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The association between prenatal famine, DNA methylation and mental disorders: a systematic review and meta-analysis

Abstract

Background

Undernutrition in pregnant women is an unfavorable environmental condition that can affect the intrauterine development via epigenetic mechanisms and thus have long-lasting detrimental consequences for the mental health of the offspring later in life. One epigenetic mechanism that has been associated with mental disorders and undernutrition is alterations in DNA methylation. The effect of prenatal undernutrition on the mental health of adult offspring can be analyzed through quasi-experimental studies such as famine studies. The present systematic review and meta-analysis aims to analyze the association between prenatal famine exposure, DNA methylation, and mental disorders in adult offspring. We further investigate whether altered DNA methylation as a result of prenatal famine exposure is prospectively linked to mental disorders.

Methods

We conducted a systematic search of the databases PubMed and PsycINFO to identify relevant records up to September 2022 on offspring whose mothers experienced famine directly before and/or during pregnancy, examining the impact of prenatal famine exposure on the offspring’s DNA methylation and/or mental disorders or symptoms.

Results

The systematic review showed that adults who were prenatally exposed to famine had an increased risk of schizophrenia and depression. Several studies reported an association between prenatal famine exposure and hyper- or hypomethylation of specific genes. The largest number of studies reported differences in DNA methylation of the IGF2 gene. Altered DNA methylation of the DUSP22 gene mediated the association between prenatal famine exposure and schizophrenia in adult offspring. Meta-analysis confirmed the increased risk of schizophrenia following prenatal famine exposure. For DNA methylation, meta-analysis was not suitable due to different microarrays/data processing approaches and/or unavailable data.

Conclusion

Prenatal famine exposure is associated with an increased risk of mental disorders and DNA methylation changes. The findings suggest that changes in DNA methylation of genes involved in neuronal, neuroendocrine, and immune processes may be a mechanism that promotes the development of mental disorders such as schizophrenia and depression in adult offspring. Such findings are crucial given that undernutrition has risen worldwide, increasing the risk of famine and thus also of negative effects on mental health.

Background

Unfavorable environmental conditions during pregnancy have been shown to promote the onset of mental disorders in the offspring [1,2,3] via epigenetic mechanisms [4,5,6]. One epigenetic mechanism that can be changed by adverse intrauterine exposure and influences the development of offspring health is deoxyribonucleic acid (DNA) methylation [5, 7,8,9,10]. DNA methylation is the addition of methyl groups to cytosine-guanine dinucleotides (CpG), with the potential to regulate gene expression [11,12,13,14,15]. For instance, Palma-Gudiel et al. [16] reported increased methylation of the glucocorticoid receptor gene (NR3C1), a gene involved in the regulation of the hypothalamic–pituitary–adrenal (HPA) axis in the offspring, following exposure to prenatal stress. Increased NR3C1 methylation has, in turn, been associated with mental disorders [17,18,19] such as depression [20].

Undernutrition in pregnant women is an unfavorable environmental condition that can affect the intrauterine development and may thus have long-lasting detrimental consequences for the mental health of the offspring later in life [21]. The effect of prenatal undernutrition on mental health can be analyzed through natural experiments (quasi-experimental studies), in which undernutrition (e.g. famine) occurs naturally in a specific population [22, 23]. Meta-analytic results have already demonstrated an increased risk of suffering from psychotic, affective, and personality disorders in adults who were exposed to famine during prenatal development [24].

One important mechanism to explain how unfavorable maternal food consumption leads to an increased susceptibility to mental disorders in the offspring in adulthood may be altered DNA methylation patterns [25,26,27]. Rijlaarsdam et al. [28] reported that an unhealthy high-fat and high-sugar prenatal diet was positively associated with changes in the insulin-like growth factor gene (IGF2) in the offspring, which was in turn related to increased attention deficit hyperactivity disorder (ADHD) symptoms in adolescence [28]. Moreover, hypomethylation of this IGF2 gene has been found in adult offspring who were prenatally exposed to famine [29]. Less is known, however, about whether altered DNA methylation mediates the effects of prenatal famine exposure on mental disorders in the offspring.

In summary, undernutrition during pregnancy appears to increase the susceptibility to mental disorders in the offspring. However, the aforementioned meta-analysis did not include a quality assessment [24]. To date, therefore, no quality assessment has been conducted on the myriad of published studies examining the effects of prenatal famine exposure on offspring mental health. Moreover, it remains to be elucidated whether changes in DNA methylation are the mechanism linking prenatal famine exposure to the development of mental disorders in adult offspring. The purpose of this study is thus to provide the first systematic review of the existing literature on the impact of prenatal famine exposure on offspring mental health and altered DNA methylation, and to integrate the findings by means of a meta-analysis.

Methods

Search strategy

We conducted a literature search of the databases PubMed and PsycINFO to identify relevant records up to September 2022. The search strategies included the words (a) “famine” and related terms, (b) “pregnancy” and related terms, (c) “DNA methylation” and related terms, or (d) “mental disorders” and related terms. The search followed a systematic approach in accordance with the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) guidelines [30]. This systematic review and meta-analysis was registered on the Open Science Framework (OSF): osf.io/3hn5p.

Screening and selection procedure

First, duplicates of the identified records were removed. Titles and abstracts were screened, and records that did not meet the eligibility criteria, such as non-human studies and non-empirical research, were excluded. The articles yielded by the literature search were screened and selected using the following inclusion criteria: (1) offspring whose mothers experienced famine during pregnancy and including either (2) a measure of DNA methylation or (3) a measure of psychopathology. A full-text reading of all remaining articles was performed. Studies were included in the meta-analyses if they (1) used the same questionnaire to measure symptoms of psychopathology, (2) included a categorical outcome (mental disorders) irrespective of which clinical interview was used to establish the diagnosis, and (3) provided adequate data for statistical analysis.

Data extraction

Included articles were examined for information about the first author, year of publication, cohort, sample description, assessment of symptoms of psychopathology, and main results. Articles on DNA methylation were examined for information about chromosome number and location, gene, number of CpGs, method for DNA methylation analysis, and main results. Data extraction was performed by one of the authors (HE) and a research assistant. Risk of bias was assessed using a modified version of the Newcastle–Ottawa scale [31, 32], containing the following seven items: sampling representativeness, sample size, exposure definition, famine severity assessment, confounding adjustment, outcome assessment, and statistical methods. Each item was scored as either good, fair, or poor [31]. The items outcome assessment and sample size were modified for studies on mental disorders, epigenome-wide DNA methylation analyses, and targeted candidate gene analyses (see Additional file 1: Tables S1–S3). Risk of bias assessment was performed by one of the authors (HE) and a senior researcher from our workgroup.

Data analysis

To assess the association between prenatal famine exposure and symptoms of psychopathology or mental disorders in adulthood, we calculated the effect size across studies as the overall pooled log10 odds ratio (logOR) of the number of individuals with and without symptoms or a mental disorder in the prenatal famine group and in the control group. The logOR was used for the depression and schizophrenia studies. The control group consisted of offspring who were exposed to famine during childhood (non-prenatal famine exposure) and/or offspring who were not exposed to famine at all (non-exposure). For two studies that used the Hospital Anxiety and Depression Scale (HADS), we used means and standard deviations to calculate Hedges’ g. One of these studies did not report the specific standard deviations for each of the two subscales of the HADS (anxiety and depression) and instead only provided overall standard deviations, which were therefore used as a reference. Results were considered statistically significant if the p value was < 0.05. Meta-analyses were conducted if at least two studies used the same outcome measurement. Studies with insufficient data were only included in the systematic review, and not in the meta-analyses. Random-effects meta-analyses were conducted using the meta-analysis function integrated in SPSS version 28.0.1.1, which also allowed us to create forest plots. The Q and I2 statistics were calculated to assess the heterogeneity of the included studies. Subgroup analyses were performed to detect whether a more homogenous effect size could be calculated. Following the Cochrane Handbook for Systematic Reviews of Interventions [33], when 10 or more studies were included in our meta-analyses, we used the trim-and-fill procedure and visual inspection of funnel plots to detect publication bias [34].

