Uncovering aggregated epigenetic index effects on intergenerational transmission of physical neglect and its psychiatric, cognitive, and bonding impact

doi:10.21203/rs.3.rs-3343636/v1

Download PDF

Article

Uncovering aggregated epigenetic index effects on intergenerational transmission of physical neglect and its psychiatric, cognitive, and bonding impact

https://doi.org/10.21203/rs.3.rs-3343636/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

Life adverse events induce DNA methylation changes, but less is known about their impact on maladaptive parenting. We use a trajectory inference method to define a personalized aggregated epigenetic maternal neglect score (EMNs) from saliva DNA methylation data. We evaluate this index as a mediator between having suffered negligence as a daughter and practicing maternal neglect to their children in a cohort of 138 mothers (n = 51 in the neglect group; n = 87 in the control non-neglect group) and with their corresponding young children. Differential methylation between the two groups was used to provide the EMNs after adjusting CpGs by the level of education, age, experimental variables, and leukocytes. Later, the explanatory role of EMNs in connecting suffered negligence in childhood with their posterior maternal neglect profile was tested through structural equation modelling (SEM). The SEM model (X² (29) = 39.590; p = 0.091; RMSEA = 0.052) confirmed the EMNs as a direct mediator between mothers’ reported childhood physical neglect and psychopathological symptoms, poor cognitive integrity, and observed poor mother-child emotional availability. A third of the genes annotated to the CpGs that affect EMNs are related to cognitive impairment, neurodegenerative and psychopathological disorders. These results establish our EMNs as a novel index to assess the contribution of DNA methylation changes to the intergenerational transmission of neglectful parental behavior and its associated effects. The evidence provided expands the possibilities for earlier and targeted interventions of the neglect condition to prevent and ameliorate the negative impact of maternal adversities on mother-child care, helping to break the cycle of maltreatment.

Biological sciences/Psychology

Biological sciences/Genetics

DNA methylation

machine-learning trajectory inference

maternal life adversity

psychopathology

cognitive impairment

mother-child emotional availability

maternal neglect

epigenetic intergenerational transmission

Maternal neglect is the most common and severe form of child maltreatment with long-term behavioral, neurobiological, and psychopathological consequences (1–4). Behavioral evidence has shown an intergenerational association between parent and child life experiences of adversity (5). Moreover, mothers more frequently exposed to childhood maltreatment and/or have a history of adverse life events have a higher probability of practicing neglectful caregiving (6). They also show psychopathological vulnerability, early childbearing of many children, and suffer intimate partner violence, and most of them have a low educational level and live in families facing substantial economic hardship (7–9). Additionally, they are more likely to have cognitive impairments indexed by poor cognitive integrity (10).

Emerging evidence indicate that exposure to childhood maltreatment and adverse life events induces DNA methylation (DNAm) changes (11, 12), revealing a biological link whereby life adversity may confer risk for variation in the quality of maternal caregiving (13) and later physical and mental health problems (1, 14, 15). Methylation changes using the PhenoAge clock in the neglect context have shown a significant increase in epigenetic age acceleration in mothers of a neglect group, even when these were chronologically younger than those of a control group on average (16). The same study shows that mothers with partners doubled the risk of epigenetic age acceleration compared to those living alone in the neglect group. In contrast, a lower level of education correlated with a higher epigenetic aging rate in the neglect group. Similarly, a cumulative score of sexual, emotional, and physical abuse and neglect maltreatment was associated with DNA methylation age acceleration using the Horvarth clock in women (17). Finally, DNAm has also been suggested to impact adult susceptibility to complex psychiatric phenotypes (18, 19).

Despite the evidence demonstrating the relevance of DNAm in these adverse contexts, the extent to which the epigenetic link mediates the relationship between adverse parental experience and consequential effects in dysfunctional parenting has yet to be defined. Therefore, compound epigenetic indexes are needed to better explore antecedent and consequential effects on dysfunctional parenting. Here, we first use saliva DNAm data and a novel machine-learning trajectories inference approach (20) to define, for the first time to our knowledge, an aggregated quantitative index of individualized epigenetic maternal neglect (EMNs). Second, we test the EMNs as an inferential index that explains the role of DNAm as a likely biological mediator between maternal life adversity (own childhood physical neglect and other types of maltreatment and adverse events) and the current maternal neglectful behavior, in support of the physical neglect transmission hypothesis. Third, we subsequently test the power of EMNs to predict negative and positive outcomes, such as psychopathological vulnerability, cognitive integrity and the quality of the mothers’ interactive bonding with their young children of high predictive value for a secure infant attachment (8, 21, 22). Lastly, enrichment analyses explore whether the genes annotated to the top regions contributing to the EMNs are clustered onto any relevant biological pathway for psychological impairments or psychopathological disorders.

Participants

One hundred thirty-eight mothers (51 neglect and 87 control mothers were recruited through the same Municipal Social Services and Primary Health Centers from Tenerife, Spain. The general inclusion criteria for both groups were being the biological mother of a child under seven years old who had not been placed in foster care at any point in their history nor been born prematurely or suffered perinatal or postnatal medical complications, according to the pediatricians’ reports. Specific inclusion criteria for the mothers in the neglect group was a substantiated case of child neglect registered in the last 12 months by Child Protective Services (CPS) according to the reports of the Social Services and complying with all the indicators of the Maltreatment Classification System (MCS) for severe neglect (23) according to the pediatrician of the Primary Health Center in charge of the case. For the mothers in the control non-neglect group the specific inclusion criteria was being biological mothers of children having negative scores in all the MCS neglect indicators and the absence of CPS or Preventive Services records for the family. The mothers in the neglect group were younger, had more children, were less likely to live in two-parent families, more likely to show a lower educational level and to receive financial assistance than those in the control group. In both groups, the mothers showed a moderate high percentage of unemployment and a lower percentage of mothers living in rural than in urban areas, whereas their children have similar mean age and sex distribution. (Table 1).

