lay the groundwork for prognostic and treatment biomarkers.

The original goal for our scoping review was to prepare for a systematic review and meta-analysis in the service of identifying correlates with potential to move to the next stage of biomarker development. Clinical biomarker development requires that there is accurate selection of the clinical population, a feasible and standardized process for biomarker data collection and processing, and replicability of results in the appropriate sub-populations [149, 150]. After these criteria have been satisfied, characteristics of the marker such as sensitivity, specificity, PPV, and NPV [151] must be established.

Progression to the next stage of biomarker development is not possible for any of the peripheral or neural correlates identified in our review, due to the small numbers of studies, concerns about self-harm classification, variability of findings, and methodologic weaknesses for each specific correlate. These features also mean that it is unwise to use the current research to generate pathoetiologic theories or develop new treatments.

But this body of work could serve as an excellent platform for biomarker discovery if four improvements are made in future research. The first and most important improvement pertains to the classification of self-harm. In the early to mid-2000s, there was widespread discussion of whether suicidality and NSSI lay on a continuum, i.e., with a predictable pattern of progression from NSSI to suicidal behavior or on a spectrum, i.e., co-occurring disorders that partially overlapped in characteristics and etiology, but were distinct clinical syndromes. The concept of a spectrum gained momentum, culminating in the US with the designation of NSSI and suicidal behavior disorder as separate disorders in need of further study in psychiatry’s Diagnostic and Statistical Manual (DSM)-5[152].

However, concerns remain that this approach may be difficult to use, based as it is in self-report about intention to die when engaging in self-harm. Some researchers assert this two-category conceptualization of self-harm has been inadequately validated [153], with concerns that investigations based on this schema will lead to invalid phenotyping [154–158].

To continue to acquire knowledge about correlates of self-harm in children and adolescents, despite disagreements about the phenomenology, we suggest several changes in methods used to classify child and adolescent subjects with self-harm. Studies should collect comprehensive information about all types of self-harm, even if the study aim is to focus on one type. This will optimize the chance that homogeneous samples will be created. To increase the validity of classification, instruments with good psychometric properties in children and adolescents should be used. Approaches using one or two questions from instruments measuring other constructs such as MDD or BPD are ill-advised [159]. Furthermore, as the type of instrument, e.g., self-report checklist vs. clinician-rated instruments, can produce different prevalence rates of self-harm [160], we recommend classifying self-harm in subjects based on a transparent integration of data from several types of instruments [161]. We also recommend more research on the use of cognitive tasks (instead of, or in addition to self-report) to classify self-harm in children and adolescents, especially in younger children [162].

The second set of improvements involves minimizing bias in future correlate studies. All the methodologic issues in design, sample construction, correlate and outcome measurement discussed in this review are well-described in the epidemiologic literature. However, to ensure that bias and measurement errors are maximally mitigated, we suggest that researchers use one of the risk of bias instruments in the study development phase as a planning guide [163].

Advancement in this field will be stalled unless measurement of specific correlates and outcomes is standardized across studies using validated and reliable methods. The third improvement needed for future research is that researchers working in each specific correlate area should agree on how to best measure those correlates and associated outcomes to minimize chances that low inter-study agreement is due to information bias or measurement error.

Most of the 28 specific correlates investigated in our dataset were derived from research on adults. Our fourth recommendation is to encourage future researchers to search for new potential correlates specifically for child and adolescent self-harm. One possible source of new correlates is post-mortem studies of completed suicide in the pediatric age range. No post-mortem studies met our inclusion criteria for this review. But studies modeled after the pioneering work of Pandey and colleagues on [164] need to be conducted in 3-19- year-olds who have completed suicide. Another source of possible correlates are genome-wide association studies not just of persons who have completed suicide, but of those with other features of suicidality[165]or NSSI [166]. Most of this work to date has been in adults, but studies specifically examining children and adolescents in large datasets could be very useful.

A final approach to identify new potential correlates or biomarkers is the use of machine learning, either with electronic health record (EHR) data [167] or in analyzing neural signatures in response to cognitive tasks [168]. There are many reservations in the use of these new approaches [169, 170], but as the machine learning field matures, strategies such as these may provide promising leads.

Limitations

While having several strengths, our review also has limitations. First, we only obtained papers written in English, so may have missed important studies on the topic not written in English. Second, our search used only two databases, PubMed and Embase. However, these two cover medical and biomedical research from 1947 to the present, including Medline, conference abstracts, ebooks, and citations in non-medical journals. PubMed has 25 million records, while Embase has 29 million. Therefore, we do not think this search strategy missed studies, but it is possible. Third, we did not search the grey literature, nor did we write to prominent authors looking for unpublished studies, especially those with negative findings. Publication bias thus might explain why nearly every study in our dataset reported some association between self-harm and the specific correlate under investigation. Fourth, our categorization of self-harm studies was based on how investigators described their populations of interest or samples. Our classification system was too high-level for us to report on the more nuanced features of suicidality, e.g., suicidal plans, ideation, attempts or on specific NSSI behaviors, e.g. cutting or burning. Future researchers will likely want more detail on specific behavioral manifestations, but if so, details on the studies are supplied in Supplements 3 and 4.

This comprehensive scoping review demonstrates that this corpus of research is not sufficiently mature for a meta-analysis to identify potential biomarkers individually or in panels. Many conflicting results are reported for the 28 specific correlates investigated, and interpretation of the divergent results is hampered by methods that may have produced biased findings and samples that are mainly generalizable to clinical populations and to girls, not boys. Most of the work was done in adolescents, not children younger than 11 years. Although the research is not currently robust enough to identify potential biomarkers, generate pathoetiologic theories, or suggest new treatment targets, it provides an excellent platform for the next level of work. Our suggestions to improve future research should significantly advance the field and help promote biomarker development for the diagnosis, prognosis, and treatment of the growing problem of child and adolescent self-harm.

Ethics approval and consent to participate: N/A

Consent for Publication: N/A

Availability of Data and Materials: Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Competing Interests: None of the authors has any conflict of interest to declare.

Funding: None to disclose.

Authors’ contributions: KP, LC, VSD conceptualized the study. VSD, KP developed and conducted searches. All co-authors, except WG participated in screening and data extraction. LDC, AH, AH, PC, and KP did bias ratings. VSD, KP, STB, and WG drafted several versions, KP did the descriptive analyses, WG and KP created figures and tables, all other co-authors provided editing at each stage of manuscript preparation.

Acknowledgments: We thank Huma Muhammadi for work on an earlier brief annotated search of this topic.

Authors’ Information: N/A

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No competing interests reported.

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Peripheral and Neural Correlates of Self-Harm in Children and Adolescents: A Scoping Review

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Abstract

Background

Methods

Results

Conclusions

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Background

Methods

Search strategy and information sources

Eligibility criteria

Screening process and data extraction

Assessment of risk of bias

Results

Study designs, samples, identifying self-harm, specific correlates studied bias/quality ratings

Designs

Samples

Methods to identify self-harm

Specific Correlates

Risk of bias ratings

Summary of study findings by type of self-harm

Discussion

Limitations

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1