Results

Search results

The literature search yielded 2697 articles, of which 239 were duplicates and removed. Of the remaining 2458 articles, a further 2382 were excluded due to publication in a language other than English, non-empirical research, or irrelevant title/abstract. Of the final 76 articles assessed for eligibility, 39 were excluded for as they did not assess the outcome, only examined exposure to nutrient deficiency, were exclusively polymorphism analyses, or assessed different exposure periods. Thus, in total, 37 studies were eligible for data extraction and were included in this systematic review. Of these studies, 22 reported effects of prenatal famine exposure on symptoms of psychopathology or mental disorders, and 14 studies reported effects of famine during pregnancy on DNA methylation. The remaining study analyzed the mediating effect of DNA methylation on mental disorders in adults prenatally exposed to famine. Eleven of the 37 studies reported sufficient data to be included in meta-analyses. The study selection is summarized in Fig. 1.

Fig. 1
figure 1

Screening and selection process of studies displayed by a PRISMA flowchart

Study characteristics

Characteristics of the included studies are shown in Tables 1, 2, 3 and 4. Articles were published between 1992 and 2022. All participants were adults. The sample size ranged from 13 to 494,684. All studies focused either on the Dutch Famine (1944–1945) or the Chinese Famine (1959–1961), with one exception, the Bangladesh Famine (1974–1975). Individuals without prenatal famine exposure were either born after the famine (non-exposure: had not experienced famine in their life) or before the famine (non-prenatal exposure: experienced famine during infancy, childhood, adolescence, or adulthood). Most DNA methylation studies (67%) used either sibling or time controls. Sibling controls were siblings of prenatally exposed adults and were mostly younger than their exposed siblings. Time controls were adults who were born either before or after the famine. As the respective authors did not specify how many control adults were in each group, it was not possible to assign them to the non-prenatal exposure or non-exposure group. Periconceptional exposure referred to exposure to famine during conception and the 1st trimester.

Table 1 Effects of prenatal exposure to famine on mental disorders/symptoms in offspring
Table 2 Effects of prenatal exposure to famine on (epi)genome-wide DNA methylation of the offspring
Table 3 Effects of prenatal exposure to famine on targeted DNA methylation of the offspring
Table 4 Effects of prenatal exposure to famine on genome-wide DNA methylation and mental disorders

Risk of bias assessment

The risk of bias assessment is presented in Additional file 2: Table S4. Quality ratings ranged from poor to good, with only two studies rated good on all study items [35, 36].

Of the studies examining symptoms of psychopathology and mental disorders, most scored highest on the statistical methods item. Most studies (86%) used proper statistical analyses and conducted sensitivity analyses. The sample size item was generally rated as good for the mental disorders or symptoms studies (77%). Of the 22 studies, 14 studies (64%) defined famine exposure both quantitatively and qualitatively. Half of the studies (50%) used a good outcome assessment by a psychiatrist or clinical psychologist according to International Classification of Diseases (ICD) or Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria. Only 36% of the studies adjusted for confounders and explained why they did so. 32% of the studies had good sampling representativeness. Sampling representativeness was rated as fair if the sample was drawn from only one hospital registry or survey. The lowest ratings were achieved for the item famine severity assessment, with 55% of the studies failing to include excess death rates (EDR), cohort size shrinkage index (CSSI) or global hunger index (GHI) to measure the severity of famine (for more information, see [37]).

Of the DNA methylation studies, most (73%) used proper statistical analyses and conducted sensitivity analyses. Adjustment for confounding factors was good in 53% of these studies. Only 27% defined famine exposure both quantitatively and qualitatively, and only 27% used a good description of the DNA methylation assay. A small proportion of the studies (13%) had good sampling representativeness and sample size. None of the DNA methylation studies were rated as showing a good famine severity assessment (0%).

Effects of prenatal famine exposure on offspring symptoms/mental disorders

Twenty-two studies investigated the effect of prenatal famine exposure on offspring symptoms of psychopathology and/or mental disorders.

As shown in Table 1, one study found higher psychopathology, as measured with the Mental Health Inventory (MHI-5) in individuals who experienced famine during prenatal development compared to individuals who did not [38]. Five studies reported increased depressive symptoms [39,40,41,42,43] in individuals with prenatal famine exposure compared to individuals with non-prenatal exposure and/or non-exposure. One study reported an association between prenatal exposure to famine and increased anxiety and depressive symptoms, as measured with the HADS [44]. In contrast, another study found no significant association between prenatal famine exposure and anxiety and depressive symptoms (HADS) as compared to non-prenatal exposure and non-exposure [45].

With regard to mental disorders, one study found a generally increased risk of mental disorders [46] after prenatal exposure compared to non-exposure. Six studies consistently reported an increased risk of schizophrenia after prenatal exposure compared to non-prenatal and/or non-exposure to famine [35, 36, 47,48,49,50]. In contrast, one study found a higher risk of developing schizophrenia in adults with non-exposure to famine than in adults with prenatal exposure [51]. An increased risk of major affective disorders was found to be linked to in utero exposure to famine as compared to non-exposure in two studies [52, 53]. One study reported an increased risk of antisocial personality disorder [54] and another an increased risk of schizoid personality disorder [55] in men after prenatal exposure compared to non-exposure to famine. Addictive disorders [56] and addictive behaviors [57] in adults were related to prenatal famine exposure but not to non-prenatal famine exposure.

In terms of depressive symptoms, two studies [39, 42] provided sufficient data for meta-analysis based on OR, with results varying by exposure period. On the one hand, adults prenatally exposed to famine showed a decreased risk of depressive symptoms compared to adults with no exposure to famine and adults who were exposed to famine after gestation (logOR = 0.96, 95% CI [0.79, 1.14]; Z = 10.75, p < 0.001; Q = 8.56, I2 = 88%). On the other hand, adults prenatally exposed to famine showed an increased risk of depressive symptoms compared to adults with no exposure to famine (logOR = 1.14, 95% CI [0.94, 1.34]; Z = 11.31, p < 0.001; Q = 6.87, I2 = 86%). In terms of anxiety and depressive symptoms as measured by the HADS, meta-analysis confirmed the null-findings (HADS-A: g = 0.08, 95% CI [− 0.05, 0.21]; Z = 1.17, p = 0.241; Q = 0, I2 = 0%; HADS-D: g = 0.06, 95% CI [− 0.08, 0.19]; Z = 0.84, p = 0.403; Q = 0.23, I2 = 0%). Meta-analysis confirmed the increased risk of suffering from schizophrenia in adulthood after prenatal famine exposure compared to non-prenatal exposure and non-exposure together (logOR = 1.13, 95% CI [0.97, 1.29]; Z = 13.97, p < 0.001). Heterogeneity was high (Q = 9.02, I2 = 89%), see Fig. 2. The results remained unchanged when subgroup analyses were conducted for the Dutch and the Chinese famine (two Dutch famine studies: logOR = 1.21, 95% CI [0.85, 1.57]; Z = 6.57, p < 0.001; Q = 1.13, I2 = 11% and five Chinese famine studies: logOR = 1.12, 95% CI [0.92, 1.33]; Z = 10.74, p < 0.001; Q = 18.25, I2 = 95%). Insufficient data were available for meta-analyses on major affective disorders, antisocial and schizoid personality disorder, as well as addictive disorders.

Fig. 2
figure 2

Forest plot of studies comparing adults prenatally exposed to famine with adults non-prenatally and non-exposed to famine regarding risk of developing schizophrenia. Conducting subgroup analyses for the Dutch and the Chinese famine did not alter the results

Effects of prenatal famine exposure on offspring DNA methylation (epigenome-wide analysis)

Nine studies, which are listed in Table 2, investigated DNA methylation by conducting (epi)genome-wide analysis in adults prenatally exposed to famine [58,59,60,61,62,63,64,65,66]. All of these used whole blood as tissue.

Four studies determined DNA methylation using the HumanMethylation450 BeadChip microarray, which has a coverage of over 450,000 sites [67, 68]. The first of these four studies did not find significantly differentially methylated regions (DMRs) in adult offspring following prenatal famine exposure as compared to non-prenatal exposure and non-exposure [59]. The second study identified that prenatal exposure to famine during early gestation was significantly associated with 613 DMRs as compared to non-exposure [58]. The authors specifically reported hypomethylated regions in four genes, namely CCDC51, TMA7, ENO2 and ZNF226 [58]. The third study found a variety of hyper- (FAM150B/TMEM18, PPAP2C, SLC38A2) and hypomethylated (OSBPL5/MRGPRG) genes in adult offspring exposed to famine during early gestation as compared to time and sibling controls. In addition, exposure during conception was associated with decreased methylation of TMEM105/SLC38A10, and exposure during any week of gestation was associated with increased methylation of the genes TACC1 and ZNF385A compared to time and sibling controls [60].