Table 1

Sociodemographic profile in Control and Neglect Groups
	Control Group (n = 87) M (SD) or %	Neglect Group (n = 51) M (SD) or %	t(136) /χ2
Mean age of mother	33.98 (6.13)	30.67 (7.36)	2.84 **
Age at child’s birth	31.05 (6.04)	28.09 (6.98)	2.61**
Number of children	1.66 (0.73)	2.49 (1.29)	−4.25 ***
Mean age of target child	3.84 (2.17)	4.06 (2.77)	-0.47
Child gender male %	58	45	1.45
Two-parent family %	72	49	6.63 **
Level of education (%)			18.76 ***
Primary school	43	80
≥Secondary school	57	20
Rural areas (%)	26	43	3.35
Unemployment (%)	59	71	1.49
Financial assistance %	24	68	24.58 ***

* p < .0.05; ** p < 0.01; *** p < 0.001. Note: M: mean score, SD: standard deviation; t: student statistic; χ2: Chi-Square statistic. Group comparisons with mean scores were performed with t statistic, while those with percentage values were performed with Chi-Square (χ2) statistic.

Psychological and behavioral measures

-The Childhood Trauma Questionnaire-Short Form (24, 25) was used to evaluate the personal history of abuse and neglect. It consists of 28 items with a 5-point Likert scale (1 = never; 5 = always) and five subtypes: physical neglect (a = 0.71), emotional abuse (a = 0.92), physical abuse (a = 0.88), sexual abuse (a = 0.94) and emotional neglect (a = 0.93) for our sample. The total score for each subscale was calculated by adding the score for each item on the corresponding subscale.
-The Stressful and Risky Events Inventory (SREI) was developed by combining relevant items from other questionnaires (ACE, (26); ISER, (27)). It comprises 16 self-reported adverse life events (e.g., divorce, economic pressure, chronic illness, eviction, unwanted pregnancy). Each item was categorically rated (no/yes occurrence) and its emotional impact with a 3-point Likert scale (0 = no occurrence; 1 = little impact; 3 = very high impact). The total emotional impact was obtained from a cumulative scoring of the emotional intensity of the events suffered.
-The Mini International Neuropsychiatric Interview (MINI; (28), Spanish version) categorically assesses (no/yes) symptoms of the 16 most common psychopathological disorders in DSM-IV and ICD-10. Scores for each disorder correspond to a cumulative scoring of symptoms and not a categorical diagnosis classification. None of the mothers in either group was under medication for psychiatric disorders at the time of testing. We calculated average scores, except for Anorexia, Bulimia and Psychosis with zero scores, for internalizing* (to experience distress inwards) and externalizing** (to experience distress outwards) dimensions (29): Major Depressive Episode*, Dysthymia*, Hypo/Manic Episode*, Generalized Anxiety Disorder*, General Panic Disorder*, Agoraphobia*, Social Phobia*, Obsessive-Compulsive Disorder*, Post-traumatic Stress Disorder*, Alcohol Dependence/Abuse**, Drug Dependence/Abuse**, Suicidality**, and Antisocial Personality**.
-The Mini-Mental State Examination (MMSE; (30) Spanish version) measures impairments in cognitive function. It evaluates in 30 questions with a cumulative score the abilities in the spatial-temporal orientation, attention span, concentration and memory, abstraction capacity (calculation), language ability, visuo-spatial perception and ability to follow basic instructions. The higher the score, the more cognitive integrity the mothers had.
- The mother-child Emotional Availability was assessed using the Infancy to Early Childhood Version Scale (31, 32). Emotional availability is defined as the mother and child's ability to read and respond appropriately to each other’s communications during a play task with a novel toy. Two external observers, blind to the mothers’ grouping, made the videos’ ratings, and the inter-rater reliability was calculated. For the mother’s behavior: sensitivity (the mother shows contingent responsiveness to child signals and demands, (Kappa score (K) = 0.94); structuring (the mother appropriately facilitates the child’s play, K = 0.90); non-intrusiveness (the mother can support the child’s play without being over directive and/or interfering, K = 0.87); non-hostility (the mother behaves with the child in a way that is not rejecting or antagonistic, K = 0.92). For the child’s behavior: responsiveness (the child’s ability and interest in exploring on his or her own and in responding to the parent’s bids, K = 0.92), and involvement (the child’s ability and willingness to engage the mother in interaction, K = 0.86). Mean scores were calculated for each mother and child dimensions.

Procedure

Social workers reported on the characteristic of the participant's family and asked mothers for permission to be contacted by phone. Those mothers who gave permission were informed about the study and the procedure upon their written acceptance. Then, the collaborator visited them at their homes, collected the mother’s responses to the questionnaires, recorded the video and collected the saliva using a Real Saliva DNA Sample Collection Kit (RBMSAL01, Real Laboratory, Valencia, Spain) as recommended. A monetary compensation of EUR 50 was given to the mothers at the end of the session.