Lastly, an association was found between prenatal famine exposure and hypo-methylation of the genes CRELD2, LRRC8D, LOC100132354, OSBPL5/MRGPRG, TXNIP, PFKFB3 as well as hypermethylation of the genes ABCG1, CCDC155, FAM150B, METTL8, PNPO, PPAP2C, SLC38A2, SYNGR1, TACC1 and ZNF385A compared to controls [64].

Two studies used methylation analyses, which cover over 850,000 sites [69]. One study reported evidence of 601 hypermethylated and 360 hypomethylated sites after prenatal famine exposure as compared to time controls [63]. The other study reported no significant differentially methylated sites after controlling for multiple testing [65].

The two studies measuring global DNA methylation via pyrosequencing did not find a link between prenatal famine exposure and altered methylation patterns as compared to sibling controls and time controls [62, 66]. One of these studies also analyzed global DNA methylation via MethyLight and LUminometric Methylation Assay (LUMA), yielding no significant findings [62].

One study used reduced representation bisulfite sequencing (RRBS) to assess DMRs and found hypermethylation in 60.8% out of 181 identified sites and hypomethylation in 39.2% following periconceptional exposure to famine compared to sibling controls [61]. In the present analysis, we solely reported on genes for which there was a significant association between DNA methylation and prenatal famine exposure. Using the data published in the included papers, we verified whether genes that were significant in some studies were also significant in others, and mostly found no concordance. For instance, only six genes identified by Tobi et al. [60] were replicated in another study by Tobi et al. [64], even though methylation analysis was performed on the same sample. Meta-analysis was not suitable due to different DNA methylation microarrays/data processing approaches and partially unavailable data.

Effects of prenatal famine exposure on offspring DNA methylation (candidate gene analysis)

As can be seen in Table 3, candidate gene DNA methylation analyses revealed significant associations between prenatal famine exposure and a variety of hyper- and hypomethylated genes as compared to the different control groups.

Compared to sibling controls, periconceptional famine exposure was associated with hypomethylation of KLF13 [61], IGF2 [29, 66], and INSIGF [66, 70]. Besides periconceptional exposure, prenatal exposure during late gestation was associated with hypomethylation of the GNASAS gene [70]. Compared to sibling and time controls, prenatal exposure to famine was related to hypermethylation in several genes (CDH23, CPT1A, INSR, SMAD7 [61]; ABCA1, IL-10, LEP, GNASAS and MEG [70]). Compared to time controls only, prenatal famine exposure was related to hypomethylation of the AGTR1 and PRKCA genes [63] and hypermethylation of the IGF2 and INSR genes [72].

As compared to non-prenatal exposure and non-exposure, adults prenatally exposed to famine showed decreased methylation of the ZFP57 and PRDM9 genes and increased methylation of the PAX8 gene [59]. Moreover, prenatal exposure to famine was related to hypomethylation of VTRNA2-1 and EXD3 compared to non-prenatal exposure only [59]. One study reported no association of GR 1-C, LPL, PI3kinase, and PPARy with in utero exposure to famine compared to non-prenatal exposure and non-exposure [73].

In sum, the candidate genes most affected by prenatal famine exposure are IGF2 and INSR. In addition, prenatal famine exposure was not associated with several other candidate genes, which are reported in Table 3 [59, 61, 70, 73, 74].

Although a few significant candidate genes were replicated in other studies, it is possible that methylation analyses were performed on the same sample. Candidate-gene studies were not eligible for meta-analysis due to the heterogeneity of affected genes and partially unavailable data.

DNA methylation as a mediator between famine exposure during pregnancy and mental disorders

Table 4 presents a more recent study by Boks et al. [75], who analyzed changes in DNA methylation in individuals exposed to famine during the first 3 months of prenatal development and their susceptibility to schizophrenia in adulthood. The authors reported that prenatally exposed adults with schizophrenia showed hypermethylation of the DUSP22 gene compared to non-exposed patients and healthy controls [75].

Discussion

In the present systematic review and meta-analysis, we investigated the association between prenatal famine exposure, DNA methylation and mental disorders in adult offspring. We report three main findings: First, meta-analysis confirmed that exposure to famine during prenatal development increases the offspring’s risk of suffering from schizophrenia. With regard to depression, meta-analyses yielded contradictory findings, showing either increased or decreased risk of depressive symptoms depending on exposure periods. Anxiety and depressive symptoms, as measured with the HADS, were not associated with prenatal famine exposure. Prenatal famine exposure was further associated with addictive disorders and behaviors as well as antisocial and schizoid personality disorder. Second, we found that prenatal famine exposure is associated with hypo- and hypermethylation of a variety of genes. The largest number of studies reported differences in DNA methylation of the IGF2 gene. Third, only one mediation study has been conducted to date, which described altered DNA methylation of the DUSP22 gene as a potential mechanism underlying the association between prenatal famine exposure and schizophrenia in adult offspring.

With regard to the first finding, additional studies confirm the increased risk for the development of schizophrenia in offspring prenatally exposed to a (natural) disaster such as an earthquake [76, 77], a terrorist attack [78], infections, and lead exposure [79]. There are several potential reasons for this effect of unfavorable environmental circumstances on an increased susceptibility to schizophrenia. According to the neurodevelopmental hypothesis proposed by Weinberger [80] and Murray and Lewis [81], such conditions impair the neurodevelopment of the fetus by adversely altering gene expression [81,82,83,84,85,86,87]. In particular, shortly after fertilization, a complete demethylation of the genome occurs, which is then re-established during embryogenesis [88]. Adverse environmental circumstances during this periconceptional period can thus permanently alter the DNA methylation of genes involved in neural pathways, impair brain development, and predispose the offspring to an increased risk of schizophrenia [84]. Moreover, researchers have found that schizophrenia shares common features with other mental disorders such as schizoaffective disorders and depression [89, 90], suggesting that the same epigenetic mechanisms are involved in its pathogenesis. However, the inconclusive findings of the meta-analyses on depressive symptoms may also be explained by the fact that environmental conditions influence DNA methylation at other life stages, in addition to early prenatal development [91]. Indeed, offspring exposed to famine in infancy or childhood exhibit more depressive symptoms than offspring exposed to famine prenatally. Nevertheless, prenatal exposure to famine increases the risk of depressive symptoms in adult offspring compared to offspring who have never been exposed to famine. Furthermore, the inconclusive findings regarding depressive symptoms and the null findings regarding anxiety may be attributable to the fact that only two studies could be included in the meta-analyses due to the heterogeneity of the examined exposure periods and different methods of statistical analysis.

With respect to the finding that IGF2 appears to be the gene that is most affected by prenatal famine exposure, the studies in this review revealed both hyper- and hypomethylation of the IGF2 gene in offspring. The reason for this finding of both increased and decreased methylation, despite the fact that all offspring were prenatally exposed to famine, might lie in a dose–response relationship in terms of duration and severity of prenatal famine exposure and IGF2 DNA methylation. Specifically, the Chinese famine was more severe and lasted for longer (3 years) compared to the Dutch famine, which was less severe and lasted for only 6 months [92]. More severe and longer exposure may have led to increased DNA methylation [72], whereas shorter and less severe exposure may have resulted mainly in decreased methylation of the IGF2 gene [29, 66]. This assumption is in line with the study by Shen et al. [92], who reported increased methylation of the IGF2 gene in offspring exposed to severe famine compared to offspring exposed to moderate famine. Moreover, different genomic positions annotated to the IGF2 gene were examined [29, 66], which could be another reason for differences in the direction of DNA methylation.

As for the third finding, there is evidence that DUSP family genes are involved in neural functions and play a role in the pathophysiology of mental disorders such as depression, bipolar disorder, and schizophrenia [93]. This supports the involvement of the DUSP22 gene in the etiology of schizophrenia in adults prenatally exposed to famine [75]. In addition, we suggest that altered DNA methylation of the aforementioned IGF2 gene may contribute to an increased risk of mental disorders, as this gene is also involved in neuronal functions. Specifically, it is an important contributor to fetal growth and development of the central nervous system [94,95,96], with increased methylation of the IGF2 gene in the placenta, for example, showing an association with higher birth weight [94]. However, another study found that increased methylation of this gene (in maternal blood) was associated with lower birth weight [97], and others found no significant association [98]. In terms of the central nervous system, dysregulations of this gene are associated with various mental disorders such as depression and schizophrenia [99].