DNAm assay and methylation analyses

DNA was extracted from the saliva samples, and its concentration was measured by spectrophotometry at the University Hospital N. S. de Candelaria (Tenerife, Spain). Library preparation and methylation sequencing were conducted at the University of Michigan Epigenomics Core in Ann Arbor (Michigan, United States) as described previously (33). Given that DNA derived from saliva shows cellular heterogeneity, the value of the epithelial cells was calculated using the estimate LC function from ewastools R-package (34, 35) with the Houseman algorithm.

Statistical analysis of EMN scores

Firstly, the epigenetic information stored in 771,785 CpGs was statistically adjusted by the covariates: level of education, mother´s age, plate position, and leukocyte concentration. The latter covariable was included after outlier imputation by the mean leukocytes by group, considering the values below or above the mean ± 3 * standard deviation as outliers. Next, we used a novel contrastive trajectories inference (cTI) method to extract enriched latent information from cross-sectional data sets and estimate the EMNs, which has been extensively validated in the context of neurodegenerative diseases (20, 36, 37). Initially, the original high dimensional data (CpGs in this context) is automatically reduced to an enriched space (about five-eight principal components) via a contrastive principal component analysis algorithm (cPCA) (38), which optimizes the exploration and visualization of the target population’s data relative to the control group. Next, in the contrasted principal components (cPC) space, each subject is assigned to a distinctive epigenetic trajectory. The subject’s position in the corresponding trajectory reflects the individual proximity to the neglect-free state (the background) and, if analyzed in the inverse direction, to the extreme neglect state. An individual EMNs is subsequently calculated, reflecting the distance to these two extremes (background or extreme maternal neglect). This analysis used the cTI approach implemented in the open-access NeuroPM toolbox (36). To reduce our data’s high dimensionality only features with a higher probability (> 95%) of being involved in a trajectory were considered.

Statistical analyses of EMNs and psychological and behavioral variables

Structural equation modelling (SEM) was used to test the maltreatment transmission hypothesis and its potential effects. We introduced three components: (a). Two positive pathways of antecedents (physical neglect and intensity of adverse events) on EMNs, to examine whether the specific version (only physical neglect) or combined with general adverse events (or even other types of maltreatment) would fit the model better. (b). One positive pathway relating EMNs with psychopathology. And (c) Two negative pathways relating EMNs to cognitive integrity and emotional availability (EA). The (a), (b) and/or (c) evidence the mediator role of EMN. Model goodness of fit was tested using the following indexes (39–41): non-significant chi-square/degrees of freedom (χ2/df ≤ 3), comparative fit index (CFI ≥ .95), Tucker-Lewis index (TLI ≥ .95), Bentler-Bonnet normed fit index, non-normed fit index, and the McDonald’s fit index (NFI/NNFI/MFI ≥ 0.90), and root mean square error of approximation (RMSA 0.05–0.08). All the analyses were performed with R and ULLRToolbox (42).

Gene enrichment analysis

Gene enrichment analysis was performed with the EnrichR tool online (43). We independently analyzed 1022 genes annotated to the first 1500 differentially methylated CpGs by order of statistical difference (by p-value) and 322 genes annotated to the EMN CpGs. The first 20 categories (by p-value) more relevant to our study from the 1022 genes were selected from DisGenET supra-category from EnrichR. A dot plot was generated with the ggplot2 R package (44). A logic heatmap showing the genes and their respective categories (both for the general list and the EMN genes) were generated with the ComplexHeatmap package in R (45, 46). A network predicting functional relationships between 13 EMN genes overlapping the list of 1022 genes was generated with the Enrichr-KG tool online (EnrichR tools were accessed during March and April 2023).

Psychological and behavioral group differences

Mothers in the neglect group had perceived higher emotional intensity in adverse events, were more likely to have experienced childhood maltreatment of all subtypes, were more likely to had internalizing and externalizing symptoms, and lower cognitive integrity than the mothers in the control group. Moreover, a poorer mother-child interaction was observed in the neglect group, with lower scores than the controls in the four dimensions of maternal behavior and child responsiveness behavior (Table 2).

Table 2

Group comparison of psychological measures
	Control Group (n = 87) M (SD)	Neglect Group (n = 51) M (SD)	t(136)	Size effectδ
Intensity adverse events	11.59 (7.70)	16.76 (8.65)	3.63***	0.64
Childhood maltreatment
Physical neglect	5.84 (1.72)	8.27 (4.01)	-4.13***	0.72
Emotional abuse	7.28 (4.03)	11.39 (6.70)	-3.98***	0.70
Physical abuse	6.15 (2.25)	8.33 (4.81)	-3.05***	0.53
Sexual abuse	5.83 (2.97)	8.98 (5.95)	-3.53***	0.62
Emotional neglect	8.89 (4.27)	10.98 (5.73)	-2.26*	0.40
Psychopathology
Internalizing symptoms	-0.24 (0.80)	0.41 (1.16	-3.87***	0.68
Externalizing symptoms	-0.20 (0.56)	0.35 (1.41)	-3.24***	0.57
Cognitive integrity	27.52 (2.27)	26.35 (2.35)	2.87**	0.51
Emotional Availability
Mother Sensitivity Mother Structuring Mother Non-intrusiveness Mother Non-hostility	6.85 (1.4)	5.71 (1.27)	4.79***	0.85
	4.17 (0.92)	3.37 (0.95)	4.87***	0.86
	4.69 (0.5)	3.92 (1.14)	4.54***	0.80
	5 (0.24)	4.85 (0.51)	2.56*	0.45
Child Responsiveness Child Involvement	5.93 (0.99)	4.89 (1.08)	5.72***	1.01
Child Responsiveness Child Involvement	5.11 (0.66)	4.79 (1.18)	1.78	0.31
* p < .05; p < .01; * p < .001. Note: M: mean score, SD: standard deviation; t: student statistic