The phenotype of adults prenatally exposed to famine may additionally be caused by altered DNA methylation of candidate genes in the neuroendocrine and immune systems [17, 100, 101]. Specifically, the LEP gene affects the HPA axis activity by inhibiting the release of corticotropin-releasing hormone (CRH), thereby suppressing its activity and reducing glucocorticoid production [102,103,104]. Hypermethylation of the LEP gene can lead to decreased gene expression [105] and possibly inhibits its role in suppressing HPA axis activity. In addition, hypermethylation of this gene has been associated with schizophrenia [106], and hyperactivity of the HPA axis is an underlying biological mechanism of depression [107, 108]. The findings of our review demonstrate that prenatal famine exposure is associated with hypermethylation of the LEP gene in adult offspring [70]. Furthermore, the function of the neuroendocrine system is closely linked to the function of the immune system, and the HPA axis acts as a mediator between the two systems [109,110,111,112]. The IL-10 gene, an anti-inflammatory cytokine of the immune system, influences the HPA axis activity [112,113,114] by increasing the production of CRH and adrenocorticotropic hormone (ACTH) in the pituitary [109, 110]. Differences in its gene expression have been found in adults suffering from a major affective disorder or schizophrenia [115,116,117]. Evidence indicates that prenatal exposure to famine is related to increased methylation of the IL-10 gene in adult offspring [70].

The present review is the first to systematically and quantitatively present the effects of prenatal famine exposure on both mental disorders or symptoms of psychopathology and DNA methylation. Its strengths include the comprehensive literature search and rigorous quality assessment (risk of bias). However, the results of the meta-analyses, particularly the omission of a meta-analysis for the whole-genome DNA methylation results, should be interpreted with caution because the authors did not to obtain all affected genes from all whole-genome DNA methylation analysis studies. In addition, we are unable to rule out publication bias due to the very small number of studies suitable for meta-analyses. All methylation studies presented in this review used whole blood as a tissue. One might consider whether DNA methylation in peripheral specimens serves as a marker for DNA methylation in brain tissue as there is evidence that epigenetic differences in peripheral specimens do not always correlate with differences in brain tissue [118, 119]. For example, Walton et al. [120] found that only 7.9% of CpGs were broadly correlated between blood and living brain tissue from the same individuals. However, they were able to identify CpG markers from blood tissue that significantly correlated with brain tissue and were involved in biological pathways affected in individuals with schizophrenia [120]. As a further limitation, the heterogeneity of genes affected by prenatal famine exposure might result from the lack of power of small sample sizes and different DNA methylation techniques across the included studies. However, it is noteworthy that most of the associations found were statistically significant at the p < 0.001 level (Tables 2, 3 and 4), even after Bonferroni correction [65, 70, 72, 74] and Benjamin-Hochberg adjustment [60, 66] for multiple testing. Candidate gene analyses have the distinct advantage of enabling a more thorough investigation of specific regions of interest by assessing the overall methylation of a target region and allowing researchers to identify specific CpG sites involved in disease pathogenesis [121]. Epigenome-wide DNA methylation analyses enable the analysis of the entire genome, as generally speaking, more than one gene is involved in the pathogenesis of diseases [122], but cover only small numbers of CpG sites per gene [123, 124]. Moreover, as the examined famine cohorts were geographically diverse, the different methylated genes may be attributable to ethnicity. For instance, Elliott et al. [125] found large differences in DNA methylation between European and South Asian individuals due to ethnically different cell composition. Additionally, the cause of the famines also differed, with the Dutch famine being the result of a food embargo during World War II [23] and the Chinese famine being due to political and economic mismanagement combined with drought [126]. This may further have exposed the two cohorts to distinct psychosocial stressors, which might have influenced their DNA methylation differently.

Conclusion

Prenatal famine exposure has been associated with altered DNA methylation of genes involved in neuronal, neuroendocrine, and immune processes, which may causally promote the development of mental disorders, specifically schizophrenia and depression in adult offspring. Further genome-wide and hypothesis-driven candidate gene mediation analyses, preferably with a longitudinal design and large sample sizes, are warranted to obtain a complete picture of the role of DNA methylation in the association between prenatal exposure to famine and the development of mental disorders. A better understanding may improve the diagnosis and treatment of schizophrenia and depression, as DNA methylation can be reversed by pharmacological drugs [127,128,129], and may inform the development of nutritional intervention programs for pregnant women affected by famine.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ABCA1:

ATP-binding cassette subfamily A member 1

ABCG1:

ATP binding cassette subfamily G member 1

ACTH:

Adrenocorticotropic hormone

ADHD:

Attention deficit hyperactivity disorder

AGTR1:

Angiotensin II receptor type 1

AKAP12:

A-kinase anchoring protein 12

APOC1:

Apolipoprotein C1

ASPD:

Antisocial personality disorder

ATP5B:

ATP synthase subunit beta

BOLA:

Bola family member

CCDC51:

Coiled-coil domain containing 51

CCMD:

Chinese classification of mental disorders

CDH23:

Cadherin-related 23

CES-D:

Center for epidemiologic studies depression scale

CpG:

Cytosine guanine dinucleotides

CPT1A:

Carnitine palmitoyltransferase 1A

CRELD2:

Cysteine rich with EGF-like domains 2

CRH:

Corticotropin-releasing hormone

CSSI:

Cohort size shrinkage index

CTCF:

CCCTC-binding factor

DMR:

Differentially methylated region

DNA:

Deoxyribonucleic acid

DUSP22:

Dual specificity phosphatase 22

DSM:

Diagnostic and statistical manual of mental disorders

EDR:

Excess death rate

ENO2:

Enolase 2

EXD3:

Exonuclease 3′–5′ domain containing 3

FTO:

Alpha-ketoglutarate-dependent dioxygenase

FAM150B:

Family with sequence similarity 150 member B

GDS:

Geriatric depression scale

GHI:

Global hunger index

GHQ-12:

General health questionnaire

GNASA/B:

G protein alpha S

GNASAS:

GNAS antisense RNA

GRB10:

Growth factor receptor-bound protein 10

GR 1-C:

Glucocorticoid receptor

HADS-A/-D:

Hospital anxiety and depression scale

HLA-DQB2:

Histocompatibility complex class II DQ beta 2

HPA:

Hypothalamic–pituitary–adrenal

ICD-10:

International statistical classification of diseases and related health problems

IGF2R:

Insulin-like growth factor 2 receptor

IGF2:

Insulin-like growth factor 2

INSIGF:

Insulin-induced gene

INSR:

Insulin receptor

KCNQ1OT1:

KCNQ1 opposite strand/antisense transcript 1

KLF13:

Kruppel-like factor 13

LEP:

Leptin

LINE-1:

Long interspersed nucleotide element-1

LOC10012354:

LOC100132354

LPL:

Lipoprotein lipase

LRRC8D:

Leucine rich repeat containing 8 VRAC subunit D

LRRC14B:

Leucine rich repeat containing 14B

LUMA:

Luminometric methylation assay

MEG3:

Maternally expressed 3

MHI-5:

Mental health inventory

MRGPRG:

MAS related GPR family member G

NR3C1:

Nuclear receptor subfamily 3 group C member 1

OSBPL5:

Oxysterol binding protein-like 5

OSF:

Open science framework

OR:

Odds ratio

PARD6G:

Par-6 family cell polarity regulator gamma

PAX8:

Paired box 8

PCR:

Polymerase chain reaction

PI3kinase:

Phosphatidylinositol 3-kinase p85

PLD6:

Phospholipase D family member 6

PNPO:

Pyridoxamine 5′-phosphate oxidase

PPAP2C:

Phosphatidic acid phosphatase 2c

PPARy:

Peroxisome proliferator-activated receptor gamma

PRDM9:

PR/SET domain 9

PRISMA-P:

Preferred reporting items for systematic review and meta-analysis protocols

PRKCA:

Protein kinase C alpha

RBM46:

RNA-binding motif protein 46

RFTN1:

Raftlin lipid raft linker 1

RRBS:

Reduced representation bisulfite sequencing

Sat2:

Satellite repeat-2

SLC28A2:

Solute carrier family 38 member 2

SLC38A10:

Solute carrier family 38 member 10

SMAD7:

SMAD family member 7

SPG20:

Spartin gene

SYNGR1:

Synaptogyrin 1

SZ:

Schizophrenia

TACC1:

Transforming acidic coiled-coil-containing protein 1

TMA7:

Translation machinery-associated protein 7

TMEM18:

Transmembrane protein 18

TMEM105:

TMEM105 long non-coding RNA

TNF:

Tumor necrosis factor

TXNIP:

Thioredoxin interacting protein

VTRNA2-1:

Vault RNA 2-1

ZFP57:

Zinc-finger transcription factor 57

ZFYVE28:

Zinc-finger FYVE-type containing 28

ZNF226:

Zinc-finger protein 226

ZNF385A:

Zinc-finger protein 385A

ZNF678:

Zinc-finger protein 678

References

  1. Brannigan R, Tanskanen A, Huttunen MO, Cannon M, Leacy FP, Clarke MC. The role of prenatal stress as a pathway to personality disorder: longitudinal birth cohort study. Br J Psychiatry. 2020;216:85–9.

    PubMed  Google Scholar 

  2. Kingsbury M, Weeks M, MacKinnon N, Evans J, Mahedy L, Dykxhoorn J, et al. Stressful life events during pregnancy and offspring depression: evidence from a prospective cohort study. J Am Acad Child Adolesc Psychiatry. 2016;55:709–16.

    PubMed  Google Scholar 

  3. Kleinhaus K, Harlap S, Perrin M, Manor O, Margalit-Calderon R, Opler M, et al. Prenatal stress and affective disorders in a population birth cohort. Bipolar Disord. 2013;15:92–9.

    PubMed  Google Scholar 

  4. Babenko O, Kovalchuk I, Metz GAS. Stress-induced perinatal and transgenerational epigenetic programming of brain development and mental health. Neurosci Biobehav Rev. 2015;48:70–91.

    PubMed  Google Scholar 

  5. Cao-Lei L, de Rooij SR, King S, Matthews SG, Metz GAS, Roseboom TJ, et al. Prenatal stress and epigenetics. Neurosci Biobehav Rev. 2020;117:198–210.

    CAS  PubMed  Google Scholar 

  6. Linnér A, Almgren M. Epigenetic programming—the important first 1000 days. Acta Paediatr Int J Paediatr. 2020;109:443–52.

    Google Scholar 

  7. James P, Sajjadi S, Tomar AS, Saffari A, Fall CHD, Prentice AM, et al. Candidate genes linking maternal nutrient exposure to offspring health via DNA methylation: a review of existing evidence in humans with specific focus on one-carbon metabolism. Int J Epidemiol. 2018;47:1910–37.

    PubMed  PubMed Central  Google Scholar 

  8. Oberlander TF, Weinberg J, Papsdorf M, Grunau R, Misri S, Devlin AM. Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics. 2008;3:97–106.

    PubMed  Google Scholar 

  9. Gervin K, Nordeng H, Ystrom E, Reichborn-Kjennerud T, Lyle R. Long-term prenatal exposure to paracetamol is associated with DNA methylation differences in children diagnosed with ADHD. Clin Epigenet. 2017;9:1–9.

    Google Scholar 

  10. Wiklund P, Karhunen V, Richmond RC, Parmar P, Rodriguez A, De Silva M, et al. DNA methylation links prenatal smoking exposure to later life health outcomes in offspring. Clin Epigenet. 2019;11:1–16.

    CAS  Google Scholar 

  11. Siegfried Z, Simon I. DNA methylation and gene expression. Wiley Interdiscip Rev Syst Biol Med. 2010;2:362–71.

    CAS  PubMed  Google Scholar 

  12. Kumar S, Chinnusamy V, Mohapatra T. Epigenetics of modified DNA bases: 5-methylcytosine and beyond. Front Genet. 2018;9:1–14.

    Google Scholar 

  13. Klose RJ, Bird AP. Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci. 2006;31:89–97.

    CAS  PubMed  Google Scholar 

  14. Handel AE, Ebers GC, Ramagopalan SV. Epigenetics: molecular mechanisms and implications for disease. Trends Mol Med. 2010;16:7–16.

    CAS  PubMed  Google Scholar 

  15. Razin A, Cedar H. DNA methylation and gene expression. Microbiol Rev. 1991;55:451–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Palma-Gudiel H, Córdova-Palomera A, Eixarch E, Deuschle M, Fañanás L. Maternal psychosocial stress during pregnancy alters the epigenetic signature of the glucocorticoid receptor gene promoter in their offspring: a meta-analysis. Epigenetics. 2015;10:893–902.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Liu L, Wu J, Qing L, Li J, Yang H, Ji A, et al. DNA methylation analysis of the NR3C1 gene in patients with schizophrenia. J Mol Neurosci. 2020;70:1177–85.

    PubMed  Google Scholar 

  18. Miller O, Shakespeare-Finch J, Bruenig D, Mehta D. DNA methylation of NR3C1 and FKBP5 is associated with posttraumatic stress disorder, posttraumatic growth, and resilience. Psychol Trauma Theory Res Pract Policy. 2020;12:750–5.

    Google Scholar 

  19. Klengel T, Pape J, Binder EB, Mehta D. The role of DNA methylation in stress-related psychiatric disorders. Neuropharmacology. 2014;80:115–32.

    CAS  PubMed  Google Scholar 

  20. Farrell C, Doolin K, O’Leary N, Jairaj C, Roddy D, Tozzi L, et al. DNA methylation differences at the glucocorticoid receptor gene in depression are related to functional alterations in hypothalamic–pituitary–adrenal axis activity and to early life emotional abuse. Psychiatry Res. 2018;265:341–8.

    CAS  PubMed  Google Scholar 

  21. Roseboom TJ. Epidemiological evidence for the developmental origins of health and disease: effects of prenatal undernutrition in humans. J Endocrinol. 2019;242:T135–44.

    CAS  PubMed  Google Scholar 

  22. Thapar A, Rutter M. Do natural experiments have an important future in the study of mental disorders? Psychol Med. 2019;49:1079–88.

    PubMed  PubMed Central  Google Scholar 

  23. Bleker LS, De Rooij SR, Painter RC, Ravelli ACJ, Roseboom TJ. Cohort profile: the Dutch famine birth cohort (DFBC)—a prospective birth cohort study in the Netherlands. BMJ Open. 2021;11:e042078.

    PubMed  PubMed Central  Google Scholar 

  24. Dana K, Finik J, Koenig S, Motter J, Zhang W, Linaris M, et al. Prenatal exposure to famine and risk for development of psychopathology in adulthood: a meta-analysis. J Psychiatry Psychiatr Disord. 2019;3:227–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Szyf M. The early life environment and the epigenome. Biochim Biophys Acta Gen Subj. 2009;1790:878–85.

    CAS  Google Scholar 

  26. Perera F, Herbstman J. Prenatal environmental exposures, epigenetics, and disease. Reprod Toxicol. 2011;31:363–73.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Waterland RA, Michels KB. Epigenetic epidemiology of the developmental origins hypothesis. Annu Rev Nutr. 2007;27:363–88.

    CAS  PubMed  Google Scholar 

  28. Rijlaarsdam J, Cecil CAM, Walton E, Mesirow MSC, Relton CL, Gaunt TR, et al. Prenatal unhealthy diet, insulin-like growth factor 2 gene (IGF2) methylation, and attention deficit hyperactivity disorder symptoms in youth with early-onset conduct problems. J Child Psychol Psychiatry Allied Discip. 2017;58:19–27.

    Google Scholar 

  29. Heijmans BT, Tobi EW, Stein AD, Putter H, Blauw GJ, Susser ES, et al. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci USA. 2008;105:17046–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, The PRISMA, et al. statement: an updated guideline for reporting systematic reviews. Int J Surg. 2020;2021:88.

    Google Scholar 

  31. Li C, Lumey LH. Exposure to the Chinese famine of 1959–61 in early life and long-term health conditions: a systematic review and meta-analysis. Int J Epidemiol. 2017;46:1157–70.