Group differences and psychological correlates of epigenetic maternal neglect scores (EMNs)

The genetic data comprised 7718 CpG islands after pruning the residuals by comparing Control and Neglect groups. The threshold was set to p < 0.021, corresponding to the first percentile of p values. As expected, EMNs, indexing the mothers’ epigenetic load, were significantly lower in the non-neglect control group than in the neglect group: CG (M = 0.152; SD = 0.05); NG (M = 0.430; SD = 0.202): t-test (53.668) = -9.614, p < .0001, using the mother’s educational level, age, the plate position, and leukocytes as covariates (Fig. 1 left showing outliers). EMNs correlated positively with physical neglect, intense events, internalizing and externalizing symptoms, and negatively with cognitive integrity and mother sensitivity, mother structuring, mother non-intrusiveness, mother non-hostility, child responsiveness and child involvement. Only correlation with mother non-hostility was not significant (Fig. 1 right). (Supplementary Information Fig. 1 depicts the initial group contrast without showing the outliers, and Supplementary Information Fig. 2 shows the correlations among all study variables).

Testing of the structural equation model

Our model tested the relationships between the mother’s childhood physical neglect and the Intensity of adverse events on EMNs, which mediates psychopathology, cognitive integrity, and emotional availability (EA) effects. We measured physical neglect and intensity of adverse events, EMNs, and cognitive integrity score. The latent variables were psychopathology and emotional availability, measured with the internalizing and externalizing scores, and with the four variables of the mother (Sensitivity, Structuring, Non-Intrusiveness, Non-Hostility and the two of the child Responsiveness, and Involvement), respectively. However, this model did not converge in a solution given the presence of negative variances and the lack of fitness: X² (40) = 113.2; p < .0001; CFI = 0.874; TLI = 0.830; NFI = 0.822; NNFI = 0.830; MFI = 0.764; RMSEA = 0.116; 90% CI= [0.091; 0.141]. Thus, a new model was tested: (a) With one unique pathway of physical neglect to EMN; (b) Keeping the positive pathway relating EMN with psychopathology; and (c) Keeping the two negative pathways from EMN to cognitive integrity and emotional availability. The measurements also suggested eliminating the child responsiveness in the emotional availability latent variable. The resulting model in Fig. 2 showed a good fit to the data: X²(29) = 39.590; p = 0.091; CFI = 0.968; TLI = 0.950; NFI = 0.894; NNFI = 0.950; MFI = 0.962; RMSEA = 0.052; 90% CI = [0; 0.089].

The model shows that EMN mediates the relationship between having suffered physical neglect, more significant psychopathological symptoms, lower cognitive integrity, and lower emotional availability effects. Table 3 show significant unstandardized and standardized path coefficients for measurement and structural models.

Table 3

Unstandardized and standardized path coefficients for measurement and structural models
Measurement Model
		Estimate	Standard error	z value	p		Standardized estimation
Psychopathological Symptoms
	Internalizing	1					0.769
	Externalizing	0.483	0.203	2.380	0.017		0.372
Emotional Availability
	Sensitivity	1					0.509
	Structuring	0.895	0.113	7.938	0.000		0.651
	Non-Intrusiveness	0.606	0.163	3.709	0.000		0.500
	Non-Hostility	0.155	0.060	2.589	0.010		0.301
	Child Involvement	0.764	0.187	4.077	0.000		0.624
	Structural Model
		Estimate	Standard error	z value	p	Standardized estimation
Epigenetic Maternal Neglect score
	Physical Neglect	0.026	0.005	5.337	0.000		0.416
	Psychopathology	1.737	0.433	4.015	0.000		0.420
	Cognitive Integrity	-2.680	1.025	-2.615	0.009		-0.219
	Emotional Availability	-2.339	0.541	-4.323	0.000		-0.602

It was also necessary to rule out the possibility that the intensity of adverse events or other types of childhood maltreatment also suffered by the mother, different from physical neglect, could separately contribute to the adjustment of the final model. This was not the case in those subsequent models. However, no SEM results with those variables showed fitting results. (See Supplementary Information Additional SEM results).

Gene enrichment analysis

To assess the biological annotations represented in our data, we performed a gene enrichment analysis. Specifically, to identify gene-disease associations we used DisGenET database with EnrichR tool online (43, 47–49). The analysis of 1022 genes annotated to the first 1500 CpGs (ordered by statistical significance) identified 234 genes associated with cognitive impairment, neurodegenerative and psychopathological disorders. These include several categories related to cognitive functions and depression, and neurobiological diseases such as schizophrenia and autism (Fig. 3A, B). Given that 1022 genes account for about 30% of all the functionally defined genes annotated to the differentially methylated CpGs included in the study, we estimate that this is a reasonable subset of genes and thus the results may be considered representative of the data. Interestingly, the 13 genes shared between the 234 cognitive/ neurodegenerative /psychopathology-related genes and the EMN genes appear to be associated almost exclusively with cognitive categories, including performance and intelligence impairments, suggesting a predominant association between these categories and the outcome of our model (Fig. 3C). Supporting these results, we found that 71 out of 320 genes annotated to the 385 EMN CpGs are associated with cognitive impairment and neurodegenerative and psychopathological disorders in the DisGenET database (Fig. 3D); whereas a group of 34 genes are associated with a distinctive cluster of cognitive impairment categories, the rest are associate to other neurodegenerative and psychopathological disorders such as Alzheimer's Diseases, senile dementia, schizophrenia, autism, hyperactive behavior, self-harm behavior, mental depression, anxiety, and bipolar diseases. Overall, cognitive categories show a higher contribution to the EMN, measured as the median of the contribution of all the genes included within each category. In concert, the gene-disease association composition of our differentially methylated data, both in the general and the EMN lists, suggest that cognitive dysfunction plays a crucial role in the epigenetic makeup, along with other neurodegenerative disorders and psychopathologies.

This study presents first-time results uncovering the explanatory role that an aggregated epigenetic index (EMNs), based on DNAm data associated with maternal negligence, plays between the condition of having suffered negligence as a daughter and the mirroring practice toward her child. Notably, the EMNs also predicts psychopathological symptoms, less cognitive integrity, and lower emotional availability in the mother-child interaction. Our findings validate the use of differential methylation patterns in clinically and socially relevant neglect behavior, and the potential role of novel machine learning algorithms (e.g. (36)) to extract a useful and predictive epigenetic index.

Our first analyses confirmed the behavioral subclinical profile of maternal neglect (1–3). As expected, the degree of the epigenetic load was higher in the neglect group than in the control group, once controlled for chronological age, educational level, experimental variables, and cell composition of the sample. This finding converged with the DNAm pattern of epigenetic age acceleration obtained with the PhenoAge clock in a neglect group of mothers (16), and with accelerated epigenetic aging in adults from exposure to childhood maltreatment (50) or lifetime stress (51). The overall findings on the impact of psychological regressors on EMNs also confirm the relevance of our index in the profile of life adversities and negative consequences that concur in maternal neglect (7–10)

Our findings add to prior evidence showing that alterations in DNAm profiles is related to several types of life adversities in adults (12, 18). We also provide additional behavioral evidence of the intergenerational transmission of adverse childhood experiences (5), abusive and neglect behavior (6, 52) and the high heredability effects found with respect to experiencing neglect (53). However, to our knowledge, this is the first study in a human cohort where the intergenerational transmission of physical neglect behavior is demonstrated to be mediated via epigenetic load. In this context, our results support the value of EMNs as an explanatory index linking the antecedent of having suffered physical neglect to a higher epigenetic load reflecting the extreme-neglect state.

In our SEM model, EMNs was positively related to internalizing and externalizing symptoms and negatively to cognitive integrity, in line with the neglect vulnerability profile. DNAm has already shown underlying susceptibility to complex psychiatric phenotypes in adults (18, 19), and a single episode of psychosis (54). This relation of EMN to psychopathological symptoms and poor cognitive integrity also converged with longitudinal behavioral evidence showing the impact of childhood neglect on cognitive function and poor educational outcomes in adolescence, as well as mental health problems, including anxiety, depression, PTSD, and psychosis in young adulthood (22).

Finally, EMNs was negatively related to a sensitive-responsive interaction in mother-child bonding (32). It seems that the heavy epigenetic burden of adverse experiences may ultimately divert caregiving focus reducing the quality of mother-child emotional availability (EA), which is typical of maternal neglect (8, 55). EA refers to both emotional exchanges and cognitive organization of coordinated mother-child play actions towards achieving joint goals, which is related to the infant’s attachment quality with high relevance for healthy development (21, 56).

Research on the epigenetic component of infant attachment is limited by candidate-gene approaches (57). Our model based on an EWAS analysis shows that a higher epigenetic load is a biological risk factor for adequate mother-child bonding with a potential negative impact on the child. Some direct findings evidencing a mother-child epigenetic sharing of life adversity and psychopathology-related genes in differentially methylated regions also support this transmission to the offspring in neglect contexts (33). However, other ways of transmission may also be possible, like epigenetic changes in the mother that are passed on to her developing offspring through the offspring’s fetal germ cells. Another is from the mother’s exposure to risk factors during pregnancy, with both paths working simultaneously (58).

Consistent with neglect profile, enrichment analysis revealed that genes annotated to the differentially methylated CpGs in our data are associated with cognitive impairment and neurodegenerative and psychopathological disorders. Of these, cognitive-related categories appear to have a stronger contribution to the EMN index, again in line with the behavioral evidence of poor developmental and learning outcomes (22). In sum, our gene enrichment data is a useful resource to validate the findings emerging from the multivariate behavioral model and, importantly, highlight genes and pathways with potential causal, mediating or consequential relationship to neglect and the mechanisms of its intergenerational transmission.

This study has several limitations. First, the difficulty in identifying a larger number of dyads that met our strict physical neglect requirements, affected the final sample size. However, even with this sample size that is around the median of other epigenetic studies for child maltreatment conducted on separate child and adult samples (12), we have obtained significant results in a large set of variables. Second, the cross-sectional design does not allow us to assess causal relations properly among the variables. Third, given the predictive nature of the enrichment analysis and the fact that we do not conduct functional studies, we cannot establish the causality relationship between any of these genes and the phenotype of interest. Finally, additional explanatory data of the biological transmission, such as genetic variants (Single Nucleotide Polymorphism) affecting the differentially methylated CpGs, were not assessed. Future studies should consider SNP genotyping to be integrated into the analyses.