    PubMed  Google Scholar 

  32. Wells GA, Shea B, O’Connel D, Peterson J, Welch V, Losos M, Tugwell P. Newcastle-Ottawa quality assessment scale cohort studies. Ottawa: University of Ottawa; 2014.

    Google Scholar 

  33. Page MJ, Higgins J, Sterne J. Assessing risk of bias due to missing results in a synthesis. In: Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al., editors. Cochrane handbook for systematic reviews of interventions. 2022. Available from:www.training.cochrane.org/handbook

  34. Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method. Biometrics. 2000;56:455–63.

    CAS  PubMed  Google Scholar 

  35. St. Clair D, He L. Rates of adult schizophrenia following of 1959–1961. JAMA. 2005;294:557–62.

    CAS  PubMed  Google Scholar 

  36. Xu MQ, Sun WS, Liu BX, Feng GY, Yu L, Yang L, et al. Prenatal malnutrition and adult Schizophrenia: further evidence from the 1959–1961 Chinese famine. Schizophr Bull. 2009;35:568–76.

    PubMed  PubMed Central  Google Scholar 

  37. Li Y, Sunder N. What doesn’t kill her, will make her depressed. Econ Hum Biol. 2021;43:101064.

    PubMed  Google Scholar 

  38. van den Broek T, Fleischmann M. Prenatal famine exposure and mental health in later midlife. Aging Ment Heal. 2019;23:166–70.

    Google Scholar 

  39. Li Y, Zhao L, Yu D, Ding G. Exposure to the Chinese famine in early life and depression in adulthood. Psychol Heal Med. 2018;23:952–7.

    CAS  Google Scholar 

  40. Li C, Miles T, Shen L, Shen Y, Liu T, Zhang M, et al. Early-life exposure to severe famine and subsequent risk of depressive symptoms in late adulthood: the China Health and retirement longitudinal study. Br J Psychiatry. 2018;213:579–86.

    PubMed  PubMed Central  Google Scholar 

  41. Stein AD, Pierik FH, Verrips GHW, Susser ES, Lumey LH. Maternal exposure to the Dutch Famine before conception and during pregnancy: quality of life and depressive symptoms in adult offspring. Epidemiology. 2009;20:1–14.

    Google Scholar 

  42. Zhou Z, Zhang W, Fang Y. Early-life exposure to Chinese famine and stroke risk in mid- to late life: the mediating roles of cognitive function and depression. BMC Geriatr. 2022;22:1–10.

    CAS  Google Scholar 

  43. He S, Li J, Wang Z, Wang L, Liu L, Sun X, et al. Early-life exposure to famine and late-life depression: Does leukocyte telomere length mediate the association? J Affect Disord. 2020;274:223–8.

    PubMed  Google Scholar 

  44. De Rooij SR, Painter RC, Phillips DI, Rikknen K, Schene AH, Roseboom TJ. Self-reported depression and anxiety after prenatal famine exposure: Mediation by cardio-metabolic pathology? J Dev Orig Health Dis. 2011;2:136–43.

    PubMed  Google Scholar 

  45. Franke K, Gaser C, Roseboom TJ, Schwab M, de Rooij SR. Premature brain aging in humans exposed to maternal nutrient restriction during early gestation. Neuroimage. 2018;173:460–71.

    PubMed  Google Scholar 

  46. Huang C, Phillips MR, Zhang Y, Zhang J, Shi Q, Song Z, et al. Malnutrition in early life and adult mental health: evidence from a natural experiment. Soc Sci Med. 2013;97:259–66.

    PubMed  Google Scholar 

  47. Wang C, Zhang Y. Schizophrenia in mid-adulthood after prenatal exposure to the Chinese Famine of 1959–1961. Schizophr Res. 2017;184:21–5.

    PubMed  Google Scholar 

  48. Susser E, Neugebauer R, Hoek HW, Brown AS, Lin S, Labovitz D, et al. Schizophrenia after prenatal famine: further evidence. Arch Gen Psychiatry. 1996;53:25–31.

    CAS  PubMed  Google Scholar 

  49. Susser ES, Shang P. Schizophrenia after prenatal exposure to the dutch hunger winter of 1944–1945. Arch Gen Psychiatry. 1992;49:983–8.

    CAS  PubMed  Google Scholar 

  50. He P, Chen G, Guo C, Wen X, Song X, Zheng X. Long-term effect of prenatal exposure to malnutrition on risk of schizophrenia in adulthood: evidence from the Chinese famine of 1959–1961. Eur Psychiatry. 2018;51:42–7.

    PubMed  Google Scholar 

  51. Song S, Wang W, Hu P. Famine, death, and madness: schizophrenia in early adulthood after prenatal exposure to the Chinese great leap forward famine. Soc Sci Med. 2009;68:1315–21.

    PubMed  Google Scholar 

  52. Brown AS, Van Os J, Driessens C, Hoek HW, Susser ES. Further evidence of relation between prenatal famine and major affective disorder. Am J Psychiatry. 2000;157:190–5.

    CAS  PubMed  Google Scholar 

  53. Brown AS, Susser ES, Lin SP, Neugebauer R, Gorman JM. Increased risk of affective disorders in males after second trimester prenatal exposure to the Dutch hunger winter of 1944–45. Br J Psychiatry. 1995;166:601–6.

    CAS  PubMed  Google Scholar 

  54. Neugebauer R, Hoek HW, Susser E. Prenatal exposure to wartime famine and development of antisocial personality disorder in early adulthood. J Am Med Assoc. 1999;282:455–62.

    CAS  Google Scholar 

  55. Hoek HW, Lumey LH, Buck KA, Gorman JM. Schizoid personality disorder after prenatal exposure to famine. Am J Psychiatry. 1996;153:1637–9.

    CAS  PubMed  Google Scholar 

  56. Franzek EJ, Sprangers N, Janssens ACJW, Van Duijn CM, Van De Wetering BJM. Prenatal exposure to the 1944–45 Dutch “hunger winter” and addiction later in life. Addiction. 2008;103:433–8.

    PubMed  Google Scholar 

  57. Franzek EJ, Akhigbe KO, Willems IEMG. Prenatal malnutrition and its devastating consequences on mental health later in life. Open J Nutr Food Sci. 2019;1:21–6.

    Google Scholar 

  58. He Y, De Witte LD, Houtepen LC, Nispeling DM, Xu Z, Yu Q, et al. DNA methylation changes related to nutritional deprivation: a genome-wide analysis of population and in vitro data. Clin Epigenet. 2019;11:1–8.

    Google Scholar 

  59. Finer S, Iqbal MS, Lowe R, Ogunkolade BW, Pervin S, Mathews C, et al. Is famine exposure during developmental life in rural Bangladesh associated with a metabolic and epigenetic signature in young adulthood? A historical cohort study. BMJ Open. 2016;6:e011768.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Tobi EW, Slieker RC, Stein AD, Suchiman HED, Eline Slagboom P, Van Zwet EW, et al. Early gestation as the critical time-window for changes in the prenatal environment to affect the adult human blood methylome. Int J Epidemiol. 2015;44:1211–23.

    PubMed  PubMed Central  Google Scholar 

  61. Tobi EW, Goeman JJ, Monajemi R, Gu H, Putter H, Zhang Y, et al. DNA methylation signatures link prenatal famine exposure to growth and metabolism. Nat Commun. 2014;5:5592.

    CAS  PubMed  Google Scholar 

  62. Lumey LH, Terry MB, Delgado-Cruzata L, Liao Y, Wang Q, Susser E, et al. Adult global DNA methylation in relation to pre-natal nutrition. Int J Epidemiol. 2012;41:116–23.

    CAS  PubMed  Google Scholar 

  63. Jiang W, Han T, Duan W, Dong Q, Hou W, Wu H, et al. Prenatal famine exposure and estimated glomerular filtration rate across consecutive generations: association and epigenetic mediation in a population-based cohort study in Suihua China. Aging (Albany NY). 2020;12:12206–21.

    CAS  PubMed  Google Scholar 

  64. Tobi EW, Slieker RC, Luijk R, Dekkers KF, Stein AD, Xu KM, et al. DNA methylation as a mediator of the association between prenatal adversity and risk factors for metabolic disease in adulthood. Sci Adv. 2018;4:eaao4364.

    PubMed  PubMed Central  Google Scholar 

  65. Li S, Wang W, Zhang D, Li W, Lund J, Kruse T, et al. Differential regulation of the DNA methylome in adults born during the Great Chinese Famine in 1959–1961. Genomics. 2021;113:3907–18.

    CAS  PubMed  Google Scholar 

  66. Tobi EW, Slagboom PE, van Dongen J, Kremer D, Stein AD, Putter H, et al. Prenatal famine and genetic variation are independently and additively associated with dna methylation at regulatory loci within IGF2/H19. PLoS ONE. 2012;7:1–11.

    Google Scholar 

  67. Sandoval J, Heyn HA, Moran S, Serra-Musach J, Pujana MA, Bibikova M, et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics. 2011;6:692–702.

    CAS  PubMed  Google Scholar 

  68. Price ME, Cotton AM, Lam LL, Farré P, Emberly E, Brown CJ, et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenet Chromatin. 2013;6:1–15.

    Google Scholar 

  69. Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17:1–17.

    Google Scholar 

  70. Tobi EW, Lumey LH, Talens RP, Kremer D, Putter H, Stein AD, et al. DNA methylation differences after exposure to prenatal famine are common and timing- and sex-specific. Hum Mol Genet. 2009;18:4046–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Zhang H, Qu X, Wang H, Tang K. Early life famine exposure to the Great Chinese Famine in 1959–1961 and subsequent pregnancy loss: a population-based study. BJOG An Int J Obstet Gynaecol. 2020;127:39–45.

    CAS  Google Scholar 

  72. Wang Z, Song J, Li C, Li Y, Shen L, Dong B, et al. DNA methylation of the INSR gene as a mediator of the association between prenatal exposure to famine and adulthood waist circumference. Sci Rep. 2020;10:1–8.

    CAS  Google Scholar 

  73. Veenendaal MV, Costello PM, Lillycrop KA, de Rooij SR, van der Post JA, Bossuyt PM, et al. Prenatal famine exposure, health in later life and promoter methylation of four candidate genes. J Dev Orig Health Dis. 2012;3:450–7.

    CAS  PubMed  Google Scholar 

  74. Wang Z, Song J, Li Y, Dong B, Zou Z, Ma J. Early-life exposure to the Chinese famine is associated with higher methylation level in the INSR gene in later adulthood. Sci Rep. 2019;9:1–9.

    Google Scholar 

  75. Boks MP, Houtepen LC, Xu Z, He Y, Ursini G, Maihofer AX, et al. Genetic vulnerability to DUSP22 promoter hypermethylation is involved in the relation between in utero famine exposure and schizophrenia. NPJ Schizophr. 2018;4:16.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Zhang YS, Rao WW, Zhang LL, Jia HX, Bi H, Wang HL, et al. Incidence rate of schizophrenia after the Tangshan earthquake in China: a 44-year retrospective birth cohort study. Transl Psychiatry. 2022;12:365.

    PubMed  PubMed Central  Google Scholar 

  77. Guo C, He P, Song X, Zheng X. Long-term effects of prenatal exposure to earthquake on adult schizophrenia. Br J Psychiatry. 2019;215:730–5.

    PubMed  PubMed Central  Google Scholar 

  78. Weinstein Y, Levav I, Gelkopf M, Roe D, Yoffe R, Pugachova I, et al. Association of maternal exposure to terror attacks during pregnancy and the risk of schizophrenia in the offspring: a population-based study. Schizophr Res. 2018;199:163–7.

    PubMed  Google Scholar 

  79. Brown AS. The environment and susceptibility to schizophrenia. Prog Neurobiol. 2011;93:23–58.

    CAS  PubMed  Google Scholar 

  80. Weinberger DR. Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiatry. 1987;44:660–9.

    CAS  PubMed  Google Scholar 

  81. Murray RM, Lewis S. Is schizophrenia a neurodevelopmental disorder? Br Med J (Clin Res Ed). 1987;295:681.

    CAS  PubMed  Google Scholar 

  82. Owen MJ, O’Donovan MC, Thapar A, Craddock N. Neurodevelopmental hypothesis of schizophrenia. Br J Psychiatry. 2011;198:173–5.

    PubMed  PubMed Central  Google Scholar 

  83. McGrath JJ, Féron FP, Burne THJ, Mackay-Sim A, Eyles DW. The neurodevelopmental hypothesis of schizophrenia: a review of recent developments. Ann Med. 2003;35:86–93.

    PubMed  Google Scholar 

  84. Kofink D, Boks MPM, Timmers HTM, Kas MJ. Epigenetic dynamics in psychiatric disorders: environmental programming of neurodevelopmental processes. Neurosci Biobehav Rev. 2013;37:831–45.

    PubMed  Google Scholar 

  85. Bassett AS, Chow EWC, Oneill S, Brzustowicz LM. Genetic insights into the neurodevelopmental origins of schizophrenia. Schizophr Bull. 2001;17:417–30.

    Google Scholar 

  86. Weinberger DR. Future of days past: neurodevelopment and schizophrenia. Schizophr Bull. 2017;43:1164–8.

    PubMed  PubMed Central  Google Scholar 

  87. Fatemi SH, Folsom TD. The neurodevelopmental hypothesis of Schizophrenia, revisited. Schizophr Bull. 2009;35:528–48.

    PubMed  PubMed Central  Google Scholar 

  88. Reik W, Dean W, Walter J. Epigenetic reprogramming in mammalian development. Science. 2001;293:1089–93.

    CAS  PubMed  Google Scholar 

  89. Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. 2016;388:86–97.

    PubMed  PubMed Central  Google Scholar 

  90. Owen MJ, O’Donovan MC. Schizophrenia and the neurodevelopmental continuum: evidence from genomics. World Psychiatry. 2017;16:227–35.

    PubMed  PubMed Central  Google Scholar 

  91. Szyf M, McGowan PO, Meaney MJ. The social environment and the epigenome. Environ Mol Mutagen. 2008;49:46–60.

    CAS  PubMed  Google Scholar 

  92. Shen L, Li C, Wang Z, Zhang R, Shen Y, Miles T, et al. Early-life exposure to severe famine is associated with higher methylation level in the IGF2 gene and higher total cholesterol in late adulthood: The Genomic Research of the Chinese Famine (GRECF) study. Clin Epigenet. 2019;11:1–9.

    CAS  Google Scholar 

  93. An N, Bassil K, Al Jowf GI, Steinbusch HWM, Rothermel M, de Nijs L, et al. Dual-specificity phosphatases in mental and neurological disorders. Prog Neurobiol. 2021;198:101906.

    CAS  PubMed  Google Scholar 

  94. St-Pierre J, Hivert MF, Perron P, Poirier P, Guay SP, Brisson D, et al. IGF2 DNA methylation is a modulator of newborn’s fetal growth and development. Epigenetics. 2012;7:1125–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Chao W, D’Amore PA. IGF2: Epigenetic regulation and role in development and disease. Cytokine Growth Factor Rev. 2008;19:111–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  96. O’Kusky J, Ye P. Neurodevelopmental effects of insulin-like growth factor signaling. Front Neuroendocrinol. 2012;33:230–51.

    PubMed  PubMed Central  Google Scholar 

  97. Montoya-Williams D, Quinlan J, Clukay C, Rodney NC, Kertes DA, Mulligan CJ. Associations between maternal prenatal stress, methylation changes in IGF1 and IGF2, and birth weight. J Dev Orig Health Dis. 2018;9:215–22.

    CAS  PubMed  Google Scholar 

  98. Tobi EW, Heijmans BT, Kremer D, Putter H, Delemarre-van de Waal HA, Finken MJJ, et al. DNA methylation of IGF2, GNASAS, INSIGF and LEP and being born small for gestational age. Epigenetics. 2011;6:171–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Pardo M, Cheng Y, Sitbon YH, Lowell JA, Grieco SF, Worthen RJ, et al. Insulin growth factor 2 (IGF2) as an emergent target in psychiatric and neurological disorders. Neurosci Res. 2019;149:1–13.

    CAS  PubMed  Google Scholar 

  100. Liu C, Jiao C, Wang K, Yuan N. DNA methylation and psychiatric disorders. Prog Mol Biol Transl Sci. 2018;157:175–232.