In conclusion, the individualized aggregated epigenetic score obtained in this study, higher in neglect group, has shown itspower in modeling the unique impact of suffering physical neglect when explaining its effects on psychopathological vulnerability, cognitive deficits, and poor mother-child bonding. This study has shown that using EMNs allows the detection of enriched DNAm patterns and functional genes related to cognitive impairment and neurodegenerative and psychopathological disorders in neglect motherhood. Such findings supporting some biological basis in the intergenerational transmission of physical neglect and their associated effects would broaden the possibilities for an early diagnosis of the neglect condition and targeted interventions to prevent and ameliorate the negative impact of maternal adversities on mother-child care and subsequent child physical and mental health problems.

Supplementary information is available at MP’s website.

ETHICS APPROVAL

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the protocol of the Ethical Committee of Investigation of the Canary Islands University Hospital Complex (code: CHUC_2018_63; date of approval: 14 December 2018).

FUNDING

This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under Grant RTI 2018-098149-B-100 to MR and IL.

ACKNOWLEDGMENTS

We thank the Health and Social Services staff and all the mothers and their children who participated in this study.

AUTHOR CONTRIBUTIONS

IL, MJR and YIM developed the idea for the study. SHR collected the data. DGL, JF, CM, and MLR did the analyses. IL, MJR, SHR and MLR wrote the manuscript. All authors contributed to the article and approved the submitted version.

COMPETING INTEREST

The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

AVAILABILITY OF DATA

The Data Set files analyzed during the current study are available online: Data Set file 1: Excel sheet 1 including the value of the beta in each CpG for both groups that were used to calculate the EMNs index; Excel sheet 2 including the values of the covariates that were used for the EMNs index; Excel sheet 3 including the values of the variables that were used to test the SEM models. Data Set file 2: Excel sheet 1 including the genes used in the enrichment; Excel sheets 2 and 3 including the partial and full list of annotated genes, respectively; and Excel sheets 4 and 5 presenting data of functional categories of the corresponding genes. The full disclosure of the raw sequencing data of mothers and their minors is subject to confidentiality restrictions of the Canary Islands Child Protection Services.