    CAS  PubMed  Google Scholar 

  101. Chen D, Meng L, Pei F, Zheng Y, Leng J. A review of DNA methylation in depression. J Clin Neurosci. 2017;43:39–46.

    CAS  PubMed  Google Scholar 

  102. Bornstein SR, Uhlmann K, Haidan A, Ehrhart-Bornstein M, Scherbaum WA. Evidence for a novel peripheral action of leptin as a metabolic signal to the adrenal gland: leptin inhibits cortisol release directly. Diabetes. 1997;46:1235–8.

    CAS  PubMed  Google Scholar 

  103. Roubos EW, Dahmen M, Kozicz T, Xu L. Leptin and the hypothalamo-pituitary-adrenal stress axis. Gen Comp Endocrinol. 2012;177:28–36.

    CAS  PubMed  Google Scholar 

  104. Heiman ML, Chen Y, Caro JF. Leptin participates in the regulation of glucocorticoid and growth hormone axes. J Nutr Biochem. 1998;9:553–9.

    CAS  Google Scholar 

  105. Tate PH, Bird AP. Effects of DNA methylation on DNA-binding proteins and gene expression. Curr Opin Genet Dev. 1993;3:226–31.

    CAS  PubMed  Google Scholar 

  106. Song J, Chen Y, Zhao Q, Li H, Li W, Chen K, et al. Leptin methylation and mRNA expression associated with psychopathology in schizophrenia inpatients. Front Psychiatry. 2022;13:1–9.

    Google Scholar 

  107. Pariante CM, Lightman SL. The HPA axis in major depression: classical theories and new developments. Trends Neurosci. 2008;31:464–8.

    CAS  PubMed  Google Scholar 

  108. Mikulska J, Juszczyk G, Gawrońska-Grzywacz M, Herbet M. Hpa axis in the pathomechanism of depression and schizophrenia: new therapeutic strategies based on its participation. Brain Sci. 2021;11:1298.

    PubMed  PubMed Central  Google Scholar 

  109. Tu H, Rady PL, Juelich T, Tyring SK, Koldzic-Zivanovic N, Smith EM, et al. Interleukin-10 regulated gene expression in cells of hypothalamic-pituitary-adrenal axis origin. Cell Mol Neurobiol. 2007;27:161–70.

    CAS  PubMed  Google Scholar 

  110. Smith EM, Cadet P, Stefano GB, Opp MR, Hughes TK. IL-10 as a mediator in the HPA axis and brain. J Neuroimmunol. 1999;100:140–8.

    CAS  PubMed  Google Scholar 

  111. Turnbull AV, Rivier CL. Regulation of the hypothalamic-pituitary-adrenal axis by cytokines: actions and mechanisms of action. Physiol Rev. 1999;79:1–71.

    CAS  PubMed  Google Scholar 

  112. Tsigos C, Chrousos GP. Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. J Psychosom Res. 2002;53:865–71.

    PubMed  Google Scholar 

  113. Baumeister D, Russell A, Pariante CM, Mondelli V. Inflammatory biomarker profiles of mental disorders and their relation to clinical, social and lifestyle factors. Soc Psychiatry Psychiatr Epidemiol. 2014;49:841–9.

    PubMed  Google Scholar 

  114. Silverman MN, Sternberg EM. Glucocorticoid regulation of inflammation and its functional correlates: from HPA axis to glucocorticoid receptor dysfunction. Ann N Y Acad Sci. 2012;1261:55–63.

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Iacob E, Light KC, Tadler SC, Weeks HR, White AT, Hughen RW, et al. Dysregulation of leukocyte gene expression in women with medication-refractory depression versus healthy non-depressed controls. BMC Psychiatry. 2013;13:1–10.

    Google Scholar 

  116. López-gonzález I, Pinacho R, Vila È, Escanilla A, Ferrer I, Ramos B. Neuroinflammation in the dorsolateral prefrontal cortex in elderly chronic schizophrenia. Eur Neuropsychopharmacol. 2019;29:384–96.

    PubMed  Google Scholar 

  117. Pandey GN, Rizavi HS, Zhang H, Ren X. Abnormal gene and protein expression of in fl ammatory cytokines in the postmortem brain of schizophrenia patients. Schizophr Res. 2018;192:247–54.

    PubMed  Google Scholar 

  118. Smith AK, Kilaru V, Klengel T, Mercer KB, Bradley B, Conneely KN, et al. DNA extracted from saliva for methylation studies of psychiatric traits: evidence tissue specificity and relatedness to brain. Am J Med Genet Part B Neuropsychiatr Genet. 2015;168:36–44.

    CAS  Google Scholar 

  119. Nishitani S, Isozaki M, Yao A, Higashino Y, Yamauchi T, Kidoguchi M, et al. Cross-tissue correlations of genome-wide DNA methylation in Japanese live human brain and blood, saliva, and buccal epithelial tissues. Transl Psychiatry. 2023;13:72.

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Walton E, Hass J, Liu J, Roffman JL, Bernardoni F, Roessner V, et al. Correspondence of DNA methylation between blood and brain tissue and its application to schizophrenia research. Schizophr Bull. 2016;42:406–14.

    PubMed  Google Scholar 

  121. Palma-Gudiel H, Córdova-Palomera A, Navarro V, Fañanás L. Twin study designs as a tool to identify new candidate genes for depression: a systematic review of DNA methylation studies. Neurosci Biobehav Rev. 2020;112:345–52.

    CAS  PubMed  Google Scholar 

  122. Szyf M, Bick J. DNA methylation: a mechanism for embedding early life experiences in the genome. Child Dev. 2013;84:49–57.

    PubMed  Google Scholar 

  123. Morris TJ, Beck S. Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) data. Methods. 2015;72:3–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Teh AL, Pan H, Lin X, Lim YI, Patro CPK, Cheong CY, et al. Comparison of methyl-capture sequencing vs. infinium 450K methylation array for methylome analysis in clinical samples. Epigenetics. 2016;11:36–48.

    PubMed  PubMed Central  Google Scholar 

  125. Elliott HR, Burrows K, Min JL, Tillin T, Mason D, Wright J, et al. Characterisation of ethnic differences in DNA methylation between UK-resident South Asians and Europeans. Clin Epigenet. 2022;14:1–17.

    Google Scholar 

  126. Smil V. The great Chinese famine. BMJ. 1999;319:1619–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Szyf M. Towards a pharmacology of DNA methylation. Trends Pharmacol Sci. 2001;22:350–4.

    CAS  PubMed  Google Scholar 

  128. Szyf M, Pakneshan P, Rabbani SA. DNA methylation and breast cancer. Biochem Pharmacol. 2004;68:1187–97.

    CAS  PubMed  Google Scholar 

  129. Alladi CG, Etain B, Bellivier F, Marie-Claire C. DNA methylation as a biomarker of treatment response variability in serious mental illnesses: a systematic review focused on bipolar disorder, schizophrenia, and major depressive disorder. Int J Mol Sci. 2018;19:1–19.

    Google Scholar 

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Acknowledgements

We warmly thank Sarah Mannion for proofreading the article and Dr. Susanne Fischer for her support in the risk of bias assessment.

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HE was responsible for the conception, acquisition of data (systematic search and screening of the literature), analyzing (meta-analyses) and interpretation of data, and drafting of the manuscript. UE was responsible for the conception, interpretation of data, and revision of the manuscript.

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Correspondence to Ulrike Ehlert.

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Additional file 1: Table S1.

Quality assessment scale (risk of bias) of adults prenatally exposed to famine who suffered from symptoms of psychopathology or a mental disorder; modified from Li and Lumey [31] and Newcastle–Ottawa Scale by Wells et al. [32]. Table S2. Quality assessment scale (risk of bias) of adults prenatally exposed to famine with alterations in (epi)genome-wide DNA methylation; modified from Li and Lumey [31] and Newcastle–Ottawa Scale by Wells et al. [32]. Table S3. Quality assessment scale (risk of bias) of adults prenatally exposed to famine with alterations in candidate gene DNA methylation; modified from Li and Lumey [31] and Newcastle–Ottawa Scale by Wells et al. [32].

Additional file 2: Table S4.

Risk of bias assessment for the effect of famine on symptoms of psychopathology/mental disorders, and DNA methylation.

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Eichenauer, H., Ehlert, U. The association between prenatal famine, DNA methylation and mental disorders: a systematic review and meta-analysis. Clin Epigenet 15, 152 (2023). https://doi.org/10.1186/s13148-023-01557-y

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