Jaffee SR. Child maltreatment and risk for psychopathology in childhood and adulthood. Annu Rev Clin Psychol. 2017;13:525–51. https://doi.org/10.1146/annurev-clinpsy-032816-045005.
Petersen AC, Joseph J, Feit M, on Law C, Council NR, others. Consequences of child abuse and neglect. Washington, DC: National Academies Press, 2014.
Stoltenborgh M, Bakermans-Kranenburg MJ, van IJzendoorn MH. The neglect of child neglect: a meta-analytic review of the prevalence of neglect. Soc Psychiatry Psychiatr Epidemiol. 2013;48:345–55. https://doi.org/10.1007/s00127-012-0549-y
Eicher MH, Samson JA, Anderson CM, Ohashi K. The effects of childhood maltreatment on brain structure, function and connectivity. Nat Rev Neurosci. 2016;17:652–66. https://doi.org/10.1038/nrn.2016.111
Schickedanz A, Escarce JJ, Halfon N, Sastry N, Chung PJ. Intergenerational Associations between Parents’ and Children’s Adverse Childhood Experience Scores. Children. 2021;8:747. https://doi.org/10.3390/children8090747
Gilbert R, Lacey R. Intergenerational transmission of child maltreatment. Lancet Public Health. 2021;6: e435–6. https://doi.org/10.1016/S2468-2667(21)00076-1
Bartlett JD, Raskin M, Kotake C, Nearing KD, Easterbrooks MA. An ecological analysis of infant neglect by adolescent mothers. Child Abuse Negl. 2014;38:723–34. https://doi.org/10.1016/j.chiabu.2013.11.011
Herrero-Roldán S, León I, Hernández-Cabrera JA, Rodrigo MJ. Improving Early Diagnosis of Child Neglect for a Better Response in Healthcare Settings. Children 2021;8: 859. https://doi.org/10.3390/children8100859
Mulder TM, Kuiper KC, van der Put CE, Stams GJJ, Assink M. Risk factors for child neglect: A meta-analytic review. Child Abuse Negl. 2018;77:198–210. https://doi.org/10.1016/j.chiabu.2018.01.006
Rodrigo MJ, León I, Góngora D, Herníandez-Cabrera JA, Byrne S, Bobes MA. Inferior fronto-temporo-occipital connectivity: A missing link between maltreated girls and neglectful mothers. Soc Cogn Affect Neurosci. 2016;11:1658–65. https://doi.org/10.1093/scan/nsw080
Cecil CAM, Zhang Y, Nolte T. Childhood maltreatment and DNA methylation: A systematic review. Neurosci Biobehav Rev. 2020;112:392–409. https://doi.org/10.1016/j.neubiorev.2020.02.019
Parade SH, Huffhines L, Daniels TE, Stroud LR, Nugent NR, Tyrka AR. A systematic review of childhood maltreatment and DNA methylation: candidate gene and epigenome-wide approaches. Transl Psychiatry. 2021;11:134. https://doi.org/10.1038/s41398-021-01207-y
Provenzi L, Brambilla M, Scotto Di Minico G, Montirosso R, Borgatti R. Maternal caregiving and DNA methylation in human infants and children: Systematic review. Genes Brain Behav. 2020;19: e12616. https://doi.org/10.1111/gbb.12616
Felitti VJ. Adverse Childhood Experiences and Adult Health. Acad Pediatr. 2009;9:131–2. ttps://doi.org/10.1016/j.acap.2009.03.001
Nelson CA, Bhutta ZA, Burke Harris N, Danese A, Samara M. Adversity in childhood is linked to mental and physical health throughout life. BMJ. 2020; 371;m3048. https://doi.org/10.1136/bmj.m3048
Herrero-Roldán S, Rodrigo MJ, Hernández-Cabrera JA, Mitchell C, López M, Alcoba-Florez J, et al. Reduction in Epigenetic Age Acceleration Is Related to Empathy in Mothers with Neglectful Caregiving. Brain Sci. 2021;11:1376. https://doi.org/10.3390/brainsci11111376
Lawn RB, Anderson EL, Suderman M, Simpkin AJ, Gaunt TR, Teschendorff AE, et al. Psychosocial adversity and socioeconomic position during childhood and epigenetic age: analysis of two prospective cohort studies. Hum Mol Genet. 2018;27:1301–8. https://doi.org/10.1093/hmg/ddy036
Cecil CAM, Smith RG, Walton E, Mill J, McCrory EJ, Viding E. Epigenetic signatures of childhood abuse and neglect: Implications for psychiatric vulnerability. J Psychiatr Res. 2016;83:184–94 ttps://doi.org/10.1016/j.jpsychires.2016.09.010
Walton E, Pingault JB, C.A., Gaunt, TR, Relton CL, Mill J, et al. Epigenetic profiling of ADHD symptoms trajectories: a prospective, methylome-wide study. Mol Psychiatry. 2017;22:250–256. https://doi.org/10.1038/mp.2016.85
Iturria-Medina Y, Khan AF, Adewale Q, Shirazi AH, the Alzheimer’s Disease Neuroimaging Initiative. Blood and brain gene expression trajectories mirror neuropathology and clinical deterioration in neurodegeneration. Brain. 2020;143:661–73. https://doi.org/10.1093/brain/awz400
Biro S, Alink LRA, Huffmeijer R, Bakermans-Kranenburg MJ, Van IJzendoorn MH. Attachment quality is related to the synchrony of mother and infant monitoring patterns. Attach Hum Dev. 2017;19:243–58. https://doi.org/10.1080/14616734.2017.1302487
Strathearn L, Giannotti M, Mills R, Kisely S, Najman J, Abajobir A. Long-term cognitive, psychological, and health outcomes associated with child abuse and neglect. Pediatrics. 2020;146:e20200438. https://doi.org/10.1542/peds.2020-0438.
Barnett D, Manly JT, Cicchetti D. Defining child maltreatment: The interface between policy and research. Child Abuse Child Dev Soc Policy. 1993;8:7–73.
Bernstein DP, Fink L. Childhood Trauma Questionnaire: A retrospec- tive self-report (CTQ). NCS Pearson, Inc. San Antonio, TX, 1998..
Hernandez A, Gallardo-Pujol D, Pereda N, Arntz A, Bernstein DP, Gaviria AM, et al. Initial Validation of the Spanish Childhood Trauma Questionnaire-Short Form: Factor Structure, Reliability and Association With Parenting. J Interpers Violence. 2013;28:1498–518. https://doi.org/10.1177/0886260512468240
Dube SR, Anda RF, Felitti VJ, Edwards VJ, Croft JB. Adverse childhood experiences and personal alcohol abuse as an adult. Addict Behav. 2002;27:713–25. https://doi.org/10.1016/s0306-4603(01)00204-0
Hidalgo V, Menéndez S, Sánchez J, López I, Jiménez L, Lorence B. Inventario de Situaciones Estresantes y de Riesgo. Documento no publicado, Universidade de Sevilla, Sevilla, España, 2005.
Ferrando L, Bobes J, Gibert J, Soto M, Soto O. 1.1. MINI Entrevista Neuropsiquiátrica Internacional (MINI International Neuropsychiatric Interview, MINI). Instrum Detección Orientación Diagnóstica. 2000; 1–25
Carragher N, Krueger RF, Eaton NR, Slade T. Disorders without borders: current and future directions in the meta-structure of mental disorders. Soc Psychiatry Psychiatr Epidemiol. 2015;50:339–350. https://doi.org/10.1007/s00127-014-1004-z
Blesa R, Pujol M, Aguilar M, Santacruz P, Bertran-Serra I, Hernández G, et al. Clinical validity of the ‘mini-mental state’for Spanish speaking communities. Neuropsychologia. 2001;39:1150–7. https://doi.org/10.1016/s0028-3932(01)00055-0
Biringen Z. Emotional availability: Conceptualization and research findings. Am J Orthopsychiatry. 2000;70:104–14. https://doi.org/10.1037/h0087711
Biringen Z, Derscheid D, Vliegen N, Closson L, Easterbrooks MA. Emotional availability (EA): Theoretical background, empirical research using the EA Scales, and clinical applications. Dev Rev. 2014;34:114–67. https://doi.org/10.1016/j.dr.2014.01.002
León I, Herrero Roldán S, Rodrigo MJ, López Rodríguez M, Fisher J, Mitchell C, et al. The shared mother-child epigenetic signature of neglect is related to maternal adverse events. Front Physiol 2022;13:966740. https://doi.org/10.3389/fphys.2022.966740
Heiss JA, Just AC. Identifying mislabeled and contaminated DNA methylation microarray data: an extended quality control toolset with examples from GEO. Clin Epigenetics. 2018;10:73. https://doi.org/10.1186/s13148-018-0504-1
Middleton LY, Dou J, Fisher J, Heiss JA, Nguyen VK, Just AC, et al. Saliva cell type DNA methylation reference panel for epidemiological studies in children. Epigenetics. 2022;17:161–77. https://doi.org/10.1080/15592294.2021.1890874
Iturria-Medina Y, Carbonell F, Assadi A, Adewale Q, Khan AF, Baumeister TR, et al. Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box. Commun Biol. 2021;4:614. ttps://doi.org/10.1038/s42003-021-02133-x
Iturria-Medina Y, Adewale Q, Khan AF, Ducharme S, Rosa-Neto P, O’Donnell K, et al. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity. Sci Adv. 2022;8:eabo6764. https://doi.org/10.1126/sciadv.abo6764
Abid A, Zhang MJ, Bagaria VK, Zou J. Exploring patterns enriched in a dataset with contrastive principal component analysis. Nat Commun. 2018;9:2134. https://doi.org/10.1038/s41467-018-04608-8
Brown TA. Confirmatory factor analysis for applied research. 2nd ed. The Gildford Press: New York, 2015.
Hair JR, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 7th ed. Pearson Prentice-Hall: Upper Saddle River, 2010.
Kline RB. Convergence of Structural Equation Modeling and Multilevel Modeling. In: The SAGE Handbook of Innovation in Social Research Methods. 2011. p. 562–89.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2020.
Xie Z, Bailey A, Kuleshov MV, Clarke DJB, Evangelista JE, Jenkins SL, et al. Gene Set Knowledge Discovery with Enrichr. Curr Protoc. 2021;1:e90. https://doi.org/10.1002/cpz1.90
Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag: New York, 2016.
Gu Z. Complex heatmap visualization. iMeta. 2022;1:1–15. https://doi.org/10.1002/imt2.43
Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847–9. https://doi.org/10.1093/bioinformatics/btw313
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128–141. https://doi.org/10.1186/1471-2105-14-128
Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44:W90–7. https://doi.org/10.1093/nar/gkw377
Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2019;48: D845–D855. https://doi.org/10.1093/nar/gkz1021
Wolf EJ, Maniates H, Nugent N, Maihofer AX, Armstrong D, Ratanatharathorn A, et al. Traumatic stress and accelerated DNA methylation age: A meta-analysis. Psychoneuroendocrinology. 2018;92:123–34.
Zannas AS, Arloth J, Carrillo-Roa T, Iurato S, Röh S, Ressler KJ, et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 2015;16:266–77. https://doi.org/10.1186/s13059-015-0828-5
Greene D, Boyes M, Hasking P. The associations between alexithymia and both non-suicidal self-injury and risky drinking: A systematic review and meta-analysis. J Affect Disord. 2020;260:140–66. ttps://doi.org/10.1016/j.jad.2019.08.088
Pittner K, Bakermans-Kranenburg MJ, Alink LRA, Buisman RSM, Van Den Berg LJM, Block LHCGCC de, et al. Estimating the Heritability of Experiencing Child Maltreatment in an Extended Family Design. Child Maltreat. 2020;25:289–99. https://doi.org/10.1177/1077559519888587
Alameda L, Liu Z, Sham P, Aas M, Trotta G, Rodriguez V, et al. Exploring the mediation of DNA methylation across the epigenome between childhood adversity and First Episode of Psychosis—findings from the EU-GEI study. Mol Psychiatry. 2023;1–12. https://doi.org/10.1038/s41380-023-02044-9
Rodrigo MJ, León I, García-Pentón L, Hernández-Cabrera JA, Quiñones I. Neglectful maternal caregiving involves altered brain volume in empathy-related areas. Dev Psychopathol. 2019;34: 1534–1543. https://doi.org/10.1017/s0954579419001469
Altenhofen S, Clyman R, Little C, Baker M, Biringen Z. Attachment security in three-year-olds who entered substitute care in infancy. Infant Ment Health J. 2013;34:435–45. https://doi.org/10.1002/imhj.21401
Darling Rasmussen P, Storebø OJ. Attachment and epigenetics: a scoping review of recent research and current knowledge. Psychol Rep. 2021;124:479–501. https://doi.org/10.1177/0033294120901846
Breton CV, Landon R, Kahn LG, Enlow MB, Peterson AK, Bastain T, et al. Exploring the evidence for epigenetic regulation of environmental influences on child health across generations. Commun Biol. 2021;4:769. https://doi.org/10.1038/s42003-021-02316-6

The authors have declared there is NO conflict of interest to disclose

DataSetfile1.xlsx
Data Set file_1
DataSetfile2.xlsx
Data Set file_2.
SupplementaryInformation.docx
Supplementary Information

Download PDF

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

Uncovering aggregated epigenetic index effects on intergenerational transmission of physical neglect and its psychiatric, cognitive, and bonding impact

Status:

Version 1

Abstract

Figures

INTRODUCTION

METHODS

Participants

Procedure

DNAm assay and methylation analyses

Statistical analysis of EMN scores

Statistical analyses of EMNs and psychological and behavioral variables

Gene enrichment analysis

RESULTS

Psychological and behavioral group differences

Group differences and psychological correlates of epigenetic maternal neglect scores (EMNs)

Testing of the structural equation model

Gene enrichment analysis

DISCUSSION

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1